This paper examines how a manager uses voluntary disclosure to influence corporate control by a short-term shareholder. Because a short-term shareholder intervenes excessively, the manager’s disclosure strategy is determined by the trade-off between excessive and insufficient intervention. In equilibrium, when shareholder short-termism is not too high, the manager discloses both good and bad news and withholds intermediate news. Alternatively, when shareholder short-termism is high, the manager only discloses good news and withholds bad news. In both equilibria, withholding information is value-enhancing for the nondisclosing firms. We also show that the likelihood of disclosure weakly decreases as the shareholder is more short-term-oriented. Moreover, nondisclosing firms are more likely to face shareholder intervention than disclosing firms.
We show that websites’ vendor use falls after the European Union’s (EU’s) General Data Protection Regulation (GDPR), but that market concentration also increases among technology vendors that provide support services to websites. We collect panel data on the web technology vendors selected by more than 27,000 top websites internationally. The week after the GDPR’s enforcement, website use of web technology vendors falls by 15% for EU residents. Websites are relatively more likely to retain top vendors, which increases the concentration of the vendor market by 17%. Increased concentration predominantly arises among vendors that use personal data, such as cookies, and from the increased relative shares of Facebook and Google-owned vendors, but not from website consent requests. Although the aggregate changes in vendor use and vendor concentration dissipate by the end of 2018, we find that the GDPR impact persists in the advertising vendor category most scrutinized by regulators. Our findings shed light on potential explanations for the sudden drop and subsequent rebound in vendor usage.
Motivated by operations research applications, such as inventory control and real-time bidding, we consider undiscounted reinforcement learning in Markov decision processes under model uncertainty and temporal drifts. In this setting, both the latent reward and state transition distributions are allowed to evolve over time, as long as their respective total variations, quantified by suitable metrics, do not exceed certain variation budgets. We first develop the sliding window upper confidence bound for reinforcement learning with confidence-widening (SWUCRL2-CW) algorithm and establish its dynamic regret bound when the variation budgets are known. In addition, we propose the bandit-over-reinforcement learning algorithm to adaptively tune the SWUCRL2-CW algorithm to achieve the same dynamic regret bound but in a parameter-free manner (i.e., without knowing the variation budgets). Finally, we conduct numerical experiments to show that our proposed algorithms achieve superior empirical performance compared with existing algorithms. Notably, under nonstationarity, historical data samples may falsely indicate that state transition rarely happens. This thus presents a significant challenge when one tries to apply the conventional optimism in the face of uncertainty principle to achieve a low dynamic regret bound. We overcome this challenge by proposing a novel confidence-widening technique that incorporates additional optimism into our learning algorithms. To extend our theoretical findings, we demonstrate, in the context of single-item inventory control with lost sales, fixed cost, and zero lead time, how one can leverage special structures on the state transition distributions to achieve improved dynamic regret bound in time-varying demand environments.
We develop a model of private equity capturing two fundamental features of this market: the fund structure and illiquidity. A fund structure with sequential capital calls arises as an optimal solution to fund managers’ (GPs) moral hazard problem but exposes investors (LPs) to illiquidity risk. Funds with more illiquidity-tolerant LPs realize higher returns, leading to different expected returns across both funds and LPs in equilibrium. GPs may inefficiently accelerate drawdowns to avoid default by LPs on capital commitments. With a secondary market for LP claims, differences in fund returns are attenuated but differences in LP returns remain. The model can rationalize several empirical findings on primary and secondary private equity markets.
Policy learning using historical observational data are an important problem that has widespread applications. Examples include selecting offers, prices, or advertisements for consumers; choosing bids in contextual first-price auctions; and selecting medication based on patients’ characteristics. However, existing literature rests on the crucial assumption that the future environment where the learned policy will be deployed is the same as the past environment that has generated the data: an assumption that is often false or too coarse an approximation. In this paper, we lift this assumption and aim to learn a distributionally robust policy with incomplete observational data. We first present a policy evaluation procedure that allows us to assess how well the policy does under worst-case environment shift. We then establish a central limit theorem type guarantee for this proposed policy evaluation scheme. Leveraging this evaluation scheme, we further propose a novel learning algorithm that is able to learn a policy that is robust to adversarial perturbations and unknown covariate shifts with a performance guarantee based on the theory of uniform convergence. Finally, we empirically test the effectiveness of our proposed algorithm in synthetic datasets and demonstrate that it provides the robustness that is missing using standard policy learning algorithms. We conclude the paper by providing a comprehensive application of our methods in the context of a real-world voting data set.
Despite intense scrutiny from investors, markets, and regulators, many public companies have no formal succession plans. Anecdotal evidence links succession risk to significant value destruction, but there is limited academic research evaluating the effects of succession planning on chief executive officer (CEO) turnover outcomes. We provide evidence that succession planning reduces the cost of management transitions by improving their efficiency. Firms with succession plans experience not only lower uncertainty around turnover events but also a faster reduction in uncertainty over the incoming CEO’s tenure, consistent with faster learning about CEO-firm fit. Succession planning also raises the quality of the CEO-firm match, as evidenced by longer CEO tenure, and improves the board’s readiness to replace an underperforming CEO, increasing turnover performance sensitivity.
This paper analyzes a dynamic relational contract for employees with reciprocal preferences. A model of a long-term employment relationship is developed that implies that generous upfront wages activate the norm of reciprocity and then are more important when an employee is close to retirement. In earlier stages, direct incentives promising a bonus in exchange for effort are more effective. Hence, direct and reciprocity-based incentives reinforce each other and should be used in combination. Moreover, a more competitive labor market may increase the utilization of reciprocity-based incentives.
We study the problem when a firm sets prices for products based on the transaction data, that is, which product past customers chose from an assortment and what were the historical prices that they observed. Our approach does not impose a model on the distribution of the customers’ valuations and only assumes, instead, that purchase choices satisfy incentive-compatible constraints. The uncertainty set of potential valuations of each past customer can then be encoded as a polyhedral set, and our approach maximizes the worst case revenue, assuming that new customers’ valuations are drawn from the empirical distribution implied by the collection of such polyhedra. We study the single-product case analytically and relate it to the traditional model-based approach. Then, we show that the optimal prices in the general case can be approximated at any arbitrary precision by solving a compact mixed-integer linear program. We further design three approximation strategies that are of low computational complexity and interpretable. In particular, the cutoff pricing heuristic has a competent provable performance guarantee. Comprehensive numerical studies based on synthetic and real data suggest that our pricing approach is uniquely beneficial when the historical data has a limited size or is susceptible to model misspecification.
We study the classical newsvendor problem in which the decision maker must trade off underage and overage costs. In contrast to the typical setting, we assume that the decision maker does not know the underlying distribution driving uncertainty but has only access to historical data. In turn, the key questions are how to map existing data to a decision and what type of performance to expect as a function of the data size. We analyze the classical setting with access to past samples drawn from the distribution (e.g., past demand), focusing not only on asymptotic performance but also on what we call the transient regime of learning, that is, performance for arbitrary data sizes. We evaluate the performance of any algorithm through its worst-case relative expected regret, compared with an oracle with knowledge of the distribution. We provide the first finite sample exact analysis of the classical sample average approximation (SAA) algorithm for this class of problems across all data sizes. This allows one to uncover novel fundamental insights on the value of data: It reveals that tens of samples are sufficient to perform very efficiently but also that more data can lead to worse out-of-sample performance for SAA. We then focus on the general class of mappings from data to decisions without any restriction on the set of policies and derive an optimal algorithm (in the minimax sense) and characterize its associated performance. This leads to significant improvements for limited data sizes and allows to exactly quantify the value of historical information.
Historical data are typically limited. We study the following fundamental data-driven pricing problem. How can/should a decision maker price its product based on data at a single historical price? How valuable is such data? We consider a decision maker who optimizes over (potentially randomized) pricing policies to maximize the worst-case ratio of the garnered revenue compared to an oracle with full knowledge of the distribution of values, when the latter is only assumed to belong to a broad nonparametric set. In particular, our framework applies to the widely used regular and monotone nondecreasing hazard rate (mhr) classes of distributions. For settings where the seller knows the exact probability of sale associated with one historical price or only a confidence interval for it, we fully characterize optimal performance and near-optimal pricing algorithms that adjust to the information at hand. The framework we develop is general and allows to characterize optimal performance for deterministic or more general randomized mechanisms and leads to fundamental novel insights on the value of data for pricing. As examples, against mhr distributions, we show that it is possible to guarantee 85% of oracle performance if one knows that half of the customers have bought at the historical price, and if only 1% of the customers bought, it still possible to guarantee 51% of oracle performance.
We consider a platform facilitating trade between sellers and buyers with the objective of maximizing consumer surplus. Even though in many such marketplaces, prices are set by revenue-maximizing sellers, platforms can influence prices through (i) price-dependent promotion policies that can increase demand for a product by featuring it in a prominent position on the web page and (ii) the information revealed to sellers about the value of being promoted. Identifying effective joint information design and promotion policies is a challenging dynamic problem as sellers can sequentially learn the promotion value from sales observations and update prices accordingly. We introduce the notion of confounding promotion policies, which are designed to prevent a Bayesian seller from learning the promotion value (at the expense of the short-run loss of diverting some consumers from the best product offering). Leveraging these policies, we characterize the maximum long-run average consumer surplus that is achievable through joint information design and promotion policies when the seller sets prices myopically. We then construct a Bayesian Nash equilibrium, in which the seller’s best response to the platform’s optimal policy is to price myopically in every period. Moreover, the equilibrium we identify is platform optimal within the class of horizon-maximin equilibria, in which strategies are not predicated on precise knowledge of the horizon length and are designed to maximize payoff over the worst-case horizon. Our analysis allows one to identify practical long-run average optimal platform policies in a broad range of demand models.
Outpatient care providers usually allow patients to access service via scheduling appointments or direct walk-in. Patients choose strategically between these two access channels (and otherwise balking) based on the trade-off of appointment delay and in-clinic waiting. How to manage outpatient care with such dual access channels, taking into account patient strategic choice behavior, is a challenge faced by providers. We study three operational levers to address this management challenge: service capacity allocation between these two channels, appointment delay information revelation via the choice and design of online scheduling systems, and a walk-in triage system that restricts the use of walk-in hours only for acute care. By studying a stylized queueing model, we find that neither a real-time online scheduling system (which offers instant access to appointment delay information at time of booking) nor an asynchronous online system (which does not directly provide delay information) can be universally more efficient. Although real-time systems appear more popular in practice, asynchronous systems sometimes can result in higher operational efficiency. Under the provider’s optimal capacity allocation, which scheduling system is more efficient hinges on two key factors: the patient demand–provider capacity relationship and patient willingness to wait. For the walk-in triage system, we find that it may or may not be beneficial to adopt; the provider’s own cost trade-off between lost demand and overtime work is the key determinant. Our research highlights that there is no one-size-fits-all model for outpatient care management, and the best use of operational levers critically depends on the practice environment.
The COVID-19 pandemic has seen dramatic demand surges for hospital care that have placed a severe strain on health systems worldwide. As a result, policy makers are faced with the challenge of managing scarce hospital capacity to reduce the backlog of non-COVID patients while maintaining the ability to respond to any potential future increases in demand for COVID care. In this paper, we propose a nationwide prioritization scheme that models each individual patient as a dynamic program whose states encode the patient’s health and treatment condition, whose actions describe the available treatment options, whose transition probabilities characterize the stochastic evolution of the patient’s health, and whose rewards encode the contribution to the overall objectives of the health system. The individual patients’ dynamic programs are coupled through constraints on the available resources, such as hospital beds, doctors, and nurses. We show that the overall problem can be modeled as a grouped weakly coupled dynamic program for which we determine near-optimal solutions through a fluid approximation. Our case study for the National Health Service in England shows how years of life can be gained by prioritizing specific disease types over COVID patients, such as injury and poisoning, diseases of the respiratory system, diseases of the circulatory system, diseases of the digestive system, and cancer.
Healthcare policy makers use wait-time metrics to encourage hospital managers to improve patient experience. In 2002, Massachusetts mandated that hospital managers develop processes to respond to boarding crises, which occur when emergency department (ED) patients experience long waits for inpatient beds. Performance improvement theory suggests that patients would be better served by preventing boarding crises rather than responding urgently after they occur. To empirically test this theory, we use data from a Massachusetts hospital that has two physician-based processes related to boarding and patient flow. First, to comply with the state mandate, the hospital developed processes to identify when the hospital is in a boarding crisis, a code yellow (CY), and subsequently request that physicians prioritize patient discharge (urgent response). Second, physicians can use predischarge orders, optional written communication about discharge barriers, to avoid discharge delays for patients approaching discharge (prevention response). Our data supports the existence of a trade-off between these two responses. Counter to our hypothesis, the state-mandated urgent response does not have any impact on length of stay (LOS). We also find that a CY has no impact on ED hourly occupancy, marginally decreases ED wait times, and increases boarding time. The prevention response is associated with a 26% reduction in LOS. Furthermore, we find that the urgent response reduces the likelihood of physicians’ ability to use the prevention response by 27.3%. We conclude that the state policy has unintended negative consequences that stymie hospital efforts to create longer term improvement.
Effective treatment of Parkinson’s disease (PD) is a continual challenge for healthcare providers, and providers can benefit from leveraging emerging technologies to supplement traditional clinic care. We develop a data-driven reinforcement learning (RL) framework to optimize PD medication regimens through wearable sensors. We leverage a data set of n = 26 PD patients who wore wrist-mounted movement trackers for two separate six-day periods. Using these data, we first build and validate a simulation model of how individual patients’ movement symptoms respond to medication administration. We then pair this simulation model with an on-policy RL algorithm that recommends optimal medication types, timing, and dosages during the day while incorporating human-in-the-loop considerations on medication administration. The results show that the RL-prescribed medication regimens outperform physicians’ medication regimens, despite physicians having access to the same data as the RL agent. To validate our results, we assess our wearable-based RL medication regimens using n = 399 PD patients from the Parkinson’s Progression Markers Initiative data set. We show that the wearable-based RL medication regimens would lead to significant symptom improvement for these patients, even more so than training RL policies directly from this data set. In doing so, we show that RL models from even small data sets of wearable data can offer novel, generalizable clinical insights and medication strategies, which may outperform those derived from larger data sets without wearable data.
In this paper, we study a family of sales-based rebate mechanisms as an effective tool to implement price discrimination across a population of buyers with correlated heterogeneous valuations on indivisible goods and services. In order to implement such sales-based rebate mechanisms, the seller charges each buyer a fixed price at the time of purchase contingent on a rebate that is a function of the ex post sales volume to be realized at the end of the sales period. The seller declares both a price and a menu of rebates as a function of sales. We show that, when there is a common component of uncertainty in consumers’ valuations (to which we refer as the quality of the product), such rebates enable a seller to effectively induce different expected net prices at different valuations. Importantly, this effective price discrimination over valuations is achieved keeping both the base price and the rebate uniform across all buyers. This uniformity of price and rebate across buyers is a key advantage of our proposed rebate mechanism, thereby providing a new mechanism for price discrimination in crowd-based markets. We use tools and techniques from game theory and variational optimization to provide insight into the economics of such mechanisms. In particular, we identify two mechanisms that are monotone functions of the sales volume that are easy to implement in practice and perform well when compared with the much more complex optimal mechanism. We provide a rigorous analysis of the optimal mechanism and discuss practical limitations in implementing the globally optimal design, further demonstrating the efficacy of our proposed monotone mechanisms.
We consider a class of three-objective mixed-integer linear programs (MILPs) where at least one of the objective functions takes only discrete values. These problems commonly occur in MILPs where one or more of the three objective functions contain only integer decision variables. In such problems, the nondominated set consists of the union of nondominated edges and individual nondominated points. The nondominated edges can provide valuable insights on the trade-offs between the two continuous objectives at different levels of the discrete-valued objective. We develop an objective-space search algorithm that keeps partitioning the search space by progressively creating cones in the two-dimensional feasible space of the two continuous objectives for relevant values of the discrete-valued objective. The algorithm generates the nondominated points or edges in the nonincreasing order of the feasible values of the selected discrete-valued objective. Additionally, the algorithm uncovers all efficient integer variable vectors, including different vectors that lead to the same nondominated points or edges. We apply the algorithm to the day-ahead electricity market clearing problem.
We study the relative merits of different dispute resolution mechanisms in two-sided platforms in the context of disputes involving malicious reviews and blackmail. We develop a game-theoretic model of the strategic interactions between a seller and a (potentially malicious) consumer. In our model, the seller takes into account the impact of consumer reviews on his future earnings; recognizing this, a malicious consumer may attempt to blackmail the seller by purchasing the product, posting a negative review, and demanding ransom to remove it. Without a dispute resolution mechanism in place, the presence of malicious consumers in the market can lead to a significant decrease in seller profit, especially in settings characterized by high uncertainty about product quality. The introduction of a standard centralized dispute resolution mechanism (whereby the seller can report allegedly malicious reviews to the host platform, which then judges whether to remove the review) can restore efficiency to some extent but requires the platform’s judgments to be both very quick and highly accurate. We demonstrate that a more decentralized mechanism (whereby the firm is allowed to remove reviews without consulting the platform, subject to ex post penalties for wrongdoing) can be much more effective, while simultaneously alleviating—almost entirely—the need for the platform’s judgments to be quick. Our results suggest that decentralization, when implemented correctly, may represent a more efficient approach to dispute resolution.
Partial vertical ownership describes a situation in which a firm holds financial shares in either its supplier (referred to as partial backward integration) or its customer (partial forward integration). We study the effect of such financial interconnectedness on two operational decisions: capacity investment and information exchange. In our model, a retailer, who has superior information about the future market demand, has passive financial holdings in the supplier. Although this passive financial investment does not enable the retailer to directly influence the supplier’s operational decisions, it does affect the market equilibrium. Specifically, financial interconnectedness between the firms can result in the retailer financing the entire capacity in the market. In addition, we characterize the conditions that ensure that information between the retailer and the supplier can be exchanged via cheap-talk communication. Interestingly, a high level of information asymmetry facilitates the exchange of information via cheap talk in the presence of these financial links. When cheap talk is not possible, we study the separating equilibrium that is achieved through the retailer’s commitment to order in advance. In this case, the separating quantity can either increase or decrease with the level partial vertical ownership, and this trend does not depend the actual level of the financial holdings. We further analyze the incentive of the retailer to conceal demand information by choosing a pooling equilibrium and conclude with discussing the effect of the financial interconnectedness on the parties’ operational payoffs.
An important problem in single-player video game design is how to sequence game elements within a level (or “chunk”) of the game. Each element has two critical features: a reward (e.g., earning an item or being able to watch a cinematic) and a degree of difficulty (e.g., how much energy or focus is needed to interact with the game element). The latter property is a distinctive feature in video games. Unlike passive services (like a trip to the spa) or passive entertainment (like watching sports or movies), video games often require concerted effort to consume. We study how to sequence game elements to maximize overall experienced utility subject to the dynamics of adaptation to rewards and difficulty and memory decay. We find that the optimal design depends on the relationship between rewards and difficulty, leading to qualitatively different designs. For example, when the proportion of reward-to-difficulty is high, the optimal design mimics that of more passive experiences. By contrast, the optimal design of games with low reward-to-difficulty ratios resembles work-out routines with “warm-ups” and “cool-downs.” Intermediate cases may follow the classical “mini-boss, end-boss” design where difficulty has two peaks. Numerical results reveal optimal designs with “waves” of reward and difficulty with multiple peaks. Level designs with multiple peaks of difficulty are ubiquitous in video games. In summary, this paper provides practical guidance to game designers on how to match the design of single-player games to the relationship between reward and difficulty inherent in their game’s mechanics. Our model also has implications for other interactive services that share similarities with games, such as summer camps for children.
Airfares evolve dynamically, giving rise to a so-called price path. This price path is controlled via two levers: (i) a fare ladder, which defines a set of airfares before the selling season, and (ii) revenue management algorithms, which control how fares evolve along the ladder during the season. We hypothesize that the current policies to control both levers—which do not account for quality differences between competing airlines—give rise to an inefficient price path and, accordingly, a loss of potential revenue. We substantiate this hypothesis via a field experiment. By partnering with an airline, we introduced quality considerations in the design of fare ladders, across 5,000 itineraries, to show that current ladder-design policies indeed lead to a suboptimal price path. We also show that this inefficiency can be mitigated by incorporating quality differences between competing airlines. This creates a smoother (and more profitable) price path.
Quality certification is ubiquitously used in markets with asymmetric information, but little is known about the impact of the number of certification tiers on market outcomes. Exploiting a field experiment on a large e-commerce marketplace with an existing two-tier certification, we study the impact of introducing a new certification tier that is less history dependent and less demanding than the existing top tier. Consistent with the theoretical literature on information design, we find that the change in information granularity among the previously noncertified sellers increases demand for high-quality young sellers, incentivizes their quality provision, and increases their chance of eventually obtaining the top-tier certification. Moreover, the three-tier certification structure prompts established sellers with higher effort cost to reduce their effort, opt out of the top tier, and adopt the new tier instead. Lastly, we provide evidence that the net impact of introducing the new certification tier on seller effort depends on how history dependent the certification requirements are in a market.
On-demand delivery through sharing platforms represents a rapidly expanding segment of the global workforce. The emergence of sharing platforms enables gig workers to choose when and where to work, allowing them to do so in a flexible manner. However, such flexibility brings notorious challenges to platforms in managing the gig workforce. Thus, understanding the incentive and behavioral issues of gig workers in this new business model is inherently meaningful. This paper investigates how the incentive mechanisms of sharing platforms—earnings, ratings, and penalties—affect the working decisions of gig workers and their nuanced relationships. To achieve this goal, we use data from one leading on-demand delivery platform with more than 50 million active consumers in China and implement a two-stage Heckman model with instrumental variables to estimate the impact of earnings, ratings, and penalties. We first show that better ratings motivate gig workers to work more. However, interestingly, when ratings are employed together with earnings, the two positive effects of ratings and earnings can be substitutes for each other. Second, we reveal that higher past penalties discourage workers from working more, whereas, interestingly, workers with higher past penalties tend to be more sensitive toward an increase in earnings. Finally, we conduct follow-up surveys to understand the underlying mechanisms of the observed moderating effects from both psychological and economic perspectives. The ultimate goal of this work is to provide managerial implications to help platform managers understand how earnings, ratings, and penalties work together to affect gig workers’ working decisions and how to manage high- and low-quality workers.
We study the joint implementation of reservation and de-reservation policies in India that has been enforcing comprehensive affirmative action since 1950. The landmark judgment of the Supreme Court of India in 2008 mandated that whenever the OBC category (with 27% reservation) has unfilled positions, they must be reverted to general category applicants in admissions to public schools without specifying how to implement it. We disclose the drawbacks of the recently reformed allocation procedure in admissions to technical colleges and offer a solution through “de-reservation via choice rules.” We propose a novel priority design—Backward Transfers (BT) choice rule—for institutions and the deferred acceptance mechanism under these choice rules (DA-BT) for centralized clearinghouses. We show that DA-BT corrects the shortcomings of existing mechanisms. By formulating India’s legal requirements and policy goals as formal axioms, we show that the DA-BT mechanism is unique for the concurrent implementation of reservation and de-reservation policies.
In environments with impending regime shifts, previous research studies probability judgments of decision makers (DMs) regarding a regime shift and finds evidence for the system neglect hypothesis: In unstable environments with precise signals, DMs exhibit relatively more underreaction in their probability judgments, and in stable environments with noisy signals, they exhibit relatively more overreaction. In the present paper we focus on pricing decisions and study how risk attitudes, as well as probability judgments regarding a regime shift, affect these decisions. We develop a model that integrates prospect theory with the system neglect hypothesis and compare changes in empirical pricing decisions to those of a Bayesian risk-neutral agent. We find a pattern of under- and overreaction in pricing decisions that mimics the system neglect hypothesis in probability judgments only when the DM’s prior probability of a regime shift is low; the system neglect pattern vanishes when the DM’s prior probability of a regime shift is high. We argue that in the case of low priors, changes in risk attitude in reaction to signals indicative of a regime shift amplify system neglect in probability judgments. In the case of high priors, on the other hand, changes in risk attitude dampen the effect of system neglect. We test our model predictions in experimental insurance markets and offer various extensions to check for the robustness of our findings. Finally, we discuss implications for managerial practice in organizational contexts that are characterized by continuous change.
We use eBay data to investigate how within-U.S. trade is influenced by differences in socioeconomic characteristics. States’ similarity in cultural characteristics (ethnicity, religious affiliations, and political behavior) predicts online trade; cultural similarity similarly predicts trade within product categories. The culture-trade relationship is stronger for transactions with sellers who lack extensive reputations or certification, suggesting that consumers infer seller trustworthiness from cultural similarity. There is no correlation between cultural similarity and buyer satisfaction, suggesting that perceived differences in trustworthiness are not validated in actual transactions.
We study whether, when, and how better connectivity through nonstop flights leads to positive innovation outcomes for firms in the global context. Using unique data of all flights emanating from 5,015 airports around the globe from 2005 to 2015 and exploiting a regression discontinuity framework, we report that a 10% increase in nonstop flights between two locations leads to a 3.4% increase in citations and a 1.4% increase in the production of collaborative patents between those locations. This effect is driven primarily by firms as opposed to academic institutions. We further study the characteristics of firms and firm locations that are salient to the relation between nonstop flights and innovation outcomes across countries. Using a gravity model, we posit and find that the positive effect of nonstop flights on innovation is stronger for firms and subsidiaries with greater innovation mass (e.g., stocks of inventors and R&D spending), located in innovation hubs or countries that are deemed technology leaders, and that are separated by large cultural or temporal distance.
Financing frictions may create a misallocation of assets in a market, thus depressing output, productivity, and asset values. This paper empirically explores how liquidity shocks generate a reallocation that diminishes this misallocation. Using a unique data set of agricultural outcomes, I explore how farmers respond to a relaxation of financial constraints through a liquidity shock unrelated to farming fundamentals, namely exogenous cash inflows caused by an expansion of hydraulic fracturing (fracking) leases. Farmers experiencing positive cash flow shocks increase their land purchases, which results in a reallocation effect. Examining purchases across areas, I find that farmers in high-productivity areas who receive cash flow shocks buy farmland in low-productivity areas, but farmers in low-productivity areas receiving positive cash flow shocks do not. Moreover, farmers increase their purchases of vacant (undeveloped) land. Average output, productivity, equipment investment, and profits all increase significantly following these positive cash flow shocks. Farmland prices also rise significantly, consistent with a cash-in-the-market pricing effect. These effects are consistent with an efficient reallocation of land toward more productive users.
We study the involvement of corporate executives in U.S. politics over the last 40 years. First, we document that the share of business politicians in federal elected office increased from 13.3% in 1980% to 22.6% in 2018, with most of the increase occurring over the last two decades. Second, we find that business politicians enjoy an early fundraising advantage over their opponents, both because they are more likely to self-fund their campaigns and because they receive more campaign contributions from their firms. Third, the election of business politicians benefits their industries and firms, which experience positive abnormal stock returns when their executives win political office. We also show that business politicians, once elected, vote for policies that shift the balance of power toward corporate interests. Using close elections for identification, we show that this policy shift cannot be attributed solely to the changes in the underlying preferences of the electorate. Overall, our results indicate that corporate executives have become more involved in U.S. politics and that this involvement has benefited business interests and affected aggregate legislative outcomes.
We study the relationship between new intermediaries and regulatory arbitrage, analyzing marketplace credit around a tightening of mortgage loan-to-value (LTV) caps in several cities in China in 2013. Using novel data covering over 20% of Chinese marketplace credit as well as the universe of loans and loan applications at a leading online lending platform, we provide evidence consistent with home buyers borrowing online to bypass the tighter LTV cap. Our findings point to new, lightly regulated financial intermediaries as a driver of household leverage, suggesting that they can open nonnegligible regulatory arbitrage channels.
This study tests whether modulating a brain region associated with cognitive control using noninvasive brain stimulation affects the disposition effect. A participant trades stocks in an experimental asset market while their level of cognitive control is exogenously manipulated by applying weak current stimulation to a control-related cortical region. We find that the stimulation significantly affects the participant’s cognitive control and tendency to exhibit the disposition effect. This result is also obtained for financial professionals. We thus provide evidence that cognitive control is important for the disposition effect.
We evaluate the impact of complexity and information content of 10-K filings on uncertainty dynamics following the filings. We have three main findings. First, the option-implied volatility on average increases in the first four weeks after the filings, followed by a net decrease in the subsequent six weeks. Second, this hump-shaped volatility dynamic is more pronounced for firms with larger 10-K file sizes. Third, we provide a novel decomposition of 10-K file size based on the individual sections’ disclosure amount and topic analysis and find that the discussions on topics in the “risk factors” section mainly capture the complexity aspect, whereas the discussions on topics in the “managerial discussion and analysis” section mainly capture the information content aspect of the 10-K filings. Our findings highlight the importance of timing for understanding the opposing effects of complexity and information content on asset prices.
Using a supervised machine learning framework on a large training set of questions and answers, we identify 1,364 trigrams that signal nonanswers in earnings call questions and answers (Q&A). We show that this glossary has economic relevance by applying it to contemporaneous stock market reactions after earnings calls. Our findings suggest that obstructing the flow of information leads to significantly lower cumulative abnormal stock returns and higher implied volatility. As both our method and glossary are free of financial context, we believe that the measure is applicable to other fields with a Q&A setup outside the contextual domain of financial earnings conference calls.
Banks strategically choose and dynamically restructure deposits and nondeposit debt in response to the minimum requirements on total capital and tangible equity. We derive the optimal strategic liability structure and show that it minimizes the protection for deposits conditional on capital requirements. Although, given any liability structure, regulators can set capital requirements high enough to remove the incentive for risk substitution, the strategic response to the capital requirements always preserves this incentive. Banks reduce leverage but increase the proportion of nondeposit debt if regulations raise the capital requirements.
An IPO is a significant event for an individual firm, and the literature examining initial public offering (IPO) firms is extensive. However, there is little evidence on the subsequent effect of a significant IPO on incumbent firms. We extend this literature by using firms’ tax policies as a powerful setting to identify whether incumbent firms respond to a significant IPO in their industry. Specifically, we use a first difference analysis to examine whether incumbent firms herd their effective tax rates (ETRs) toward a significant IPO’s ETR. We provide robust evidence that incumbent firms adjust their ETR either up or down by one to two percentage points, on average, for up to three years following a significant IPO entrance to the industry. Cross-sectional analyses support attention-based and tax-specific motives for herding, and additional analysis documents market attention-based consequences. Finally, we find that incumbents weigh the benefits of herding against the cost of adjusting their ETR and that incumbent firms use discretionary tax accruals as a less costly mechanism to imitate IPO firms’ generally accepted accounting principles (GAAP) ETRs. We contribute to an IPO literature that focuses almost exclusively on the IPO firm. We extend the literature by providing evidence that a significant IPO in an industry can result in a herding response in incumbent firms’ policy choices.
This paper studies the effect of trust on the perceived credibility of earnings news. Using earnings response coefficients, we find that firms located in low-trust regions of the United States experience significantly lower stock price reactions to earnings news. Additional tests indicate that managers can counterbalance investors’ dependence on trust by employing reputable auditors or signaling the quality of their earnings using dividends to improve the perceived credibility of their financial reports. Overall, our findings suggest that trust affects the pricing of earnings news in capital markets.
Over the past decade, blockchains and cryptocurrencies have taken a central stage in financial technology (FinTech) innovation. In 2020–2021, as the academic finance and management community began actively investigating this domain, we issued a call for papers for a special issue to encourage interdisciplinary research in this emerging area. This section of Management Science presents the first systematic collection of knowledge, both theoretical and empirical, focusing on blockchain economics, crypto assets, decentralized finance, and Web3 ecosystems. We describe the editorial protocol employed for this special issue (now included in this volume as a special section), summarize what we learn about the field, and introduce the 15 articles included in the special section. We also offer several observations to highlight foundational issues in the new field and to guide future research in this exciting new area at the intersection of technology and finance.
We present the first systematic approach to detect fake transactions on cryptocurrency exchanges by exploiting robust statistical and behavioral regularities associated with authentic trading. Our sample consists of 29 centralized exchanges, among which the regulated ones feature transaction patterns consistently observed in financial markets and nature. In contrast, unregulated exchanges display abnormal first significant digit distributions, size rounding, and transaction tail distributions, indicating widespread manipulation unlikely driven by a specific trading strategy or exchange heterogeneity. We then quantify the wash trading on each unregulated exchange, which averaged more than 70% of the reported volume. We further document how these fabricated volumes (trillions of dollars annually) improve exchange ranking, temporarily distort prices, and relate to exchange characteristics (e.g., age and user base), market conditions, and regulation. Overall, our study cautions against potential market manipulations on centralized crypto exchanges with concentrated power and limited disclosure requirements and highlights the importance of fintech regulation.
Does the proof-of-work consensus protocol serve its intended purpose of supporting decentralized cryptocurrency mining? To address this question, we develop a game-theoretical model in which miners first invest in hardware to improve the efficiency of their operations and then compete for mining rewards in a rent-seeking game. We show that centralization grows with heterogeneity in mining costs, but hardware capacity constraints prevent the most efficient miners from monopolizing the mining process. Investment leads to a more decentralized network unless larger miners have a significant comparative advantage in acquiring new hardware. Our model generates empirically supported implications: (i) mining centralization is countercyclical with respect to mining reward, and (ii) a change in mining reward leads to a less-than-proportional change in hash rates.
We assess how the cost structure of cryptocurrency mining affects the response of miners to exchange rate fluctuations and the immutability of cryptocurrency ledgers that rely on proof-of-work. We show that the amount of mining power supplied to currencies that rely on specialized hardware, such as Bitcoin, responds less to adverse exchange rate shocks than other currencies respond to such shocks, a fact that is instrumental to avoiding double-spending attacks. The results may change if mining equipment used for one cryptocurrency can be transferred to another. For smaller currencies with low exchange rate correlation, transferability eliminates the protection that fixed costs provide. Our results weaken doomsday predictions for Bitcoin and other cryptocurrencies with declining block rewards.
Blockchain-based cryptocurrencies must solve the problem of assigning priorities to competing transactions. The most widely used mechanism involves each transaction offering a fee to be paid once the transaction is processed, but this discriminatory price mechanism fails to yield stable equilibria with predictable prices. We propose an alternate fee setting mechanism, StableFees, that is based on uniform price auctions. We prove that our proposed protocol is free from manipulation by users and miners as the number of users and miners increases and show empirically that gains from manipulation are small in practice. We show that StableFees reduces the fees paid by users and reduces the variance of fee income to miners. Data from December 2017 show that, if implemented, StableFees could have saved Bitcoin users $272,528,000 USD in transaction fees while reducing the variance of miner’s fee income, on average, by a factor of 7.4. We argue that our fee protocol also has important social welfare and environmental benefits.
In the high-stakes race for scalability, some blockchains have turned to committee-based consensus (CBC), whereby the chain’s recordkeeping rights are entrusted to a committee of block producers elected via approval voting. Smaller committees boost speed and scalability but can compromise security when voters have limited information. In this environment, voting strategies are naturally nonlinear, and equilibria can become intractable. Despite this, we show that elections converge to optimality asymptotically (in voter numbers), exponentially quickly, and under relatively weak informational requirements. Compared to popular stake-weighted lottery and single-vote protocols used in practice, we find that CBC, when paired with approval voting, can offer meaningful efficiency and robustness gains if enough voters are engaged.
Real-world contractual agreements between firms are often incomplete, leading to suboptimal investment and loss of value in supply chain relationships. To what extent can blockchain technology help alleviate problems arising from contractual incompleteness? We examine this issue by exploiting a quasi-natural experiment based on the staggered adoption of U.S. state laws that increased firms’ in-state ability to develop, adopt, and use blockchain technology. We find that, after exposure to a pro-blockchain law, firms with greater asset specificity exhibit more positive changes to Tobin’s Q, research and development, and blockchain-related innovation. Also, such firms appear to rely less on vertical integration, form more strategic alliances, and shift their emphasis to less geographically proximate customers. Overall, our results suggest that blockchain technology can help firms remedy constraints and inefficiencies arising from contractual incompleteness.
In a token offering, investors fund a venture in exchange for tokens that grant rights to future economic output. To many financial industry insiders, tokens have no intrinsic merit and exist only as a way to evade regulations. We demonstrate that generic revenue-based token contracts are indeed economically inferior to equity and lead to over- or underproduction. However, an optimally designed token contract, which is a combination of an output presale and an incremental revenue-sharing agreement, yields the same payoffs as equity and debt. Moreover, with entrepreneurial moral hazard, tokens can finance a strictly larger set of ventures than equity.
Using the market for initial coin offerings (ICOs) as a laboratory, we provide evidence that entrepreneurs use retention to alleviate information asymmetry. The underlying technology and the absence of regulation make the ICO market well suited to study this question empirically. Using a hand-collected data set, we show that ICO issuers that retain a larger fraction of their tokens are more successful in their funding efforts and are more likely to develop a working product. Moreover, we find that retention is a stronger signal when markets are crowded, and investors do not have as much time to conduct due diligence.
We examine digital product markets in which consumers are heterogeneous in their propensity to actively interact with other users and valuations increase with the share of active users (e.g., social network platforms). We propose a model in which entrepreneurs can issue digital claims (tokens) to promise exclusive access to benefits that specifically enhance the utility of active users. This allows entrepreneurs to extract consumer surplus through price discrimination. Because there is an incentive to renege on the “exclusivity” promise ex post and expand the network of active users, the credibility of this commitment resides in a costly technology (blockchain) that embeds automatic contracts in the tokens sold and limits entrepreneurial discretion. We show that the profitability of token-based sales increases with entrepreneurial ability and the intensity of network effects.
This paper studies the contribution of analysts to the functioning and failure of the market for initial coin offerings (ICOs). The assessments of freelancing analysts exhibit biases because of reciprocal interactions of analysts with ICO team members. Even favorably rated ICOs tend to fail raising some capital when a greater portion of their ratings reciprocate prior ratings. Ninety days after listing on an exchange, the market capitalization relative to the initial funds raised is smaller for tokens with more reciprocal ratings. These findings suggest that conflicts of interest help explain the failure of ICOs.
Traditional two-sided platforms (e.g., Amazon, Uber) rely primarily on commission contracts to generate revenues and fuel growth, whereas their decentralized counterparts (e.g., Uniswap, Filecoin) often forego these in favor of token retention. What economics underpin this choice? We show that with properly designed initial coin offerings (ICOs), both mechanisms can independently alleviate market failures at the initial fundraising stage and incentivize long-term platform building. However, they achieve this in different ways. Although commission contracts often lead to higher profits for founders, token retention leads to higher service levels, benefiting the users and service providers. In essence, token retention surrenders a fraction of earnings to better align with the tenets of decentralized governance. Combining both mechanisms can add value, but only in relatively limited cases. These findings offer guidance and a possible rationale for why platforms may want to favor one mechanism over the other or use both.
We model cryptocurrencies as utility tokens used by a decentralized digital platform to facilitate transactions between users of certain goods or services. The network effect governing user participation, in conjunction with the nonneutrality of the token price, can cause the token market to break down. We show that token retradeability mitigates this risk of breakdown on younger platforms by harnessing user optimism but worsens this fragility when sentiment trading by speculators crowds out users. Elastic token issuance mitigates this fragility, but strategic attacks by miners exacerbate it because users’ anticipation of future losses depresses the token’s resale value.
Many central banks are considering issuing a central bank digital currency (CBDC). How will the CBDC affect the macroeconomy? Will its design matter? To answer these questions, we theoretically and quantitatively assess the effects of a CBDC on consumption, banking, and welfare. Our model captures the competition between different means of payments and incorporates a novel general equilibrium feedback effect from transactions to deposits creation. The general equilibrium effects of a CBDC are decomposed into three channels: payment efficiency, price effects, and bank funding costs. We show that a cash-like CBDC is more effective than a deposit-like CBDC in promoting consumption and welfare. Interestingly, a cash-like CBDC can also crowd in banking, even in the absence of bank market power. In a calibrated model, at the maximum, a cash-like CBDC can increase bank intermediation by 10.2% and welfare by 0.059%, and it can capture up to 23.3% of the payment market. We also discuss some lessons for designing a CBDC.
Decentralized exchanges (DEXs) are an essential component of the nascent decentralized finance (DeFi) ecosystem. The most common DEXs are so-called automated market makers (AMMs): smart contracts that pool liquidity and process trades as atomic swaps of tokens. AMMs price transactions with a deterministic liquidity invariance rule that only uses the AMM’s token deposits as inputs and that has no precedent in traditional finance. Yet, in the context of transparent and open blockchain operations, any liquidity invariance pricing function allows so-called sandwich attacks (akin to front running) that increase the cost of trading and threaten the long-term viability of the DeFi ecosystem. Invariance pricing is also not regret free. Linear pricing rules have similar problems except for uniform pricing, which has regret-free prices and limits sandwich attack profits but which invites excessive order splitting. Comparing trading costs using a model of liquidity provision, constant product pricing is often cheaper except when the variance of the underlying asset is small or when the order is large.
Cryptocurrencies provide a unique opportunity to identify how derivatives impact spot markets. They are fully fungible and trade across multiple spot exchanges at different prices, and futures contracts were selectively introduced on Bitcoin (BTC) exchange rates against the U.S. dollar (USD) in December 2017. Following the futures introduction, we find a significantly greater increase in cross-exchange price synchronicity for BTC–USD relative to other exchange rate pairs as demonstrated by an increase in price correlations and a reduction in arbitrage opportunities and volatility. We also find support for an increase in price efficiency, market quality, and liquidity. The evidence suggests that futures contracts allowed investors to circumvent arbitrage frictions associated with short-sale constraints, arbitrage risk associated with block confirmation time, and market segmentation. Overall, our analysis supports the view that the introduction of BTC–USD futures was beneficial to the Bitcoin spot market by making the underlying prices more informative.
Hospitals throughout the developed world are reimbursed based on diagnosis-related groups (DRGs). Under this scheme, patients are divided into clinically meaningful groups, and hospitals receive a fixed fee per patient episode tied to the patient DRG. The fee is based on the average cost of providing care to patients who belong to the same DRG across all hospitals. This scheme, sometimes referred to as “yardstick competition,” provides incentives for cost reduction, as no hospital wants to operate at a higher cost than average, and can be implemented using accounting data alone. Nevertheless, if costs within a DRG are heterogeneous, this scheme may give rise to cherry-picking incentives, where providers “drop” patients who are more expensive to treat than average. To address this problem, regulators have tried to reduce within-DRG cost heterogeneity by expanding the number of DRG classes. In this paper, we show that even if cost heterogeneity is eliminated, such expansion will fail to completely eliminate patient cherry picking. In equilibrium, the market will bifurcate into two groups, one of which will continue to cherry-pick patients and underinvest in cost reduction, whereas the other group treats all patients. Furthermore, we show that DRG expansion is particularly problematic if hospitals are also able to “upcode” patients, that is, intentionally assign patients to a more resource-intensive DRG than needed to increase income. Upcoding increases within-DRG cost heterogeneity and amplifies cherry-picking incentives. We examine potential solutions involving yardstick competition based on input statistics.
As the industrial Internet-of-things (IIoT) is becoming increasingly valuable, manufacturers are eager to establish IIoT-based platforms for preventative maintenance (PM). These platforms reposition the roles of manufacturers and reshape the patterns of the after-sales service market. Manufacturers can adopt either the competitive strategy by introducing improved after-sales services to compete with independent maintenance, repair, and operations firms (MROs) or the “coopetitive” strategy by simultaneously opening the platform to these MROs. However, relevant research on this topic remains scarce. Hence, our study fills this gap by investigating a manufacturer’s decision for the IIoT-based platform and the subsequent relationship with an MRO. First, we find that even when the product value is relatively low and the IIoT adoption increases PM cost; interestingly, it is sometimes beneficial for the manufacturer to establish the platform. Next, we find that even if the royalty revenue is lower than the increased IIoT costs, the manufacturer may sometimes still adopt the coopetitive strategy of opening the platform. Moreover, with the opening of the platform, the manufacturer pays more technology investment, even in the competitive market. Furthermore, whenever the manufacturer opens the platform, the MRO can profit more by accessing the platform. Hence, there is sometimes a win-win equilibrium with the IIoT adoption. In addition, we find that the opening of the platform sometimes causes less customer surplus but generates more social welfare. Following the IIoT adoption, our findings offer insightful takeaways for the manufacturer’s decisions on the establishment and opening of the platform, the MRO’s reaction, and policymakers’ welfare policies.
We study the allocative challenges that governmental and nonprofit organizations face when tasked with equitable and efficient rationing of a social good among agents whose needs (demands) realize sequentially and are possibly correlated. As one example, early in the COVID-19 pandemic, the Federal Emergency Management Agency faced overwhelming, temporally scattered, a priori uncertain, and correlated demands for medical supplies from different states. In such contexts, social planners aim to maximize the minimum fill rate across sequentially arriving agents, where each agent’s fill rate (i.e., its fraction of satisfied demand) is determined by an irrevocable, one-time allocation. For an arbitrarily correlated sequence of demands, we establish upper bounds on the expected minimum fill rate (ex post fairness) and the minimum expected fill rate (ex ante fairness) achievable by any policy. Our upper bounds are parameterized by the number of agents and the expected demand-to-supply ratio, yet we design a simple adaptive policy called projected proportional allocation (PPA) that simultaneously achieves matching lower bounds for both objectives (ex post and ex ante fairness) for any set of parameters. Our PPA policy is transparent and easy to implement, as it does not rely on distributional information beyond the first conditional moments. Despite its simplicity, we demonstrate that the PPA policy provides significant improvement over the canonical class of nonadaptive target-fill-rate policies. We complement our theoretical developments with a numerical study motivated by the rationing of COVID-19 medical supplies based on a standard compartmental modeling approach that is commonly used to forecast pandemic trajectories. In such a setting, our PPA policy significantly outperforms its theoretical guarantee and the optimal target-fill-rate policy.
The internet advertising market is a multibillion dollar industry in which advertisers buy thousands of ad placements every day by repeatedly participating in auctions. An important and ubiquitous feature of these auctions is the presence of campaign budgets, which specify the maximum amount the advertisers are willing to pay over a specified time period. In this paper, we present a new model to study the equilibrium bidding strategies in standard auctions, a large class of auctions that includes first and second price auctions, for advertisers who satisfy budget constraints on average. Our model dispenses with the common yet unrealistic assumption that advertisers’ values are independent and instead assumes a contextual model in which advertisers determine their values using a common feature vector. We show the existence of a natural value pacing–based Bayes–Nash equilibrium under very mild assumptions. Furthermore, we prove a revenue equivalence showing that all standard auctions yield the same revenue even in the presence of budget constraints. Leveraging this equivalence, we prove price of anarchy bounds for liquid welfare and structural properties of pacing-based equilibria that hold for all standard auctions. In recent years, the internet advertising market has adopted first price auctions as the preferred paradigm for selling advertising slots. Our work, thus, takes an important step toward understanding the implications of the shift to first price auctions in internet advertising markets by studying how the choice of the selling mechanism impacts revenues, welfare, and advertisers’ bidding strategies.
In several transportation systems, vehicles can choose where to meet customers rather than stopping in fixed locations. This added flexibility, however, requires coordination between vehicles and customers that adds complexity to routing operations. This paper develops scalable algorithms to optimize these operations. First, we solve the one-stop subproblem in the ℓ1 space and the ℓ2 space by leveraging the geometric structure of operations. Second, to solve a multistop problem, we embed the single-stop optimization into a tailored coordinate descent scheme, which we prove converges to a global optimum. Third, we develop a new algorithm for dial-a-ride problems based on a subpath-based time–space network optimization combining set partitioning and time–space principles. Finally, we propose an online routing algorithm to support real-world ride-sharing operations with vehicle–customer coordination. Computational results show that our algorithm outperforms state-of-the-art benchmarks, yielding far superior solutions in shorter computational times and can support real-time operations in very large-scale systems. From a practical standpoint, most of the benefits of vehicle–customer coordination stem from comprehensively reoptimizing “upstream” operations as opposed to merely adjusting “downstream” stopping locations. Ultimately, vehicle–customer coordination provides win–win–win outcomes: higher profits, better customer service, and smaller environmental footprint.
Folklore suggests that policy gradient can be more robust to misspecification than its relative, approximate policy iteration. This paper studies the case of state-aggregated representations, in which the state space is partitioned and either the policy or value function approximation is held constant over partitions. This paper shows a policy gradient method converges to a policy whose regret per period is bounded by ϵ, the largest difference between two elements of the state-action value function belonging to a common partition. With the same representation, both approximate policy iteration and approximate value iteration can produce policies whose per-period regret scales as ϵ/(1−γ), where γ is a discount factor. Faced with inherent approximation error, methods that locally optimize the true decision objective can be far more robust.
Gender segregation remains a significant problem in many occupations and organizations. To solve this problem, many U.S. employers now seek to craft gender-neutral job postings. In this article, we investigate whether such employer recruitment efforts are successful in encouraging women and men to apply equally for jobs. Specifically, we move beyond the well-studied effects of the gender typing of occupations, organizations, and industries to study the extent to which the recruiting language used in job postings influences the actual preapplication behavior of job seekers of different genders. Using unique data from both a large-sample observational field study (Study 1) and a field experiment study (Study 2) of real online job postings, we first assess the gendered language mechanism by testing whether stereotypical femininity in the wording that recruiters use to advertise otherwise identical jobs differently influences female and male job seekers’ interest in applying. We then assess the recruiter gendering mechanism by testing whether the gender of the recruiter and the femininity in the wording recruiters use when presenting themselves to job seekers further contribute to gender job search disparities. Our analyses ultimately show negligible effects for both the gendering of job postings or of the job poster, and we therefore conclude that, in practice, employers’ efforts to simply tweak the language of recruitment messages do not matter much for gender equality and diversity.
Affirmative action policies are often implemented through reserve systems. In this study, we demonstrate that reserve systems face widespread misunderstanding by the public. This misunderstanding can lead individuals to support policies that ineffectively pursue their interests. To establish these claims, we present 1,013 participants in the Understanding America Study with choices between pairs of reserve systems. Participants are members of the group receiving affirmative action and are financially incentivized to choose the system that maximizes their chance of admission. Using this data, we apply a novel approach to identifying the rate of uptake of different decision rules used by participants. We find that participants rarely use a fully optimal decision rule. In contrast, we find that many choices—40% in our primary estimates—are rationalized by a nearly correct decision rule, with errors driven solely by failing to appreciate the importance of processing order. Failing to account for processing order causes individuals to fail to distinguish between two policies that achieve different degrees of affirmative action: policies that provide nonbinding minimum guarantees of the number of spaces allocated and policies that provide spaces over-and-above what would be allocated absent a reserve. Confusion about the importance of processing order helps to explain otherwise surprising decisions made in field applications of reserve systems. We discuss implications for managers and policy makers who are trying to implement reserve systems and who are accountable to the public.
In nearly every betting market, gamblers overvalue the riskiest bets. This “longshot bias” is usually explained by a tendency to overweight low-probability events. We offer a novel explanation: contrast effects enhance the attractiveness of longshots because gambles presented in terms of their payoffs are easier to compare along the payoff dimension than along the probability dimension. Our analysis of historical betting odds suggests that contrasts between gamble payoffs can explain the longshot bias. Laboratory experiments confirm those contrast effects and show that the longshot bias disappears when gamblers consider bets in isolation or when winning probabilities are easier to compare.
Choosing job applicants to hire in online labor markets is hard. To identify the best applicant at hand, employers need to assess a heterogeneous population. Recommender systems can provide targeted job-applicant recommendations that help employers make better-informed and faster hiring choices. However, existing recommenders that rely on multiple user evaluations per recommended item (e.g., collaborative filtering) experience structural limitations in recommending job applicants: Because each job application receives only a single evaluation, these recommenders can only estimate noisy user-user and item-item similarities. On the other hand, existing recommenders that rely on classification techniques overcome this limitation. Yet, these systems ignore the hired worker’s performance—and, as a result, they uniformly reinforce prior observed behavior that includes unsuccessful hiring choices—while they overlook potential sequential dependencies between consecutive choices of the same employer. This work addresses these shortcomings by building a framework that uses job-application characteristics to provide recommendations that (1) are unlikely to yield adverse outcomes (performance-aware) and (2) capture the potentially evolving hiring preferences of employers (sequence-aware). Application of this framework on hiring decisions from an online labor market shows that it recommends job applicants who are likely to get hired and perform well. A comparison with advanced alternative recommender systems illustrates the benefits of modeling performance-aware and sequence-aware recommendations. An empirical adaptation of our approach in an alternative context (restaurant recommendations) illustrates its generalizability and highlights its potential implications for users, employers, workers, and markets.
I study how much consumers benefit from new products in markets with information frictions. I analyze new products in the U.S. hard drive market, which is characterized by ample product innovation. Using unique click-stream data, I measure the magnitude of two frictions, category consideration and costly search, and show that both play a crucial role in shaping consumer demand. To estimate consumer surplus from new products, I develop a search model that captures both frictions and propose a novel Bayesian estimation method to recover its parameters. I then show that ignoring information frictions leads researchers to underestimate the consumer surplus from new hard drives because it appears that consumers do not value the combinations of attributes these hard drives offer. Partly eliminating frictions, through marketing efforts or market-wide transparency initiatives, can help consumers to more fully internalize the benefits of new product launches.
The well-documented rating inflation of incentivized reviews (IRs) can mislead consumers into choosing a product that they would otherwise not buy. To protect consumers from this undesirable influence, the U.S. Federal Trade Commission recommends that reviewers conspicuously disclose any material connection they may have with sellers. In theory, such disclosures safeguard consumers by motivating reviewers to be truthful and inducing consumers to discount inflated IR ratings. Our research finds, however, that IR disclosure accomplishes neither. Specifically, our empirical analysis of consumer reviews on Amazon reveals that, even with disclosure, (1) rating inflation of IRs remains, and (2) this inflation boosts sales at consumers’ expense. Finally, we propose an alternative approach to eliminate rating inflation of IRs and empirically demonstrate its effectiveness. These findings have important implications for consumers, firms, and ongoing policy discussions around IRs.
We examine strategic communication in labor market recommendations. Our formal model features two-sided asymmetric information: An adviser has private information about his own preference bias for a focal candidate and a signal of the quality of this candidate, whereas the hiring firm has private information about the quality of an alternative candidate. The adviser can choose whether to recommend his focal candidate to the firm. If he recommends and the firm hires the candidate, then the adviser pays a reputational cost (receives a reputation boost) if the firm later learns that the hire has low quality (high quality). Our main results describe how the equilibrium behavior of advisers (lying choices) and firms (hiring choices) depend on the intricate interplay between preference biases, reputation, lying costs, and the hiring firm’s labor market strength (access to alternative candidates with higher quality). We show that the equilibrium features assortative matching: advisers with a higher (lower) reputation choose to lie less (more), and consequently, their candidates are more likely to be hired by firms with strong (weak) access to high-skilled outside candidates. Two equilibrium forces create a “rich get richer” effect. First, advisers choose to lie less to hiring firms with access to better top candidates, further benefiting those firms. Second, advisers with a higher (lower) reputation choose to lie less (more), which increases (decreases) their future reputation, creating a “reputation trap.” We discuss the implications of our model for hiring strategy, referral systems, and the ability to accrue and sustain human capital-based competitive advantages.
This paper examines how a firm’s choice of the type of experiment impacts on its potential exploitation of new technological opportunities. It does so in the context of the failure of successful firms (or disruption) where the literature has informally suggested that firms undertake errors in experimental choice. It is shown that firms will generically choose experiments that minimize false positives (a high-bar) or minimize false negatives (a low-bar) rather than strike a balance between the two. This is done to better inform decisions regarding the exploitation of technological opportunities. It is shown that these choices can differ between incumbents and entrants based on their fundamentals and because of the anticipation of competition between them.
The prior literature on analyst forecasts has focused almost exclusively on firms that just meet or beat the mean or median consensus analyst forecast, without much regard to alternative benchmarks within the forecast distribution. Anecdotal evidence suggests that there is institutional significance to the lowest (minimum) and highest (maximum) analyst earnings forecast. We rigorously explore whether these two new benchmarks actually have incremental significance and, if so, whether there are differences in how managers and investors perceive the importance of these three benchmarks (i.e., minimum, mean, and maximum). Consistent with the theory of investor ambiguity aversion, which predicts an asymmetric market response to good and bad news, our results support the notion that of the three benchmarks we explore, firms act most aggressively to exceed the minimum forecast, followed by the mean, and then finally the maximum. This order is consistently supported by the following evidence: the existence of higher incentives to beat the benchmark; the likelihood of earnings management to beat the benchmark; accrual reversal after firms just barely achieve each benchmark; accrual mispricing around each benchmark; and, finally, a faster incorporation into the stock price of the bad news that a firm misses the minimum than of the good news that a firm meets or beats the maximum. These findings fill a void in academic research on these two new benchmarks and offer a consistent explanation as to why the popular press and managers frequently highlight and discuss beating these benchmarks as a separate and notable achievement.
We examine how brokerage firm initial public offerings (IPOs) influence the research quality of sell-side analysts employed by the brokerage. Our main results focus on earnings forecast bias and absolute forecast errors as proxies for research quality. Using a staggered difference-in-differences analysis, we document significant decreases in forecast bias and absolute forecast error during the two-year period centered on the analysts’ brokerage house IPO. In additional analyses, we explore several potential explanations for the short-term benefits of brokerage house IPOs. We find some evidence that IPOs delay the departure of more talented analysts and that the effects are more concentrated among analysts and brokers that face more scrutiny.
Using intertemporal variation in the bounding of a state’s minimum wage by the federal rate and business credit-score data for 15.2 million establishments, we find that the increase in labor costs caused by a higher federal minimum wage leads to lower business credit scores and worsens the financial health of small businesses in the affected states. In particular, small, young, labor-intensive, and minimum-wage-sensitive establishments located in affected states and those located in competitive and low-income areas experience higher financial stress. Increases in the minimum wage are associated with employment reductions and a higher exit rate for small businesses. Our results document some potential costs of a one-size-fits-all nationwide minimum wage for some small businesses.
We study incentive problems associated with the compensation of servicers during default remediation. First, we fill a gap in the literature by identifying stylized facts about servicing fees. Next, we present evidence showing that servicing fees drive mortgage modifications and foreclosures, likely to the detriment of investors. Servicers modify loans paying high servicing fees and delay their foreclosure to protect servicing cash flows. These effects are causal. Voluntary mortgage renegotiation by servicers is unlikely to reduce foreclosures. In addition to ex post government intervention, special servicing and innovative mortgage contracts allowing for affordable modifications that benefit investors may improve renegotiation outcomes.
The U.S. healthcare system is undergoing a period of substantial change with hospitals purchasing many physician practices (“vertical integration”). In theory, this vertical integration could improve quality by promoting care coordination but could also worsen it by impacting the care delivery patterns. The evidence quantifying these effects is limited because of the lack of understanding of how physicians’ behaviors alter in response to the changes in financial ownership and incentive structures of the integrated organizations. We study the impact of vertical integration by examining Medicare patients treated by gastroenterologists, a specialty with a large outpatient volume and a recent increase in vertical integration. Using a causal model and large-scale patient-level national panel data that includes 2.6 million patient visits across 5,488 physicians, we examine changes in various measures of care delivery. We find that physicians significantly alter their care process (e.g., in using anesthesia with deep sedation) after they vertically integrate, and there is a substantial increase in patients’ postprocedure complications. We further provide evidence that the financial incentive structure of the integrated practices is the main reason for the changes in physician behavior because it discourages the integrated practices from allocating expensive resources to relatively unprofitable procedures. We also find that, although integration improves operational efficiency (e.g., measured by physicians’ throughput), it negatively affects quality and overall spending. Finally, to shed light on potential mechanisms through which policymakers can mitigate the negative consequences of vertical integration, we perform both mediation and cost-effectiveness analyses and highlight some useful policy levers.
We consider the classic stochastic multiarmed bandit problem with a constraint that limits the total cost incurred by switching between actions to be no larger than a given switching budget. For this problem, we prove matching upper and lower bounds on the optimal (i.e., minimax) regret and provide efficient rate-optimal algorithms. Surprisingly, the optimal regret of this problem exhibits a nonconventional growth rate in terms of the time horizon and the number of arms. Consequently, we discover surprising “phase transitions” regarding how the optimal regret rate changes with respect to the switching budget: when the number of arms is fixed, there are equal-length phases, in which the optimal regret rate remains (almost) the same within each phase and exhibits abrupt changes between phases; when the number of arms grows with the time horizon, such abrupt changes become subtler and may disappear, but a generalized notion of phase transitions involving certain new measurements still exists. The results enable us to fully characterize the trade-off between the regret rate and the incurred switching cost in the stochastic multiarmed bandit problem, contributing new insights to this fundamental problem. Under the general switching cost structure, the results reveal interesting connections between bandit problems and graph traversal problems, such as the shortest Hamiltonian path problem.
We report experimental and quasi-experimental evidence from a “sharing economy” marketplace that transitioned from decentralized to centralized pricing. Centralized pricing increased the utilization of providers’ assets, resulting in higher revenues but also higher transaction costs. Providers who were barred from accessing the price system made nonprice adjustments, including reducing the availability of their assets, canceling booked transactions, and exiting the market. Providers who retained partial pricing control reacted substantially less but experienced similar revenue increases. We highlight the challenges of implementing centralized pricing and assessing its welfare effects. We show that partial control can mitigate these challenges, allowing providers to express their private and heterogeneous preferences while maintaining the benefits of centralization.
Artist collaborations in music have been on the rise, and they tend to produce commercially and critically successful songs. We seek to uncover the effect of these collaborative projects on career trajectories and identify the factors that lift an artist’s profile in the short and long term. We develop a theory of collaboration based on the transfer of capital between the collaborating artists that facilitates spillovers across time. To validate the theory, we use weekly radio plays of individual songs across 25 European countries between the years 2011 to 2018, together with a multiattribute Spotify data set of songs and Hofstede’s cultural dimensions in relation to artist origins. We create pairs of similar artists who released a collaboration and a solo song in the same week and measure the impact of collaborations based on the difference-in-differences methodology. We find that releasing a collaboration song, in comparison with a solo song, increases the number of plays of an artist in the future by +4.6%. This lift can be broken down into +9.6% for the current song and +7.7% for subsequently released songs, whereas past songs are unaffected. The effect is moderated by the difference in economic, social, and cultural capitals and is significantly larger when one’s partner has higher economic and social capital or is highly dissimilar along the cultural dimension. Our theoretical and empirical exploration of such strategic alliances uncovers several underlying mechanisms at play in the success of these pairings and can serve as the basis for future work targeted at prescriptive contributions.
The trust game, a simple two-player economic exchange, is extensively used as an experimental measure for trust and trustworthiness of individuals. We construct deep neural network–based artificial intelligence (AI) agents to participate a series of experiments based upon the trust game. These artificial agents are trained by playing with one another repeatedly without any prior knowledge, assumption, or data regarding human behaviors. We find that, under certain conditions, AI agents produce actions that are qualitatively similar to decisions of human subjects reported in the trust game literature. Factors that influence the emergence and levels of cooperation by artificial agents in the game are further explored. This study offers evidence that AI agents can develop trusting and cooperative behaviors purely from an interactive trial-and-error learning process. It constitutes a first step to build multiagent-based decision support systems in which interacting artificial agents are capable of leveraging social intelligence to achieve better outcomes collectively.
Although the rate of invention by firms and the effect on firm performance have been central themes in economics and strategy, the position and differentiation of invention by firms have received less attention. We develop a method to characterize a firm’s technology portfolio based on the semantic content of patents that allows us to map a firm’s unique spatial position relative to every other firm in technology space and to measure the overall differentiation of a firm’s technology portfolio. Using a large panel of U.S. public firms from 1980 to 2015, we illustrate that technology differentiation has a strong positive correlation with firm performance, particularly in research and development-intensive industries and industries with strong product market rivalry. We also show that technology differentiation is associated with subsequent differentiation from competitors in the product market and a reduction in outgoing technology spillovers to other firms. We provide open access to code and data to characterize the technology portfolio of firms and to measure the technological position and differentiation of U.S. public firms.
Immigration has proven to be a major force driving entrepreneurial dynamics. In this paper I investigate how the geographical distribution of immigrants within a given country, and in particular the presence of “enclaves,” affects the relationship between immigration and entrepreneurship. I examine the impact of Polish immigration to Great Britain following the unprecedented migration wave caused by the European Union enlargement in 2004. I address omitted variable concerns by using information on the location of historical Polish military settlements and the occupational composition and growth of Polish immigration in Ireland to construct instruments for enclaves and location choices of immigrants. The econometric results indicate, on the one hand, that immigration does increase immigrant entrepreneurship, but not in existing immigrant enclaves. On the other hand, immigrant entrepreneurs outside enclaves tend to achieve worse growth outcomes than those in enclaves. Further analyses provide an explanation to these findings due to blocked labor markets and to the higher prevalence of “necessity entrepreneurship” outside of enclaves. These results offer new insights to understand the influence of geography on entrepreneurship in the presence of immigration.
We show that managerial learning from stock prices can lead to feedback loop vulnerability: corrective actions based on perceived negative market signals reduce the sensitivity of asset payoffs to stock market information. Less sensitivity discourages liquidity provision and increases the price impact of liquidity shocks. Interestingly, overconfident managers who disregard stock price information may be less vulnerable to the adverse price impact of nonfundamental liquidity shocks. Our empirical evidence strongly supports the model’s underlying premises and predictions: First, investment decisions of overconfident CEOs are significantly less responsive to stock price fluctuations. Second, the price impact of liquidity shocks, for example, mutual fund fire sales, is substantially smaller for firms with overconfident CEOs.
Blockchain-based smart contracts can potentially replace certain traditional contracts through decentralized enforcement and reduced transaction costs. However, scalability is a key bottleneck hindering their broader application and adoption, often leading to concentrated or exclusive networks. To avoid falling short of the original promise of the technology, firms actively explore “layer-2” methods for scaling. We provide some initial evidence on the economic implications of a layer-2 scaling solution, which moves information aggregation from on-chain to off-chain peer-to-peer networks. A parallel-system experiment allows clean identification because we observe the same unit in the treatment and control systems at the same time. We find that this scaling solution reduces operating costs by 76%, and importantly, leads to decentralization with lower market concentration and more participation, which in turn improves data accuracy. The findings provide insights on how blockchain and smart contracting technologies evolve toward achieving decentralized and scalable trust.
E-commerce retailers are increasingly faced with challenges of finding ways to provide a seamless shopping experience to customers. The checkout process and its related touch points are especially critical in shaping the customer experience. We study the impact of adopting one-click buying, a feature that reduces the number of steps required to place a purchase order to a single click, on subsequent customer behavior. Using quasi-experimental data over a period of 35 months from an online retailer before and after the launch of one-click buying, we find adopting one-click buying is effective in lifting customer purchases and does so by making treated customers purchase more often and more items. The impact of adopting one-click buying on customer purchases after adoption is economically significant, persistent over time, and heterogeneous across customers. Analyzing clickstream data of customer activity online and purchases across product categories, we provide evidence that the increase in purchases is driven by richer engagement through both more visits to the website and more page views on visit and the expansion of purchases across categories. We discuss the implications of our findings for customer experience and targeting.
Recent shifts in societal discourse have led digital platforms to support equity, inclusivity, and diversity by introducing identity-signaling features, for example, indicators of owner race or gender. In this work, we explore whether, when, and how using those features may impact retailer demand. We tackle this question via a multimethod study. We begin by conducting a controlled experiment on Prolific.co, presenting subjects with actual Google Places business profiles for a set of Black-owned restaurants in Chicago. We randomly vary the presence of Black- and women-owned labels in these profiles and assess subjects’ expectations of popularity and quality along various restaurant dimensions. Our results demonstrate that the Black-owned label, in particular, drives significant increases in all outcomes with the effects arising primarily from Black and democratically liberal subjects. Next, we conduct an archival analysis of the effect label adoption has on the physical foot traffic that retailers receive based on SafeGraph’s mobile patterns database. Our difference-in-differences estimations yield consistent results; we find evidence of a positive average effect on foot traffic volumes. Further, we show that a rise in visitors from liberal-leaning geographies drives these effects. We discuss implications for digital platform operators and retailers.
We link records of firm performance, equity analyst forecast errors, and stock returns around companies’ earnings announcements to firm-specific measures of heat exposure for more than 17,000 firms in 93 countries from 1995 to 2019. We find that increased exposure to extremely high temperatures reduces firms’ revenues and operating income. A one-standard-deviation increase in the number of hot days decreases revenues (operating income) by 0.6% (1.8%) of the average quarterly revenue (operating income). Moreover, we provide evidence that increased heat exposure impacts negatively on firm financial performance relative to analyst predictions and on earnings announcement returns. These findings indicate that capital market participants do not fully anticipate the economic consequences of heat as a first order physical climate risk.
Unchecked climate change will cause precipitation volatility to increase around the world, leading to economic damages in the face of adjustment costs. We estimate these damages for construction—an economically important, climate exposed industry. Empirically, employment falls in response to forecasted rainfall and more so as the forecast horizon increases. This pattern allows for identification of labor adjustment costs via a multisector model of local labor markets calibrated to our estimates. When rainfall is anticipatable one month ahead, construction firms pay 10% of monthly profit to adjust. They pay less than 1% for rainfall anticipatable six months ahead. Without further adaptation or forecast improvements, increased rainfall volatility due to climate change is projected to lead to more costly adjustment.
I apply a novel machine-learning based “weather index” method to daily store-level sales data for a national apparel and sporting goods brand to examine short-run responses to weather and long-run adaptation to climate. I find that even when considering potentially offsetting shifts of sales between outdoor and indoor stores, to the firm’s website, or over time, weather has significant persistent effects on sales. This suggests that weather may increase sales volatility as more severe weather shocks become more frequent under climate change. Consistent with adaptation to climate, I find that sensitivity of sales to weather decreases with historical experience for precipitation, snow, and cold weather events, but—surprisingly—not for extreme heat events. This suggests that adaptation may moderate some but not all the adverse impacts of climate change on sales. Retailers can respond by adjusting their staffing, inventory, promotion events, compensation, and financial reporting.
This study uses disaggregated establishment-level data to identify a firm’s exposure to physical climate risk and examines investors’ reaction to natural disasters in both the U.S. corporate bond and stock markets. We find that, when a firm is exposed to disasters, investors overreact by depressing the current bond and stock prices, causing future returns to be higher. However, firms with a strong environmental profile experience lower selling pressure on their bonds and stocks, although their fundamentals weakened following disasters. The evidence suggests that corporate investment in improving environmental profiles pays off when climate change risk is materialized.
Enthusiasm for “greening the financial system” is welcome, but a fundamental challenge remains: financial decision makers lack the necessary information. It is not enough to know that climate change is bad. Markets need credible, digestible information on how climate change translates into material risks. To bridge the gap between climate science and real-world financial indicators, we simulate the effect of climate change on sovereign credit ratings for 109 countries, creating the world’s first climate-adjusted sovereign credit rating. Under various warming scenarios, we find evidence of climate-induced sovereign downgrades as early as 2030, increasing in intensity and across more countries over the century. We find strong evidence that stringent climate policy consistent with limiting warming to below 2 °C, honoring the Paris Climate Agreement and following representative concentration pathway (RCP) 2.6, could nearly eliminate the effect of climate change on ratings. In contrast, under higher emissions scenarios (i.e., RCP 8.5), 59 sovereigns experience climate-induced downgrades by 2030, with an average reduction of 0.68 notches, rising to 81 sovereigns facing an average downgrade of 2.18 notches by 2100. We calculate the effect of climate-induced sovereign downgrades on the cost of corporate and sovereign debt. Across the sample, climate change could increase the annual interest payments on sovereign debt by US$45–$67 billion under RCP 2.6, rising to US$135–$203 billion under RCP 8.5. The additional cost to corporations is US$10–$17 billion under RCP 2.6 and US$35–$61 billion under RCP 8.5.
We use firm-level data that measure forward-looking physical climate risk to examine the impact of climate risk on capital structure. We find that greater physical climate risk leads to lower leverage in the post-2015 period (i.e., after the Paris Agreement and the first step of standardization of disclosure of climate risk information). Our results hold after controlling for firm characteristics known to determine leverage, including credit ratings. Our evidence shows that the reduction in leverage related to climate risk is shared between a demand effect (the firm’s optimal leverage decreases) and a supply effect (bankers and bondholders increase spreads when lending to firms with the greatest risk). Our results are consistent with the hypothesis that physical climate risk affects leverage via larger expected distress costs and higher operating costs.
We find that manufacturing firms adopt more conservative capital structures in response to the Nitrogen Oxides (NOx) Budget Trading Program (NBP) of 2004, a regional cap-and-trade program aimed at mitigating the NOx emissions of power plants in 11 midwestern and southeastern states in the United States. Our further analysis demonstrates that, because the NBP induces an electricity price shock, it affects manufacturers’ financial decisions by raising their operating leverage and distress risk. We also find that firms respond to the NBP’s adoption heterogeneously: they adjust their financial leverage more dramatically when facing greater electricity intensity, financial distress threats, or competitive pressure. In addition, firms adapt not only capital structure, but also other financial policies in response to the regulation. Overall, our study shows that climate policy risk constitutes an essential consideration in firm financial decisions. It also highlights potential unintended consequences of policy responses to climate change for the corporate sector.
We estimate the risk premium for firm-level climate change exposure among S&P 500 stocks and its time-series evolution between 2005 to 2020. Exposure reflects the attention paid by market participants in earnings calls to a firm’s climate-related risks and opportunities. When extracted from realized returns, the unconditional risk premium is insignificant but exhibits a period with a positive risk premium before the financial crisis and a steady increase thereafter. Forward-looking expected return proxies deliver an unconditionally positive risk premium with maximum values of 0.5%–1% p.a., depending on the proxy, between 2011 and 2014. The risk premium has been lower since 2015, especially when the expected return proxy explicitly accounts for the higher opportunities and lower crash risks that characterize high-exposure stocks. This finding arises as the priced part of the risk premium primarily originates from uncertainty about climate-related upside opportunities. In the time series, the risk premium is negatively associated with green innovation; Big Three holdings; and environmental, social, and governance fund flows and positively associated with climate change adaptation programs.
Climate change and uncertainty about its potential consequences has become a central concern for economists, investors, and policymakers alike. I use a stochastic, dynamic general equilibrium model where final output is produced using a mix of cheap, dirty inputs and expensive, clean inputs and preferences incorporate aversion to climate model misspecification to analyze the implications of climate change and climate model uncertainty on economic and financial market outcomes. I find that climate change leads to increased clean input production and reduced emissions and that there is a negative price of climate risk that is significantly amplified and increasing in magnitude as climate change increases due to aversion to climate model uncertainty. Existing empirical estimates are consistent with the model implications, highlighting the potentially significant influence of climate model uncertainty on macroeconomic and asset pricing outcomes.
We compute the social cost of carbon (SCC) when decision makers want robust estimates in the face of deep (or “Knightian”) uncertainty. We introduce the notion of full-path accumulated robust preferences from stochastic control theory to an integrated assessment model. Robust preferences are appropriate for analyzing climate-related problems because, given the large uncertainty in climate science, they enable decision makers to attain solutions that are robust to a wide range of climate change scenarios. We solve the resulting model, which includes uncertainty about climate change and the ensuing economic damage and show the existence of optimal solutions and time-consistent optimal deterministic Markov policies. Additionally, we also prove that the standard Hansen–Sargent recursive utility provides an upper bound of this full-path utility. In our baseline model specification, we find that the year 2020’s optimal SCC is US$162 per tCO2 with an average annual growth rate of 2.5%, setting the world on a 1.37°C path, which requires full decarbonization by 2068. We introduce the notion of the SCC robustness premium, which we define as the additional SCC price tag for robustness. For a plausible range of preference parameters, the SCC robustness premium in 2020 is between US$1.41 and US$25.89 per tCO2 with US$2.20 per tCO2 in our baseline calibration. Over time, this premium grows significantly. The forecasts of our model facilitate managerial decision making during the world’s transition from a carbon- and emission-intensive economy to a regenerative economy. The high estimates for the SCC predict drastic rises in emission costs for high-emission industries.
We empirically test the prediction of Pástor et al. (2021) that green firms outperform brown firms when concerns about climate change increase unexpectedly, using data for S&P 500 companies from January 2010 to June 2018. To capture unexpected increases in climate change concerns, we construct a daily Media Climate Change Concerns index using news about climate change published by major U.S. newspapers and newswires. We find that on days with an unexpected increase in climate change concerns, the green firms’ stock prices tend to increase, whereas brown firms’ prices decrease. Furthermore, using topic modeling, we conclude that this effect holds for concerns about both transition and physical climate change risk. Finally, we decompose returns into cash flow and discount rate news components and find that an unexpected increase in climate change concerns is associated with an increase (decrease) in the discount rate of brown (green) firms.
Is it feasible to build desalination plants for the coproduction of salt and fresh water from U.S. seawater that could lead to a restructuring of supply chains for salt imports? As it is predicted that climate change will increase water stress worldwide, an increasing number of countries are using desalination plants to generate fresh water. In most such cases, residual concentrates must be disposed of, and the disposal cost is increasing as countries are becoming more environmentally conscious. Selective salt recovery can help to alleviate this issue as it reduces the need for concentrate disposal and generates additional revenue. To gain some insights into the costs and benefits of coproduction plants, we have collected data on current desalination practices and salt imports in the United States along with the manufacturing costs and energy requirements for coproduction plants. We have used this data to build an optimization model to determine an optimal number and location of coproduction plants in the United States and their potential markets for the sale of coproduced salt. In our analysis, we consider a different total number of coproduction facilities, and for each configuration, we evaluate the resulting net water cost and carbon emissions impact. Our results indicate that there exists the potential for building several coproduction plants in the United States that would be both financially competitive with existing desalination plants and lead to a reduction in carbon emissions. This information might be of use to both governments and businesses when they make decisions about the type of desalination facilities built and the implemented “polluter pays” policies.
This paper shows how green investing spurs companies to mitigate their carbon emissions by raising the cost of capital of the most carbon-intensive companies. Companies’ emissions decrease when the wealth share of green investors and their sensitivity to climate externalities increase. We show that the impact of green investors primarily governs companies’ long-run emissions. Companies are further incentivized to reduce their emissions when green investors anticipate tighter climate regulations and climate-related technological innovations. However, heightened uncertainty regarding future climate risks alleviates green investors’ pressure on the cost of capital of companies and pushes them to increase their emissions. Calibrated on U.S. data, our model suggests that, albeit effective, the impact of green investors remains limited given their current wealth share and practices.
We study collaborative shareholder engagements on climate change issues. These engagements involve coalitions of investors pursuing behind-the-scenes dialogue to encourage target firms to adopt environmental sustainability practices. Drawing on a unique data set of 553 engagements coordinated by the United Nations–supported Principles for Responsible Investment—and an innovative mixed-methods approach integrating fuzzy-set qualitative comparative analysis with regression analysis and qualitative interviews—we investigate how four coalition composition levers (coalition size, shareholding stake, experience, local access) combine to enable or hinder engagement success. We find that successful coalitions use four configurations of coalition composition levers that are tailored to target firms’ financial capacity and environmental predispositions, that is, target firms’ receptivity. Unsuccessful configurations instead emphasize single levers at the expense of others. Drawing on qualitative interviews, we identify three mechanisms (synchronizing, contextualizing, overfocusing) that plausibly underly the identified configurations and provide investor coalitions with knowledge about target firms and their local contexts, thus enhancing communication and understanding between investor coalitions and target firms. Our study contributes an emerging “tailor-to-target” theory of collaborative shareholder engagement that extends the literature by showing the importance of designing investor coalitions for effective climate-related engagement and the value of conceiving coalitions as different configurations of the same levers that can fit a target firm’s receptivity. From a practical perspective, our study prompts investors to move beyond one-size-fits-all approaches to instead tailor their engagement strategies to target firms’ receptivity.
In a 2010 special report, The Economist magazine termed the resurgence of state-owned, publicly listed enterprises “Leviathan Inc.” and criticized the poor governance and low efficiency of these firms. We compile a new comprehensive data set of state ownership of publicly listed firms in 44 countries over the period of 2004–2017 and show that state-owned enterprises are more responsive to environmental issues. The effect is more pronounced in economies lacking energy security and strong environmental regulation, and among firms with more local operations and higher domestic government ownership. We find a similar effect on corporate social engagement but not on governance quality. These results suggest a different role for “Leviathan Inc.,” especially in dealing with environmental externalities.
This study examines how electric utilities and regulators can encourage residential consumers to conserve electricity during the hottest summer days and shift electricity load from the day to off-peak, nighttime hours. We analyze a two-year field experiment involving 280 Texas households that explores approaches to conservation and load-shifting to enable emission reductions and reduce generation costs. Our critical peak pricing intervention reduces electricity consumption by 14% on the peak hours of the hottest days, leading to greenhouse gas emission reductions of about 16%. A key contribution of this study is the use of high-frequency appliance-level data. We show that 74% of the critical peak response is from reducing air conditioning. In a complementary nighttime pilot program, consumers respond strongly to lower prices by programming the timing of electric vehicle charging. Our work highlights how automation can influence the consumer tradeoffs relating to effort costs, discomfort, monetary incentives, and warm glow.
Private sector innovation is critical to mitigating and adapting to climate change. This paper studies innovation in solar energy technology, a key source of clean energy that has experienced rapid price declines over the past decade. To understand the causes and effects of innovation, I estimate a dynamic structural model of competition among solar panel manufacturers. The model captures important features of the industry, including the role of government subsidies for solar adoption, and I employ a unique measure of technological progress that is observable and verifiable. The results produce two main insights. First, ignoring innovation by firms can generate biased estimates of the effects of government policy. Second, decentralized government intervention in a global market generates spillovers; a subsidy in one country causes international firms to innovate more, leading to lower prices and increased adoption elsewhere. This spillover underscores the need for international coordination by governments and the private sector to address climate change.
Many countries have adopted payment for ecosystem services (PES) programs to reduce deforestation. Empirical evaluations find such programs, which pay forest owners to conserve forest, can lead to anywhere from no impact to a 50% reduction in deforestation level. To better understand the potential effectiveness of PES contracts, we use a principal–agent model, in which the agent has an observable amount of initial forest land and a privately known baseline conservation level. Commonly used conditional contracts perform well when the environmental value of forest is sufficiently high or sufficiently low, but can do arbitrarily poorly compared with the optimal contract for intermediate values. We identify a linear contract with a distribution-free per-unit price that guarantees at least half of the optimal contract payoff. A numerical study using U.S. land use data supports our findings and illustrates when linear or conditional contracts are likely to be more effective.
We examine whether the Environmental Protection Agency (EPA) uniformly enforces the Clean Air Act for politically connected and unconnected firms using a close election setting. We find no difference in regulated pollutant emissions or EPA investigations between the two groups, although connected firms experience less regulatory enforcement and lower penalties. These results are more pronounced for firms connected to politicians capable of influencing regulatory bureaucrats and for connected firms that are more important to their supported politicians. Taken together, our results show that campaign contributions can indirectly benefit firms by way of reduced environmental regulatory enforcement and penalties.
How do firms address complex collective action problems effectively? Institutional and stakeholder research suggests that firms may avoid the tragedy of the commons by aligning the interests of critical proximate stakeholders in ways that governments cannot accomplish. This phenomenological paper investigates this possibility by analyzing Amazon rainforest preservation by Natura, a Brazilian cosmetics company. The results indicate that Natura internalized environmental externalities by linking ecologically conscious consumers with rural Amazonian communities. A differences-in-differences analysis compares forest preservation and fire activity in the municipalities that Natura entered with those in which it did not enter. Natura’s impact is identified through an instrumental variable analysis using missing satellite images, which Natura relied upon to decide which municipalities to enter. Quantitative results tie Natura’s entry into municipalities with forest preservation. Analysis of three mechanisms associates Natura’s involvement with stakeholder decisions to cultivate diverse forest-generated crops instead of clearing the land for conventional agriculture. This study contributes to the management literature by suggesting how firms can address important global challenges, such as rainforest preservation, by investing in stakeholder capability development and by creating institutional arrangements in line with those envisioned elsewhere.
We investigate the role of physical distance in corporate lending by exploiting infrastructure improvements as shocks to travel time. Lower travel time increases the likelihood of initiating a new banking relationship, consistent with an economic surplus from lower transaction costs. In existing lending relationships, banks capture part of this surplus by increasing interest rates, in particular, if banks have higher bargaining power. Reductions in travel time to competing banks have the opposite effects. Banks benefit from improved infrastructure through an increase in clients, and lenders that rely more on technology do not exhibit sensitivity to changes in distance.
The United Nations Principles for Responsible Investment (PRI) is the largest global environmental, social, and governance (ESG) initiative in the asset-management industry to date. We analyze what happens after active U.S. mutual funds sign the PRI to assess whether they exhibit ESG implementation. We find that PRI signatories attract a large fund inflow, but we do not observe improvements in fund-level ESG scores or fund returns. We consider a battery of ways to proxy for funds’ ESG incorporation (e.g., entry/exit, screening, engagement, voting for pro-ESG proposals), but fail to observe evidence of meaningful on average follow-through. Next, we explore cross-sectional fund characteristics and find that only quant funds exhibit small improvements in ESG performance versus other funds, mainly through buying high-ESG-performing stocks. Furthermore, we note that signatories are not superior performers in ESG issues prior to joining the PRI relative to non-PRI funds, but PRI affiliation tends to be widely advertised on company websites, marketing materials, and fund documents. Overall, a reasonable reader may perceive our findings as consistent with PRI funds’ greenwashing. We note, however, that what we uncover is based only on outcome-based measures and may miss some actual efforts of signatories.
We investigate a data-driven multiperiod inventory replenishment problem with uncertain demand and vendor lead time (VLT) with accessibility to a large quantity of historical data. Different from the traditional two-step predict-then-optimize (PTO) solution framework, we propose a one-step end-to-end (E2E) framework that uses deep learning models to output the suggested replenishment amount directly from input features without any intermediate step. The E2E model is trained to capture the behavior of the optimal dynamic programming solution under historical observations without any prior assumptions on the distributions of the demand and the VLT. By conducting a series of thorough numerical experiments using real data from one of the leading e-commerce companies, we demonstrate the advantages of the proposed E2E model over conventional PTO frameworks. We also conduct a field experiment with JD.com, and the results show that our new algorithm reduces holding cost, stockout cost, total inventory cost, and turnover rate substantially compared with JD’s current practice. For the supply chain management industry, our E2E model shortens the decision process and provides an automatic inventory management solution with the possibility to generalize and scale. The concept of E2E, which uses the input information directly for the ultimate goal, can also be useful in practice for other supply chain management circumstances.
Several financial exchanges (e.g., IEX and NYSE American) recently introduced messaging delays to protect ordinary investors from high-frequency traders who exploit stale orders. To capture the impact of such delays, we propose a simple parametric model of the continuous double auction market format. The model examines the dynamics of midpoint pegged order queues and finds their steady states. It shows how messaging delays can protect pegged orders and improve investor welfare, but typically increase queuing costs. Recently available field data show that the empirical distribution of queued pegged orders is highly leptokurtotic and resembles the discrete Laplace distribution predicted by the model.
Health information exchanges (HIEs) are designed to improve the quality and efficiency of healthcare by facilitating improved information sharing between health entities. This study systematically examines the impact of HIE use in emergency departments (EDs) on the quality and efficiency of medical care. We focus on the length of stay (LOS) and the 30-day readmission rate to capture healthcare efficiency and quality, respectively. We also examine whether the breadth of patient health information and physicians’ experience with the HIE moderates these effects. We leverage a unique panel data that tracks actual HIE access by physicians who practice in a set of hospitals that participate in the focal HIE. The patient-level encounter data set—which involves more than 80,000 ED encounters attended by more than 300 physicians over a 19-month period—comprises detailed medical provider information, patient-level medical information, and various other information related to procedures that were performed. After controlling for a battery of patient-specific, physician-specific, disease-specific, and ED visit-specific variables, our results show that HIE access in information-intensive environments (such as EDs) reduces LOS and 30-day readmission rate. We find that breadth of patient health information and physicians’ HIE experience amplify these benefits. We account for endogeneity issues and perform additional falsification tests and robustness checks. We document that benefits of HIE access are amplified for noninjury, chronic condition, and uncommon diagnoses related patient visits. Based on our results, we offer insights to practitioners and academicians alike on how HIEs can yield better patient-level and provider-level outcomes.
Restaurant delivery platforms collect customer orders via the Internet, transmit them to restaurants, and deliver the orders to customers. They provide value to restaurants by expanding their markets, but critics claim they destroy restaurant profits by taking a percentage of revenues and generating congestion that negatively impacts dine-in customers. We consider these tensions using a model of a restaurant as a congested service system. We find that the predominant industry contract, in which the platform takes a percentage cut of each delivery order (a “commission”), fails to coordinate the system because the platform does not internalize its effect on dine-in revenues; this leads to prices that are too low, reducing the restaurant’s margins and leaving money on the table for both firms. Two commonly proposed remedies to this problem (commission caps and allowing the restaurant to set a price floor on the platform) can increase restaurant revenue but do not solve the coordination issue. We thus propose an alternative, practical coordinating contract that is a variation of the current industry standard: for each delivery order, the platform pays the restaurant a percentage revenue share and a fixed fee. We show that this contract, appropriately designed, coordinates the system, protects restaurant margins by ensuring a lower bound on its revenue per delivery order, and allocates revenue between the restaurant and the platform with a high degree of flexibility.
This paper investigates how the pricing policy of a revenue-maximizing monopolist is influenced by the social learning dynamics of customers who use online reviews to estimate the quality of the product. A salient feature of our problem is that the customers’ willingness to pay, and hence the demand function, evolves over time in conjunction with the online reviews. The monopolist strives to maximize its total expected revenue over a finite horizon by adjusting prices in response to these dynamics. The revenue maximization problem is studied using two different review models: a quality-based review model, where customers report their experienced quality, and a value-based review model, where reviews internalize experienced quality as well as the purchase price. To formulate the problem in tractable form, we derive a fluid model that provides a good approximation of the system dynamics when the volume of sales is large. This formulation lends itself to key structural insights into the interactions between optimal pricing policies and review dynamics. In particular, we identify critical time scales and social learning regimes that sharply separate the efficacy of dynamic pricing vis-à-vis fixed-price strategies. Furthermore, we demonstrate the impact of the quality-based and value-based review models on key structural properties of the optimal pricing policies. These structural insights are also elucidated in an illustrative simulation study based on data from an online marketplace.
We consider periodic review perishable inventory systems with a fixed product lifetime. Unsatisfied demand can be either lost or backlogged. The objective is to minimize the long-run average holding, penalty, and outdating cost. The optimal policy for these systems is notoriously complex and computationally intractable because of the curse of dimensionality. Hence, various heuristic replenishment policies are proposed in the literature, including the base-stock policy, which raises the total inventory level to a constant in each review period. Whereas various studies show near-optimal numerical performances of base-stock policies in the classic system with zero replenishment lead time and a first-in-first-out issuance policy, the results on their theoretical performances are very limited. In this paper, we first focus on this classic system and show that a simple base-stock policy is asymptotically optimal when any one of the product lifetime, demand population size, unit penalty cost, and unit outdating cost becomes large; moreover, its optimality gap converges to zero exponentially fast in the first two parameters. We then study two important extensions. For a system under a last-in-first-out or even an arbitrary issuance policy, we prove that a simple base-stock policy is asymptotically optimal with large product lifetime, large unit penalty costs, and large unit outdating costs, and for a backlogging system with positive lead times, we prove that our results continue to hold with large product lifetime, large demand population sizes, and large unit outdating costs. Finally, we provide a numerical study to demonstrate the performances of base-stock policies in these systems.
The notion of omnichannel, an integration of brick-and-mortar stores with online channels, has been thriving in recent years and is reforming the traditional service industry. Many service chains, such as Starbucks and McDonald’s, established omnichannel capability by allowing customers to order online in advance before visiting stores for pickup. The premise of omnichannel services is that when customers take advantage of the low-cost-of-waiting online channel, both their utility and the provider’s revenue will increase. Although simply adding an online-ordering option to the conventional walk-in model stimulates revenue, it also inflicts interference on the walk-in channel. We show that online ordering inadvertently reduces customers’ individual utility and social welfare when both channels are used in equilibrium. Moreover, the less it costs to order and wait online, the more the social welfare is reduced. We then evaluate two industry state-of-the-art operational remedies: regulating the use of the online channel and establishing channel-dedicated capacities. Although both remedies may improve the throughput over the walk-in-only service or even the first-come-first-served omnichannel service, they are unlikely to achieve this without jeopardizing the social welfare. We thus propose prioritizing walk-in customers and show that such prioritization can deliver this premise—that is, simultaneously benefiting the service provider and customers in comparison with the conventional walk-in-only service when both channels are used in equilibrium. Our results highlight that creating an efficient marketplace requires synergy between innovative technology and effective operational strategies.
We study a single product pricing problem with demand censoring in an offline data-driven setting. In this problem, a retailer has a finite amount of inventory and faces a random demand that is price sensitive in a linear fashion with unknown price sensitivity and base demand distribution. Any unsatisfied demand that exceeds the inventory level is lost and unobservable. We assume that the retailer has access to an offline data set consisting of triples of historical price, inventory level, and potentially censored sales quantity. The retailer’s objective is to use the offline data set to find an optimal price, maximizing his or her expected revenue with finite inventories. Because of demand censoring in the offline data, we show that the existence of near-optimal algorithms in a data-driven problem—which we call problem identifiability—is not always guaranteed. We develop a necessary and sufficient condition for problem identifiability by comparing the solutions to two distributionally robust optimization problems. We propose a novel data-driven algorithm that hedges against the distributional uncertainty arising from censored data, with provable finite-sample performance guarantees regardless of problem identifiability and offline data quality. Specifically, we prove that, for identifiable problems, the proposed algorithm is near-optimal and, for unidentifiable problems, its worst-case revenue loss approaches the best-achievable minimax revenue loss that any data-driven algorithm must incur. Numerical experiments demonstrate that our proposed algorithm is highly effective and significantly improves both the expected and worst-case revenues compared with three regression-based algorithms.
The impact of product reviews on consumer purchasing behavior is empirically well documented. This can create perverse incentives for firms to offer reviewers side payments (“bribes”) in exchange for biased reviews for their products. The presence of bribes distorts the information in reviews and leads to detrimental effects on consumer utility. This paper builds a two-sided reputation model where a reviewer can inflate her reviews in exchange for bribes. If the reviewer accepts bribes and misrepresents her reviews, then she builds her reputation as an inaccurate reviewer and makes consumers less likely to follow her recommendations. This in turn makes firms no longer interested in offering her a bribe. Can the reviewer retain influence over consumers’ purchasing decisions while simultaneously accepting bribes and misrepresenting her reviews? We provide a characterization of the environments that allow this kind of manipulation and show that regulatory policies that aim to reduce bribes can lead to undesirable outcomes. Finally, we show that eliminating bribes from the market can increase the welfare of all market participants, even for those firms who would have otherwise bribed in exchange for more favorable reviews.
We derive generalized bounds on conditional expected excess returns that can be computed from option prices. The generalized lower bound may serve as an expected excess return proxy for individual and basket-type assets, is conditionally tight, accounts for the entire risk-neutral distribution of returns, and outperforms existing variance-based models in out-of-sample predictions. Bounds calibrated to realized returns correspond to reasonable risk aversion and prudence. On average, expected stock returns given by the bounds decrease on even weeks of the Federal Open Market Committee cycle. Cross-sectional tests deliver a reasonable market risk premium.
This paper examines startups’ positioning within technological cycles. We use patent text to measure whether innovation pertains to a technological area that is rapidly evolving or stable. We show that innovation in rapidly evolving areas (i.e., early in the cycle) substitute for existing technologies, whereas innovation in stable areas (i.e., later in the cycle) complement them. Our new measure is distinct from existing characterizations of innovation and is economically important. We find that startups in rapidly evolving areas tend to exit via initial public offering, thus remaining independent, consistent with technological substitution. In contrast, startups in stable areas tend to sell out, consistent with technological complementarity and synergies.
We study the spread of misinformation in a social network characterized by unequal access to learning resources. Agents use social learning to uncover an unknown state of the world, and a principal strategically injects misinformation into the network to distort this learning process. A subset of agents throughout the network is endowed with knowledge of the true state. This gives rise to a natural definition of inequality: privileged communities have unrestricted access to these agents, whereas marginalized communities do not. We show that the role that this inequality plays in the spread of misinformation is highly complex. For instance, communities who hoard resources and deny them to the larger population can end up exposing themselves to more misinformation. Conversely, although more inequality generally leads to worse outcomes, the prevalence of misinformation in society is nonmonotone in the level of inequality. This implies that policies that decrease inequality without substantially reducing it can leave society more vulnerable to misinformation.
Willingness to vaccinate and test are critical in the COVID-19 pandemic. We study the effects of two measures to increase the support of vaccination and testing: defaults and monetary compensations. Some organizations, such as restaurants, fire departments, hospitals, or governments in some countries, have used these measures. Yet there is the concern that compensations could erode intrinsic motivation and decrease vaccination intentions. We show that, in the early stages of the pandemic, both approaches, compensations and defaults, significantly increased COVID-19 test demand and vaccine intentions. Compensations for vaccines, however, need to be large enough because low compensations can backfire. We estimate heterogeneous treatment effects to document which groups are more likely to respond to these measures. The results show that defaults and avoidance of small compensations are especially important for individuals who are more skeptical of the vaccine, measured by their trust in the vaccine and their political views. Hence, both measures could be used in a targeted manner to achieve stronger results.
We report experimental data from standard market entry games and winner-take-all games. At odds with traditional decision-making models with risk aversion, the winner-take-all condition results in substantially more entry than the expected-payoff-equivalent market entry game. We explore three candidate explanations for excess entry: blind spot, illusion of control, and joy of winning, none of which receive empirical support. We provide a novel theoretical explanation for excess entry based on cumulative prospect theory and test it empirically. Our results suggest that excess entry into highly competitive environments is not caused by a genuine preference for competing, but is instead driven by probability weighting. Market entrants overweight the small probabilities associated with the high payoff outcomes in winner-take-all markets, while they underweight probable failures.
When drawing inferences about a person’s personal characteristics from the person’s actions, “correspondence bias” is the tendency to overestimate the influence of those characteristics and underestimate the influence of situational factors, such as incentives the individual faces. We build a simple framework to formalize correspondence bias and test its predictions in an online experiment. Consistent with correspondence bias, subjects are, on average, willing to pay to receive the dictator-game givings of an individual with whom they are randomly assigned to play a game that encourages cooperation rather than one with whom they play a game that encourages selfish behavior. We show, further, that experiencing both games oneself, as opposed to playing one and observing the other, reduces the bias, and receiving information about how each of the players behaved in both games eliminates it.
The strength of weak ties and brokerage theory both rely on the argument that weak bridging ties deliver novel information to create “vision advantages” for actors in brokerage positions. However, our conceptualization of novelty is itself fundamentally underdeveloped. We, therefore, develop a theory of how three distinct types of novelty—diversity, non-redundancy, and uniqueness—combine with network structure to create vision advantages in social networks. We test this theory using panel data on an evolving corporate email network. Three main results emerge from our analysis. First, we confirm the diversity-bandwidth tradeoff (DBT) at the heart of the vision advantage. As brokers’ networks become more diverse, their channel bandwidth contracts, creating countervailing effects on access to novel information. Second, we uncover a mechanism driving the DBT, which helps explain differences in vision advantages across strong and weak ties. Strong, cohesive ties deliver greater information diversity and non-redundancy, whereas weak bridging ties contribute the most unique information (the information that is most different from what other contacts deliver). Third, we find network diversity (in contrast to network constraint) to be positively associated with longitudinal entropy, a measure of the accumulation of novel information over time. This suggests that weak bridging ties, which provide the most unique information through low bandwidth, structurally diverse channels, contribute the most to one’s aggregation of novel information over time. Collectively, these results take a step toward resolving a long-standing debate in network theory about whether strong, cohesive networks or weak bridging networks contribute more to vision advantages. This work firmly establishes that it depends.
The Becker-DeGroot-Marschak (BDM) mechanism is frequently used in experimental research to measure willingness-to-pay (WTP). The mechanism is very appealing given its incentive compatibility property. However, a growing body of experimental evidence finds that WTP is sensitive to the underlying price distribution of the BDM mechanism. The literature suggests that these findings can be rationalized only if subjects have context-dependent preferences. Under context-dependent preferences, the use of the BDM mechanism momentarily changes subjects’ preferences; subjects do not have incentives to report their intrinsic valuation, which is what the BDM attempts to measure, but instead they report the amount they are willing to pay after their preferences have momentarily changed. This lack of incentive compatibility would raise serious concerns about the validity of the BDM as an elicitation procedure; indeed, virtually all studies that use the BDM mechanism choose that elicitation procedure precisely for its incentive compatibility property. In this paper, we propose that an assumption of context dependence is not necessary to rationalize the aforementioned experimental findings. We provide an alternative plausible theory and experimental evidence to support it. Our theory relies on the premise that subjects need to exert effort to learn their preferences. We find that the price distribution of the BDM mechanism influences the subject’s incentives to exert effort. The results presented here are particularly appealing to the experimental research: our theory is consistent with the experimental findings that WTP is sensitive to the price distribution of the BDM mechanism while preserving the incentive compatibility property.
The limits of arbitrage explain how a speculative bubble is sustained; they do not explain how likely one is to occur. To do that, you need a theory about the thing that sporadically causes arbitrageur constraints to bind. I propose a first such theory, which is based on social interactions between speculators. The theory says that bubbles should be more likely in assets where increases in past returns make excited-speculators relatively more persuasive to their peers. I empirically verify this ex ante prediction about bubble likelihoods and show that it is robust to some ex post disagreement about bubble definitions.
We examine the impact of social investing on charitable donations using a unique data set consisting of investment behaviors and donation transactions for more than 10,000 customers of an investment app platform. We find that investors switching to a recently introduced social fund reduced their donations, mainly in charities supporting causes similar to those of the social fund. However, 79% of the investors that switched to the social fund did not donate before switching, so the social fund attracted more people to fund social causes. Still, because of the substitution effect, we estimate social funds have a positive effect on society only if their annual contributions to social causes are greater than 3.2% of the balance invested.
We study mismarking of newly purchased odd lot and two classes of round lot structured product positions in mutual funds. Such mismarking artificially inflates net asset values and overstates cumulative returns. Applied to funds launched after January 2010, a simulation-tested mismarking fund filter identifies 12 Highly Questionable funds managing $75 billion. The performance of these funds matches closely the predicted pattern of mismarking: extremely high alpha and skewness, particularly immediately after launch. We show that structured product mismarking can seriously inflate return-since-inception metrics. We also provide evidence consistent with return smoothing for one quarter of the sample structured product funds. The inflated performance metrics benefit fund managers through significantly higher Sharpe ratios, Morningstar ratings, and asset growth but cause material losses to later investor cohorts.
Return on investment (ROI) and residual income (RI) are two important accounting measures that are commonly used to evaluate managers’ performance, and evidence suggests that both ROI and RI can help motivate long-term investments. Research is limited, though, regarding whether ROI and RI differentially affect managers’ actions, and, more specifically, research has not examined the effects of ROI and RI on risk-taking. We conduct an experiment to examine the separate and interactive effects of individuals’ risk preferences and ROI and RI performance measures on risk-taking in capital investment decisions. We predict and find that the use of ROI as a performance measure leads to riskier choices, as compared with RI, and that this effect is concentrated in relatively more risk-averse individuals. We also provide process evidence that reveals some of the ways in which ROI and RI performance measures affect decision making. Collectively, our results contribute to literature examining the effects of accounting information and performance measures on managers’ risk-taking behaviors.
Online platforms that match customers with suitable service providers utilize a wide variety of matchmaking strategies; some create a searchable directory of one side of the market (i.e., Airbnb, Google Local Finder), some allow both sides of the market to search and initiate contact (i.e., Care.com, Upwork), and others implement centralized matching (i.e., Amazon Home Services, TaskRabbit). This paper compares these strategies in terms of their efficiency of matchmaking as proxied by the amount of communication needed to facilitate a good market outcome. The paper finds that the relative performance of these matchmaking strategies is driven by whether the preferences of agents on each side of the market are easy to describe. Here, “easy to describe” means that the preferences can be inferred with sufficient accuracy based on responses to standardized questionnaires. For markets with suitable characteristics, each of these matchmaking strategies can provide near-optimal performance guarantees according to an analysis based on information theory. The analysis provides prescriptive insights for online platforms.
We study a large-scale (n = 50,000) natural field experiment implemented by the U.S. Social Security Administration aimed at increasing the timely and accurate self-reporting of wages by Supplemental Security Income (SSI) recipients. A letter reminding SSI recipients of their wage reporting responsibilities significantly increased both the likelihood of reporting any earnings and the total earnings reported. However, the specific letter content—providing social information or highlighting the salience of penalties—had no systematic effect. We develop a conservative estimate that the letters generated roughly $5.91 in savings per dollar spent, highlighting the value of such a nudge in this important context.
We initiate the study of incentive-compatible forecasting competitions in which multiple forecasters make predictions about one or more events and compete for a single prize. We have two objectives: (1) to incentivize forecasters to report truthfully and (2) to award the prize to the most accurate forecaster. Proper scoring rules incentivize truthful reporting if all forecasters are paid according to their scores. However, incentives become distorted if only the best-scoring forecaster wins a prize, since forecasters can often increase their probability of having the highest score by reporting more extreme beliefs. In this paper, we introduce two novel forecasting competition mechanisms. Our first mechanism is incentive compatible and guaranteed to select the most accurate forecaster with probability higher than any other forecaster. Moreover, we show that in the standard single-event, two-forecaster setting and under mild technical conditions, no other incentive-compatible mechanism selects the most accurate forecaster with higher probability. Our second mechanism is incentive compatible when forecasters’ beliefs are such that information about one event does not lead to belief updates on other events, and it selects the best forecaster with probability approaching one as the number of events grows. Our notion of incentive compatibility is more general than previous definitions of dominant strategy incentive compatibility in that it allows for reports to be correlated with the event outcomes. Moreover, our mechanisms are easy to implement and can be generalized to the related problems of outputting a ranking over forecasters and hiring a forecaster with high accuracy on future events.
Corporate bonds with large increases in implied volatility over the past month underperform those with large decreases in implied volatility by 0.6% per month. In contrast to existing studies that show implied volatility changes carry information about fundamental news, our evidence suggests that implied volatility changes contain information about uncertainty shocks to the firm. Our results are consistent with the notion that informed traders with new information about firm risk prefer to trade in the option market and the corporate bond market underreacts to this information.
We introduce a new distributionally robust optimization model to address a two-period, multiitem joint pricing and production problem, which can be implemented in a data-driven setting using historical demand and side information pertinent to the prediction of demands. Starting from an additive demand model, we introduce a new partitioned-moment-based ambiguity set to characterize its residuals, which also determines how the second-period demand would evolve from the first-period information in a data-driven setting. We investigate the joint pricing and production problem by proposing a cluster-adapted markdown policy and an affine recourse adaptation, which allow us to reformulate the problem as a mixed-integer linear optimization problem that we can solve to optimality using commercial solvers. We also extend our framework to ensemble methods using a set of ambiguity sets constructed from different clustering approaches. Both the numerical experiments and case study demonstrate the benefits of the cluster-adapted markdown policy and the partitioned moment-based ambiguity set in improving the mean profit over the empirical model—when applied to most out-of-sample tests.
Industries differ in the extent to which they can offshore their production. I document that industries with low offshoring potential carry a sizeable risk premium compared with industries with high offshoring potential, suggesting that the possibility to offshore affects industry risk. A two-country dynamic trade model in which offshoring allows firms to secure market share against foreign competitors can rationalize the empirical findings.
Understanding the drivers of market concentration in the generic pharmaceutical industry is essential to guaranteeing the availability of low-cost generics. In this paper, we develop a structural model to capture the multiple determinants governing manufacturers’ entry decisions; in particular, we focus on how manufacturing complexity and the regulatory environment for generics approval affect concentration in drug markets. We estimate the model using data collated from six disparate sources. We find that manufacturing complexity, as reflected in the number of active ingredients, for example, significantly reduces the likelihood of generics entry. Moreover, the delay in the review process for the generics applications significantly affects the number of firms entering a market. Our policy simulations suggest that a shortened drug approval review time significantly increases the average number of entrants per market and reduces the fraction of markets with no generics entry; however, a notable portion of markets would still lack any generic competition.
In this paper, we consider a container leasing firm that has elementary and premium containers, which are downward substitutable and for use by elementary contract customers (ECCs), premium contract customers (PCCs), as well as walk-in customers (WICs). ECCs can be satisfied by elementary containers or premium ones at discounted prices while PCCs only accept premium containers. WICs can be satisfied by any type of container at different prices. The objective is to maximise the expected total rental revenue by managing its limited capacity. We formulate this problem as a discrete-time Markov Decision Process and show the submodularity and concavity of the value function. Based on this, we show that the optimal policy can be characterised by a series of rationing thresholds, a series of substitution thresholds and a priority threshold, all of which depend on the system states. We further give conditions under which the optimal policy can be simplified. Numerical experiments are conducted to show the impact of the substitution of two items on the revenue, to compare the performance of the optimal policy with those of the commonly used policies and to investigate the influence of arrival rates on the optimal policy. Last, we extend the basic model to consider different rental durations, ECCs’ acceptance behaviour and endogenous prices for WICs.
We consider a mixed duopoly service system with two service providers (SPs): one private and the other public. The public SP’s objective is welfare maximization, whereas the private SP is profit driven. Customers are heterogeneous in terms of their quality tastes and choose from three options, namely, joining the private queue, joining the public queue, and balking (or taking an outside option). We first consider the scenario where the private SP provides a premium service and the public SP provides a regular service. Paradoxically, we find that although the public SP’s objective is welfare maximization, welfare in the system can be less than that obtained with only profit-seeking private SPs. We further demonstrate that the maximum social welfare is achieved by partially privatizing the public SP, that is, by including both welfare and profit maximization as arguments in its objective function. In extreme cases in which the public SP’s capacity is very small, fully privatizing the public SP can be socially desirable. We then consider the alternate setting where the public SP provides a premium service and the private SP provides a regular service and obtain similar conclusions. These findings, however, rely on the assumption that an outside option exists for balking customers, and one should be cautious in generalizing to no-balking situations.
We examine the effect of plant managers on productivity using unique matched manager-plant panel data on U.S. auto-assembly plants during 1993–2007. Our econometric approach is two-pronged. Our first approach relies on using the panel nature of our data to measure variation in productivity due to managerial influence. We estimate the interquartile range of the effect of individual plant managers on average hours-per-vehicle to be about 30%. Further, we find that plant managers’ experience with the models that are in production ameliorates the negative impact of new model introductions on productivity. We also observe evidence that managers’ plant-specific tenure has a positive impact on productivity. In our second approach, we use high-frequency time-series data, along with structural-break tests and machine-learning methodologies, to predict variation in production using plant-manager switches. We find that a plant manager’s identity is predictive of changes in both the mean and variance of production, further highlighting their channels of managerial influence. These findings are robust to narrowing the sample to focus on retirements as an exogenous source of managerial switches.
Improving traceability in food supply chains has been a big part of ongoing effort to reduce contamination risks and food waste. Recent technology advancement such as blockchain technology has propelled innovative retailers to promote food traceability system adoption in their supply chains. We develop a three-tier supply chain model with multiple upstream (tier 2) suppliers to investigate how traceability technology adoption affects incentives of supply chain members and whether and how its anticipated benefits can be realized. We find that full traceability brings direct revenue benefit to every supply chain member by saving uncontaminated food from disposal (pure traceability effect) but also leaves each tier of the supply chain vulnerable to its immediate downstream buyer’s exploitation through strategically lowering the purchasing price (strategic pricing effect). The interplay of the two effects may result in some of the supply chain members (even the retailer) being worse off with traceability adoption and the system being exposed to higher contamination risk; the latter is because of the weakened upstream supplier’s incentive to exert contamination risk-reduction effort. Moreover, we find that the supply chain network structure also influences the benefit distribution of traceability adoption: The retailer always benefits from traceability adoption in network structures where the tier 1 supplier’s strategic pricing power is eliminated or weakened; all supply chain members benefit from traceability adoption in a network with a large number of tier 2 suppliers. Finally, we show that alternative risk-mitigation schemes such as tier 2 coordination can diminish the value of traceability adoption, and partial traceability enabled by tier 1 product inspection can be more beneficial to the retailer than full traceability.
Operating under both supply-side and demand-side uncertainties, a mobile-promotion platform conducts advertising campaigns for individual advertisers. Campaigns arrive dynamically over time, which is divided into seasons; each campaign requires the platform to deliver a target number of mobile impressions from a desired set of locations over a desired time interval. The platform fulfills these campaigns by procuring impressions from publishers, who supply advertising space on apps via real-time bidding on ad exchanges. Each location is characterized by its win curve, that is, the relationship between the bid price and the probability of winning an impression at that bid. The win curves at the various locations of interest are initially unknown to the platform, and it learns them on the fly based on the bids it places to win impressions and the realized outcomes. Each acquired impression is allocated to one of the ongoing campaigns. The platform’s objective is to minimize its total cost (the amount spent in procuring impressions and the penalty incurred due to unmet targets of the campaigns) over the time horizon of interest. Our main result is a bidding and allocation policy for this problem. We show that our policy is the best possible (asymptotically tight) for the problem using the notion of regret under a policy, namely the difference between the expected total cost under that policy and the optimal cost for the clairvoyant problem (i.e., one in which the platform has full information about the win curves at all the locations in advance): The lower bound on the regret under any policy is of the order of the square root of the number of seasons, and the regret under our policy matches this lower bound. We demonstrate the performance of our policy through numerical experiments on a test bed of instances whose input parameters are based on our observations at a real-world mobile-promotion platform.
We extend the existing literature on gender differences in competitive behavior by investigating tournament entry choices when a principal decides for an agent. In a laboratory experiment, we randomly assign subjects the role of either principal or agent. The principal decides whether the agent performs a real-effort task under piece-rate or tournament incentives. When deciding, the principal is informed about the agent’s previous performance, age, and residency. Between treatments, we vary whether the principal knows the agent’s gender. In a baseline treatment, we replicate the standard setting in which subjects decide for themselves whether to compete. Our main findings are, first, that there is no gender gap in tournament entry when principals decide for agents as opposed to the baseline treatment. Second, the gender gap closes because more women are made to compete by principals. Third, whereas there is no gender gap in either of the principal treatments, revealing the agent’s gender is associated with higher overall tournament entry rates. Exploratory analyses of principals’ choice determinants reveal a positive effect of preferences to take risks, competitiveness, and confidence in agents’ performances on making agents compete. In addition, we find no difference in how principals evaluate male and female agents’ performances. Finally, we test the efficiency of principals’ competition choices and show that they lead to fewer payoff-maximizing outcomes than when subjects decide for themselves. Additionally, overall tournament performances and winners’ performances are lower when agents are made to compete, but this effect is not robust to controlling for agents’ previous performances.
Many important managerial outcomes hinge on the co-occurrence of multiple uncertain events, a situation termed conjunctive risk. Whereas past literature has addressed the psychology of choosing to enter situations with conjunctive risk, this article elucidates a novel way in which the psychology of managing conjunctive risk is importantly distinct. We examine a case in which there are two independent events, one is currently less likely than the other, both are required for overall success, and the decision maker must evaluate opportunities to increase the chance of the less-likely or more-likely requirement. We introduce the hypothesis of a worst-first heuristic. Decision makers intuitively evaluate improvements in conjunctive risk according to their impact on the biggest barrier to success, the least likely of the required events. We find evidence for such a worst-first heuristic across nine experiments (n = 3,653, including samples from the United States and United Kingdom in Studies 1–5 and Studies S1–S3 in the online supplement, as well as a sample of managers in Study 6). Participants invest more to improve chances of less-likely requirements than more-likely requirements, even when the latter improvements have at least as much impact on the aggregate chance of success. Moreover, we find that decision makers exhibit this behavior particularly when managing conjunctive risk, as doing so makes them attend to which threat is the worst. Conversely, they do not appear to exhibit the behavior when making formally equivalent decisions about choosing between conjunctive risks. This bias toward underinvesting in stronger-links holds important implications for decision making in contexts subject to conjunctive risk—both managerial and societal.
Hiring in online labor markets involves considerable uncertainty: which hiring choices are more likely to yield successful outcomes and how do employers adjust their hiring behaviors to make such choices? We argue that employers will initially explore the value of available information. When employers observe successful outcomes, they will keep reinforcing their hiring strategies; but when the outcomes are unsuccessful, employers will adjust their hiring behaviors. To investigate these dynamics, we propose a two-component framework that links hiring choices with task outcomes. The framework’s first component, a Hidden Markov Model, captures how employers transition from unsuccessful to successful hiring decisions. The framework’s second component, a conditional logit model, estimates employer hiring choices. Analysis of 238,364 hiring decisions from a large online labor market shows that, often, employers initially explore cheaper contractors with a lower reputation. When these options result in unsuccessful outcomes, employers learn and adjust their hiring behaviors to rely more on reputable contractors who are not as cheap. Such hiring tends to be successful, guiding employers to reinforce their hiring processes. As a result, the market observes employers transition from cheaper, lower-reputation options with poorer performance to more expensive reputable options with better performance. We attribute part of this behavior to employer confidence and risk attitude, which can change over time. This work is the first to investigate how employers learn to make successful hiring choices in online labor markets. As a result, it provides platform managers with new knowledge and analytics tools to target employer interventions.
Efforts to promote Health Information Exchanges (HIEs) on a nationwide scale are beset with major challenges, and one of them is its meaningful use for both physicians and patients. Referrals potentially provide a context for the meaningful use of HIE, and we are yet to understand how HIEs affect referrals. This research studies the impact of HIE on referral patterns. We establish that participation in an HIE network increases the referrals sent to and received from other HIE participants. We investigate this relationship using both econometric and network-analytic methods. Whereas the econometric analysis focuses on the underlying associations between HIE adoption and referral patterns, the network analysis addresses the transformation process by which HIE adoption and referrals coevolve over time. This study has significant implications for healthcare policy making, development of innovative HIE business models, and management of healthcare organizations.
In programmatic advertising, firms outsource the bidding for ad impressions to ad platforms. Although firms are interested in targeting consumers that respond positively to advertising, ad platforms are usually rewarded for targeting consumers with high overall purchase probability. We develop a theoretical model that shows if consumers with high baseline purchase probability respond more positively to advertising, then firms and ad platforms agree on which consumers to target. If, conversely, consumers with low baseline purchase probability are the ones for which ads work best, then ad platforms target consumers that firms do not want to target—the incentives are misaligned. We conduct a large-scale randomized field experiment, targeting 208,538 individual consumers, in a display retargeting campaign. Our unique data set allows us to both causally identify advertising effectiveness and estimate the degree of incentive misalignments between the firm and ad platform. In accordance with the contracted incentives, the ad platform targets consumers that are more likely to purchase. Importantly, we find no evidence that ads are more effective for consumers with higher baseline purchase probability, rendering the ad platform’s bidding suboptimal for the firm. A welfare analysis suggests that the ad platform’s bidding optimization leads to a loss in profit for the firm and an overall decline in welfare. To remedy the incentive misalignment, we propose a solution in which the firm restricts the ad platform to target only consumers that are profitable based on individual consumer-level estimates for baseline purchase probability and ad effectiveness.
The availability of consumer data is inducing a growing number of firms to adopt more personalized pricing policies. This affects both the performance of, and the competition between, alternative distribution channels, which in turn has implications for firms’ distribution strategies. We develop a formal model to examine a brand manufacturer’s choice between mono distribution (selling only through its own direct channel) or dual distribution (selling through an independent retailer as well). We consider different demand patterns, covering both horizontal and vertical differentiation and different pricing regimes, with the manufacturer and retailer each charging personalized prices or a uniform price. We show that dual distribution is optimal for a large number of cases. In particular, this is always the case when the channels are horizontally differentiated, regardless of the pricing regime; moreover, if both firms charge personalized prices, a well-designed wholesale tariff allows them to extract the entire consumer surplus. These insights obtained here for the case of intrabrand competition between vertically related firms are thus in stark contrast to those obtained for interbrand competition, where personalized pricing dissipates industry profit. With vertical differentiation, dual distribution remains optimal if the manufacturer charges a uniform price. By contrast, under personalized pricing, mono distribution can be optimal when the retailer does not expand demand sufficiently. Interestingly, the industry profit may be largest in a hybrid pricing regime, in which the manufacturer forgoes the use of personalized pricing and only the retailer charges personalized prices.
In order to thrive, organizations need to build and maintain an ability to meet unexpected external challenges. Yet many organizations are sluggish: their capabilities can only undergo incremental changes over time. What are the stochastic processes governing “routinely occurring” challenges that best prepare a sluggish organization for unexpected challenges? We address this question with a stylized principal-agent model. The “agent” represents a sluggish organization that can only change its capability by one unit at a time, and the “principal” represents the organization’s head or its competitive environment. The principal commits ex ante to a Markov process over challenge levels. We characterize the process that maximizes long-run capability for both myopic and arbitrarily patient agents. We show how stochastic, time-varying challenges dramatically improve a sluggish organization’s preparedness for sudden challenges.
This research investigates the role of parents in explaining the surprisingly low presence of women among inventors despite their increase among graduates from science, technology, engineering and mathematics (STEM) subjects. With Danish registry data on the population born between 1966 and 1985 and an experimental setting crafted on siblings’ gender composition, we find that the transmission of inventorship from parents to children disfavors daughters if they have a (second-born) brother. We complement this analysis with evidence about the role of parental factors at different stages of children’s education. Overall, our results confirm that parental role models matter for children’s education, especially at early stages and, through this, increase the probability of a child’s becoming an inventor. However, the direct transmission of inventorship that favors boys much more than girls seems to be affected by gendered expectations developed by parents about daughters’ and sons’ returns from inventorship. Our study contributes to explaining who becomes an inventor and why by adding an important boundary condition to the literature: Parents are intermediaries who, based on their own interpretation of external information about inventive jobs, contribute to create or limit opportunities for their children.
In many markets, buyers sign advance contracts before actual decisions on transactions or consumptions are made. Therefore, a buyer may have private information on expected payoff at the contracting stage, and as time moves on, new information on other components of payoff may arrive. However, prior information can be losable, forgettable, or unattended. In this paper, we investigate how limited memory may influence the optimal design of contracts for sequential screening. Despite memory loss, the buyer can make ex post inference about her initially informed type from the chosen contract. As ex ante screening facilitates subsequent retrospection, the chosen contract can serve as a self-reminding instrument. This would yield an endogenous demand for separation in ex ante contract choice. In response, distortions in the optimal contract design can be either mitigated or intensified, leading to improved or undermined social welfare, respectively. As a result, the equilibrium buyer surplus can be higher than that under perfect memory. We also show that the buyer can exhibit the so-called flat-rate bias, even though her preference is time consistent and perfectly predicted. In addition, as memory can be perfectly recovered from the equilibrium contract choice, investing on any other memory-improving instrument is redundant. Moreover, the buyer’s demand for screening can induce her to choose dominated refund contract. Nevertheless, when dominance must be obeyed, the seller may offer a menu of refund contracts with two-way distortions.
We examine the contractual implications of a lender’s trust for corporate loans. We measure how trusting a lender is using the average trust attitude in the chief executive officer’s ancestral country of origin. We find that banks with trusting CEOs charge lower interest rates in U.S. syndicated loans. This effect is identified within existing lender–borrower relationships and similar types of loans. Further analyses indicate that trust reduces the cost of credit by boosting the perceived credibility of borrower information and by mitigating contracting problems. We corroborate our findings by conducting a survey of loan officers with experience in loan syndication.
We propose a two-step machine learning algorithm—the Supervised Adaptive Group LASSO (SAGLasso) method—that is suitable for constructing parsimonious return predictors from a large set of macro variables. We apply this method to government bonds and a set of 917 macro variables and construct a new, transparent, and easy-to-interpret macro variable with significant out-of-sample predictive power for excess bond returns. This new macro factor, termed the SAGLasso factor, is a linear combination of merely 30 selected macro variables out of 917. Furthermore, it can be decomposed into three sublevel factors: a novel housing factor, an employment factor, and an inflation factor. Importantly, the predictive power of the SAGLasso factor is robust to bond yields, namely, the SAGLasso factor is not spanned by bond yields. Moreover, we show that the unspanned variation of the SAGLasso factor cannot be attributed to yield measurement error or macro measurement error. The SAGLasso factor therefore provides a potential resolution to the spanning controversy in the macro-finance literature.
This paper provides novel evidence suggesting that securities class action lawsuits, a central pillar of the U.S. litigation and corporate governance system, can constitute an obstacle to valuable corporate innovation. We first establish that valuable innovation output makes firms particularly vulnerable to costly low-quality class action litigation. Exploiting judge turnover in federal courts, we then show that changes in class action litigation risk affect the value and number of patents filed, suggesting firms take into account that risk in their innovation decisions. A new perspective we provide is that innovation success, not only innovation failure, can increase firms’ securities class action litigation risk.
When the cost of hedging is nil, the conditional capital asset pricing model (CAPM) holds. We empirically test the conditional CAPM by regressing asset returns onto the product of their conditional betas and market returns. Estimated intercepts are not statistically different from zero, implying that the conditional CAPM successfully explains the conditional level of asset returns. Yet, unconditional betas do not explain the cross section of average asset returns; the unconditional CAPM fails. We show why and how the success of the conditional CAPM actually explains the failure of the unconditional CAPM, thereby rationalizing the coexistence of these two intriguing results.
We develop a theory wherein a priori identical banks may trade loans in a search market with reusable information. The equilibrium is unique, but its nature depends on the probability of a future market state. When the probability of a boom is high, all banks hold no equity and do no screening. When this probability is low, all banks choose a high level of equity and screen loans. For intermediate probability values, the equilibrium is heterogeneous, with some banks posting equity and screening and others avoiding equity and screening. This endogenously arising heterogeneity generates interbank trading. The credit market is partially frozen in a recession: only high-capital banks have continued funding access. Low-capital banks obtain funding by selling legacy loans to banks with “financial muscle,” so market funding is reallocated from low-capital to high-capital banks.
The decision usefulness of the direct versus indirect presentation method of a cash flow statement has been a long-standing issue in both practice and accounting research. By capitalizing on comparative advantages of experimental methods, we provide insights into how investors process the information contained in different presentation methods to make cash flow forecasts, especially in the context of various types of nonrecurring items. We predict and find that, when nonrecurring accrued expenses are present, the indirect method leads to lower forecast errors by activating investors’ underlying knowledge structures of operating cash flows in terms of an accrual (versus cash) basis. We also find that, in the presence of nonrecurring cash or nonrecurring accrued revenues, there is no difference in forecast errors between the indirect and direct methods. Moreover, we find that the combination of the direct and indirect methods (the direct-plus-indirect method) leads to lower forecast errors than the direct method, but it does not provide an incremental benefit for forecast accuracy beyond the indirect method.
I demonstrate that, on average, information asymmetry grows between earnings announcements and falls right after each new earnings announcement. I estimate that percent effective spreads typically grow 3.1% over the course of the inter-announcement period. I demonstrate that this inter-announcement growth in information asymmetry occurs gradually throughout the entire inter-announcement period and not just right before each new earnings announcement. This inter-announcement growth has implications for reporting frequency decisions. A semiannual reporter that switches to quarterly reporting cuts the growth time in half by cutting each semiannual reporting period into two quarterly reporting periods. As a result, it reduces information asymmetry in what would have been the second half of the semiannual reporting period. I confirm this in a European setting, showing that the average reduction in bid-ask spreads from the inter-announcement growth channel is 1.6% when firms switch from semiannual to quarterly reporting.
In their 2012 paper, Bemmaor and Glady introduced the gamma/Gompertz/negative binomial distribution model for customer base analysis. Their model uses exponentially distributed interpurchase times and a Gompertz distributed customer lifetime, where the latter distribution is nonmemoryless. This comment corrects an error in their expression for the conditional expected number of individual future purchases E(xi*) in a forecasting interval of length T*. Contrary to their approach, the correct derivation of the conditional expectation must be based on the conditional survival and density functions of the lifetime distribution. Using the wrong formula leads managers to overestimate the expected future customer purchases. Further, the comment corrects the erroneous expressions for the conditional variance Var(xi*) and the conditional mean residual lifetime E(τ−Ti).
The editors of Management Science acknowledge all our 2022 reviewers and guest associate editors who generously provided expert counsel and guidance on a voluntary basis. We are grateful for their contributions; without them, the journal could not function. Here, we list those reviewers who contributed four or more reviews in 2022.
Incentivized choice experiments are a key approach to measuring preferences in economics but are also costly. Survey measures are a low-cost alternative but can suffer from additional forms of measurement error due to their hypothetical nature. This paper seeks to leverage the strengths of both approaches by proposing a new survey module on risk aversion, time discounting, trust, altruism, positive and negative reciprocity, in which survey items are selected based on ability to predict choices in corresponding, incentivized experiments. The methodology and results provided in the paper can also potentially provide a model for researchers who have specific requirements and want to design their own modules.
We study the impact of inclusive institutions on innovation using novel, hand-collected, county-level data for Imperial Germany. We use the timing and geography of the French occupation of different German regions after the French Revolution of 1789 as an instrument for institutional quality. We find that the number of patents per capita in counties with the longest occupation was more than double that in unoccupied counties. Among the institutional changes brought by the French, the introduction of the Code civil, ensuring equality before the law, and the promotion of commercial freedom through the abolition of guilds and trade licenses had a stronger effect on innovation than the abolition of serfdom, which increased labor market mobility, and agricultural reforms that broke up the power of rural elites. The effect of institutions on innovation is particularly pronounced for high-tech innovation, suggesting that innovation might be a key channel through which institutions ultimately affect economic growth. Our findings highlight inclusive institutions as a first-order determinant of innovation.
We study contextual stochastic optimization problems, where we leverage rich auxiliary observations (e.g., product characteristics) to improve decision making with uncertain variables (e.g., demand). We show how to train forest decision policies for this problem by growing trees that choose splits to directly optimize the downstream decision quality rather than split to improve prediction accuracy as in the standard random forest algorithm. We realize this seemingly computationally intractable problem by developing approximate splitting criteria that use optimization perturbation analysis to eschew burdensome reoptimization for every candidate split, so that our method scales to large-scale problems. We prove that our splitting criteria consistently approximate the true risk and that our method achieves asymptotic optimality. We extensively validate our method empirically, demonstrating the value of optimization-aware construction of forests and the success of our efficient approximations. We show that our approximate splitting criteria can reduce running time hundredfold while achieving performance close to forest algorithms that exactly reoptimize for every candidate split.
To maximize productivity, manufacturers must organize and equip their workforces to efficiently handle variable workloads. Their success depends on their ability to assign experienced and skilled workers to specialized tasks and coordinate work on production lines. Worker turnover may disrupt such efforts. We use staffing, productivity, and pay data from within a major consumer electronics manufacturer’s supply chain to study how firms should manage worker turnover and its effects using production decisions, wages, and inventory. We find that worker turnover impedes coordination between assembly line coworkers by weakening knowledge sharing and relationships. Publicly available unit-cost estimates imply that worker turnover accounts for $206–274 million in added direct expenses alone from defectively assembled units failing the firm’s stringent quality control. To evaluate managerial alternatives, we structurally estimate a dynamic equilibrium model (an Experience-Based Equilibrium) encompassing (1) workers’ endogenous turnover decisions and (2) the firm’s weekly planning of its production scheduling and staffing in response. In counterfactual analyses, a less turnover-prone, hence more productive, workforce significantly benefits the firm, reducing its variable production costs by 4.5%, or an estimated $928 million for the studied product. Such benefits justify paying higher efficiency wages even to less skilled workforces; furthermore, interestingly, rational inventory management policies incentivize self-interested firms to reduce rather than tolerate turnover.
In-store promotions are a highly effective marketing tool that can have a significant impact on revenue. In this research, we study the question of dynamic promotion planning in the face of Bounded-Memory Peak-End demand models. In order to determine promotion strategies, we establish that a High-Low pricing policy is optimal under diagonal dominance conditions (so that the current period price dominates both past period price effects and competitive product price effects on the demand), as well as conditions on the price dispersion. We show that finding the optimal High-Low dynamic promotion policy is NP-hard in the strong sense. Nevertheless, for the special case of promotion planning for a single item, we propose a compact Dynamic Programming (DP) approach that can find the optimal promotion plan that follows a High-Low policy in polynomial time. When the diagonal dominance conditions do not hold, and, hence, a High-Low policy is not necessarily optimal, we show that the optimal High-Low policy that is found by our proposed DP can find a provably near-optimal solution. Using the proposed DP as a subroutine, for the case of multiple items, we propose a Polynomial-Time-Approximation Scheme (PTAS) that can find a solution that can capture at least 1−ε of the optimal revenue and runs in time that is exponential only in 1/ε. Finally, we test our approach on data from large retailers and demonstrate an average of 5.1−15.6% increase in revenue relative to the retailer’s current practices.
We consider derivatives written on multiple underlyings in a one-period financial market, and we are interested in the computation of model-free upper and lower bounds for their arbitrage-free prices. We work in a completely realistic setting, in that we only assume the knowledge of traded prices for other single- and multi-asset derivatives and even allow for the presence of bid–ask spread in these prices. We provide a fundamental theorem of asset pricing for this market model, as well as a superhedging duality result, that allows to transform the abstract maximization problem over probability measures into a more tractable minimization problem over vectors, subject to certain constraints. Then, we recast this problem into a linear semi-infinite optimization problem and provide two algorithms for its solution. These algorithms provide upper and lower bounds for the prices that are ε-optimal, as well as a characterization of the optimal pricing measures. These algorithms are efficient and allow the computation of bounds in high-dimensional scenarios (e.g., when d = 60). Moreover, these algorithms can be used to detect arbitrage opportunities and identify the corresponding arbitrage strategies. Numerical experiments using both synthetic and real market data showcase the efficiency of these algorithms, and they also allow understanding of the reduction of model risk by including additional information in the form of known derivative prices.
Motivated by online labor markets, we consider the online assortment optimization problem faced by a two-sided matching platform that hosts a set of suppliers waiting to match with a customer. Arriving customers are shown an assortment of suppliers and may choose to issue a match request to one of them. After spending some time on the platform, each supplier reviews all the match requests she has received and, based on her preferences, she chooses whether to match with a customer or to leave unmatched. We study how platforms should design online assortment algorithms to maximize the expected number of matches in such two-sided settings. We establish that a simple greedy algorithm is 1/2-competitive against an optimal clairvoyant algorithm that knows in advance the full sequence of customers’ arrivals. However, unlike related online assortment problems, no randomized algorithm can achieve a better competitive ratio, even in asymptotic regimes. To advance beyond this general impossibility, we consider structured settings where suppliers’ preferences are described by the multinomial logit and nested logit choice models. We develop new forms of balancing algorithms, which we call preference-aware, that leverage structural information about suppliers’ choice models to design the associated discount function. In certain settings, these algorithms attain competitive ratios provably larger than the standard “barrier” of 1−1/e in the adversarial arrival model. Our results suggest that the shape and timing of suppliers’ choices play critical roles in designing online assortment algorithms for two-sided matching platforms.
Assortment optimization concerns the problem of selling items with fixed prices to a buyer who will purchase at most one. Typically, retailers select a subset of items, corresponding to an “assortment” of brands to carry, and make each selected item available for purchase at its brand-recommended price. Despite the tremendous importance in practice, the best method for selling these fixed-price items is not well understood, as retailers have begun experimenting with making certain items available only through a lottery. In this paper, we analyze the maximum possible revenue that can be earned in this setting, given that the buyer’s preference is private, but drawn from a known distribution. In particular, we introduce a Bayesian mechanism-design problem where the buyer has a random ranking over fixed-price items and an outside option, and the seller optimizes a (randomized) allocation of up to one item. We show that allocations corresponding to assortments are suboptimal in general, but under many commonly studied Bayesian priors for buyer rankings, such as the Multinomial Logit and Markov Chain choice models, assortments are, in fact, optimal. Therefore, this large literature on assortment optimization has much greater significance than appreciated before—it is not only computing optimal assortments; it is computing the economic limit of the seller’s revenue for selling these fixed-price substitute items. We derive several further results—a more general sufficient condition for assortments being optimal that captures choice models beyond Markov Chain; a proof that Nested Logit choice models cannot be captured by Markov Chain, but can, to some extent, be captured by our condition; and suboptimality gaps for assortments when our condition does not hold. Finally, we show that our mechanism-design problem provides the tightest-known Linear Programming relaxation for assortment optimization under the ranking distribution model.
The design of performance-based incentives—commonly used in online labor platforms—can be naturally posed as a moral hazard principal-agent problem. In this setting, a key input to the principal’s optimal contracting problem is the agent’s production function: the dependence of agent output on effort. Although agent production is classically assumed to be known to the principal, this is unlikely to be the case in practice. Motivated by the design of performance-based incentives, we present a method for estimating a principal-agent model from data on incentive contracts and associated outcomes, with a focus on estimating agent production. The proposed estimator is statistically consistent and can be expressed as a mathematical program. To circumvent computational challenges with solving the estimation problem exactly, we approximate it as an integer program, which we solve through a column generation algorithm that uses hypothesis tests to select variables. We show that our approximation scheme and solution technique both preserve the estimator’s consistency and combine to dramatically reduce the computational time required to obtain sound estimates. To demonstrate our method, we conducted an experiment on a crowdwork platform (Amazon Mechanical Turk) by randomly assigning incentive contracts with varying pay rates among a pool of workers completing the same task. We present numerical results illustrating how our estimator combined with experimentation can shed light on the efficacy of performance-based incentives.
This paper studies a pricing problem for a single-server queue where customers arrive according to a Poisson process. For each arriving customer, the service provider announces a price rate and system wait time. In response, the customer decides whether to join the queue, and, if so, the duration of the service time. The objective is to maximize either the long-run average revenue or social welfare. We formulate this problem as a continuous-time control model whose optimality conditions involve solving a set of delay differential equations. We develop an innovative method to obtain the optimal control policy, whose structure reveals interesting insights. The optimal dynamic price rate policy is not monotone in the wait time. That is, in addition to the congestion effect (the optimal price rate increases in the wait time), we find a compensation effect, meaning that the service provider should lower the price rate when the wait time is longer than a threshold. Compared with the prevalent static pricing policy, our optimal dynamic pricing policy improves the objective value through admission control, which, in turn, increases the utilization of the server. In a numerical study, we find that our revenue-maximizing pricing policy outperforms the best static pricing policy, especially when the arrival rate is low, and customers are impatient. Interestingly, the revenue-maximizing policy also improves social welfare over the static pricing policy in most of the tested cases. We extend our model to consider nonlinear pricing and heterogeneous customers. Nonlinear pricing may improve the revenue significantly, although linear pricing is easier to implement. For the hetergeneous customer case, we obtain similar policy insights as our base model.
We address a very important problem in offshore wind farm design, namely, the combined optimization of the turbine location and of the connection cables required to bring the electrical power produced by the turbines to a given substation, and eventually to shore. We first describe a mixed-integer linear programming model that combines previous proposals from the literature. Then we improve it by a number of additional inequalities intended to strengthen its linear programming relaxation. In particular, we propose new classes of Benders-like cuts derived from an induced-clique substructure of the problem. The validity of these cuts is established in a purely combinatorial way, without resorting to Benders’s standard duality theory, and efficient separation procedures are proposed. The practical effectiveness of the proposed cuts is established through computational tests, showing that they do improve very significantly the dual bound provided by the standard model. We also present an exact branch-and-cut solver for the problem, which separates the new cuts at run time. Computational results confirm that the new cuts are instrumental for the success of our exact solver.
In this paper, we study the learning problem in contextual search, which is motivated by applications such as crowdsourcing and personalized medicine experiments. In particular, for a sequence of arriving context vectors, with each context associated with an underlying value, the decision maker either makes a query at a certain point or skips the context. The decision maker will only observe the binary feedback on the relationship between the query point and the value associated with the context. We study a probably approximately correct learning setting, where the goal is to learn the underlying mean value function in context with a minimum number of queries. To address this challenge, we propose a trisection search approach combined with a margin-based active learning method. We show that the algorithm only needs to make O˜(1/ε2) queries to achieve an ε-estimation accuracy. This sample complexity significantly reduces the required sample complexity in the passive setting where neither sample skipping nor query selection is allowed, which is at least Ω(1/ε3).
We study the optimal design of information nudges directed to present-biased consumers who make consumption decisions over time without exact prior knowledge of their long-term consequences. For any distribution of risks, there exists a consumer-optimal information nudge that is of cutoff type, recommending abstinence if the risk is high enough. Depending on the distribution of risks, more or fewer consumers have to be sacrificed, as they cannot be credibly warned even though they would like to be. Under a stronger present bias, the target group receiving a credible warning to abstain must be tightened, but this need not increase the probability of harmful consumption. If some consumers have a stronger present bias than others, traffic-light nudges turn out to be optimal and, when subgroups of consumers differ sufficiently, the optimal traffic-light nudge is also subgroup optimal. We finally compare the consumer-optimal nudge with those that a health authority or a lobbyist would favor.
We investigate the causal effect of performance pay and conversations about performance in 224 stores of a retail chain implementing a field experiment with a 2x2 factorial design. In the performance pay treatments, managers receive a bonus, which is a simple linear function of the profits achieved above a threshold value. In the performance review treatments, managers have to report their activities undertaken to increase profits in regular meetings. We find that whereas performance pay did not yield significant profit increases, performance review conversations increased profits by about 7%. However, when additionally receiving performance pay, the positive effect of performance reviews vanished. We provide evidence from surveys and meeting protocols that performance pay changes the nature of conversations, leading to a stronger self-reliance of store managers, which undermines the value of the performance reviews.
Combinatorial auctions have found widespread application for allocating multiple items in the presence of complex bidder preferences. The enumerative exclusive OR (XOR) bid language is the de facto standard bid language for spectrum auctions and other applications, despite the difficulties, in larger auctions, of enumerating all the relevant packages or solving the resulting NP-hard winner determination problem. We introduce the flexible use and efficient licensing (FUEL) bid language, which was proposed for radio spectrum auctions to ease both communications and computations compared with XOR-based auctions. We model the resulting allocation problem as an integer program, discuss computational complexity, and conduct an extensive set of computational experiments, showing that the winner determination problem of the FUEL bid language can be solved reliably for large realistic-sized problem instances in less than half an hour on average. In contrast, auctions with an XOR bid language quickly become intractable even for much smaller problem sizes. We compare a sealed-bid FUEL auction to a sealed-bid auction with an XOR bid language and to a simultaneous clock auction. The sealed-bid auction with an XOR bid language incurs significant welfare losses because of the missing bids problem and computational hardness, the simultaneous clock auction leads to a substantially lower efficiency than FUEL because of the exposure problem.
To shed light on the factors that affect who speaks up in teams in the workplace, we study willingness to speak up after someone has raised an opinion. We call voicing disagreement overriding and study this behavior in a laboratory experiment where participants answer multiple choice questions in pairs. In a control treatment, participants interact anonymously. In a photo treatment, both participants see the photo of the person they are matched with at the beginning of the group task. Using a series of incentivized tasks, we elicit beliefs about the likelihood that each possible answer option to a question is correct. This allows us to measure disagreement and to tease apart the role of disagreement versus preferences in the decision to override ideas in teams. Results show that anonymity increases overriding. This treatment effect is driven by social image costs. Analysis of heterogeneity in behavior by gender reveals no differences between the likelihood that men and women override. However, we find some evidence that men and women are treated differently; when participants disagree with their partner, they are more likely to override a woman than a man. Preferences seem to in part explain the differential treatment of men and women. Studying group performance, we find that overriding helps groups on average, while the gender composition of teams does not affect team performance.
Facial information is essential in daily life, but relatively little is known about whether seeing a face improves people’s decision quality. This experimental paper studies the loan-approval decisions based on the historical cash-loan data with real repayment outcomes and exogenously varies whether and how a borrower’s facial information is provided. We find that facial information does not improve subjects’ decisions, despite the fact that it can predict repayment behavior in a machine-learning algorithm. This is because subjects have various biases in evaluating facial photos, and they rely excessively on facial information in making the loan-approval decisions.
A choice rule with affirmative action decides on the recipients of a limited number of identical objects by reconciling two objectives: respecting a priority ordering over the applicants and supporting a minority group. We extend the standard formulation of a choice problem by incorporating a type function and a priority ordering and introduce monotonicity axioms on how a choice rule should respond to variations in these parameters. We show that monotonic and substitutable affirmative action rules are the ones that admit a bounded reserve representation. As a prominent class of choice rules that satisfy the monotonicity axioms, we characterize lexicographic affirmative action rules that are prevalent both in the literature and in practice. Our axiomatic approach provides a novel way to think about reserve systems and uncovers choice rules that go beyond lexicographic affirmative action rules.
Should firms that apply machine learning algorithms in their decision making make their algorithms transparent to the users they affect? Despite the growing calls for algorithmic transparency, most firms keep their algorithms opaque, citing potential gaming by users that may negatively affect the algorithm’s predictive power. In this paper, we develop an analytical model to compare firm and user surplus with and without algorithmic transparency in the presence of strategic users and present novel insights. We identify a broad set of conditions under which making the algorithm transparent actually benefits the firm. We show that, in some cases, even the predictive power of the algorithm can increase if the firm makes the algorithm transparent. By contrast, users may not always be better off under algorithmic transparency. These results hold even when the predictive power of the opaque algorithm comes largely from correlational features and the cost for users to improve them is minimal. We show that these insights are robust under several extensions of the main model. Overall, our results show that firms should not always view manipulation by users as bad. Rather, they should use algorithmic transparency as a lever to motivate users to invest in more desirable features.
One of the most commanding powers of the U.S. Patent and Trademark Office (USPTO) is to compel inventions into secrecy, withholding patent rights and prohibiting disclosure, to prevent technology from leaking to foreign competitors. This paper studies the impacts of compulsory secrecy on firm invention and the wider innovation system. In World War II, USPTO issued secrecy orders to more than 11,000 patent applications, which it rescinded en masse at the end of the war. Compulsory secrecy caused implicated firms to shift their patenting away from treated classes, with effects persisting through at least 1960. It also restricted commercialization and impeded follow-on innovation. Yet it appears it was effective at keeping sensitive technology out of public view. The results provide insight into the effectiveness of compulsory secrecy as a regulatory strategy and into the roles, and impacts, of formal intellectual property in the innovation system.
Massive online text reviews can be a powerful market research tool for understanding consumer experiences and helping firms improve and innovate. This research exploits the rich semantic properties of text reviews and proposes a novel machine learning modeling framework that can reliably and efficiently extract consumer opinions and uncover potential interaction effects across these opinions, thereby identifying hidden and nuanced areas for product and service improvement beyond existing modeling approaches in this domain. In particular, we develop an opinion extraction and effect estimation framework that allows for uncovering customer opinions’ average effects and their interaction effects. Interactions among opinions can be synergistic when the co-occurrence of two opinions yields an effect greater than the sum of two parts, or as what we call dysergistic, when the co-occurrence of two opinions results in dampened effect. We apply the model in the context of large-scale customer ratings and text reviews for hotels and demonstrate our framework’s ability to screen synergy and dysergy effects among opinions. Our model also flexibly and efficiently accommodates a large number of opinions, which provides insights into rare yet potentially important opinions. The model can guide managers to prioritize joint areas of product and service improvement and innovation by uncovering the most prominent synergistic pairs. Model comparison with extant machine learning approaches demonstrates our improved predictive ability and managerial insights.
Negative advertising provides information about the weaknesses of a competitor’s product. We study negative advertising with a focus on how its regulation impacts product positioning for profit-maximizing firms. We build a model of informative advertising competition, where product positioning is endogenous, and consumers have rational expectations. We show that despite the informational benefits of negative advertising, permitting it (as the Federal Trade Commission in the United States does) may lead to reduced product differentiation and lower consumer welfare, even in markets where firms do not use negative advertising in equilibrium. We then extend our model to political competition, where a candidate’s objective is to obtain a larger share of votes than the competitor. We show that political competition supports higher positional differentiation, along with more negative advertising than product competition, in line with observed high use of negative advertising in political races and their rarer use in product competition.
It is generally acknowledged that sunk cost bias leads to suboptimal decisions, such as escalation of commitment. Some researchers, however, suggest that sunk cost bias can be beneficial when consumers have self-control problems. In this paper we explore the case when consumers with sunk cost bias have time-inconsistent preferences and, therefore, suffer from self-control problems. We experimentally demonstrate that sunk costs can make subjects better off by inducing higher effort. We then develop an analytical model to explore the implications of sunk cost bias for firm’s pricing strategy. We find that, in the presence of sunk cost bias, higher prices can lead to higher experienced quality. We show that sunk cost bias can sometimes improve firm’s profits, lead to lower prices, and increase welfare. Our results suggest that, when consumers use a product for multiple periods, pricing policies such as 0% financing, which are often viewed as exploitative, can instead lead to lower total prices, higher profits, and higher welfare.
We study the impact of a financial transaction tax (FTT) in a model that combines asset trading and real investment. An informed trader holds private information about the fundamental value of a firm, and the firm’s manager relies on the asset price to infer such information and invest accordingly. We characterize an informative, but illiquid, equilibrium where the firm’s value is optimal but trade is inefficiently low, together with an uninformative equilibrium with maximal liquidity but inefficient firm value. Although an FTT inefficiently reduces trading, it may tilt the market’s equilibrium and make asset prices more informative. We characterize the situations in which one or the other of these two effects prevails. The analysis also helps us to reconcile some puzzling empirical evidence regarding the adoption of the FTT.
Candidate self-funding, in particular self-loans, is a significant source of funding of political campaigns. Self-funding clusters among nonincumbent campaigns, republican campaigns, and more expensive campaigns. Self-funded campaigns raise less money from individuals and special interests and also spend less. Self-funders are wealthier on average and run in more competitive elections. The analysis of self-funders’ legislative decisions shows that self-funders’ votes, especially those of republicans, are significantly more sensitive to contributions from special interests that are affected by the votes. The results highlight the importance of considering politicians’ self-funding choices in analyzing voting behavior and the value of political activism.
This paper examines how female directors (FDs) affect firm value in the absence of mandatory gender quotas. Using a newly collected data set on director deaths around the globe, we find that stock prices decrease approximately 2% more when an FD passes away, compared with a male director. What explains this negative capital market reaction? We find evidence that finding successors for deceased FDs is challenging for firms: Succession delays are longer, and although firms try to replace FDs with women, two-thirds of their successors are male. Furthermore, their successors tend to be younger, less experienced, and more often externally hired. Stock prices decline less if more potential female successors exist in a country, the firm is larger, or FDs other than the deceased woman were on the board. Because observable characteristics such as age, tenure, education, and network centrality cannot explain the negative stock market reaction, unobserved differences across genders that lead to a lower fit of male successors to the existing board are the most likely explanation for the firm value loss after the death of an FD.
We develop an approach to solve the Barberis casino gambling model [Barberis N (2012) A model of casino gambling. Management Sci. 58(1):35–51] in which a gambler whose preferences are specified by the cumulative prospect theory (CPT) must decide when to stop gambling by a prescribed deadline. We assume that the gambler can assist their decision using independent randomization. The problem is inherently time inconsistent because of the probability weighting in CPT, and we study both precommitted and naïve stopping strategies. We turn the original problem into a computationally tractable mathematical program from which we devise an algorithm to compute optimal precommitted rules that are randomized and Markovian. The analytical treatment enables us to confirm the economic insights of Barberis for much longer time horizons and to make additional predictions regarding a gambler’s behavior, including that, with randomization, a gambler may enter the casino even when allowed to play only once and that it is prevalent that a naïf never stops loss.
This paper analyzes how the degree of task-related team diversity influences the team outcome. The focus is on a novel benefit of team diversity that does not depend on complementary skills or mutual learning. We consider a multiagent setting where the employer makes use of career competition to motivate the team. Our analysis reveals incentive effects that do not exist in the well-studied case of teams of size 2. In teams with more than two members, diversity may have a positive effect on the efforts of some members although diversity leads to unbalanced career competition. As a consequence, expected profits are higher under a moderate degree of diversity compared with a situation with completely homogeneous agents. This result shows that the employer prefers a positive degree of diversity for pure incentive reasons.
Although leveraged exchange-traded funds (ETFs) are popular products for retail investors, how to hedge them poses a great challenge to financial institutions. We develop an optimal rebalancing (hedging) model for leveraged ETFs in a comprehensive setting, including overnight market closure and market frictions. The model allows for an analytical optimal rebalancing strategy. The result extends the principle of “aiming in front of target” introduced by Gârleanu and Pedersen (2013) from a constant weight between current and future positions to a time-varying weight because the rebalancing performance is monitored only at discrete time points, but the rebalancing takes place continuously. Empirical findings and implications for the weekend effect and the intraday trading volume are also presented.
The timely flow of financial information is critical for efficient capital market functioning, yet we have little understanding of firms’ and auditors’ collective abilities to maintain timely financial reporting when under duress. We use COVID as a stress test case to examine whether reporting systems can withstand systemic increases in complex economic events and coordination challenges. Despite COVID-related challenges persisting through 2020 and beyond, we document surprisingly modest average delays in financial reports during COVID and only in Q1-2020. Reporting timeliness reverts to pre-COVID levels no later than Q2-2020. We find no evidence of meaningful declines in actual reporting quality during COVID, but we do find some evidence consistent with declines in perceived reporting quality. Overall, our findings indicate that current financial reporting processes are remarkably robust and provide insights about financial reporting more broadly. In particular, given that nearly all firms were able to weather the unprecedented disruptions caused by COVID, our findings imply that most material reporting delays observed outside of COVID are likely a result of either a firm’s strategic choices or exceptionally fragile reporting processes.
“Zombie lending” remains a widespread practice by banks around the world. In this paper, we exploit a series of large-scale on-site inspections made on the credit portfolios of several Portuguese banks to investigate how these inspections affect banks’ future lending decisions. We find that an inspected bank becomes 20% less likely to refinance zombie firms, immediately spurring their default. Overall, banks seemingly reduce zombie lending because the incentives to hold these loans disappear once they are forced to recognize losses.
Telemedicine is increasingly used across the developing world to expand access to healthcare, to improve outcomes, and to reduce costs. One common model is that of telemedicine centers, which are small primary care facilities run by midlevel (nonphysician) providers who conduct a preliminary examination and then facilitate a telemedicine visit with a remote physician in real time. However, the impact of this channel of care delivery—particularly on existing physical healthcare-delivery channels—has not been thoroughly examined. We use data from one of the largest tele-ophthalmology implementations in the world to examine this issue. Using a quasi-experimental difference-in-differences approach, we find that opening a nearby telemedicine center generates a 31% increase in the overall network visit rate from the population within 10 km of the new center, 62% of which is driven by new patients, suggesting a substantial increase in access. The rate of eyeglasses prescriptions to correct for simple refractive errors increases by 18.5%, whereas the rate of cataract surgery to replace the natural lens in a patient’s eye with an artificial lens remains unchanged. The increase in access and treatment rates does not significantly impact the direct costs incurred by patients, but reduces their indirect costs (measured as travel distance) by 30% (12 km). Finally, we find significant spatial heterogeneity in these effects, which vary with the distance of patients to facilities. These results have important implications for the design of telemedicine networks and the portfolio of healthcare services provided through them.
This paper shows that investments based on deep learning signals extract profitability from difficult-to-arbitrage stocks and during high limits-to-arbitrage market states. In particular, excluding microcaps, distressed stocks, or episodes of high market volatility considerably attenuates profitability. Machine learning-based performance further deteriorates in the presence of reasonable trading costs because of high turnover and extreme positions in the tangency portfolio implied by the pricing kernel. Despite their opaque nature, machine learning methods successfully identify mispriced stocks consistent with most anomalies. Beyond economic restrictions, deep learning signals are profitable in long positions and recent years and command low downside risk.
This paper seeks to demonstrate the impact of the gig economy on product quality in seemingly unrelated local industries through the labor market. Our empirical context is the quality of service for restaurants in the city of Austin, and we examine how they were impacted by the exogenous exit and reentry of rideshare platforms, Uber and Lyft, because of regulatory changes. We leverage these exogenous shocks and combine them with sentiment-analyzed data from Yelp reviews that capture how customers assess the quality of service at each restaurant. We show that, compared with control cities, customers in Austin become more negative about service quality when Uber and Lyft are present in the city. Additionally, we use rich data on employee turnover and wages to demonstrate that service staff turnover increases in Austin when Uber and Lyft are present compared with the control cities. We also conduct several additional studies and robustness checks that are all congruent with our hypothesis that Uber and Lyft lower the quality of service in Austin restaurants by raising their staff turnover. Together, these results suggest significant ramifications of the gig economy on the broader industries through the labor market.
This paper exploits a major food safety crisis to estimate a full demand model for the unsafe product and its substitutes, recovering consumers’ preference parameters for different product characteristics. Counterfactual exercises quantify the relevance of different mechanisms driving consumers’ responses, such as changes in safety perceptions, idiosyncratic taste, product characteristics, and price. We find that consumers’ reactions are limited by their preferences for the product’s observable and unobservable characteristics. Because of the costs associated with switching from the affected product, the decline in demand following a product-harm crisis tends to understate the true weight of such events in consumers’ utility. We find that unobservable taste is a crucial driver of consumers’ responses. Our counterfactual exercises illustrate that the demand would have declined further if consumers had had access to a closer substitute. For an accurate assessment of product-harm crises, managerial strategies should therefore account for how different demand drivers bind consumers’ substitution patterns.
Effective approaches to forecast model selection are crucial to improve forecast accuracy and to facilitate the use of forecasts for decision-making processes. Information criteria or cross-validation are common approaches of forecast model selection. Both methods compare forecasts with the respective actual realizations. However, no existing selection method assesses out-of-sample forecasts before the actual values become available—a technique used in human judgment in this context. Research in judgmental model selection emphasizes that human judgment can be superior to statistical selection procedures in evaluating the quality of forecasting models. We, therefore, propose a new way of statistical model selection based on these insights from human judgment. Our approach relies on an asynchronous comparison of forecasts and actual values, allowing for an ex ante evaluation of forecasts via representativeness. We test this criterion on numerous time series. Results from our analyses provide evidence that forecast performance can be improved when models are selected based on their representativeness.
In this research we reexamine and update prior research on the association between decision conflict and choice deferral. Decision conflict has featured prominently in psychology literature, going back to the seminal work of Lewin (1933), Miller (1944), and Janis and Mann (1977). Building on this earlier work and the assumption of conflict as an inhibitor of choice, Tversky and Shafir (1992; see also Dhar 1997) argued that the tendency to defer a decision can increase when decision makers experience conflict because of the need to make trade-offs between options in a set. Tversky and Shafir (1992) showed that decision makers may defer choice more when faced with two options presenting trade-offs compared with when they are offered either a single option (see also Dhar 1997) or two options where one alternative dominates the other. Relatedly, Luce (1998) showed that decision makers may be more likely to defer choice when faced with multiple options presenting relatively difficult (versus easy) trade-offs. We reexamine published data and report the results of 40 well-powered direct and conceptual replications of these effects (total n = 26,703) that cast doubt on the replicability of prior research. Our data suggest that additional research is needed in order to reveal a robust association between experimental manipulations of decision conflict and choice deferral. We conclude our investigation by delineating a set of directions for future research on this topic.
Decision makers often ask experts to forecast a future state. Experts, however, can be biased. In the economics and psychology literature, one extensively studied behavioral bias is called herding. Under strong levels of herding, disclosure of public information may lower forecasting accuracy. This result, however, is derived only for point forecasts. In this paper, we consider experts’ probabilistic forecasts under herding, find a closed-form expression for the first two moments of a unique equilibrium forecast, and show that the experts report too similar locations and inflate the variance of their forecasts because of herding. Furthermore, we show that the negative externality of public information no longer holds. In addition to reacting to new information as expected, probabilistic forecasts contain more information about the experts’ full beliefs and interpersonal structure. This facilitates model estimation. To this end, we consider a one-shot setting with one forecast per expert and show that our model is identifiable up to an infinite number of solutions based on point forecasts but up to two solutions based on probabilistic forecasts. We then provide a Bayesian estimation procedure for these two solutions and apply it to economic forecasting data collected by the European Central Bank and the Federal Reserve Bank of Philadelphia. We find that, on average, the experts invest around 19% of their efforts into making similar forecasts. The level of herding shows an increasing trend from 1999 to 2007 but drops sharply during the financial crisis of 2007–2009 and then rises again until 2019.
Despite the truthful dominant strategy, participants in strategy-proof mechanisms submit manipulated preferences. In our model, participants dislike rejections and enjoy the confirmation from getting what they declare desirable. Formally, the payoff from a match decreases in its position in the submitted ranking such that a strategic trade-off between preference intensity and match probability arises. This trade-off can trigger the commonly observed self-selection strategies. We show that misrepresentations can persist for arbitrarily small report-dependent components. However, honesty is guaranteed to be optimal if and only if there is no conflict between the quality and feasibility of a match. We substantiate the theory with already existing evidence and provide novel testable predictions.
Fear of public speaking is very common, but we know little about its implications for individuals and organizations. We establish public speaking aversion as an economically relevant preference using three steps. First, we use laboratory and classroom experiments to show that preferences for speaking in public vary strongly across individuals with many participants willing to give up significant amounts of money to avoid giving a short presentation in front of an audience. Second, we introduce two self-reported items to elicit preferences for speaking in public through surveys. We show that these items are strongly related to choices in the incentivized laboratory experiment and that public speaking aversion is distinct from established traits and preferences, including extraversion. Third, we elicit these items in a survey of business and economics students and show that public speaking aversion predicts career expectations. Public speaking–averse individuals avoid careers in management consulting and are more attracted to data analyst and back office careers.
We provide evidence that investor behavior is sensitive to two dimensions of subjective uncertainty concerning future asset values. Investors vary in the extent to which they attribute market uncertainty to (1) missing knowledge, skill, or information (epistemic uncertainty) and (2) chance or stochastic processes (aleatory uncertainty). Investors who view stock market uncertainty as higher in epistemicness (knowability) are more likely to reduce uncertainty by seeking guidance from experts and are more responsive more responsive to available information when choosing whether to invest. In contrast, investors who view stock market uncertainty as higher in aleatoriness (randomness) are more likely to reduce uncertainty through diversification, and their risk preferences better predict whether they choose to invest. We show, further, that attributions of uncertainty can be perturbed by the format in which historical information is presented: charts displaying absolute stock prices promote perceptions of epistemicness and greater willingness to pay for financial advice, whereas charts displaying the change in stock prices from one period to the next promote perceptions of aleatoriness and a greater tendency to diversify.
Subjects observe a private signal and make an initial guess; they then observe their neighbors’ guesses, update their own guess, and so forth. We study learning dynamics in three large-scale networks capturing features of real-world social networks: Erdös–Rényi, Stochastic Block (reflecting network homophily), and Royal Family (that accommodates both highly connected celebrities and local interactions). We find that the Royal Family network is more likely to sustain incorrect consensus and that the Stochastic Block network is more likely to persist with diverse beliefs. These patterns are consistent with the predictions of DeGroot updating. It lends support to the notion that the use of simple heuristics in information aggregation is prevalent in large and complex networks.
We study direct load control contracts that utilities use to curtail customers’ electricity consumption during peak-load periods. These contracts place limits on the number of calls and total number of hours of power reduction per customer per year as well as the duration of each call. The stochastic dynamic program that determines how many customers to call and the timing and duration of each call for each day is an extremely difficult (NP-hard) optimization problem. We design a scenario-based approximation method to generate probabilistic allocation polices in a reasonable amount of time. Our approach consists of three approximations: deterministic approximation of demand, discretization of the expected demand, and aggregation/disaggregation of the resources. We show the relative information error resulting from the deterministic approximation is O(1/n), the discretization error is O(1/n), and the aggregation/disaggregation error is O(1/n), where n represents the length of the horizon. Finally, we show the total relative error is O(1/n). Our error analysis establishes that our approximation method is near optimal. In addition, our extensive numerical experiments verify the high quality of our approximation approach. The error, conservatively measured, is quite small and has an average and standard deviation of 8.6% and 1.4%, respectively. We apply our solution approach to the data provided by three major utility companies in California. Overall, our study shows our procedure improves the savings in energy-generation cost by 37.7% relative to current practices.
In this paper, we consider the yet-uncharted assortment optimization problem under the exponomial choice model, in which the objective is to determine the revenue-maximizing set of products that should be offered to customers. Our main algorithmic contribution comes in the form of a fully polynomial-time approximation scheme, showing that the optimal expected revenue can be efficiently approached within any degree of accuracy. We synthesize several ideas related to approximate dynamic programming, intended to construct a compact discretization of the continuous state space by keeping track of “key statistics” in rounded form and by operating with a suitable bit precision complexity. We complement this result by a number of NP-hardness reductions to natural extensions of this problem. Moreover, we conduct empirical and computational evaluations of the exponomial choice model and our solution method. Focusing on choice models with a simple parametric structure, we provide new empirical evidence that the exponomial choice model can achieve higher predictive accuracy than the multinomial logit (MNL) choice model on several real-world data sets. We uncover that this predictive performance correlates with certain characteristics of the choice instance—namely, the entropy and magnitude of choice probabilities. Finally, we leverage fully ranked preference data to simulate the expected revenue of optimal assortments prescribed using the fitted exponomial and MNL models. On semisynthetic data, the exponomial-based approach can lift revenues by 3%–4% on average against the corresponding MNL benchmark.
The growing complexity of consulting, new product development, and information technology projects has led firms to increasingly adopt the strategy of collaborative value cocreation with their vendors. Although such value cocreation often involves a client firm engaging with more than one vendor, research in this field has primarily focused on single-vendor cocreation, whereas research on multivendor sourcing has rarely considered collaborative value cocreation. We bridge this gap by studying a cocreation environment involving a client, a primary vendor, and a potential secondary vendor. Examining the trade-offs that arise in the strategic interactions among the three firms, we derive novel insights about how these interactions differ from those under one-vendor cocreation, and from those under traditional multivendor sourcing. Specifically, we demonstrate that the client can sometimes be better off by adding a secondary vendor that is less efficient than the primary one; and a more expensive primary vendor can sometimes make the client less inclined to add the secondary vendor. The client’s decision to add the secondary vendor is driven by the value per unit of output rather than purely cost or efficiency parameters. We show that in such collaborative environments, the primary vendor benefits from the addition of a secondary vendor whenever it benefits the client. The conceptual and managerial insights drawn from our research contribute to a better theoretical and practical understanding of strategic decision making by firms in multivendor cocreation environments that are increasingly becoming prevalent.
Exaptation refers to the emergence of novel functionalities in existing products. Exaptations frequently arise in the context of users who creatively modify (or hack) existing products to accommodate new needs. Here, we examine how product-first (compared with problem-first) search affects the occurrence of exaptations. In a product-first search, the user identifies the product to be hacked before seeking a viable need. In a problem-first search, the user has a defined problem before seeking a viable solution. We argue that users are more likely to achieve exaptations following a problem-first (compared with a product-first) search. Indeed, with problem-first search, they are less likely to face functional fixedness, and they can leverage their greater awareness of problems that may not have readily adaptable solutions, which leads them to generate exaptations. Using a novel data set comprising user hacks of IKEA products, we present evidence that hacks originating from a product-first search are less likely to generate exaptations than hacks originating from a problem-first search. We also show that this difference is mitigated when the user has hacking experience or when the IKEA product being hacked is more modular. We also explore how the mitigation happens. Increased hacking experience appears to reduce functional fixedness; meanwhile, increased product modularity increases the likelihood that users will make serendipitous discoveries leading to exaptations. We contribute to the growing literature on exaptation as a source of novelty and discuss the implications of this phenomenon for managing user innovation.
We study professional players and their roles in peer-to-peer (P2P) markets. Most notably, P2P home-sharing platforms (e.g., Airbnb) consist of both professional hosts and nonprofessional individual hosts. What are the roles of the professionals? Should home-sharing platforms regulate their participation? Professional hosts may primarily offer properties that nonprofessional hosts would not supply and attract more guests—the differentiation effect. Or they may mostly supply similar properties and compete with the nonprofessionals—the competition effect. Using a unique data set of Airbnb listings, we first find that professional hosts’ properties are more expensive and have superior characteristics than nonprofessionals’. Second, we capitalize on a quasi-experiment in which Airbnb capped the number of properties a host can manage in several cities in the United States to determine the roles of professional hosts. With different predictions (about the policy impacts) under the differentiation versus competition effects, we find evidence suggesting the dominance of the latter. In particular, the policy increased the supply from nonprofessional hosts, and the price level of nonprofessional properties as a group went up after the policy. However, our findings of heterogeneity in policy impacts suggest that the dominance of competition is less prominent in certain markets. Finally, we find that the platform was not worse off in attracting reservations or securing revenue after the policy. Our findings contribute to both theory and practice as they reveal the roles of professional players and how P2P platforms can manage their participation.
Could a salary history ban (SHB) reduce the gender wage gap? Proponents of this intervention believe the gap is sustained by the practice of eliciting salary histories from job applicants. Although observational studies suggest that SHB operates as envisioned, two features complicate the interpretation of its effects. These are, respectively, the passage of relevant legislation alongside SHB, and the presence of public campaigns that propel SHB into law. We assessed SHB in the United Kingdom, where neither potential confound was present, and did not find evidence that the intervention operated as intended. An intention-to-treat analysis of a 16-month field experiment, conducted with 230 staff hires at a private educational institution, indicates that SHB was about as likely to harm new hires as it was to help them. Additional analyses did not reveal significant differences between women and men. We supplement these results with an interrupted time series analysis of 3,687 placements made by a recruitment firm that voluntarily adopted SHB for its job candidates. Salaries were significantly lower under SHB, but were not significantly different for women versus men. Taken together, our results suggest that SHB was ineffective in isolation from contemporaneous legislative changes, proequality messaging, or some combination thereof.
Using cross-state and intertemporal variation in whether a state’s minimum wage is bound by the federal minimum wage, we provide evidence that minimum wage increases lead U.S. public firms in minimum-wage-sensitive industries (i.e., retail, restaurant, and entertainment) to cut capital expenditures. These effects are concentrated one to two years after the law goes into effect. Prior to the minimum wage increase, investment trends are similar across minimum-wage-sensitive firms in bound versus unbound states, and we find little evidence that minimum wage changes affect U.S. public firm investment outside of these industries.
This paper introduces formal monitoring procedures as a risk-management tool. Continuously monitoring risk forecasts allows practitioners to swiftly review and update their forecasting procedures as soon as forecasts turn inadequate. Similarly, regulators may take timely action in case reported risk forecasts become poor. Extant (one-shot) backtests require, however, that all data are available prior to testing and are not informative of when inadequacies might have occurred. To monitor value-at-risk and expected shortfall forecasts “online”—that is, as new observations become available—we construct sequential testing procedures. We derive the exact finite-sample distributions of the proposed procedures and discuss the suitability of asymptotic approximations. Simulations demonstrate good behavior of our exact procedures in finite samples. An empirical application to major stock indices during the COVID-19 pandemic illustrates the economic benefits of our monitoring approach.
In a parsimonious regime switching model, we find strong evidence that expected consumption growth varies over time. Adding inflation as a second variable, we uncover two states in which expected consumption growth is low, one with high and one with negative expected inflation. Embedded in a general equilibrium asset pricing model with learning, these dynamics replicate the observed time variation in stock return volatilities and stock-bond return correlations. They also provide an alternative derivation for a measure of time-varying disaster risk suggested by Watcher [Wachter J (2013) Can time-varying risk of rare disasters explain aggregate stock market volatility? J. Finance 68(3):987–1035]. implying that both the disaster and the long-run risk paradigm can be extended toward explaining movements in the stock-bond correlation.
This paper studies the investor’s investment decision in a standard financial contracting model. The investor chooses among entrepreneurs with heterogeneous production technologies who generate different probability distributions of cash flow under effort. I provide a complete characterization of optimal contracts, agency rents, and the investor’s investment decision. Differences in entrepreneurs’ production technologies imply differences in optimal contracts and agency rents across equally productive entrepreneurs, which biases and potentially distorts the investor’s investment decision. The results provide a complete characterization of entrepreneurs’ financial constraints in the presence of heterogeneous production technologies and uncover a fundamental link between production technologies, financial contracts, and financial constraints.
We document and examine a significant shift in the comovement of asset returns and macroeconomic volatility during the Great Moderation. Strong U.S. stock and bond return predictability from several macroeconomic volatility series before 1982 was followed by a significant predictability decline during the Great Moderation (1982–2008). These findings are robust to alternative empirical specifications and out-of-sample tests. In a calibrated equilibrium model with time-varying volatility, the predictability decline requires changes in several model elements. Lower return predictability is consistent with stronger policy responses to inflation and output, a larger slope in the New Keynesian Phillips curve, and reduced sensitivity of both macroeconomic and financial variables to a volatility factor. Our results contribute to the examination of macroeconomic volatility as a driver of expected asset returns and the instability in predictive regressions. We further identify sources of the Great Moderation using asset price dynamics.
This paper examines an innovation in capital formation that has spurred contentious debate: the Umbrella Partnership Corporation (“Up-C”) IPO. Advisors and underwriters argue that the Up-C deal structure is a driver of post-IPO value and, thus, is a value-enhancing means of raising capital that may be one solution to concerns regarding the drop in the number of publicly traded companies. Consistent with these claims, recent research suggests that organizing soon-to-be public businesses as pass-through entities (as is the case for Up-Cs) leads to superior future performance. Yet, broadening the analysis to consider abnormal stock performance and post-IPO litigation of a larger and more recent sample of exclusively Up-C IPOs, we conclude just the opposite. While the Up-C deal structure increases IPO valuations and predicts positive post-IPO operating performance, the return performance of Up-C IPOs indicates that Up-C deals harm public shareholders. Further, despite their superior earnings performance, Up-C IPOs face a significantly higher rate of post-IPO litigation as compared with non-Up-C IPOs. Because IPO investors seemingly do not anticipate the myriad ways in which the Up-C deal structure might facilitate opportunism by pre-IPO owners, they frequently turn to litigation as an ex-post settling-up mechanism. Consequently, our paper offers the first empirical evidence of downsides associated with the Up-C deal structure for public shareholders and, in so doing, affords the rarity of having academic evidence lead (as opposed to respond to) a controversial debate.
We study the effect of proximity to corporate headquarters on the productivity of inventors and research and development (R&D) facilities. Distant inventors and R&D facilities are less productive, and plausibly exogenous reductions in the travel time from these inventors or facilities to headquarters increase their productivity and creativity. We hypothesize that these improvements in the management of innovation production occur because proximity improves monitoring, managerial ability to provide direction, relationship building that supports creativity, and information exchange, including advice from headquarters. Consequently, our results suggest that proximity can help managers balance both exploration and exploitation when overseeing innovation. Naturally, these results then beg the question of why firms do not locate all inventors and R&D facilities in close proximity to headquarters. We find that distant inventors and R&D facilities are located in areas with better general economic indicators, and especially more favorable tax rates, than headquarters, suggesting firms balance these benefits against the benefits of proximity when locating innovation production.
When reporting after-tax non–generally accepted accounting principles (GAAP) earnings, firms are required to adjust for the tax effects of exclusions. Since 2010, the Securities and Exchange Commission (SEC) has issued and updated compliance and disclosure interpretations (C&DIs), which specifically require firms to disclose the tax effects of exclusions. We assemble a detailed, hand-collected data set of S&P 1500 firms’ disclosures to provide the first large-sample evidence on the reporting of the tax effects of non-GAAP exclusions. We find three key results. First, echoing the SEC’s concern, a significant proportion of non-GAAP reporting firms do not follow the C&DI guidelines (i.e., they do not disclose the tax effects of exclusions). Second, among firms that disclose the tax effects of exclusions, we find that managers strategically select the tax rates applied to exclusions to achieve after-tax earnings targets. Third, manager-reported non-GAAP earnings are less persistent for future operating earnings and cash flows relative to non-GAAP earnings calculated by applying various benchmark tax rates to exclusions. This evidence suggests that managers’ strategic behavior in selecting the tax rates applied to exclusions pollutes reported non-GAAP earnings and reduces their usefulness for predicting future performance. Overall, our results shed light on a specific channel through which firms use non-GAAP reporting to meet or beat earnings expectations.
We examine the incentive effects of subjectivity in allocating tournament-based rewards and punishments. We use data from a company where reward and punishment decisions are based on a combination of objective metrics and subjective performance assessments. Rankings based on the objective metrics and the ultimate payoff allocations are disclosed to all members of the organization. This information allows employees to observe whether and how managers subjectively override the objective rankings. Consistent with expectancy theory, we predict and find that subjective rewards and punishments manifesting as favorable (unfavorable) deviations from formula-based payoff expectations are associated with subsequent performance improvements (declines). These performance responses are incremental to the effects of receiving a reward or punishment per se. Our results suggest that managers can benefit from using subjective rewards, but using subjective punishments can be very costly in the absence of sufficiently strong ex ante incentive effects associated with the prospect of subjective penalties. Our findings contribute to the literature on subjectivity in performance evaluations and have important practical implications for designing incentive systems.
We examine the relationship between pre-grant patent disclosure and analyst forecast accuracy. We take advantage of the passage of the American Inventor’s Protection Act (1999), which mandates the pre-grant public disclosure of all information in patent application documents within 18 months of the initial filings. We find that, on average, the pre-grant patent disclosure of corporate inventions significantly improves the accuracy of analyst forecasts about the patenting firm and this improvement is greater for firms with higher research and development intensity. Nevertheless, improvements in the accuracy of analysts’ forecasts are smaller when firms issue more original and scientifically broader patents. Also, this effect is weaker for firms in states without legal protection for trade secrets.
We study the incentives of a digital business to collect and protect users’ data. The users’ data the business collects improve the service it provides to consumers, but they may also be accessed, at a cost, by strategic third parties in a way that harms users, imposing endogenous users’ privacy costs. We characterize how the revenue model of the business shapes its optimal data strategy: collection and protection of users’ data. A business with a more data-driven revenue model will collect more users’ data and provide more data protection than a similar business that is more usage driven. Consequently, if users have small direct benefit from data collection, then more usage-driven businesses generate larger consumer surplus than their more data-driven counterparts (the reverse holds if users have large direct benefit from data collection). Relative to the socially desired data strategy, the business may over- or undercollect users’ data and may over- or underprotect it. Restoring efficiency requires a two-pronged regulatory policy, covering both data collection and data protection; one such policy combines a minimal data protection requirement with a tax proportional to the amount of collected data. We finally show that existing regulation in the United States, which focuses only on data protection, may even harm consumer surplus and overall welfare.
Much of the evidence on managerial biases in corporate finance focuses on the CEO and, in particular, CEO overconfidence. This singular focus can lead to misattribution as it ignores the roles of other managers who are responsible for a given corporate outcome. We evaluate the influence of the CFO and other C-suite executives as compared with the CEO. Mirroring the widely used Longholder CEO measure of CEO overconfidence, we construct Longholder CFO and Longholder Other measures. For financing decisions, we find that CEO overconfidence becomes an insignificant predictor of most decisions when included jointly with the CFO proxy, whereas CFO overconfidence has strong predictive power. The reverse holds for nonfinancing decisions: CEO beliefs predict the risk and return of investment projects and, thus, their cost of financing as well as acquisitions. Other C-suite managers’ overconfidence is not significant in either of these two realms. CEO overconfidence does remain significant even for financing decisions in the subsample of firms with “powerful” (entrenched) CEOs. We also show that overconfident CEOs tend to hire overconfident CFOs, which generates a multiplier effect and explains the misattribution to the CEO in analyses that do not account for the roles of other managers. Our results imply that analyses of managerial biases need to identify the dominant decision makers and account for their respective influence.
We study healthcare operations of emergency departments (EDs) by examining the practice styles and skills of ED physicians. Our data include all residents of Montreal, Canada, with an initial ED visit in Montreal during a nine-month period. For each visit, our data record the initial treating hospital, ED physician, ED billed expenditures, and all interactions with the health system within the subsequent 90 days. Physicians in Montreal rotate across shifts between simple and difficult cases, implying a quasi-random assignment of patients to physicians within an ED. We consider three medical conditions that present frequently in the ED and for which mistreatment may have dramatic consequences—angina, appendicitis, and transient ischemic attacks—jointly examining diagnostic and disposition skills. To control for variation in diagnosis, our sample for each condition consists of patients with a broader set of symptoms and signs potentially indicative of the condition. Separately by condition, we regress healthcare usage and cost measures on indicators for physicians to estimate the skill and practice style of each physician. We then evaluate the variation across physicians in their practice style and skills and the correlations between different measures of skill and practice style. We find significant variation across physicians in their practice styles and skills. We also find that physicians with costly practice styles often have worse outcomes in terms of more ED revisits and more hospitalizations. Finally, the practice styles and skills of physicians correlate positively across the three conditions that we consider.
Free trial promotions are a commonly used customer acquisition strategy in the Software as a Service industry. We use data from a large-scale field experiment to study the effect of trial length on customer-level outcomes. We find that, on average, shorter trial lengths (surprisingly) maximize customer acquisition, retention, and profitability. Next, we examine the mechanism through which trial length affects conversions and rule out the demand cannibalization theory, find support for the consumer learning hypothesis, and show that long stretches of inactivity at the end of the trial are associated with lower conversions. We then develop a personalized targeting policy that allocates the optimal treatment to each user based on individual-level predictions of the outcome of interest (e.g., subscriptions) using a lasso model. We evaluate this policy using the inverse propensity score reward estimator and show that it leads to 6.8% improvement in subscription compared with a uniform 30-days for-all policy. It also performs well on long-term customer retention and revenues in our setting. Further analysis of this policy suggests that skilled and experienced users are more likely to benefit from longer trials, whereas beginners are more responsive to shorter trials. Finally, we show that personalized policies do not always outperform uniform policies, and we should be careful when designing and evaluating personalized policies. In our setting, personalized policies based on other methods (e.g., causal forests, random forests) perform worse than a simple uniform policy that assigns a short trial length to all users.
Hedge funds actively acquire publicly available financial disclosures. Funds acquiring such information subsequently earn 1.5% higher annualized abnormal returns than nonacquirers. Trades by the same fund in the same quarter are more profitable when accompanied by public information acquisition. Acquiring public filings is relatively less profitable when macrouncertainty is high. Funds employ a wide range of strategies for acquiring public filings. Those that systematically scrape large volumes of information, specialize in certain filing types, acquire filings with more content changes, or access information immediately outperform other funds.
We show that firms strategically alter their disclosures when investors’ access to information via search engines is interrupted. We conduct a textual analysis and exploit an exogenous event—Google’s 2010 surprising withdrawal from mainland China, which significantly hampered domestic investors’ ability to search for foreign information but did not affect their cost to access domestic information. Following Google’s exit, Chinese firms’ announcements on foreign transactions become more bullish relative to domestic transactions. Optimism in disclosure is especially rosy if the press releases are conveying negative news or are issued by poorly governed firms. This effect is mitigated in the presence of foreign investors or analysts affiliated with foreign brokers who are not subject to foreign information censorship by the government. The increase in search cost also appears to leave domestic news media and financial analysts more vulnerable to the influence of corporate disclosure. These optimistic announcements allow insiders to harvest higher returns from selling their shares and are associated with a higher likelihood of corporate misconduct.
We study a compensation problem for salespeople with learning potential. In our model, both the firm and sales agent are risk neutral and forward-looking; the agent can privately observe his skill, exert effort, and learn from experience; the firm can learn from the agent’s choice and revise sales targets over time. The problem entails a dynamic tradeoff between exploiting learning, screening information, and maximizing efficiency. We find the optimal compensation plan differs substantially from the existing ones: it sets aggressive targets for expediting skill development, and pays the information rent for neutralizing the agent’s misbehaving temptation over the entire relationship. We find learning drives the long-run outcomes; ignoring it can mislead compensation design and inflict substantial losses. Our results shed light on when and why firms distort sales, favor incumbents, and prefer long-term plans. By highlighting the critical role of learning in long-run performance, this study advances our understanding of salesforce theory and practice.
Although the literature on network effects has focused on single-network firms, many industries feature multinetwork firms that play more complex dynamic pricing games. In this paper, we estimate the network effect at the smartphone operating system (OS) level and study multi-OS telecommunication carriers’ dynamic pricing strategies in an oligopolistic setting, using data on the U.S. smartphone industry. We find a positive OS network effect. Counterfactual analysis indicates that if the carriers were single-OS sellers, they would increase the phone prices of large OSs and lower the prices of small OSs, which reduces the consumer surplus by $6.99 billion. Further analyses show that the multi-OS carriers choose lower prices for large OSs than for small OSs because of their preference for OS concentration.
Given the ranking of competitors, how should the prize endowment be allocated? This paper introduces and axiomatically studies the prize allocation problem. We focus on consistent prize allocation rules satisfying elementary solidarity and fairness principles. In particular, we derive several families of rules satisfying anonymity, order preservation, and endowment monotonicity, which all fall between the equal division rule and the winner-takes-all rule. Our results may help organizers to select the most suitable prize allocation rule for rank-order competitions.
In a finitely repeated game with asymmetric information, we experimentally study how individuals adapt the nature of their lies when settings allow for reputation building. Although some lies can be detected ex post by the uninformed party, others remain deniable. We find that traditional market mechanisms, such as reputation, generate strong changes in the way people lie and lead to strategies in which individuals can maintain plausible deniability; people simply hide their lies better by substituting deniable lies for detectable lies. Our results highlight the limitations of reputation to root out fraud when a deniable lie strategy is available.
We document that women compete worse against men in field tournaments in over 150 countries and across all ages. Our field setting is the game of chess and we benefit from a large and rich data set to investigate the robustness and heterogeneity of our uncovered gender differences in competition. We find a macro gender gap in every country: there are fewer female than male players, especially at the top, and women have lower average rankings. Moreover, comparing millions of individual games, we find a small but robust micro gender gap: women’s scores are about 2% lower than expected when playing a man rather than a woman with an identical rating, age and country. Using a simple theoretical model, we show how this small micro gap may affect women’s long-run human-capital formation. By reducing effort and increasing the probability of quitting, both effects accumulate to explain a larger share of the macro gap.
We experimentally study how presentation formats for return distributions affect investors’ diversification choices. We find that sampling returns alleviates correlation neglect and constitutes an effective way to improve financial decisions. When participants get a description of the probabilities for outcomes of the joint return distribution, we confirm the findings of others that investors neglect the correlation between assets in their diversification choices. However, when participants sample from the joint distribution, they change their allocation between two assets in response to a change in their correlation in the predicted direction. The results are robust across two experiments that have participants with varying experience (students versus private investors).
We construct an economic framework for understanding the incentives of the participants of a permissioned blockchain for supply chains and other related industries. Our study aims to determine whether adoption of blockchain is socially beneficial and whether such adoption arises in equilibrium. We find that blockchain reduces information asymmetry for consumers, thereby enhancing consumer welfare. Consumer welfare gains can be sufficiently large that blockchain adoption is socially beneficial; nonetheless, we find that blockchain adoption does not arise in equilibrium. This situation arises because blockchain adoption costs are borne by manufacturers, and manufacturers cannot extract consumer gains through prices due to the competitive nature of the manufacturing sector. We offer a system of transfers to generate blockchain adoption in equilibrium when it is socially beneficial.
We consider the optimal operating policies of a free-to-play multiplayer game with a premium subscription to maximize its lifetime operating profit. Accounting for social comparisons between free and premium players, we model the game attracting or losing players with a hybrid of the Bass diffusion model and the replicator equation in evolutionary game theory. Leveraging optimal control theory, we characterize optimal dynamic pricing and advertising policies and show that the developer should prioritize initial growth through aggressive advertising, while postponing the introduction of a premium subscription. Surprisingly, the optimal subscription price may start high and gradually decrease. We further show that the developer should strengthen social-comparison effects, that payment-based matchmaking can be an effective monetization driver, and that our main findings remain robust when allowing individual in-game item purchases/partial premium subscription. These findings are potentially instructive for game developers adopting the premium subscription model.
In the ranking and selection problem, a sampling budget is allocated among a finite number of designs with the goal of efficiently identifying the best. Allocations of this budget may be static (with no dependence on the random values of the samples) or adaptive (decisions are made based on the results of previous decisions). A popular methodological strategy in the simulation literature is to first characterize optimal static allocations by using large deviations theory to derive a set of optimality conditions, and then to use these conditions to guide the design of adaptive allocations. We propose a new methodology that can be guaranteed to adaptively learn the solution to these optimality conditions in a computationally efficient manner, without any tunable parameters, and under a wide variety of parametric sampling distributions.
We analyze the design of a randomization procedure in a field setting with high stakes and substantial public interest: matching sports teams in the Union of European Football Association Champions League. While striving for fairness in the chosen lottery—giving teams similar distributions over potential partners—the designers seek to balance two conflicting forces: (i) imposing a series of combinatorially complex constraints on the feasible matches; and (ii) designing an easy-to-understand and credible randomization. We document the tournament’s solution, which focuses on sequences of uniform draws over each element in the final match, assisted by a computer to form the support for each draw. We first show that the constraints’ effects within this procedure are substantial, with shifts in expected prizes of up to a million euro and large distortions in match likelihoods of otherwise comparable team pairs. However, examining all possible counterfactual lotteries over the feasible assignments, we show that the generated inequalities are, for the most part, unavoidable and that the tournament design is close to a constrained-best. In two extensions, we outline how substantially fairer randomizations are possible when the constraints are weakened, and how the developed procedure can be adopted to more-general settings.
In complex procurement projects, it is difficult to write enforceable contracts that condition price upon quality. Supplier nonperformance becomes an acute risk, particularly when there is intense competition for the contract. An established incentive mechanism used to mitigate the problem of supplier nonperformance is retainage, in which the buyer sets aside a portion of the purchase price. After project completion, the buyer determines the amount of retainage that is released to the seller, considering any defects that arise. Although generally a feasible contract form to implement, the practical difficulties in assessing completion introduce a moral hazard for the buyer. We develop a structurally new game and experimental design to offer managerial insights on how retainage principles mediate trust and trustworthiness in competitive procurement settings with moral hazard. The experimental results suggest that if trust in the procurement relationship is strong enough, then retainage can mitigate the seller-side moral hazard problem and substitute for reputation in a fragmented supply chain at the cost of inflated tender prices. In high retainage structures, there is a tradeoff between trade efficiency and supplier participation in request for bids. We further develop a model of fair payment norms and offer managerial insights on how to design the retainage mechanism, conditional on prevailing levels of trust and beliefs about fairness.
Health systems are placing increasing emphasis on improving the design and operation of clinical trials with the aim of making the health technology adoption process more value-based. We present a model of a value-based, two-armed clinical trial in which both the recruitment rate and trial length are optimized. The model is value-based because it balances the cost of the trial with the expected benefit it generates for patients, valued by the relative health benefits and costs of the technologies. We consider a wide range of regulatory and practical contexts that address how patient health is valued (discount rate, time horizon, pragmatic trials). We present comparative statics and asymptotic analysis together with a retrospective application to a recent health technology assessment and an extension for adaptive trials. Results challenge traditional perceptions concerning the efficiency, length, and knowledge that may be gained from clinical research for trial managers or funders charged with delivering value efficiently: we highlight trade-offs between trial costs and population health benefits influenced by trial outcomes and the importance of optimizing both recruitment rate and trial duration rather than sample size alone.
We consider the problem of a seller of data who sells information to a buyer regarding an unknown (to both parties) state of the world. Traditionally, the literature explores one-round strategies for selling information because of the seller’s holdup problem: once a portion of the data set is released, the buyer’s estimate improves, and as a result, the value of the remaining data set drops. In this paper, we show that this intuition is true when the buyer’s objective is to improve the precision of the estimate. On the other hand, we establish that when the buyer’s objective is to improve downstream operational decisions (e.g., better pricing decisions in a market with unknown elasticity) and when the buyer’s initial estimate is misspecified, one-round strategies are outperformed by selling strategies that initially provide free samples. In particular, we provide conditions under which such free-sample strategies generate strictly higher revenues than static strategies and illustrate the benefit of providing data samples for free through a series of examples. Furthermore, we characterize the optimal dynamic pricing strategy within the class of strategies that provide samples over time (at a constant rate), charging a flow price until some time when the rest of the data set is released at a lump-sum amount.
Motivated by the agricultural industries, this paper studies the economic and environmental implications of biomass commercialization; that is, converting organic waste into a saleable product from the perspective of a processor that uses a commodity input to produce both a commodity output and biomass. We characterize the economic value of biomass commercialization and examine how input and output spot price uncertainties affect this value. Using a model calibration, we find that lower input spot price variability or higher output spot price variability or correlation between the two spot prices increases this value for a typical palm oil mill. To measure the environmental impact, we use total expected carbon emissions resulting from profit-maximizing decisions and characterize the change in total expected emissions after commercialization. Our analysis reveals that, although higher biomass demand or biomass price always increases the value of biomass commercialization, these changes are not necessarily environmentally beneficial as they may increase the emissions associated with biomass commercialization. We also characterize conditions under which biomass commercialization is environmentally beneficial or harmful; that is, it leads to a reduction or an increase in the total expected emissions, respectively. In comparison with the existing understanding which does not take into account optimization of operational decisions, our analysis highlights two types of misconceptions (and characterizes the specific conditions under which they appear): (i) we would mistakenly think that biomass commercialization is environmentally beneficial when it is not, and (ii) we would mistakenly think that biomass commercialization is environmentally harmful when it is not.
We present a novel explanation of why organizations tend to lose their agility over time despite their efforts to foster worker initiative in adapting to local information. Worker initiative ensures efficiency but requires strong incentives. When incentives are relational and the firm faces shocks to its credibility, it may adopt standardized work processes that ignore local information but yield satisfactory (though suboptimal) performance. The adoption of such standardized processes helps the firm survive the current shock but inflicts inefficiencies in the future. Although the firm may recover, it becomes more vulnerable to future shocks, and consequently, more reliant on the standardized work procedures.
We consider a nondurable good monopolist that collects data on its customers in order to profile them and subsequently practice price discrimination on returning customers. The monopolist’s price discrimination scheme is leaky in the sense that an endogenous fraction of consumers choose to incur a privacy cost to conceal their identity when they return in the following periods. We characterize the Markov perfect equilibrium of the game under two alternative customer profiling regimes: full information acquisition (FIA) and purchase history information (PHI). In both cases, we find that, contrary to what could be expected, the monopolist’s aggregate profit is not monotonically increasing in the level of the privacy cost, but a U-shaped function of it, leading to ambiguous profit effects: a reduction in privacy costs increases the fraction of customers who choose to be anonymous (detrimental profit effect), but it also softens the firm’s introductory price, reducing the pace at which prices targeted to new customers fall over time (positive profit effect). When comparing results under FIA and PHI, we find that market expansion is faster, and more customers conceal their identity under FIA than under PHI. Equilibrium profits are also higher in the FIA case. Although equilibrium profits are U-shaped functions of the privacy cost in both profiling regimes, they tend to be globally decreasing with the privacy cost under PHI and globally increasing under FIA.
Drawing on the notion of compensatory behavior, this paper studies how students compensate for learning loss during a pandemic and what role artificial intelligence (AI) plays in this regard. We further probe into a difference in compensatory behavior for learning loss in terms of quantity, pattern, and pace (i.e., tripartite aspect of learning behavior) of AI-powered learning app usage depending on the level of pandemic threat and the proximity of a goal to students. Results show that the pandemic threat affects student learning behavior differently. Immediately following the COVID-19 outbreak, students who live in the epicenter of the outbreak (versus those who do not) use the app less at first, but with time, they use it more (quantity), on a more regular basis (pattern), and rebound to a curriculum path (pace) comparable to students who do not live in the outbreak’s epicenter. These findings collectively explain behavior that is consistent with compensation for learning loss. The results also partially corroborate the goal-proximity effect, revealing that proximity to a goal (e.g., the degree to which the national university admission exam is approaching) has a moderating role in explaining the tripartite perspective of student learning behavior. Overall, these findings have important theoretical and practical implications for understanding how innovative education technologies can not only facilitate student learning during adversity, but also support learning recovery after adversity.
We study the effects of implicit government guarantee (IGG) on Chinese state-owned enterprises (SOEs). We find that SOEs reduce their investments by 2.4% of book assets, on average, relative to matched non-SOEs after the first SOE default in China’s onshore bond markets in 2015. The investment reduction concentrates among SOEs that are financially constrained, yet SOEs financed by large state banks are hardly affected. Bondholders require more stringent default protection in newly issued SOE bonds. We also find that the investment reduction is more pronounced for SOEs with severe agency problems and that SOEs experienced more positive market reactions to acquisition announcements after 2015. Our results suggest that the reduction in IGG has confounding effects on Chinese firms. Although the weakening of IGG may help mitigate overinvestment, it exacerbates financial constraints of those with limited access to alternative sources of financing.
We analyze the relation between time-series predictability and factor investing. We use a large set of financial, macroeconomic, and technical variables to time-series-manage the market portfolio. A combination of the out-of-sample market excess return forecasts of all variables yields a managed market portfolio that generates alphas relative to cross-sectional factor models that exceed 5% per annum. More broadly, the relation between time-series evaluation measures and (multifactor) alphas is weakly positive but complex. The variables’ predictability for future returns is more important than that for volatility. Finally, we document that managed market portfolios based on lagged factor realizations also perform well.
We attempt to replicate a seminal paper that offered support for the rational expectations hypothesis and reported evidence that markets with certain features aggregate dispersed information. The original results are based on only a few observations, and our attempt to replicate the key findings with an appropriately powered experiment largely fails. The resulting poststudy probability that market performance is better described by rational expectations than the prior information (Walrasian) model under the conditions specified in the original paper is very low. As a result of our failure to replicate, we investigate an alternate set of market features that combines aspects of the original experimental design. For these markets, which include both contingent claims and homogeneous dividend payments (as in many prediction markets), we do find robust evidence of information aggregation in support of the rational expectations model. In total, our results indicate that information aggregation in asset markets is fragile and should only be expected in limited circumstances.
A firm’s publicly available information reflects ex ante committed information releases and ex post discretionary disclosures. In a setting wherein a firm’s CEO is concerned with stock market valuation and confrontations with the employee union, this paper endogenizes when the CEO retains ex post disclosure discretion and when the CEO curtails discretion, opting instead to ex ante commit to not gathering information or publicly disclosing information. The underlying economic forces entail a trade-off between the time-inconsistency problem (the CEO’s ex post preferences can diverge from the CEO’s ex ante goals) and how silence (absence of information) under the ex ante and ex post paths is valued differentially by the union. In effect, silence under different systems “speaks” differently, and this influences both the labor union’s incentives to engage in bargaining with the firm and the stock market’s pricing of the firm’s equity.
We study carbon-reduction pledges of publicly traded U.S. oil exploration and production (E&P) companies. Of the 69 firms in the sample, 18 have committed to net-zero carbon targets, 14 have announced significant emissions cuts, whereas the remaining 37 firms have not announced any formal commitment to reduce emissions. We find that a firm’s energy production and ownership by BlackRock is positively associated with its decision to announce a net-zero pledge or significant emission cuts. E&P firms are likely to announce such commitments after Engine No. 1 proposed its slate of directors for Exxon’s board. The stock market reacts more negatively to net-zero pledges relative to emission-reduction pledges on average. However, the market reaction is related to the quality and credibility of these pledges. In particular, we find that the negative market reaction to net-zero pledges is mainly driven by firms with post-2030 target years relative to firms that commit to achieving net zero by 2030. We also find that the market reacts more negatively to net-zero pledges when the firm does not show the strength of its commitment via the presence of a board-level committee to monitor the company’s progress toward its emission goals or when the top management’s pay is not linked to specific emission reduction goals.
Switchback experiments, where a firm sequentially exposes an experimental unit to random treatments, are among the most prevalent designs used in the technology sector, with applications ranging from ride-hailing platforms to online marketplaces. Although practitioners have widely adopted this technique, the derivation of the optimal design has been elusive, hindering practitioners from drawing valid causal conclusions with enough statistical power. We address this limitation by deriving the optimal design of switchback experiments under a range of different assumptions on the order of the carryover effect—the length of time a treatment persists in impacting the outcome. We cast the optimal experimental design problem as a minimax discrete optimization problem, identify the worst-case adversarial strategy, establish structural results, and solve the reduced problem via a continuous relaxation. For switchback experiments conducted under the optimal design, we provide two approaches for performing inference. The first provides exact randomization-based p-values, and the second uses a new finite population central limit theorem to conduct conservative hypothesis tests and build confidence intervals. We further provide theoretical results when the order of the carryover effect is misspecified and provide a data-driven procedure to identify the order of the carryover effect. We conduct extensive simulations to study the numerical performance and empirical properties of our results and conclude with practical suggestions.
We study the effectiveness of information design in reducing congestion in social services catering to users with varied levels of need. In the absence of price discrimination and centralized admission, the provider relies on sharing information about wait times to improve welfare. We consider a stylized model with heterogeneous users who differ in their private outside options: low-need users have an acceptable outside option to the social service, whereas high-need users have no viable outside option. Upon arrival, a user decides to wait for the service by joining an unobservable first-come-first-serve queue, or leave and seek her outside option. To reduce congestion and improve social outcomes, the service provider seeks to persuade more low-need users to avail their outside option, and thus better serve high-need users. We characterize the Pareto-efficient signaling mechanisms and compare their welfare outcomes against several benchmarks. We show that if either type is the overwhelming majority of the population, then information design does not provide improvement over sharing full information or no information. On the other hand, when the population is sufficiently heterogeneous, information design not only Pareto-dominates full-information and no-information mechanisms, in some regimes it also achieves the same welfare as the “first-best,” that is, the Pareto-efficient centralized admission policy with knowledge of users’ types.
Motivated by online decision making in time-varying combinatorial environments, we study the problem of transforming offline algorithms to their online counterparts. We focus on offline combinatorial problems that are amenable to a constant factor approximation using a greedy algorithm that is robust to local errors. For such problems, we provide a general framework that efficiently transforms offline robust greedy algorithms to online ones using Blackwell approachability. We show that the resulting online algorithms have O(T) (approximate) regret under the full information setting. We further introduce a bandit extension of Blackwell approachability that we call Bandit Blackwell approachability. We leverage this notion to transform greedy robust offline algorithms into a O(T2/3) (approximate) regret in the bandit setting. Demonstrating the flexibility of our framework, we apply our offline-to-online transformation to several problems at the intersection of revenue management, market design, and online optimization, including product ranking optimization in online platforms, reserve price optimization in auctions, and submodular maximization. We also extend our reduction to greedy-like first-order methods used in continuous optimization, such as those used for maximizing continuous strong DR monotone submodular functions subject to convex constraints. We show that our transformation, when applied to these applications, leads to new regret bounds or improves the current known bounds. We complement our theoretical studies by conducting numerical simulations for two of our applications, in both of which we observe that the numerical performance of our transformations outperforms the theoretical guarantees in practical instances.
We investigate the effects of a tick-size reduction on market quality in a multiperiod limit order book market. For illiquid stocks, reducing the tick size facilitates undercutting and discourages liquidity provision, resulting in deteriorating market quality but higher volume. For liquid stocks, reducing the tick size curtails queues, resulting in lower depth and volume but narrower spread. With a competing crossing network, a tick-size reduction results in worse market quality for all stocks due to migration of order flows. We empirically test our model predictions and find support for recent tick-size reductions in Japan and the United States.
Cold start describes a commonly recognized challenge for online advertising platforms: with limited data, the machine learning system cannot accurately estimate the click-through rates (CTR) of new ads and, in turn, cannot efficiently price these new ads or match them with platform users. Traditional cold start algorithms often focus on improving the learning rates of CTR for new ads to improve short-term revenue, but unsuccessful cold start can prompt advertisers to leave the platform, decreasing the thickness of the ad marketplace. To address these issues, we build a data-driven optimization model that captures the essential trade-off between short-term revenue and long-term market thickness on the platform. Based on duality theory and bandit algorithms, we develop the shadow bidding with learning (SBL) algorithms with a provable regret upper bound of O(T2/3K1/3(log T)1/3d1/2), where K is the number of ads and d captures the error magnitude of the underlying machine learning oracle for predicting CTR. Our proposed algorithms can be implemented in a real online advertising system with minimal adjustments. To demonstrate this practicality, we have collaborated with a large-scale video-sharing platform, conducting a novel, two-sided randomized field experiment to examine the effectiveness of our SBL algorithm. Our results show that the algorithm increased the cold start success rate by 61.62% while compromising short-term revenue by only 0.717%. Our algorithm has also boosted the platform’s overall market thickness by 3.13% and its long-term advertising revenue by (at least) 5.35%. Our study bridges the gap between the theory of bandit algorithms and the practice of cold start in online advertising, highlighting the value of well-designed cold start algorithms for online advertising platforms.
The log realized volatility (RV) is often modeled as an autoregressive fractionally integrated moving average model ARFIMA(1,d,0). Two conflicting empirical results have been found in the literature. One stream shows that log RV has a long memory (i.e., the fractional parameter d > 0). The other stream suggests that the autoregressive coefficient α is near unity with antipersistent errors (i.e., d < 0). This paper explains how these conflicting empirical findings can coexist in the context of ARFIMA(1,d,0) model by examining the finite sample properties of popular estimation methods, including semiparametric methods and parametric maximum likelihood methods. The finite sample results suggest that it is challenging to distinguish Model 1 (ARFIMA(1,d,0) with α close to 0 and d close to 0.5) from Model 2 (ARFIMA(1,d,0) with α close to unity and d close to –0.5). An intuitive explanation is given. For the 10 financial assets considered, despite that no definitive conclusions can be drawn regarding the data-generating process, we find that the frequency domain maximum likelihood (or Whittle) method can generate the most accurate out-of-sample forecasts.
A novel laboratory experiment is used to show that mismatching between task preferences and task assignment strongly undermines worker performance and leads to free riding in teams. Unlike prior experiments using real effort tasks, task preferences are elicited from all workers. Under team-based remuneration (revenue sharing), free riding is significant, but this effect is largely driven by those working on undesired tasks. Workers’ endogenous sorting into tasks improves productivity as it mitigates task mismatching although workers’ task selection per se has only small effects on work performance and effort provisions beyond the positive sorting effects.
Trust is an important element of many financial transactions. Yet, the financial industry has been struggling with public mistrust. One explanation for this could be the selection of individuals who wish to work in and get job offers from the financial industry. In this paper, we examine the selection into the financial industry based on social preferences. We identify the social preferences of business and economics students, and, for more than six years, follow up on their early career choices and their job placement after graduation. Students eager to work in the financial industry behave in a substantially less trustworthy manner and show less willingness to cooperate than those with other career plans. The job market does not alleviate this selection. Those subjects who find their first permanent job in finance behave in significantly less trustworthy manner in a trust game than those working in other industries.
Health wearables in combination with gamification enable interventions that have the potential to increase physical activity—a key determinant of health. However, the extant literature does not provide conclusive evidence on the benefits of gamification, and there are persistent concerns that competition-based gamification approaches will only benefit those who are highly active at the expense of those who are sedentary. We investigate the effect of Fitbit leaderboards on the number of steps taken by the user. Using a unique data set of Fitbit wearable users, some of whom participate in a leaderboard, we find that leaderboards lead to a 370 (3.5%) step increase in the users’ daily physical activity. However, we find that the benefits of leaderboards are highly heterogeneous. Surprisingly, we find that those who were highly active prior to adoption are hurt by leaderboards and walk 630 fewer steps daily after adoption (a 5% relative decrease). In contrast, those who were sedentary prior to adoption benefited substantially from leaderboards and walked an additional 1,300 steps daily after adoption (a 15% relative increase). We find that these effects emerge because sedentary individuals benefit even when leaderboards are small and when they do not rank first on them. In contrast, highly active individuals are harmed by smaller leaderboards and only see benefit when they rank highly on large leaderboards. We posit that this unexpected divergence in effects could be due to the underappreciated potential of noncompetition dynamics (e.g., changes in expectations for exercise) to benefit sedentary users, but harm more active ones.
With one-third of marriages in the United States beginning online, online dating platforms have become important curators of the modern social fabric. Prior work on online dating has elicited two critical frictions in the heterosexual dating market. Women, governed by age-old social norms of not making the first move, are inhibited in their interactions in that they do not initiate contact with men. On the other side, men send an abundance of messages, the majority of which do not convert to matches. A key distinguishing feature of online dating versus its traditional counterpart is the ability to leave a range of digital signals not replicable in the offline world. These digital signals can impact the nature of online dating platform outcomes. In this paper, we study the impact of a feature that reveals “who likes you” (WLY) on engagement, the number of matches, match efficiency, and match sorting in online dating. This feature reveals the identity of the voters who have rated the focal user with a like. To causally identify the effect of this feature, we conduct a large-scale randomized control trial in collaboration with a major North American dating platform. The treatment causes women to be more proactive, sending 7.4% more messages, which is a highly desirable market improvement given that men send double the number of messages compared with women. Further, we find that the women endowed with this feature increase their matches by 14.4%, whereas men increase their matches by 11.5%. Analyzing the moderating impact of desirability—a key aspect of the WLY feature—provides us with nuanced findings. Depending on the levels of each of the two parties’ desirability, we see evidence of sorting, encouragement, and discouragement.
The scalability of a marketplace depends on the operations of the marketplace platform and its sellers’ capacities. In this study, we explore one strategy that a marketplace platform can use to enhance its scalability: providing an ancillary service to sellers. In our model, a platform can choose whether and when to provide this service to sellers and, if so, what prices to charge and which types of sellers to serve. Although such a service helps small sellers, we highlight that the provision of such a service can diminish the incentives of large sellers to make their own investment, thereby reducing their potential output. When the output reduction by large sellers is substantial, the platform may not want to provide the ancillary service, and, even if it does, it may choose to set a price higher than its marginal cost to motivate large sellers to scale. The platform may also choose to strategically delay the provision of the service.
When acquiring consumer data for marketing or new business initiatives, it is important to decide what attributes or features of potential customers should be acquired. We study a new feature selection problem in the context of customer data acquisition in which different features have different acquisition costs. This feature selection problem is studied for linear regression and logistic regression. We formulate the feature selection and acquisition problems as nonlinear discrete optimization problems that minimize prediction errors subject to a budget constraint. We derive the analytical properties of the solutions for the problems, develop a computational procedure for solving the problems, provide an intuitive interpretation for the feature selection criteria, and discuss managerial implications of the solution approach. The results of the experimental study demonstrate the effectiveness of our approach.
We study the maintenance of a machine that deteriorates according to a Markov process until it fails. When failure occurs (which is observable), corrective replacement is made. Otherwise, the machine can be in one of two unobservable working states, and the decision maker can choose production, inspection, or preventive replacement. The state is revealed upon inspection and is reset by corrective or preventive replacement. The objective is to minimize the expected total discounted cost over an infinite horizon. We derive an exact, analytical solution to this problem via a dual framework for partially observable Markov decision processes. The solution can be easily computed without value iteration. We identify six possible structures of the optimal solution, which are represented as graphs. Each graph contains an absorbing, cyclic subgraph that governs the steady-state behavior of the machine. The exact analytical solution facilitates comparative statics analysis, comprehensive numerical studies, and the generation of insights.
One of the most prevalent demand models in the revenue management literature is based on dividing the selling horizon into a number of time periods such that there is at most one customer arrival at each time period. This demand model is equivalent to using a discrete-time approximation to a Poisson process, but it has an important shortcoming. If the mean number of customer arrivals is large, then the coefficient of variation of the number of customer arrivals has to be small. In other words, large demand volume and large demand variability cannot coexist in this demand model. In this paper, we start with a revenue management model that incorporates general mean and variance for the number of customer arrivals. This revenue management model has a random selling horizon length, capturing the distribution of the number of customer arrivals. The question we seek to answer is the form of the fluid approximation that corresponds to this revenue management model. It is tempting to construct the fluid approximation by computing the expected consumption of the resource capacities in the constraints and the total expected revenue in the objective function through the distribution of the number of customer arrivals. We demonstrate that this answer is wrong in the sense that it yields a fluid approximation that is not asymptotically tight as the resource capacities get large. We give an alternative fluid approximation where perhaps surprisingly, the distribution of the number of customer arrivals does not play any role in the constraints. We show that this fluid approximation is asymptotically tight as the resource capacities get large. A numerical study also demonstrates that the policies driven by the latter fluid approximation perform substantially better, so there is practical value in getting the fluid approximation right under high-variance demand.
Buying display ad impressions via real-time auctions comes with significant allocation and price uncertainties. We design and analyze a contract that mitigates this uncertainty risk by providing guaranteed allocation and prices while maintaining the efficiency of buying in an auction. We study how risk aversion affects the desire for guarantees and how to price a guaranteed allocation. We propose to augment the traditional auction with a programmatic purchase option (which we call a Market-Maker contract) that removes allocation and price uncertainties. Instead of participating in the auction, advertisers can secure impressions in advance at a fixed premium price offered by the Market-Maker. It is then the responsibility of the Market-Maker to procure these impressions by bidding in the auction. We model buyers as risk-averse agents and analyze the equilibrium outcome when buyers face two purchase options (auction and Market-Maker contract). We derive analytical expressions for the Market-Maker price that reveal insightful relationships with uncertainties in the auction price and buyers’ risk levels. We also show the existence of a Market-Maker price that simultaneously improves the seller’s revenue and the sum of buyers’ utilities. As a building block to our analysis, we establish the truthfulness of the multiunit auction when buyers have nonquasilinear utilities because of risk aversion. Recently, the Google’s Display & Video 360 platform started offering a product akin to Market-Maker called “Guaranteed Packages,” which was inspired by this paper.
Government bonds might provide reference entities that reduce corporate bond yields. We study China’s 2017 issuance of two U.S. dollar (USD)-denominated sovereign bonds when there were (effectively) no outstanding USD sovereigns. We find that Chinese corporate USD bonds experienced a four- to nine-basis-point decline in yield spreads, whereas corporate renminbi (RMB) bonds did not. The effect was stronger for corporate bonds with maturities similar to those of the USD sovereigns. Further consistent with the reference effect, USD-denominated corporate bonds experienced declines in bid-ask spreads and volatility; corporate bond yield spread changes became more sensitive to sovereign yield innovations; and the less-informed foreign mutual funds significantly increased their holdings of offshore USD Chinese corporate bonds. Limited evidence indicates that new corporate bond maturities shifted toward the sovereign bonds’ maturity after their issuance.
I develop a continuous-time model to examine how the interaction between competition and financial constraints affects firms’ research and development (R&D) strategies. The model integrates two key characteristics of R&D investment: accelerability (i.e., higher R&D intensity leads to faster discovery) and scalability (i.e., higher R&D intensity leads to higher project payoff). I find that firms react strategically to their rivals’ financial constraints when making investment decisions in a duopoly R&D race. In particular, firms respond positively to the R&D intensity of an unconstrained rival, while they respond in a hump-shaped fashion to the R&D intensity of a constrained rival. As a result, a constrained firm can pre-empt an unconstrained competitor in market equilibrium. Accelerability is necessary for such pre-emption to occur, and scalability generally reduces its likelihood. Comparison with a monopoly benchmark shows that the economic mechanism differs from over-investment induced by financial constraints alone. The model also generates new implications regarding how project characteristics and cash flow risks impact R&D decisions.
This paper studies the role of shared analysts, who cover the target and the acquirer, in mergers and acquisitions outcomes. We find that deals where both firms are connected through shared analysts are less likely to be completed than deals without shared analysts. If completed, however, deals with shared analysts exhibit higher expected synergy gains, acquirer announcement returns, and acquirer post-completion performance. Shared analysts are also more likely to issue estimates about the acquirer in the proximity of the impending deal and their estimates are associated with a decrease in the uncertainty about acquirer’s cash flows. Our results highlight how the position of analysts in a deal results in an informational edge that enhances the screening of deals and mitigation of the acquirer’s informational disadvantage. Shared analysts who enhance the acquirer’s ability to screen out deals also exhibit superior career outcomes post-completion. We find similar results in the pre- and post-Regulation Fair Disclosure period, using brokerage house closures as a quasi-natural experiment and after controlling for analysts’ affiliations.
Using terrorist attacks as an exogenous shock to uncertainty, we provide evidence that firms located near terrorism-stricken areas are less likely takeover targets for two years after the attack and receive lower acquisition premiums. The latter finding is reflected in lower target firm abnormal returns and synergy gains. Additionally, in terrorism-stricken areas, target firms are associated with a lower share of synergies, withdrawn deals rise, and acquirers are more likely to get involved in acquisitions of target firms located in different metropolitan statistical areas than their own or acquire faraway target firms. We attribute our results to the real options theory, which predicts that high uncertainty increases the value of the option to delay investments. Additionally, we show that the impact on target firm human capital and acquirer CEO uncertainty and fear are potential sources of terrorism-induced uncertainty with the former source prevailing over the latter.
Institutional investors’ proxy voting decisions are influenced by their neighbors. I identify peer effects in proxy voting using close-call votes on shareholder-sponsored governance proposals at publicly traded financial institutions (focal institutions). I first show that the passage of a governance proposal at a focal institution makes the institution more likely to vote against management at its portfolio firms. Using a triple-difference approach, I find that the neighboring institutions of the focal institution that passes a governance proposal become more likely to vote against management in stocks that are heavily held by the focal institution. These results suggest that peer influence is an important determinant of proxy voting behavior.
I develop a continuous-time general equilibrium model with a continuum of states of the world and a continuum of agents endowed with heterogeneous beliefs. The model permits to analyze the interactions between financial markets and production. There is a single firm that faces convex adjustment costs and maximizes its terminal value. Equivalently, the firm uses decreasing returns to scale risk-return technology. The model is tractable and matches many of the empirical regularities in aggregate output and stock prices, such as a financial volatility that is higher than the macroeconomic volatility, skewness, kurtosis, short-term momentum, and volatility risk premium during recessions. All these aspects disappear when one assumes beliefs homogeneity or constant returns to scale. In particular, the impact of beliefs heterogeneity observed in endowment economies does not pertain when introducing production unless one assumes decreasing returns to scale in the risk-return technology.
Modern consumers are concerned about not only their material payoff, but also the fairness of the transaction when making purchasing decisions. In this paper, we investigate how consumers’ inequity aversion affects a manufacturer who sources inputs from upstream suppliers. We find that, when the manufacturer sources from a single supplier or when consumers observe the manufacturer’s cost, inequity aversion hurts both the supplier’s and manufacturer’s profits. However, when the manufacturer sources from multiple suppliers and consumers do not observe the manufacturer’s cost, inequity aversion reduces both the suppliers’ and manufacturer’s margins, which significantly alleviates the double marginalization problem, increases consumer demand, and improves channel efficiency. As a result, inequity aversion benefits the suppliers, manufacturer, and consumers alike, leading to a “win–win–win” outcome. By comparing cases in which consumers observe and do not observe the manufacturer’s cost, we also find that, when faced with inequity-averse consumers, a manufacturer may find it optimal to withhold its cost information to help secure lower procurement costs from upstream suppliers.
We analyze banks’ “protected-weekend” policies that restrict junior bankers from working during weekends. We use taxi rides from bank addresses in New York City to infer bankers’ working hours. We find the policies induced bankers to shift their work to late-night hours on weekdays. We then investigate whether such shifts in working hours affected the quality of work. After the policy, analysts of the policy-implementing banks make more errors in their earnings forecasts. They also herd more toward the consensus in their forecasts. We further provide evidence that junior bankers are the most adversely affected by the policy.
We examine the sorting role of broad-based equity pay using detailed employee-level data. We propose trust in management as an important characteristic over which equity pay sorts employees, as such pay typically leaves employees with concentrated positions in employer stock and therefore more exposed to the outcomes of management’s actions. Consistent with this conjecture, we find that the relation between employees’ perceptions of management’s credibility and voluntary turnover intentions is significantly stronger in the presence of a broad-based equity plan. Our findings provide insight into how broad-based equity pay can improve firm performance despite theoretical challenges regarding its incentive effects.
Widely acknowledged as one of the most controversial standards in governmental accounting, Governmental Accounting Standards Board (GASB) Statements 67/68 radically altered both the recognition and measurement for defined benefit public pensions. The standards require the recognition of previously only disclosed unfunded pension liabilities and increase the magnitude of reported pension liabilities by requiring the partial incorporation of a market-based discount rate. Although the recognition proposal was unaltered from its initial conception to the final standards, the discount rate proposal was significantly attenuated after the initial proposal met with unanimous preparer opposition. Accordingly, we examine what incentives drive preparer lobbying positions, whether such lobbying is aligned with user interests, and how such lobbying affects ex post implementation of the final standard. Our results suggest a misalignment between preparer lobbying and a user interest perspective. In particular, although state opposition for liability-increasing proposals is positively associated with expected financial statement impact, economic constraints, and political ramifications, user lobbying exhibits the opposite relationship, suggesting that users demand greater transparency in the states most likely to eschew it. We further find that state governments consistently opposed to the market-based discount rate are more likely to maintain status quo higher discount rates that lower reported liabilities after the implementation of GASB 67/68. Examining the mechanism, we find that opposing states avoid the market-rate trigger by increasing required contributions from employees rather than by increasing government funding or implementing benefit cuts. The evidence taken together contributes to our understanding of the development and implementation of the landmark GASB 67/68 standards.
We develop a model of a manager’s equilibrium voluntary disclosure policy to study how that policy changes depending on whether the manager is prohibited from disclosing, or allowed to disclose, a half-truth; we also examine how the disclosure policy changes depending on whether the manager has a duty to update past disclosures. Among our results, we show that if a manager is prohibited from issuing half-truths, the manager discloses a wider array of information than if the manager is allowed to issue half-truths, and investors view the absence of disclosure more skeptically; we also show that imposing a duty to update on the manager does not affect the manager’s initial disclosures, but it results in the manager disclosing uniformly more information over time.
Ownership segmentation in the municipal bond market gives rise to competition among local issuers for a limited supply of capital. We consider the disclosure implications of this competition for capital, using Moody’s 2010 recalibration of the municipal rating scale. The recalibration placed lowly upgraded issuers at a disadvantage relative to their highly upgraded peers within the same market segment. We develop a model in which two municipal bond issuers compete for investors’ capital by choosing bond yields. The model predicts the issuer that is disadvantaged by the recalibration is more likely to improve its disclosure to better compete with its advantaged peer if (i) the rating upgrade that its peer receives is higher and (ii) competition for capital is fiercer. Empirically, we find that the disadvantaged issuers provide more and timelier financial disclosures after the recalibration. This improvement in disclosure quality increases in the extent of the issuer’s disadvantage, arises only if we consider peers with whom the issuer directly competes for capital, and is pronounced when the local capital supply is constrained. Our analytical and empirical analyses support the idea that a competitive disadvantage in raising capital in segmented markets can motivate issuers to improve disclosure quality.
We derive a firm’s debt issuance policy when managers have an informational advantage over creditors and face debt restructuring costs. In our model, regardless of how poor their private signal is, managers of firms that can access the credit market avoid default by issuing new debt to service existing debt. Therefore, only bonds of firms that have exhausted their ability to borrow are subject to jump-to-default risk because of incomplete information and, in turn, command a jump-to-default risk premium. We document that our model captures many salient features of the corporate bond market.
We examine nonprofit organizations’ involvement in the Paycheck Protection Program (PPP). The PPP provided participants with forgivable loans to pay employee salaries, increasing participants’ financial flexibility during the pandemic. We examine the associations between nonprofits’ prepandemic financial obligations (e.g., long-term debt and donor-restricted net assets) and PPP participation and participants’ loan characteristics. First, we find nonprofit organizations participated at a lower rate than other small business industries and that nonprofits with greater financial obligations were more likely to participate in the program. Second, we find financial obligations were positively associated with the loan amount received as a percentage of total payroll costs. Last, although approximately 11% of nonprofits failed to obtain loan forgiveness, we find nonprofits with restricted net assets were more likely to have their loans forgiven. Our results suggest nonprofits with greater debt and donor obligations used the PPP to increase their financial flexibility.
We leverage an exogenous shock—the crackdown on corrupt Chinese officials beginning in 2012—and examine how the allocation of research subsidies and innovative outcomes were affected. We argue that the staggered removal of provincial heads on corruption charges during China’s anticorruption campaign and the unanticipated departures of local government officials responsible for innovation programs led to plausibly exogenous reductions in corruption. After both events, the allocation of subsidies became more sensitive to firm merit than to corruption and subsidies became more strongly associated with future innovation. Anticorruption efforts and officials’ career incentives improved the efficacy of subsidy programs.
General Data Protection Regulation (GDPR)—the European Union’s data protection regulation—has two key principles. It recognizes that individuals own and control their personal (but not contractual) data in perpetuity, leading to three critical privacy rights, namely, the rights to (i) explicit consent (data opt-in), (ii) to be forgotten (data erasure), and (iii) portability (data transfer). It also includes data security mandates against privacy breaches through unauthorized access. We study GDPR’s equilibrium impact by including these features in a dynamic two-period model of forward-looking firms and consumers. Firms collect consumer data for personalization and price discrimination. Consumers trade off gains from personalization relative to potential losses from privacy breaches and price discrimination in their purchase, data opt-in, erasure, and transfer decisions. Though data security mandates impose fines on firms for privacy breaches, firms can benefit from higher opt-in given lower breach risk. Surprisingly, data security mandates can hurt consumers. The effect of privacy rights is nuanced. Since the right to opt in separates goods exchange from the provision of personal data, it prevents market failure under high breach risk. But it also reduces consumer opt-in and personal data availability. Erasure and portability rights reduce consumers’ hold-up concerns by disciplining firms to provide ongoing value by limiting price discrimination and not slacking off on data security; but they also reduce the incentive to offer lower initial prices that encourages opt-in. Overall, privacy rights always benefit consumers in competitive markets, but they can surprisingly hurt consumers under monopoly, as monopolists have less incentives to subsidize consumer opt-in. They raise (reduce) firm profit and social welfare when breach risk is high (low). Finally, privacy rights increase firm profit most at moderate levels of data transferability.
We examine the effect of high-frequency trading on market quality from the perspective of a limit order trader. By competing with slower limit order traders, high-frequency traders impose a welfare externality by selectively crowding out the most profitable limit orders. The order book imbalance immediately before each order submission, cancellation, and trade suggests that high-frequency traders strategically use limit order book information to supply liquidity on the thick side of the order book and demand liquidity from the thin side. This strategic behavior is more pronounced during volatile periods and when trading speeds increase.
Although variable pricing enables sports teams to respond to changing demand across games, reports point to somewhat limited implementation. In this paper, we study the implications of a switch to variable pricing on the primary and resale ticket markets using quasi-experimental data from the National Football League. Applying a difference-in-differences technique with propensity score weighting, we first show that the adoption of variable pricing increases primary market ticket sales by 1.59% per game. We then explore the mechanism behind this positive impact. We find that lower prices for the less attractive games are well received by customers, and customers do not have an unfavorable response to higher prices for the more attractive games. Our analysis of the customers’ activities in the resale market shows that the minimum resale market prices increase for the more attractive games following the switch to variable pricing. This analysis provides evidence for the increase in the option value created by the resale market and explains the positive response to higher prices for the more attractive games. Finally, we investigate when variable pricing is likely to be the most effective. We find that variable pricing has greater success in team hometowns with lower income levels and higher income diversity, and creating more game tiers in variable pricing leads to higher sales.
The paper introduces a method to conduct a forward-looking antitrust review of horizontal mergers. The method utilizes a dynamic oligopoly model in which mergers, entry/exit, and product repositioning are endogenous. The model provides long-run industry trajectories with and without the merger under review, enabling the regulator to obtain dynamically robust welfare comparisons. The paper demonstrates the framework’s application to regulate the U.S. radio broadcasting industry. In particular, it investigates the long-run efficacy of two commonly used merger heuristics: radio station ownership caps (see the Telecom Act of 1996) and static merger simulations. The paper finds that raising the ownership cap results in higher total welfare and demonstrates varying long-run effectiveness of merger simulations.
Ad targeting technology has enabled a highly personalized delivery of online ads. Behind this development is the belief that better targeting will lead to more relevant ads. This paper challenges this lay belief by showing that irrelevant advertising can arise not necessarily from technological imperfection but also from the incentive problem embedded in the ad agency-advertisers relationship. We first demonstrate that the ad agency serving multiple advertisers may strategically allocate an ad impression to a lesser-matched, sometimes totally irrelevant, niche advertiser because future impressions can match better with the mainstream advertiser. We further find that, without a contractual obligation to serve both advertisers, the agency may not deliver completely irrelevant ads to consumers. However, another type of inefficiency can arise where the agency may not send any ad to potentially interested consumers who have a strictly positive match probability with advertisers. These inefficiencies arise due to contractual restrictions, either contractual obligations or budget constraints, when the agency serves multiple advertisers. As such, we endogenize the advertisers’ contractual requirement choices and show how the contractual obligation(s) can arise in equilibrium. Finally, we show that irrelevant ads will not disappear simply because more impressions are available in the market. Our analysis suggests that as the number of impressions increases, the irrelevant ads can persist, but the probability of receiving irrelevant ads decreases.
Counterfeits are available across many marketplaces. In emerging markets, in particular, consumers often observe a mix of counterfeits and authentic products sold by retailers. It is difficult for some consumers to distinguish between the two types of products. From these consumers’ perspective, the products can be considered as probabilistic goods, and we term retailers’ strategies probabilistic selling of counterfeits. Retailers who sell counterfeits are subject to a penalty by regulation. We develop a game-theoretical model to study this dishonest strategy under regulation. Our model offers the following insights. First, a higher penalty for selling counterfeits can lead to a smaller proportion of authentic products in the market. This finding is driven by a manufacturer’s strategic response: knowing that the penalty becomes higher, the manufacturer has greater incentives to raise the wholesale price of its authentic products. From the retailers’ point of view, this elevated procurement cost can, in turn, encourage them to lower the probability of their products being authentic. Second, manufacturer profit increases with the penalty, whereas retailer profit, consumer surplus, and social welfare do not, in general, exhibit a monotonic relationship with the penalty.
Advancements in targeting technology have allowed firms to engage in more precise targeting based on several aspects of consumers’ preferences. Exposed to more targeted ads, consumers are becoming increasingly aware of being targeted and respond accordingly. This paper provides a theoretical analysis of multidimensional targeting under which consumers can draw inferences about multiple components of their utility from the advertised product. We show that the firm can be worse off under multidimensional targeting than under single-dimensional targeting, in which the firm targets consumers based only on a single component of their utility. This is because, with multidimensional targeting, targeted consumers may face greater uncertainty about on which specific dimension(s) they can expect to enjoy the advertised product. Therefore, they may be less willing to exert a costly effort of clicking the ad and making a purchase decision. When this result holds, the firm may want to adopt a single-dimensional targeting strategy. However, we show that the firm cannot credibly commit to such a strategy once given access to multiple dimensions of customer data. Interestingly, a higher unit cost of advertising can mitigate the firm’s commitment problem for using customer data and thus increase the firm’s profit. Moreover, the firm can sometimes lower the price to recover some of, but not entirely offset, the drawbacks of multidimensional targeting. We discuss the implications of our results regarding the current practice of targeted advertising and data privacy protection policies.
Research in psychology shows that consumption goals can help consumers avoid excessive consumption of vice goods and the associated long-term harm. In this paper, we propose a model of self-control with consumption goals and examine how goals moderate the behavior of consumers and the firm’s strategy. We find that consumers’ personal goals lead to a lower price for a less unhealthy product but a higher price for a more unhealthy product. Furthermore, even though personal goals reduce the sales of a product, the firm can be better off if consumers have goals rather than no goals. The improvement in the firm’s profits need not be at consumers’ expense. In fact, consumer welfare increases with personal goals. In some contexts, consumption is not driven by personal goals but shaped by social norms, such as the advice of experts or social groups. We find that, unlike personal goals, normative goals make consumers less sensitive to price and do not always improve consumer welfare. Furthermore, normative goals can hurt the firm’s profits in contexts in which personal goals could improve profits. Finally, we show that our framework with dynamically inconsistent preferences yields results that are consistent with alternative formulations of consumer self-control problems, such as the dual-self model of Thaler and Shefrin and the costly self-control model of Gul and Pesendorfer.
Whether to purchase a product is of fundamental importance to marketing, but purchasing behaviors vary widely across individuals and contexts. This paper proposes that a sizeable fraction of this variation is associated with differences in the time at which a product’s desirability and its price are processed and utilized by consumers. To test this hypothesis, participants purchased different products while their mouse cursor movements associated with purchasing an option were recorded across three laboratory studies. These natural cursor movements and estimates from a cognitive model identified the time at which product desirability and price each began to influence decisions. On average, we found that product desirability impacted the decision-making process significantly earlier than price. Moreover, the difference in the time at which product and price influenced choice explained a sizeable fraction of the variation in the option that was purchased. Additional analysis and studies revealed that the time at which an attribute begins to influence decisions can be altered by simple marketing actions, such as a product’s visual display and price discount framing, and that these actions have consequences for choice. Together, these results add to our understanding of how consumers make simple purchasing decisions.
Because the average treatment effect (ATE) measures the change in social welfare, even if positive, there is a risk of negative effect on, say, some 10% of the population. Assessing such risk is difficult, however, because any one individual treatment effect (ITE) is never observed, so the 10% worst-affected cannot be identified, whereas distributional treatment effects only compare the first deciles within each treatment group, which does not correspond to any 10% subpopulation. In this paper, we consider how to nonetheless assess this important risk measure, formalized as the conditional value at risk (CVaR) of the ITE distribution. We leverage the availability of pretreatment covariates and characterize the tightest possible upper and lower bounds on ITE-CVaR given by the covariate-conditional average treatment effect (CATE) function. We then proceed to study how to estimate these bounds efficiently from data and construct confidence intervals. This is challenging even in randomized experiments as it requires understanding the distribution of the unknown CATE function, which can be very complex if we use rich covariates to best control for heterogeneity. We develop a debiasing method that overcomes this and prove it enjoys favorable statistical properties even when CATE and other nuisances are estimated by black box machine learning or even inconsistently. Studying a hypothetical change to French job search counseling services, our bounds and inference demonstrate a small social benefit entails a negative impact on a substantial subpopulation.
Trade credit is an important source of short-term financing and an integrated part in supply contracts. Although a number of theories have been proposed on how trade credit could improve supply chain efficiency, causal studies on the impact of trade credit on operational decisions are scarce. In this study, we examine the impact of trade credit on inventory decisions using an empirical strategy that leverages (i) an exogenous shock imparted by the French government’s intervention to impose a ceiling on trade credit duration, (ii) a triple difference-in-differences identification strategy, and (iii) synthetic controls (SCs). By considering the 60-day ceiling coverage and SC construction requirements, we identify four French retail sectors as our main sample. Among them, in the postregulation period, the hardware retail sector firms on average exhibited a significant 16% decline in trade credit usage. Correspondingly, these firms also displayed a significant 11% decline in inventory level. In the remaining three sectors, we found mixed results in the main sample. All the four sectors, however, show consistent support for a causal link between trade credit and inventory in a subsample compiled using a stringent 90-day ceiling criterion. Collectively, our findings offer direct evidence that trade credit is an indispensable financing source for inventory procurement. Finally, in the postregulation period, the hardware retailers exhibited a 15.5% decline in revenue and 3.5% reduction in gross profit. This cautions policy makers that regulations limiting the use of trade credit may have unintended consequences on downstream firms, and may harm overall supply chain efficiency.
We study the design of a decentralized two-sided matching market in which agents’ search is guided by the platform. There are finitely many agent types, each with (potentially random) preferences drawn from known type-specific distributions. Equipped with knowledge of these distributions, the platform guides the search process by determining the meeting rate between each pair of types from the two sides. Focusing on symmetric pairwise preferences in a continuum model, we first characterize the unique stationary equilibrium that arises given a feasible set of meeting rates. We then introduce the platform’s optimal directed search problem, which involves optimizing meeting rates to maximize equilibrium social welfare. We first show that incentive issues arising from congestion and cannibalization make the design problem fairly intricate. Nonetheless, we develop an efficiently computable search design whose corresponding equilibrium achieves at least 1/4 the social welfare of the optimal design. In fact, our construction always recovers at least 1/4 the first-best social welfare, where agents’ incentives are disregarded. Our search design is simple and easy to implement, as its corresponding bipartite graph consists of disjoint stars. Furthermore, our design implies the platform can substantially limit choice and yet induce an equilibrium with approximately optimal welfare. We offer alternative search designs with improved approximation factors for markets with certain special structures. Finally, we show that approximation is likely the best one can hope for by establishing that the problem of designing optimal directed search is NP-hard to even approximate beyond a certain constant factor.
In after-sales product support, both literature and practice have highlighted the advantages of paying for performance under performance-based contracts (PBC) over the more traditional transaction-based contracts (TBC) that tie supplier payments to each repair incident. Although PBC is believed to better align incentives in the supply chain when the supplier’s private effort is difficult to verify, emerging technologies can make the repair process more transparent, which can eliminate the supplier’s moral hazard problem. Meanwhile, fast growth of service outsourcing for established products makes information asymmetry with regards to product failure rates a new challenge for maintenance, repair, and overhaul (MRO) operations. To analyze this changing environment, we build a stylized adverse selection model and explore which contract is more efficient in dealing with information asymmetry. We assume that equipment failure rates are only known by the customer, whose outside options are type dependent. The uninformed supplier has to design appropriate mechanisms to overcome the disadvantages of this information structure while maximizing profits. We show that the two contracts demonstrate different screening ability, and TBC may be preferred over PBC. Type-dependent outside options can lead to countervailing incentives, making PBC immune to information asymmetry and enabling it to achieve the first-best outcome. When the differential of outside options has a relatively stronger effect compared with the differential of failure rates, TBC can bring the supplier a higher ex ante payoff than PBC. Our paper brings to light a heretofore unknown advantage of TBC, and we demonstrate when these contracts are likely to be observed in the third-party MRO market.
Mergers and acquisitions in healthcare are increasingly leading to changes in firm management. This paper studies how a change in firm management impacts clinical performance using data on an understudied phenomenon: medical practice acquisitions by physician practice management companies (PPMCs). PPMCs market themselves as offloading the administrative burden of running a medical practice without compromising physician autonomy over clinical decisions. However, a PPMC’s management strategy and practices, such as performance monitoring and financial incentives, could influence physician behavior. For example, some PPMCs advertise increasing revenue through better financial management, whereas others also advertise improving quality through better clinical management. In this paper, I collect data on three large PPMCs that manage the practices of more than 40% of obstetricians and gynecologists (Ob-Gyns) in Florida between 2006 and 2014. An Ob-Gyn’s main clinical decision in childbirth involves a tradeoff between financial and clinical outcomes: cesarean sections (C-sections) are often more highly reimbursed than vaginal births but pose risks to maternal and infant health when not medically necessary. Using difference-in-differences methods, I find heterogeneous effects on C-sections depending on a PPMC’s publicized management strategy. Physicians acquired by PPMCs that focus on financial management increase the use of C-sections, resulting in less clinically appropriate care and worse patient outcomes. The opposite result is found when PPMCs focus on clinical management. I provide qualitative and quantitative evidence that differences in firm management are the most likely driver of changes in C-sections. This paper informs how the corporatization of medicine can alter clinical performance outcomes.
Online marketplaces, such as Amazon, Alibaba, Google Shopping, and JD.com, allow sellers to promote their products by charging them for the right to be displayed on top of organic search results. In this paper, we study the problem of designing auctions for sponsored products and highlight some new challenges emerging from the interplay of two unique features: substitution effects and information asymmetry. The presence of substitution effects, which we capture by assuming that consumers choose sellers according to a multinomial logit model, implies that the probability a seller is chosen depends on the assortment of sellers displayed alongside. Additionally, sellers may hold private information about how their own products match consumers’ interests, which the platform can elicit to make better assortment decisions. We first show that the first-best allocation, that is, the welfare-maximizing assortment in the absence of private information, cannot be implemented truthfully in general. Thus motivated, we initiate the study of incentive-compatible assortment optimization by characterizing prior-independent and prior-dependent mechanisms and quantifying the worst-case social cost of implementing truthful assortment mechanisms. An important finding is that the worst-case social cost of implementing truthful mechanisms can be high when the number of sellers is large. Structurally, we show that optimal mechanisms may need to downward distort the efficient allocation both at the top and the bottom.
Do political differences, which are becoming increasingly acute among Americans, inhibit market transactions? We study this by examining whether the perceived political distance between investors and borrowers in an online lending market affects whom investors choose to fund. Using two complementary empirical approaches (a gravity model and a difference-in-differences analysis), we find a nuanced effect: Investors from comparatively conservative states consider political distance when making lending decisions, whereas investors from comparatively liberal states do not. Lending activity drops by as much as 11.6% when the investor’s state is more conservative than the borrower’s state. We also find that political distance between investors and borrowers reduces the likelihood that a borrower’s listing will be funded, thereby limiting the ability of the market to fulfill its function. However, political distance does not predict loan performance, which is consistent with another finding: The relationship of political distance to lending activity is not significant for experienced investors. It may be that investors stop considering political distance after they learn from experience that it does not predict loan performance. We find evidence for two mechanisms underlying our results: (1) a preference-based mechanism, in which investors from conservative states have a general preference for borrowers from conservative states, and (2) a rationality-based mechanism in which investors from conservative states use political ideology as a signal of creditworthiness (rightly or wrongly). Our results contribute to the literatures on online frictions and political (in)tolerance and have implications for the design of online lending (and other) markets.
Decision research often takes the variability of potential outcomes as a measure of risk. It thus characterizes sure things, which, by definition, guarantee a specific outcome, as safe. But is this characterization always empirically valid? We show that, when the prevailing reference point is an uncertain option or position, sure things can be perceived as risky rather than safe. Furthermore, preferences may hinge on such perceptions: when construed as risky, sure things can be less appealing. Our findings suggest a perception-based explanation for why the classic tendency to favor sure things over uncertain options is often attenuated given an uncertain reference point. More broadly, they tap an unresolved debate about the determinants of decisions. Much research focuses on taste-based determinants, such as attitudes toward perceived risks, and thereby underplays the critical role of the underlying perceptions. Appreciating the impact of perceptions on decision making leads to novel prescriptive recommendations.
In jobs in which workers have the flexibility to decide how much work to supply, such as in the gig economy, the effect of a wage change on work supply can be hard to predict. A wage increase offers workers the opportunity to make more money. At the same time, it allows them to make money faster, so they can enjoy more leisure and do not need to work as much. Economic theory alone does not predict which outcome is more likely to occur, and the empirical evidence is also mixed. This paper provides some psychological insights into this economic problem by showing that the short-term effect of a wage change on work supply depends on how the change is framed. Given a current wage of “work l hours to earn $m,” a pay-change frame (“work the same l hours and earn $M”) facilitates a change in work supply in the same direction as the wage change, whereas a load-change frame (“work L hours and earn the same $m”) facilitates the opposite change. Hence, if strategically framed, a wage decrease can elicit the same increase in work supply as a wage increase. A set of studies demonstrate this wage frame effect on both actual work performance and expressed willingness to work. The findings from these studies offer a behavioral perspective on a classic labor economics problem, document a novel framing effect for the judgment and decision-making literature, and suggest a nudge strategy that managers and policy makers can use in incentive designs.
We analyze how changes in the market structure of one industry—airlines—affect the performance of firms in a complementary industry—hotels—using an instrumental variables strategy to account for potential correlation between unobserved shocks to both markets. We find that more intense airline competition boosts hotel performance across all standard measures: price, occupancy rate, and revenue per available room. Spillovers vary across hotel quality and passenger type: Lower-quality branded hotels serving more price-sensitive travelers, most likely brought into the market because of more intense airline competition, benefit the most. However, performance spillovers do not translate into higher hotel entry.
We examine the impact of product market competition on innovation in markets for technology. An innovator makes an investment in quality-improving innovation that can be licensed to one (targeted licensing) or all (market-wide licensing) product market competitors. Our model points to a U-shaped relationship between competition in licensee product markets and innovation in the market for technology: at low levels of competition, market-wide licensing is optimal, and competition reduces innovation, whereas at high levels of competition, targeted licensing is optimal and competition increases innovation. Our empirical analysis using a large panel of U.S. data provides clear support for these predictions linking competition, innovation, and licensing.
This study develops and tests a simple model of voluntary disclosure in which managers can choose to withhold (i.e., redact) certain elements from mandatory disclosure. We consider a setting in which mandatory disclosure is a disaggregated disclosure (e.g., a financial statement), voluntary disclosure is an aggregate disclosure (e.g., an earnings forecast), and the costs of each type of disclosure are distinct. In this setting, we show that managers endogenously substitute between the two types of disclosure; managers that choose to withhold information from mandatory disclosure are more likely to provide voluntary disclosure. We test our predictions using a comprehensive sample of mandatory disclosures in which the SEC allows the firm to redact information that would otherwise jeopardize its competitive position. Consistent with our predictions, we find strong evidence that redacted mandatory disclosure is associated with greater voluntary disclosure.
Analysts’ street earnings forecasts are sometimes based on GAAP earnings and sometimes based on non-GAAP earnings, which exclude various GAAP earnings components. Therefore, differences in analysts’ street earnings forecasts may capture differences in not only expected performance but also the earnings metric forecasted. We argue that analysts who forecast non-GAAP, rather than GAAP, street earnings are more likely to separately analyze earnings components. Consistent with this argument, we find that analysts who forecast non-GAAP street earnings issue relatively more accurate forecasts. We also argue that excluded earnings components often reflect negative transitory items, and that variation across analysts in the earnings metric forecasted suggests that the negative excluded items are forecasted by only a subset of analysts. Consistent with this assertion, we find that variation across analysts in the earnings metric forecasted is associated with a lower consensus GAAP earnings surprise and lower stock returns around the earnings announcement. Finally, although variation in the earnings metric forecasted is a source of analyst forecast dispersion, we find that it is also incrementally associated with a lower earnings response coefficient, consistent with the existence of transitory items. We therefore find that the variation in the earnings metric forecasted is an important source of analyst forecast dispersion that predicts not only a lower earnings surprise but also a lower earnings response.
Based on an online experiment with a sample of finance professionals and participants from the general population (acting as clients), we examine drivers and motives of clients’ choices to delegate investment decisions to agents. We find that clients favor delegation to investment algorithms, followed by delegation to finance professionals compensated with an aligned incentive scheme, and lastly to finance professionals receiving a fixed payment for investing on behalf of others. We show that trust in investment algorithms or finance professionals, and clients’ propensity to shift blame on others increase the likelihood of delegation, whereas clients’ own decision-making quality is associated with a decrease in delegation frequency.
This paper examines the attention effect in the options market. We show that option investors (especially retail investors) buy more calls and puts on both daily winner and loser stocks, and this buying pressure leads to an overvaluation, as shown in subsequent lower hedged returns. The overvaluation is due to a combination of differences of opinion, risk aversion, and margin requirements. The economic magnitude is large. For instance, a zero-financing portfolio involving options on loser stocks renders an alpha of 2.90% per month. Aside from contributing to the broad literature of investor attention versus asset returns, our study also sheds light on an important yet largely neglected topic: the impact of margins on option trading and pricing.
We examine whether rating agencies act defensively toward issuers with a higher likelihood of default. We find that agencies’ qualitative soft rating adjustments are more accurate as issuers’ default risk grows, as evidenced by the adjustments leading to lower type I and type II error rates and better prediction of default and default recovery losses. We also find that soft adjustments’ relevance increases with issuers’ default risk, as evidenced by the adjustments being more predictive of initial offering yields and leading to a greater market reaction to rating changes. Further, we find that the rating agencies assign better educated and more experienced analysts to higher-risk issuers, providing evidence of one mechanism used by the rating agencies to generate more accurate and relevant soft adjustments. Overall, our study suggests that as the likelihood of issuer default grows, the threat of reputational harm from discovered rating failures increasingly mitigates the rating agencies’ strategic behavior incentivized by the issuer-pay model.
We examine the relation between public pension plan chief investment officer (CIO) compensation and plans’ investment performance. Higher paid CIOs outperform their counterparts by 47–60 basis points per year, largely through increased and superior investment in private equity and real estate. This outperformance generates an additional $74.91–$95.63 million in economic value. Plans offering higher compensation hire better educated CIOs and are more likely to retain their CIOs. Higher CIO compensation is positively correlated with the use of incentive compensation, but incentive compensation does not directly affect performance. Demand- and supply-side frictions help explain the variation in CIO pay and the persistent low compensation paid by some plans despite the positive relation between compensation and performance.
We examine the link between the scarcity of arbitrage capital and investors’ response to firms’ earnings information. The cost of financing levered investment strategies (i.e., investing with borrowed funds) increases in the risk profile of the securities being traded. As a result, when capital is scarce, levered traders face heightened incentives to tilt their portfolios in the direction of firms’ earnings information as an indicator of lesser risk because doing so mitigates financing constraints. Our results suggest that these investors become more responsive to firms’ earnings announcements when capital is scarce. Moreover, market prices display a stronger reaction to firms’ announcements and less postearnings announcement drift. Together, our findings suggest the scarcity of arbitrage capital spurs both the use of and responsiveness of market prices to firms’ earnings news.
Using the setting of funds of hedge funds (FoFs), we show that prime brokers (PBs) facilitate investors’ search for informed hedge fund managers. We find that FoFs exhibit PB bias, a disproportionate preference for hedge funds serviced by their connected PBs. This PB bias is stronger when the cost of hedge fund due diligence is higher relative to capital and when the FoF’s management firm generates higher prime brokerage fees. PB bias also predicts FoF performance: the highest PB-bias quartile outperforms the rest by 2.08%–2.45% per annum after adjusting for differences in their risks.
Over the last two decades, researchers in operations management have increasingly leveraged laboratory experiments to identify key behavioral insights. These experiments inform behavioral theories of operations management, impacting domains including inventory, supply chain management, queuing, forecasting, and sourcing. Yet, until now, the replicability of most behavioral insights from these laboratory experiments has been untested. We remedy this with the first large-scale replication study in operations management. With the input of the wider operations management community, we identify 10 prominent experimental operations management papers published in Management Science, which span a variety of domains, to be the focus of our replication effort. For each paper, we conduct a high-powered replication study of the main results across multiple locations using original materials (when available and suitable). In addition, our study tests replicability in multiple modalities (in-person and online) due to laboratory closures during the COVID-19 pandemic. Our replication study contributes new knowledge about the robustness of several key behavioral theories in operations management and contributes more broadly to efforts in the operations management field to improve research transparency and reliability.
Using a unique panel data set of manufacturing firms in Spain in 1994–2006, I show that the use of flexible (shorter and cheaper-to-fire) employment contracts promotes debt financing. I build the identification strategy on the intertemporal, cross-regional, and cross-gender variation in government subsidies that differentially encouraged firms to hire workers on the less flexible contracts. A thought experiment of prohibiting an average firm from hiring workers on flexible contracts suggests that such a firm should reduce its debt-to-capital ratio by 7%. These findings suggest that flexible employment contracts reduce operating leverage and the fixity of firms’ costs, increasing financial leverage. The unique institutional environment allows me to isolate this mechanism from collective bargaining and total labor cost channels. I further show that employment flexibility increases firm performance for firms that benefit most from operating leverage reductions and depend more on external financing. The results demonstrate how management can use heterogenous labor contracts to improve firm outcomes.
“Freemium,” whereby a basic service level is provided free of charge but consumers are charged for more advanced features, has become a popular business model for firms selling digital goods. However, it is not clear whether the launch of a free version helps or hurts the demand of an existing paid version. The free version may allow consumers to sample the product before making a purchase decision and subsequently increase demand of the paid version, but it may also cannibalize demand of the paid version. We use a comprehensive data set on game apps from Apple’s App Store that tracks the launch of both the paid and the free versions of individual apps on a daily level to identify whether a freemium strategy stimulates or hurts demand of an existing paid version. We estimate the spillover effects between the free version and the paid version of the same app under a difference-in-difference framework, relying on the fact that app developers cannot predict the exact launch date of the free version of the app due to Apple’s review and approval of apps prior to release and accounting for app-level product heterogeneity. We find that the launch of a free version increases demand of the paid version of the same app. Under the main specification, if the daily number of ratings before the free version’s launch is at the mean, then all else equal, the launch of the free version leads to an 8.9% increase in the daily number of ratings. We then describe multiple robustness checks. Finally, we present evidence that the results are driven by consumers sampling the free version as well as enhanced app discovery and explore the relative importance of the two mechanisms.
Strategic decisions are increasingly delegated to algorithms. We extend previous results of the algorithmic collusion literature to the context of dynamic optimization with imperfect monitoring by analyzing a setting where a limited number of agents use simple and independent machine-learning algorithms to buy and sell a storable good. No specific instruction is given to them, only that their objective is to maximize profits based solely on past market prices and payoffs. With an original application to battery operations, we observe that the algorithms learn quickly to reach seemingly collusive decisions, despite the absence of any formal communication between them. Building on the findings of the existing literature on algorithmic collusion, we show that seeming collusion could originate in imperfect exploration rather than excessive algorithmic sophistication. We then show that a regulator may succeed in disciplining the market to produce socially desirable outcomes by enforcing decentralized learning or with adequate intervention during the learning process.
Asset prices remain depressed for years following mutual fund fire sales, but little is known about the causes of these price drops. We show that asymmetric information generates price pressure during fire sales. We separate trades into expected trades, which assume fund managers scale down their portfolio, and discretionary trades. We find that discretionary trades contain fundamental information, whereas expected trades do not. Moreover, other traders cannot distinguish between discretionary and expected trades. Our findings help explain the magnitude and persistence of fire sale discounts: fund managers choose which assets to sell, and information asymmetries make it difficult for arbitrageurs to disentangle price pressure from fundamental information.
Analyzing data from approximately 1.5 million employees across 1,108 established public and private U.S. companies, we find that the strength of employee beliefs related to purpose is weaker in public companies. Among public companies, those beliefs are stronger for firms with more committed owners. These patterns are most pronounced within the salaried middle and hourly ranks rather than senior executives. Differences across firms can be attributed, in part, to differences in leadership and incentive characteristics. Our findings are consistent with higher owner commitment and the corresponding policies that arise alongside this higher commitment, enabling a stronger sense of purpose inside organizations.
We experimentally study information transmission by experts motivated by their reputation for being well-informed. In our game of reputational cheap talk, a reporter privately observes information about a state of the world and sends a message to an evaluator; the evaluator uses the message and the realized state of the world to assess the reporter’s informativeness. We manipulate the key driver of misreporting incentives: the uncertainty about the phenomenon to forecast. We highlight three findings. First, misreporting information is pervasive even when truthful information transmission can be an equilibrium strategy. Second, consistent with the theory, reporters are more likely to transmit information truthfully when there is more uncertainty on the state. Third, evaluators have difficulty learning reporters’ strategies and, contrary to the theory, assessments react more strongly to message accuracy when reporters are more likely to misreport. In a simpler environment with computerized evaluators, reporters learn to best reply to evaluators’ behavior and, when the state is highly uncertain and evaluators are credulous, to transmit information truthfully.
A combination of point estimates from multiple judges often provides a more accurate aggregate estimate than a point estimate from a single judge, a phenomenon called “the wisdom of crowds.” However, if the judges use shared information when forming their estimates, the simple average will end up overemphasizing this common component at the expense of the judges’ private information. A decision maker could in theory obtain a more accurate estimate by appropriately combining all information behind the judges’ opinions. Although this information underlies the judges’ individual estimates, it is typically unobservable and thus cannot be directly aggregated by a decision maker. In this article, we propose a weighting of judges’ individual estimates that appropriately combines their collective information within a single estimation problem. Judges are asked to provide both a point estimate of the quantity of interest and a prediction of the average estimate that will be given by all other judges. Predictions of others are then used as part of a criterion to determine weights that are applied to each judge’s estimate to form an aggregate estimate. Our weighting procedure is robust to noise in the judges’ responses and can be expressed in closed form. We use both simulation and data from a collection of experimental studies to illustrate that the weighting procedure outperforms existing methods. An R package called metaggR implements our method and is available on the Comprehensive R Archive Network.
We show that intensified competition changes the location of business activity and, in turn, affects supply chain relationships. Using establishment-level data, we find that, when upstream product markets become more competitive, suppliers are more likely to relocate their establishments closer to customers. Following the supplier’s relocation, its sales to the customer increase, its relationship with the customer is less likely to be terminated, and its innovation is more aligned with the customer’s innovation. The relocated supplier also experiences more analyst following and institutional ownership that are in common with the customer and is more likely to issue equity than debt. However, the improved relationship, by causing the supplier to engage more in innovation dedicated to the customer, adversely affects creative innovation, which is known to drive growth.
We show that superstitions—beliefs without scientific grounding—impact the investment and risk-taking of Chinese firms. We focus on widely held beliefs in bad luck during one’s “zodiac year,” which occurs on a 12-year cycle around a person’s birth year, to study superstitions and risk taking. We first show a direct correspondence between zodiac year and risk taking via survey data: respondents are two percentage points more likely to favor no-risk investments if queried during their zodiac year. Turning to corporate decision making, we find that return volatility declines in the chairman’s zodiac year, suggesting a reduction in risk taking overall. Focusing on specific types of risk taking, investment in R&D and corporate acquisitions both decline during the chairman’s zodiac year; returns around acquisition announcements are also lower, suggesting real allocative consequences of zodiac year beliefs.
Content-sharing social network platforms rely heavily on user-generated content to attract users and advertisers, but they have limited authority over content provision. We develop an intervention that leverages social interactions between users to stimulate content production. We study social nudges, whereby users connected with a content provider on a platform encourage that provider to supply more content. We conducted a randomized field experiment (N =993,676) on a video-sharing social network platform where treatment providers could receive messages from other users encouraging them to produce more, but control providers could not. We find that social nudges not only immediately boosted video supply by 13.21% without changing video quality but also, increased the number of nudges providers sent to others by 15.57%. Such production-boosting and diffusion effects, although declining over time, lasted beyond the day of receiving nudges and were amplified when nudge senders and recipients had stronger ties. We replicate these results in a second experiment. To estimate the overall production boost over the entire network and guide platforms to utilize social nudges, we combine the experimental data with a social network model that captures the diffusion and over-time effects of social nudges. We showcase the importance of considering the network effects when estimating the impact of social nudges and optimizing platform operations regarding social nudges. Our research highlights the value of leveraging co-user influence for platforms and provides guidance for future research to incorporate the diffusion of an intervention into the estimation of its impacts within a social network.
Host-generated property images as a visual channel reveal substantial information about properties. Selecting proper images to display can lead to higher demand and increased rental revenue. In this paper, we define, estimate, and optimize the impacts of Airbnb photos on customers’ renting decisions. We apply ResNet-50, a convolutional neural network model, to build two separate, supervised learning models to evaluate the image quality and room types posted by Airbnb hosts. Then, we characterize the overall impacts of photo layout by the room type featured in the photo, photo quality, and order of display on the listings’ web pages. To address two estimation challenges in the Airbnb setting, namely, censored demand and changing consideration sets, we propose a novel pairwise comparison model that utilizes customers’ booking sequence data to consistently estimate the impact of photo layout on customers’ renting decisions. Our estimation results suggest that the cover image has a significantly larger impact than noncover photos and a high-quality bedroom cover image leads to the largest increase in demand. Furthermore, we build a nonlinear integer programming optimization problem and develop an algorithm to determine the optimal photo layout. Our counterfactual analysis suggests that a listing’s unilateral adoption of optimal photo layout leads to 11.0% more bookings on average. Moreover, depending on the neighborhood and market size, when listings simultaneously switch to the optimal photo layout, they get booked for two to five additional days in a year on average, which boosts revenue by $500 to $1,100.
Buy-one-get-one (BOGO) promotions have become popular. With BOGO, the first unit is sold for the regular price, and the second unit is discounted. We analyze BOGO in manufacturer–retailer supply chains. We identify conditions under which BOGO outperforms price reduction (PR) and everyday low price (EDLP) policies. We find that, for some products, whether consumers stockpile or not, if BOGO and PR have the same market size, BOGO has a larger retailer profit and the same or larger manufacturer profit because BOGO induces more consumers to buy and consume two units. When consumers stockpile, the retailer sets prices to prevent such behavior, and the retailer’s share of supply chain profits is largest under BOGO, whereas consumer surplus with BOGO is smaller than PR. We also find that BOGO reduces double marginalization. When PR expands market size more than BOGO, BOGO’s effectiveness diminishes. When consumers stockpile without increasing consumption and/or production cost is high, EDLP is best. Our results are robust to multiperiod with single-promotion-period settings. A large number of regular-price periods following a promotion period increases stockpiling, which erodes the retailer’s profit and favors EDLP. If promotions are offered for consecutive periods, a larger number of promotion periods increases PR’s efficacy relative to BOGO. Time-inconsistent consumers increase stockpiling and make PR outperform BOGO. Heterogeneous consumers’ holding cost and marginal utility prevent retailers from perfectly discriminating among consumers who make profit-reducing choices. Compared with retailers’ BOGO, manufacturers’ BOGO increases double marginalization and decreases retailers’ and manufacturers’ profits and consumer surplus.
There are more than 7,000 known rare diseases, but pharmaceutical manufacturers developed treatments for only 500 of them. To improve the availability and accessibility of treatments for rare diseases, governments have introduced several programs including subsidies, exogenous pricing, and outcome-based payment schemes in recent years. What kind of subsidy programs and pricing mechanisms can improve patient access? Inspired by various pilot programs, we present a multistage game theoretic model to analyze different subsidy schemes proposed for rare diseases. In addition to the traditional setting in which the manufacturer sets the price, we consider an “exogenous pricing scheme” under which an independent consortium sets the price. We also examine an outcome-based payment scheme, which offers the drug for free if it is not efficacious. We formulate a four-stage Stackelberg game to determine whether it is optimal to subsidize the pharmaceutical manufacturer, the patients, or both. Our results offer several insights. First, we find that government subsidies are crucial to entice new drug development for rare diseases. Also, we find that the pricing scheme matters: The exogenous pricing scheme can improve patient welfare, but it reduces the manufacturer’s expected profit. We find that this result is robust even when an outcome-based payment scheme is used.
In markets, such as those for airline tickets and hotel accommodations, firms sell time-dated products and have private information about unsold capacities. We show that competition under private information may explain observed phenomena, such as increased price dispersion and higher expected prices toward the deadline. We also show that private information severely limits the market power of firms and that information exchange about capacity increases firms’ profits. Finally, we inquire into the incentives to unilaterally disclose information or to engage in espionage about rival’s capacity and show that they increase firms’ profits compared with the private information setting.
Project selection decisions are complex because they must balance not only financial returns, project risk, and fit with strategy, but also competitive circumstances. A rival’s project development efforts provide two pieces of information: a market rivalry signal, indicating potentially heightened competition in a market, and a technological signal, indicating a possible solution to a problem in that market. We hypothesize that these signals affect a firm’s likelihood of project selection in opposite directions, and that the timing of the signals matters for selection. We examine the drug development pipelines of the top 15 pharmaceutical companies from 1999 to 2016 to examine how rival projects drive the decision to progress a drug from preclinical laboratory trials to clinical trials in humans. Early-stage rival projects provide a stronger market rivalry signal, and they are associated with a decreased likelihood of the firm selecting its own project to compete in the same market. Late-stage rival projects signal technological feasibility and are associated with an increase in the likelihood of selection. We then exploit heterogeneity in market potential (i.e., disorder prevalence/incidence) and a molecular compound’s technology (i.e., therapeutic modality) to independently manipulate the salience of the two signals. Finally, we provide evidence on how selection based on rival signals informs project success. Information from rival projects prompts the selection of more successful drugs, but only after a threshold when sufficient uncertainty has been resolved.
Containing a pandemic is first and foremost a management problem: one has to find ways to reduce mobility and physical contacts in order to slow down the spread of the virus. We discuss and construct a novel database of internal and external lockdown measures around the world and analyze whether they helped reduce the spread of infections and the number of deaths. We address the endogeneity of lockdowns by modeling anticipation effects. Our data cover 178 countries in the period from December 2019 to November 2020 and identify lockdown and release periods along with confirmed cases of infections and deaths resulting from COVID-19. Overall, we find that lockdowns were effective, reduced mobility, and saved about 3.6 million lives in developed countries within 100 days after they were implemented. Measures taken within countries (rather than border closure) and partial lockdowns (instead of more constraining measures) were the most effective. However, in developing countries, where the opportunity cost of staying home might be too high for people to comply, lockdowns were ineffective. Additionally, the release of lockdown measures, which started in mid-May 2020 in most countries, did not lead to a strong resurgence of the virus except for border closure releases.
The gig economy provides workers with the benefits of autonomy and flexibility but at the expense of work identity and coworker bonds. Among the many reasons why gig workers leave their platforms, one unexplored aspect is the lack of an organization identity. In this study, we develop a team formation and interteam contest field experiment at a ride-sharing platform. We assign drivers to teams either randomly or based on similarity in age, hometown location, or productivity. Having these teams compete for cash prizes, we find that (1) compared with those in the control condition, treated drivers work longer hours and earn 12% higher revenue during the contest; (2) the treatment effect persists two weeks postcontest, albeit with half of the effect size; and (3) drivers in hometown-similar teams are more likely to communicate with each other, whereas those in age-similar teams continue to work longer hours and earn higher revenue during the two weeks after the contest ends. Together, our results show that platform designers can leverage team identity and team contests to increase revenue and worker engagement in a gig economy.
Decision making on the job is becoming increasingly important in the labor market, in which there is an unprecedented rise in demand for workers with problem-solving and critical-thinking skills. This paper investigates how indoor air quality affects the quality of strategic decision making based on data from official chess tournaments. Our main analysis relies on a unique data set linking the readings of air-quality monitors inside the tournament room to the quality of 30,000 moves, each of them objectively evaluated by a powerful artificial intelligence–based chess engine. The results show that poor indoor air quality hampers players’ decision making. We find that an increase in the indoor concentration of fine particulate matter (PM2.5) by 10 μg/m3 (corresponding to 75% of a standard deviation in our sample) increases a player’s probability of making an erroneous move by 26.3%. The decomposition of the effects by different stages of the game shows that time pressure amplifies the damage of poor air quality to the players’ decisions. We implement a number of robustness checks and conduct a replication exercise with analogous move-quality data from games in the top national league showing the strength of our results. The results highlight the costs of poor air quality for highly skilled professionals faced with strategic decisions under time pressure.
Achieving success often requires persistent effort. We study the effectiveness of two reward mechanisms, all-or-nothing and piece-rate, to incentivize full completion of repeated tasks over time. Our theoretical analysis shows that exogenously imposing the all-or-nothing mechanism can be ineffective due to the potential discouragement effect. In contrast, empowering individuals to choose between the two reward mechanisms can significantly improve the full completion rate. Data from a series of field experiments and follow-up replications provide robust evidence that the all-or-nothing mechanism is effective only when it is presented as an option. Our results highlight the importance of choice in incentivizing persistent effort.
Many decision makers are thought to economize on attention by processing information at the simpler level of a category. We directly test whether such category focus reflects an adaptive response to attention constraints, in five preregistered experiments using an information sampling paradigm with mouse tracking. Consistent with rational principles, participants focus more on category-level information when individual differences are small, when the category contains more members, and when time constraints are more severe. Participants are sensitive to the statistical structure of the category even when it must be learned from experience, and they respond to a latent shift in this structure. Beliefs about category members tend to cluster together more when category focus is high—a key element of rational inattention. However, this is counteracted by greater weight placed on salient and idiosyncratic information when the category is large. Our results broadly substantiate influential theories of categorical thinking, giving us a clearer view on the drivers and consequences of inattention.
We examine the effect of a U.S. Supreme Court decision regarding abortion laws on Americans’ preferences for political candidates. The decision was leaked in advance of the official announcement, and we track the evolution of political preferences from before to after the leak and, eventually, to after the formal announcement. The abortion issue was already very important to voters before the leak, but the Court’s decision did not simply make it more important for everyone. We find that the decision decreased the importance weight of abortion for Republicans, while increasing it for independents/nonvoters. Further, the decision increased Republican support for candidates who want to ban abortions although this effect is diminished for candidates that oppose exceptions for rape, incest, or the mother’s health. Nonaffiliated voters move sharply away from candidates who want to ban abortions without exceptions. The decision also resulted in a lasting polarization along gender lines whereby men became more likely to vote for a candidate that supports a ban on abortion, while women are less likely to support candidates that ban abortions.
Brand valuation methods traditionally focus on the value a brand generates via its ability to enhance demand and, accordingly, profitability. However, this paper explores how a brand can generate value for a firm through the ability to deter entry of new competitors. In this respect, we distinguish between a brand’s direct effect on demand and its strategic effect on the behavior of rival firms. We investigate this within the context of the U.S. stacked chips category using a dynamic model that endogenizes brand building and entry decisions. We find that up to 63% of a brand’s value can be derived from its ability to deter entry. Furthermore, we find that a brand is most valuable when the cost of entry that potential entrants face is moderate: neither too high nor too low.
Success tends to increase and failure tends to decrease the chances of future success. We show that this impact of past outcomes can change how diagnostic success or failure are regarding the competence of an individual or a firm. Succeeding under adverse circumstances is especially impressive when initial failure reduces the chances of success more for low-quality agents than for high-quality agents. Succeeding after initial failure (being a “successful underdog”) can also indicate higher expected quality than succeeding twice if initial success increases the chances of success of all agents to a high level. In different circumstances, the outcome after success can be especially informative about quality, implying that failing after an initial success (a “one-hit wonder”) indicates lower quality than failing twice does. We find effects consistent with our model in data on Canadian professional hockey players and on data from the Music Laboratory experiment: Initial failure combined with eventual success is associated with high quality. The results help to clarify when failure should be attributed to the person in charge or to the situation, when underdogs and individuals who overcome adversity are especially impressive and when a naïve “more is better” heuristic for evaluating performance can be misleading.
We develop a new privacy-preserving framework for a general class of financial network models, leveraging cryptographic principles from secure multiparty computation and decentralized systems. We show how aggregate-level network statistics required for stability assessment and stress testing can be derived from real data without any individual node revealing its private information to any outside party, be it other nodes in the network, or even a central agent. Our work bridges the gap between established theories of financial network contagion and systemic risk that assume agents have full network information and the real world where information sharing is hindered by privacy and security concerns.
We provide a reduced-rank approach (RRA) to extract a few factors from a large set of factor proxies and apply the extracted factors to model the cross-section of expected stock returns. Empirically, we find that the RRA five-factor model outperforms the well-known Fama–French five-factor model as well as the corresponding principal component analysis, partial least squares, and least absolute shrinkage and selection operator models for pricing portfolios. However, at the stock level, our RRA factor model still has large pricing errors even after adding more factors, suggesting that the representative factor proxies of our study do not have sufficient information for pricing individual stocks.
Decisions in management and finance rely on information that often includes win-lose feedback (e.g., gains and losses, success and failure). Simple reinforcement then suggests to blindly repeat choices if they led to success in the past and change them otherwise, which might conflict with Bayesian updating of beliefs. We use finite mixture models and hidden Markov models, adapted from machine learning, to uncover behavioral heterogeneity in the reliance on difference behavioral rules across and within individuals in a belief-updating experiment. Most decision makers rely both on Bayesian updating and reinforcement. Paradoxically, an increase in incentives increases the reliance on reinforcement because the win-lose cues become more salient.
We propose a macrofinance model that rationalizes robust features in equity index option markets. When rare disasters are followed by economic recoveries, the slope of the implied volatility term structure is positive in good times but turns negative in bad times. Additionally, implied volatility decreases with moneyness in bad times (volatility skew), whereas the shape becomes a smile in good times in the presence of rare economic booms. Our theory contributes to understanding the dynamics of the implied volatility surface yet keeping standard asset-pricing moments realistic.
The term structure of equity risk premium is moderately downward-sloping unconditionally, markedly downward-sloping in good times, and markedly upward-sloping in bad times. An asset-pricing model featuring time-varying correlation between realized and expected cashflow shocks explains these puzzling empirical findings. Indeed, the model-implied slope of the equity term structure is in line with the data, both conditionally and unconditionally, because the estimated cashflow shock correlation is volatile, counter-cyclical, and negative on average. The model also generates realistic asset-pricing moments and a positive relation between the equity risk premium, slope of the equity term structure, and the dividend yield.
This paper studies how liquidity management affects asset markets and aggregate economic performance. We explore the connection between liquidity allocation and capital allocation. The policy that changes the liquidity premia on assets has broad implications for private-sector behavior and market efficiency. Liquidity tightening, that is, a reduction in the aggregate quantity of liquid debt, is associated with credit loosening, that is, an increase in funds raised through the issuance of private liquid debt. A positive liquidity premium on debt is required for capital-allocation efficiency, while a zero-liquidity premium is required for liquidity-allocation efficiency. The optimal policy balances these two considerations. Government purchase of private debt is needed to support low interest rates. The analysis is extended to incorporate the liquidity differential between public and private debt and partially liquid capital.
Using a new measure of shareholder inattention constructed from exogenous industry shocks to institutional investor portfolios, we find that firms with distracted shareholders are associated with a higher cost of debt. This effect is stronger for firms with more powerful CEOs, firms with higher information asymmetry, and those operating in less competitive product markets. Further testing suggests that the inattention-cost of debt relation is driven primarily by dual holders directly observing shareholder distraction. Our results are robust to controlling for inattention at the retail investor level and to other external monitors, including credit rating agencies, financial analysts, and Big 4 auditors. Overall, our evidence suggests that institutional shareholder inattention has an incrementally negative effect on bond pricing.
We explore a manager’s incentives to disclose the precision of a signal about firm profitability. Voluntary disclosure of precision information encourages traders to acquire private information, increasing price informativeness and improving the firm’s investment efficiency. We highlight a novel tradeoff: on the one hand, more precise public information crowds out traders’ information acquisition by leveling the playing field. On the other hand, there can also be a crowding-in effect because high-precision disclosures indicate greater managerial confidence and higher investment, which increases the traders’ value of information. The crowding-in effect can dominate if the firm discloses above-average profitability. We derive testable predictions regarding the financial market consequences of supplemental disclosures that are informative about the precision or relevance of payoff-related signals.
Prior studies document the role social media information plays in the stock market and the important dissimilarities between the bond and stock markets. Bridging these two types of literature, we examine the role of social media information in the corporate bond market. Analyzing a broad sample of messages by Twitter individual users, posted just prior to earnings announcements, containing bond, credit risk, and fundamental information, we find that aggregate Twitter opinion (OPI) predicts upcoming announcement bond returns and changes in credit default swap (CDS) spreads, and is associated with future changes in bond yield spreads and credit ratings, thereby providing economically important information to the bond market. This interpretation is bolstered by results from a variety of cross-sectional analyses. Finally, we document an association between OPI and future changes in default risk, which casts light on the nature of the Twitter information underlying our findings. Overall, our findings demonstrate that Twitter appears to disseminate potentially economically important information to even the presumably sophisticated bond and CDS investors, as well as information intermediaries.
This study examines a reputation-concerned entrepreneur’s incentives to provide disaggregated information about a project’s future performance when the entrepreneur seeks to increase both the market price of the project and the market assessment of the entrepreneur’s ability as a project manager. Two factors determine equilibrium: (i) the informational quality of the signal related to the entrepreneur’s ability and (ii) the magnitude of reputational concerns. If the former is relatively low, the entrepreneur with moderate reputational concerns is more likely to provide disaggregated information when the signal about the project’s overall performance is intermediate than when it is sufficiently good or bad. Also, given any value of the signal about the overall performance, this entrepreneur withholds disaggregated information when the signal about the entrepreneur’s ability is intermediate rather than sufficiently good or bad. The comparative static results provide novel empirical predictions about disclosure of aggregate versus disaggregated information.
The focal point analysis is a viable approach to analyze models of finitely durable goods in infinite time horizon. Agrawal, Ferguson, Toktay, and Thomas [Agrawal VV, Ferguson M, Toktay LB, Thomas VM (2012) Is leasing greener than selling? Management Sci. 58(3):523–533.] study the focal point of steady-state firm–consumer games for leasing and selling models. We note that the optimal solution for their selling model does not reflect self-replicated leases mirroring the lease offered by the firm and show that the solution is indeed erroneous as it violates the constraint on consumer-type cutoffs. Although this does not affect the structure of their propositions, that is, outlining thresholds for more profitability and greenness of selling compared with leasing, it renders the thresholds incorrect. We first uncover and fix the root cause of the error. We then distinguish the focal point analysis from the rental price approach and note a few remarks for future durable goods models.
Exhibiting altruism toward and cooperativeness with others is a key ingredient for successful work relationships and managerial decision making. Rising political polarization creates a hazard because it ruptures this fabric and impedes the interaction of employees, especially across political isles. This paper’s focus is to examine various behavioral-, belief-, and norm-based layers of (non)strategic decision making that are plausibly affected by polarization. I quantify this phenomenon via five preregistered studies in the context of Donald J. Trump, comprising 15 well-powered behavioral experiments and a diverse set of over 8,600 participants. To capture the pervasiveness of polarization, I contrast the findings with various political and nonpolitical identities. Overall, I consistently document strong heterogeneous effects: ingroup-love occurs in the perceptional domain (how close one feels toward others), whereas outgroup-hate occurs in the behavioral domain (how one helps/harms/cooperates with others). The rich setting also enables me to examine the mechanisms of observed intergroup conflict, which can be attributed to one’s grim expectations regarding cooperativeness of the opposing faction, rather than one’s actual unwillingness to cooperate. For the first time, the paper also tests whether popular behavioral interventions (defaults and norm-nudges) can reduce the detrimental impact of polarization in the contexts studied here. The tested interventions improve prosociality but are ineffective in closing the polarization gap.
This study demonstrates the importance of separating the probabilities of misstatement occurrence and detection when examining financial statement restatements. Despite the many benefits of examining the probability of restatements using traditional logistic models, interpretations of these models are clouded by partial observability—only subsequently detected misstatements are observable. We propose addressing this often overlooked issue by implementing a bivariate probit model with partial observability. We demonstrate the importance of separating these latent probabilities by re-examining three prior restatement studies and show the importance of separating the occurrence and detection probabilities. Our evidence suggests that future studies interested in restatements as a measure of accounting quality should consider implementing bivariate probit models as one way to address the partial observability inherent in this setting.
International Financial Reporting Standard (IFRS) 9 is of practical relevance to banks because it requires intense monitoring of borrowers to record timely loan losses. Using data from 50 countries, we find that accounting-driven bank monitoring due to IFRS 9 adoption reduces firms’ reliance on bank debt relative to public debt. This finding is consistent with firms experiencing more costly bank monitoring after a shift in regulatory reporting that requires banks to monitor borrowers more intensely. In further analyses, we find that the negative effect of IFRS 9 adoption on bank debt reliance is more pronounced with more stringent regulatory supervision of banks, consistent with regulatory stringency exacerbating costly bank monitoring for firms. We also find that the negative effect is stronger when firms can more easily switch from bank debt to public debt financing, consistent with the relevance of switching costs in firms’ decisions to avoid costly bank monitoring.
We propose a novel framework for investigating learning dynamics on the debt market. Observing a firm’s survival of apparently distressed periods, the market eliminates asset value estimates that are too low to be consistent with the observed survival. Therefore, the firm’s cost of debt becomes lower for given financials. Relative to a perfect information setting, the firm strategically delays default to benefit from a subsequently lower cost of debt. Default comes as a surprise, as it reveals the currently worst possible asset value as correct. The surprise effect is mitigated for debt with higher performance sensitivity and for lower ex ante information asymmetry.
Whereas trust is the cornerstone of any market’s functioning, it is of particular importance in markets that are unregulated, illiquid, and opaque, such as the art market. This study examines the role of authenticity, as captured by provenance information in auction catalogs, on the probability of auctioned oil paintings, watercolors, and prints being sold; their price formation; and returns. Auction catalogs include four authenticity dimensions: pedigree (ownership “blockchain,” descendance information; type of past owners, such as renowned collectors; and past sales records), exhibition history (e.g., in famous museums or galleries), literature coverage (e.g., in catalogues raisonnés or authoritative press), and certification (e.g., artist’s physical testimonial, expert opinions). We find that trust, proxied by provenance information, increases the probability of a work being sold by up to 4%, leads to hammer price premiums up to 54%, and increases annualized returns by 5%–16%. To address potential endogeneity problems between the provision of provenance and past prices/price expectations, we perform quasi-natural experiments in difference-in-differences settings on auction houses’ provenance policy changes following authenticity litigation and on a contamination effect of the discovery of fakes and forgeries on the oeuvre of forged artists. We also test transactions less affected by past prices, such as estate sales following the death of a collector. The findings on the relation between provenance and prices are robust to artist reputation, artistic style, auction house reputation, art market liquidity, and artist career timing.
Using current performance to set future targets can discourage effort and reduce performance. Our study examines whether this ratchet effect also undermines incentives of high-level managers and executives. We use a dynamic model to show that empirical tests used in prior literature can falsely reject the null hypothesis of no ratchet effect. We also motivate a new test that can better detect the adverse incentives effects of target setting. Specifically, we show that the ratchet effect can be identified as the effect of past performance on changes in perceived target difficulty. We use panel data from nine annual 2011–2019 surveys to implement this test. Similar to prior studies, we find strong evidence that targets are revised upward following good performance. Nevertheless, we reject the ratchet effect hypothesis because we further find that good performance in one period is associated with a decrease in perceived target difficulty in the next period. This finding is more pronounced in settings where well-performing managers have more private information about future performance and where long-term commitments are more credible.
We find evidence for the beta anomaly in mutual fund performance. This anomaly is not accounted for in the standard four-factor framework, nor by the addition of a betting-against-beta factor to the benchmark model. We identify the active component of alpha (active alpha) not attributable to the passive effects related to beta. Active alpha is persistent and associated with superior portfolio performance. We find that, although many investors use standard alpha to allocate capital, a subset of sophisticated investors allocate their money based on active alpha. Our procedure is useful across the commonly used benchmark models for measuring performance and can be extended to accommodate other potential factor beta anomalies.
The investment capital asset pricing model, in which expected returns vary cross-sectionally with investment, profitability, and expected growth, provides an equilibrium foundation for Graham and Dodd’s security analysis. The q5 model is a good start to explaining prominent security analysis strategies, such as Abarbanell and Bushee’s fundamental signals, Frankel and Lee’s intrinsic to market, Greenblatt’s “magic formula,” Asness et al.’s quality minus junk, Bartram and Grinblatt’s agnostic analysis, operating cash flow to market, and Penman and Zhu’s expected-return strategy as well as best performing active discretionary funds, such as Buffett’s Berkshire Hathaway.
The convention when calculating corporate bond trading costs is to estimate bid–ask spreads that customers pay, implicitly assuming that dealers always provide liquidity to customers. We show that, contrary to this assumption, customers increasingly provide liquidity following the adoption of post-2008 banking regulations, and thus, conventional bid–ask spread measures underestimate the cost of dealers’ liquidity provision. Among large trades wherein dealers use inventory capacity, customers pay 40%–60% wider spreads than before the crisis. Customers’ balance-sheet capacity and their trading relationships with dealers are important determinants of customer liquidity provision.
We study the interaction of flexible capital utilization and depreciation for expected returns and investment of firms. Empirically, an investment strategy that buys (sells) equities with low (high) utilization rates earns 5% per annum. Utilization predicts excess returns beyond other production-based variables. We reconcile this novel utilization premium quantitatively using a production model. The model suggests that flexible utilization is important for matching the cross-sectional distribution of investment and stock prices jointly. A model without flexible utilization yields many counterfactuals that flexible utilization addresses by making depreciation fluctuate endogenously. Overall, utilization tightens the link between firms’ production and valuations.
Despite a large minimum wage literature that analyzes employee outcomes, there is less evidence on firm performance, prices, and quality. Exploiting staggered changes in U.S. minimum wages during 2000–2008 and census data on more than 29,000 hotel properties, we find that doubling the minimum wage as currently debated would reduce average hotel revenues by 6% per year and their occupancy rates by 3.1%. Responses vary by employer quality and organizational form. Although luxury hotels pass through cost increases to their customers with no impact on their revenues, upscale hotels facing more price-sensitive customers reduce prices but still suffer losses in occupancy rates and revenues as they struggle with preserving the expected quality level. They are also most likely to see quality downgrades from their segment. The lower-end hotels also pass through cost increases to their customers but, unlike luxury hotels, face significant declines in occupancy rates and revenues. Overall, the performance effects of minimum wage increases are nonmonotonic across quality. We also find negative impacts on hotel entry rates, although the performance and entry effects appear to be short lived. Furthermore, the negative effects of minimum wages dominate in states without right-to-work regulation, suggesting the need for a more comprehensive approach to labor market regulation.
We propose a methodology for estimating option-implied, forward-looking variances and covariances of assets and portfolios, which may not possess actively traded options. Our approach relies on the observation that, if asset returns follow a factor structure, then the variances and covariances of the factors span the systematic variances and covariances of assets. We implement the methodology empirically and show that our forward-looking moment estimates provide useful implications for the prediction of jumps and for portfolio choice.
This study quantifies the number of estimates involved in firms’ accruals and examines whether it is informative about the relation between accruals and future earnings. We measure the number of estimates using the frequency of the use of the word estimate in the qualitative portions of a firm’s Notes to the Financial Statements (footnotes). Motivated by arguments regarding the impact of estimation errors in accruals, we hypothesize and find that the accruals of firms that have more estimates have a lower relation with future earnings (i.e., lower persistence) and a lower association with their past, current, and future cash flows.
This paper studies the cost of capital effect of a major regulatory technology, or RegTech, event: the staggered implementation of the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system of the Securities and Exchange Commission in the period from 1993 to 1996. This event represents a largely exogenous shock to corporate information dissemination technologies, resulting in a considerable reduction in information acquisition costs for investors. Using a difference-in-differences research design, we show that the cost of equity capital declines substantially after a firm switches from paper filing to mandatory electronic filing in EDGAR. The effect is stronger for small firms and firms with low analyst coverage and low institutional ownership. We identify three channels through which EDGAR affects a firm’s cost of capital: the liquidity, risk-taking, and corporate governance channels. EDGAR implementation also improves a firm’s investment efficiency significantly. We find evidence that the marginal value of a firm’s capital investment and cash is higher during the post-EDGAR period.
This paper studies how a tick size change affects market quality, price discovery, and the competition for liquidity provision by dealers and high-frequency trading firms (HFTs) in the U.S. Treasury market. Using difference-in-differences regressions around the November 19, 2018, tick size reduction in the 2-year Treasury note and a similar change in the 2-year futures eight weeks later, we find significantly improved market quality. Moreover, dealers become more competitive in liquidity provision and price improvement, consistent with the hypothesis that HFTs find liquidity provision less profitable in the smaller tick size environment. Last, we find a significant shift in short-run price discovery toward the cash market, which then reverses when the futures market tick size is reduced, suggesting that the finer pricing grid in the cash market allows traders to act on small information signals that are not profitable to exploit in the larger-tick futures market. Our findings suggest that reducing the tick size in tick-constrained and highly liquid markets like the Treasury market is on balance beneficial.
Financial covenants influence firm behavior by state-contingently allocating decision rights. I develop a quantitative model with long-term debt where shareholders cannot commit to not dilute existing lenders with new debt issuances. Lenders intervene on covenant violations but cannot commit either to any debt restructuring plan ex ante. Counterfactual experiments suggest that financial covenants significantly reduce default probability and increase firm value. However, the value creation is limited by lenders’ limited commitment. A hump-shaped relation between covenant tightness and firm value emerges, reflecting a balance between limited commitment on two sides.
This is the first study examining the key role played by venture capital and private equity (VCPE) firms in the private debt market. Private debt funds invest in companies owned (sponsored) by VCPEs and in other companies without VCPE sponsors. Using novel data, we find that private debt without VCPE sponsors generates a premium. Further analysis shows that this sponsorless premium compensates for higher risk and costs of risk mitigation as sponsorless lenders adopt a more hands-on approach emulating VCPE sponsors’ roles. Our results are robust and provide important lessons for investors and investee firms in private debt, venture lending, and VCPE.
Starting from twelve distinct factors from the recent literature, plus twelve principal components (PCs) of anomalies unexplained by the initial factors, a Bayesian comparison of approximately seventeen million models in terms of marginal likelihoods and posterior model probabilities shows that {Mkt, MOM, IA, ROE, MGMT, PERF, PEAD, FIN}, plus the nonconsecutive principal components, {PC1, PC5, PC7} are the best supported risk factors. Pricing tests and annualized out-of-sample Sharpe ratios for tangency portfolios suggest that this asset pricing model should be used for computing expected returns, assessing investment strategies and building portfolios.
We develop a new housing search index (HSI) extracted from online search activity on a limited set of keywords related to the house-buying process. We show that HSI has strong predictive power over subsequent changes in house prices, both in-sample and out-of-sample and after controlling for the effect of commonly used predictors, and relate our findings to models of search-induced frictions. Our results imply that search data can be used as an early indicator of where the market is going.
In this paper, we evaluate the economic value of a blockchain application. In the context of asset-backed securities (ABS) issuance in China, where some ABS are issued with blockchain technology and others are not, we find that the use of blockchain significantly reduces the coupon yield at issuance. Compared with other ABS, those issued using blockchain technology experience a decrease of 31.4 basis points in the yield spread, which corresponds to a relative decrease of 13%. We further document that the effect of blockchain is more pronounced for ABS deals rated by less reputable credit rating agencies and agencies that rely more on issuers for their rating business, for revolving ABS, and for ABS with a larger number of underlying assets. We also find that the use of blockchain can reduce the level of retained interest and number of credit enhancement mechanisms. This paper contributes to the literature by providing a small-sample analysis of the economic value of a blockchain application in financial markets.
Network effects are often used to justify platform strategies such as acquisitions and subsidies that aggregate users to a single dominant platform. However, when users have heterogeneous preferences, a single platform may not be as effective as multiple platforms from both a strategic perspective and an antitrust perspective. We study the role of network effects and platform differentiation in the context of a merger between the two largest platforms for pet-sitting services. To obtain causal estimates of network effects, we leverage geographic variation in premerger market shares and employ a difference-in-differences approach. Our results reveal that although users of the acquiring platform benefit from the merger thanks to network effects, those of the acquired platform are comparatively worse off because their preferred option is removed. Network effects and differentiation offset each other such that at the market level, users are not substantially better off with a combined platform than with two separate platforms. These findings have strategic and regulatory implications as well as highlight the importance of platform differentiation even in the presence of network effects.
This paper studies intertemporal asset pricing in network economies when distress shocks can propagate through the network, similarly to epidemic outbreaks. Two classes of equilibria exist. In the first, idiosyncratic shocks are diversifiable and do not affect valuations; the consumption capital asset pricing model applies. In the second, idiosyncratic shocks generate nondiversifiable long-run cascades of shocks (financial pandemics) that introduce a new risk premium component unexplained by traditional systematic factors. We derive closed solutions for asset prices as a function of the network properties and discuss their properties. After a structural break (1984), we find evidence of a network risk premium that is statistically and economically significant.
This study tests the hypothesis that competitive strategic interactions foster overconfidence. We experimentally compare a strategic environment in which players have an incentive to overstate their own ability to deter competitors and avoid competition with a nonstrategic environment in which these incentives are removed. Subsequently, we measure the participants’ confidence. Overconfidence persists in the former environment but vanishes in the latter. We provide evidence for three mechanisms that contribute to the persistence of overconfidence. First, participants who win uncontested update their confidence as if they had won in an actual competition. Second, by contrast, participants who do not compete do not update their confidence, thus creating an asymmetry in updating. Third, inflated ability messages are “contagious” because they affect positively how their receivers update their confidence. We provide empirical evidence on the role of these mechanisms to explain the Dunning–Kruger effect and gender differences in confidence.
We study how a data-rich firm can benefit by unilaterally sharing its customer data with a data-poor competitor when the data can be used for price discrimination. By sharing data on the segment of market that is more loyal to the competitor and keeping the data on the competitor’s most loyal segment to itself, the firm can induce the competitor to raise its price for consumers on which it does not have data. Such data sharing is an example of a fat-cat strategy as it softens price competition that follows data sharing. Although consumer surplus decreases as a result of data sharing, total surplus can increase when the sharing firm concedes its market share to the competitor, which improves the quality of consumer–firm matching.
In this paper, I analyze the joint design of capital requirements and fair value reporting rules for financial institutions with illiquid assets. I specifically examine how prudential regulation aimed at solving agency problems affects financial institutions’ incentives to use Level 2 versus Level 3 fair value reporting, as well as financial stability. Crucially, Level 3 reporting allows financial institutions to use their private information, whereas Level 2 fair values are only measured with public information. Interestingly, my analysis shows regulators may leave to financial institutions the discretion to report illiquid assets at Level 2 or Level 3. Financial institutions then report at Level 3 only if they have good private information about the assets’ quality. Moreover, prudential rules that only rely on Level 2 fair values may be efficient at solving agency problems within financial institutions but may also decrease financial stability. By contrast, prudential rules that leave to financial institutions the discretion to report illiquid assets at Level 2 or Level 3 while relaxing capital requirements may increase financial stability.
Reputation feedback systems assign feedback scores to traders with the aim of separating them according to reliability. There is now a substantial amount of literature on what these mechanisms do well and not so well. Conspicuously absent is a theoretical framework to guide thinking on improving these systems. Here we construct a prototype market, stylized and conceptual in nature, but also suitable for laboratory testing. We use the prototype to examine competing approaches to eliciting feedback from traders. Using entropy to benchmark informativeness, we show that the informativeness of feedback elicitation approaches depends on the ability to solve a coordination problem such that traders use a common mapping to turn experiences into ratings. In theory, different approaches can be about equally informative although the map to coordinate on is more ambiguous in some cases. We then test the approaches in the laboratory. The resulting data find that the most informative feedback is associated with the elicitation methods where the solution to the coordination problem is least ambiguous.
We develop a dynamic equilibrium model to derive the endogenous relations among firms’ ownership structures, managerial incentives, and asset returns. Ownership concentration is positively related to managerial incentives (PPS) but is negatively related to its expected stock (dollar) return, and volatility. If initial block ownership is above (below) its steady-state level, block ownership and managerial PPS decrease (increase) over time to their steady-state levels, whereas the expected stock returns, return volatilities, and the price impacts of block trades increase (decrease). The convergence of these variables to their respective steady-state levels is faster for firms that are less productive, that are riskier, and whose earnings are more sensitive to managerial actions. The price impact of block trades is larger in firms that are more productive and smaller in firms whose earnings are more sensitive to managerial actions. Changes in the expected stock return over time are negatively affected by changes in block ownership but positively affected by changes in managerial incentives. Relative to owner-managed firms, more widely owned firms have lower expected stock returns and volatilities, faster dispersion of block ownership, and smaller but more variable price impacts of block trades. Compared with diffusely owned firms, firms with significant blockholdings have lower expected stock returns, higher return volatilities, and less powerful managerial incentives.
Social reference points have been identified to be important determinants of individuals’ welfare. We investigate the consequences of social reference points for risk taking in a laboratory experiment. In the main treatments, risk-taking subjects observe the predetermined earnings of peer subjects when making a risky choice. We exogenously manipulate peers’ earnings and find a significant treatment effect: decision makers make less risk-averse choices in the case of larger peers’ earnings. The treatment effect is consistent with an application of prospect theory to social reference points and cannot be explained by reference points based on counterfactual information, anchoring, and experimenter demand effects. In additional analyses, we show that diminishing sensitivity seems to play an important role in subjects’ risky choices. We explore also whether inequity aversion and expectations-based reference points can account for our findings and conclude that they do not provide plausible alternative explanations for them.
In a large-scale natural field experiment, we partnered with a microlending company in Kyrgyzstan that asked over 180,000 of its clients for donations to social projects as a form of corporate philanthropy. In a 2 × 2 design, we explored two main (preregistered) hypotheses about giving by the poor. First, based on a conjecture that the poor are more price sensitive than the rich and in contrast to previous studies, we hypothesize that matching incentives induce crowding in of out-of-pocket donations. Second, we hypothesize that our population cares about their proximity to the charitable project. We find evidence in favor of the former hypothesis but not the latter. Previous studies of charitable giving focus on middle- or high-income earners in Western countries, neglecting the poor, although the lowest-income groups are often shown to contribute substantial shares of their income to charitable causes. Our results challenge the evidence in the extant literature but are in line with our theoretical model. The implications for fundraising managers are that the optimal design of fundraising campaigns crucially depends on the targeted groups and that donation matching is successful in stimulating participation in poorer populations.
Construct-based models have become a mainstay of management and information systems research. However, these models are likely overfit to the data samples upon which they are estimated, making them risky to use in explanatory, prescriptive, or predictive ways outside a given sample. Empirical researchers currently lack tools to analyze why and how their models may not generalize out of sample. We propose a composite overfit analysis (COA) framework that applies predictive tools to describe the sources and ramifications of overfit in terms of the focal concepts important to empirical researchers: cases, constructs, and causal paths. The COA framework begins by using a leave-one-out crossvalidation procedure to identify cases with unusually high predictive error given their in-sample fit—a difference we describe as predictive deviance. The framework then employs a novel deviance tree method to group deviant cases that have similar predictive deviance and for similar theoretical reasons. We then employ a leave-deviant-group-out method, which sequentially analyzes how each deviant group affects model parameters, thereby identifying potentially unstable paths in the model. We can then infer descriptive reasons for why and how overfit affects a given model and data sample using the grouping criteria of the deviance tree, construct scores of deviant groups, and resulting unstable paths. These insights allow researchers to identify unexpected behavior that could define boundary conditions of their theory or point to new theoretical phenomena. We demonstrate the practical utility of our analytical framework on a technology adoption model in a new context.
At the onset of the coronavirus (COVID-19) pandemic, hospitals experienced a demand surge for COVID-19–related medical care while simultaneously struggling to source prescription drugs needed to manage COVID-19 patients. This is worrisome as shortfalls in the supply of essential drugs can negatively impact patient outcomes. The popular press reporting on these challenges suggests that they are caused by supply chain disruptions. However, rigorous research on the relationship between the pandemic and prescription drug supply is limited. To address this gap, we leverage a quasi-experimental design and IQVIA’s National Sales Perspectives™ data from 2018 to 2020. We focus on prescription drugs indicated for the management of COVID-19 patients and a set of control drugs (i.e., drugs not used for COVID-19). We find that in the early phases of the pandemic, hospitals stockpiled prescription drugs indicated for the management of COVID-19, making this behavior an under-recognized cause of the sourcing challenges. The sales volume of drugs indicated for COVID-19 management was concentrated in the first two months of the pandemic, after which sales decreased significantly despite a nationwide increase in COVID-19–related hospitalizations. We investigate another potential cause of stockpiling: expected price increases. Counter to concerns of price gouging, we find little evidence of price inflation for these drugs. An implication for drug manufacturers is that orders due to stockpiling by downstream buyers early on in a pandemic may need to be discounted when predicting future demand. Our results have implications for drug suppliers, hospitals, and policymakers interested in improving medical product supply chain resilience.
Motivated by our collaboration with Anheuser-Busch InBev (AB InBev), a consumer packaged goods (CPG) company, we consider the problem of forecasting sales under the coronavirus disease 2019 (COVID-19) pandemic. Our approach combines nonparametric regression, game theory, and pandemic modeling to develop a data-driven competitive online nonparametric regression method. Specifically, the method takes the future COVID-19 case estimates, which can be simulated via the susceptible-infectious-removed (SIR) epidemic model as an input, and outputs the level of calibration for the baseline sales forecast generated by AB InBev. In generating the calibration level, we focus on an online learning setting where our algorithm sequentially predicts the label (i.e., the level of calibration) of a random covariate (i.e., the current number of active cases) given past observations and the generative process (i.e., the SIR epidemic model) of future covariates. To provide robust performance guarantee, we derive our algorithm by minimizing regret, which is the difference between the squared ℓ2-norm associated with labels generated by the algorithm and labels generated by an adversary and the squared ℓ2-norm associated with labels generated by the best isotonic (nondecreasing) function in hindsight and the adversarial labels. We develop a computationally efficient algorithm that attains the minimax-optimal regret over all possible choices of the labels (possibly non-i.i.d. and even adversarial). We demonstrate the performances of our algorithm on both synthetic and AB InBev’s data sets of three different markets (each corresponds to a country) from March 2020 to March 2021. The AB InBev’s numerical experiments show that our method is capable of reducing the forecast error in terms of weighted mean absolute percentage error (WMAPE) and mean squared error (MSE) by more than 37% for the company.
We investigate the increasingly common practice of chief executive officers (CEOs) taking public stances on social and political issues (CEO activism). We find that CEO activism stems from a CEO’s personal ideology and its alignment with investor, employee, and customer ideologies. We show that CEO activism results in positive market reactions. Furthermore, firms with CEO activism realize increased shareholdings from investors with a greater liberal leaning, who rebalance their portfolios toward these firms. Our results suggest that investors’ socio-political preferences are an important channel through which CEO activism affects equity demand and stock prices. Notably, CEOs are less likely to be fired when their activist stances generate positive market responses.
The growing prevalence of stock market chat rooms and social media suggests that communication between traders may affect market outcomes. Using data from a series of laboratory experiments, we study the causal effect of trader communication on market efficiency. We show that communication allows markets to convey private information more effectively. This effect is robust to a wide range of information settings. The presence of insiders limits the impact, whereas posted reputation scores in the communication platform magnify it. These findings illustrate the need to consider social interactions when designing market institutions to leverage the social motives that foster information aggregation.
We exploit staggered municipality-level antimafia enforcement actions in Italy over the 1995–2015 period to study how the presence of organized crime affects firms. Following enforcement actions, we find increases in competition (i) among firms and (ii) for public procurement contracts. Firms that do not exit after a weakening of organized crime shrink in size, more so when operating in the nontradable sector. Our findings are consistent with organized crime acting as a barrier to entry and affecting local economic activity.
The behavioral literature suggests that minor frictions can elicit desirable behavior without obvious coercion. Using closures of ATMs in a densely populated city as an instrument for small frictions to physical banking access, we find that customers affected by ATM closures increase their usage of the bank’s digital platform. Other spillover effects of this adoption of financial technology include increases in point-of-sale transactions, electronic funds transfers, automatic bill payments and savings, and a reduction in cash usage. Our results show that minor frictions can help overcome the status quo bias and facilitate significant behavior change.
Women are underrepresented in business, academic, and political decision-making bodies across the world. To investigate the causal effect of gender representation on multilateral negotiations, we experimentally manipulate the composition of triads in a majoritarian, divide-the-dollar game. We document a robust gender gap in earnings driven largely by the exclusion of women from alliances rather than differential shares within alliances. Experiments with different subject pools show that distinct bargaining dynamics can underlie the same inequitable outcomes; gender-biased outcomes can be caused by outright discrimination, but they can also be driven by more complex dynamics related to differences in bargaining strategies. Although replacing the male with a female majority all but eliminates the gap in one pool, it has minimal effect in the other. These findings show that there is no “one-size-fits-all” solution to the gender gap we uncovered and highlight the importance of studying bargaining dynamics in detail.
It is intuitive and theoretically sound to combine experts’ forecasts based on their proven skills, while accounting for correlation among their forecast submissions. Simpler combination methods, however, which assume independence of forecasts or equal skill, have been found to be empirically robust, in particular, in settings in which there are few historical data available for assessing experts’ skill. One explanation for the robust performance by simple methods is that empirical estimation of skill and of correlations introduces error, leading to worse aggregated forecasts than simpler alternatives. We offer a heuristic that accounts for skill and reduces estimation error by utilizing a common correlation factor. Our theoretical results present an optimal form for this common correlation, and we offer Bayesian estimators that can be used in practice. The common correlation heuristic is shown to outperform alternative combination methods on macroeconomic and experimental forecasting where there are limited historical data.
Cognitive differences can catalyze social learning through the process of one-to-one social influence. Yet the learning benefits of exposure to the ideas of cognitively dissimilar others often fail to materialize. Why do cognitive differences produce learning from interpersonal influence in some contexts but not in others? To answer this question, we distinguish between cognition that is expressed—one’s public stance on an issue and the way in which supporting arguments are framed—and cognition that is latent—the semantic associations that underpin these expressions. We theorize that, when latent cognition is obscured, one is more likely to be influenced to change one’s mind on an issue when exposed to the opposing ideas of cognitively dissimilar, rather than similar, others. When latent cognition is instead observable, a subtle similarity-attraction response tends to counteract the potency of cognitive differences—even when social identity cues and other categorical distinctions are inaccessible. To evaluate these ideas, we introduce a novel experimental paradigm in which participants (a) respond to a polarizing scenario; (b) view an opposing argument by another whose latent cognition is either similar to or different from their own and is either observable or obscured; and (c) have an opportunity to respond again to the scenario. A preregistered study (n = 1,000) finds support for our theory. A supplemental study (n = 200) suggests that the social influence of latent cognitive differences operates through the mechanism of argument novelty. We discuss implications of these findings for research on social influence, collective intelligence, and cognitive diversity in groups.
Organizations face challenges when trying to effectively introduce new operational practices that substitute for existing ones. We study how the dynamics due to social comparisons between employees give rise to individual strategic considerations and eventually shape the organizational adoption outcome. We develop an evolutionary game theory model that accounts for these microlevel individual adoption decisions and their impact on macrolevel population adoption equilibria. Social comparisons invoke dynamics that expand the possible outcomes beyond the traditional nonadoption versus full-adoption dichotomy. Specifically, ahead-seeking social comparisons drive the long-term coexistence of practices because employees seek to differentiate their choices from those of others. Meanwhile, behind-averse comparisons create a bandwagon effect that determines adoption depending on the initial fraction of adopters—that is, employees who are trained upfront. These dynamics are robust to various settings: different conceptualizations of social comparisons, each employee responding to more than one kind of social comparison, and nonhomogeneous social comparisons across employees. Moreover, they are material to organizations that seek to maximize their profit when introducing a new practice, by setting the levels of upfront training and adoption rewards. Our results call for senior managers to account for such behavioral traits when managing the introduction of new practices. Profitable adoption critically relies upon matching rewards and training to the type of social comparison.
We draw a surprising and direct mathematical equivalence between the class of fair division mechanisms, designed to allocate divisible goods without money, and the class of weakly budget-balanced wagering mechanisms, designed to elicit probabilities. Although this correspondence between fair division and wagering has applications in both settings, we focus on its implications for the design of incentive-compatible fair division mechanisms. In particular, we show that applying the correspondence to competitive scoring rules, a prominent class of wagering mechanisms based on proper scoring rules, yields the first incentive-compatible fair division mechanism that is both fair (proportional and envy-free) and responsive to agent preferences. Moreover, for two agents, we show that competitive scoring rules characterize the whole class of nonwasteful and incentive-compatible fair division mechanisms, subject to mild technical conditions. As one of several consequences, this allows us to resolve an open question about the best possible approximation to optimal utilitarian welfare that can be achieved by any incentive-compatible mechanism. Finally, because the equivalence greatly expands the set of known incentive-compatible fair division mechanisms, we conclude with an evaluation of this entire set, comparing the mechanisms’ axiomatic properties and examining their welfare performance in simulation.
We propose a method to identify the informativeness of a future scheduled announcement at the daily level, exploiting the discontinuity it creates in the term structure of option volatility. We implement the strategy in a panel data model to estimate the relation between prior signals and the future announcement. This method allows us to separate substitutes from complements, it can isolate multiple signals within the same quarter, and it can condition on the timing and signal characteristics. We find that analyst forecasts substitute earnings announcement information and that recommendations do not provide extra information on top of forecasts. Moreover, our evidence suggests that insiders sell to avoid uncertainty when the announcement is far away but pull forward earnings information when they trade one month before.
A central feature of most organizations is the use of measures to represent key elements of performance across multiple strategic objectives. Prior research demonstrates a tendency for individuals to treat these measures as though they are the higher order strategic objectives the measures were intended to represent as opposed to imperfect representations of those strategic objectives—a phenomenon labeled “surrogation.” We employ an experiment to further understand this phenomenon. In this study, we capture neural activation when processing measures and when processing strategic objectives. We find that, in regions of the brain with greater (less) activation for concrete versus abstract words, brain activation is greater (less) when processing measure phrases versus when processing strategy phrases. We further find evidence that greater brain activity and longer response times are associated with less surrogation. This affirms the notion that increased cognitive involvement can reduce surrogation.
Heuristics and biases in probabilistic belief updating have typically been examined in simple two-state experimental settings. We argue that the two-state setting has probabilistic properties that do not extend to settings with more states. With three states, we find that individuals apply similar heuristics, such as representativeness and anchoring, when providing posterior probability distributions. However, because of the different normative benchmark, the use of these heuristics results in different biases for point estimates. In particular, we demonstrate that the well-known finding of stronger underinference for larger signal sets does not translate from the two-state to the three-state setting. Our findings caution against an indiscriminate transfer of updating biases observed in two-state settings to a broad set of real-world applications.
Consumers frequently search for information before making decisions. Because their search and purchase decisions depend on the information environment, firms have a strong incentive to influence it. This paper endogenizes the consumer’s information environment from the firm’s perspective and endogenizes the search decision from the consumer’s perspective. We consider a dynamic model where a firm sequentially persuades a consumer to purchase the product. The consumer only wishes to buy the product if it is a good match. The firm designs the information structure. Given the endogenous information environment, the consumer trades off the benefit and cost of information acquisition and decides whether to search for more information. Given the information acquisition strategy of the consumer, the firm trades off the benefit and cost of information provision and determines how much information to provide. This paper characterizes the optimal information structure under a general signal space. We find that the firm only smooths information provision over multiple periods if the consumer is optimistic about the product fit before searching for information. Moreover, if the search cost for the consumer is high, the firm designs the information such that the consumer will be certain that the product is a good match and will purchase it after observing a positive signal. If the search cost is low, the firm provides noisy information such that the consumer will be uncertain about the product fit but will still buy it after observing a positive signal.
We develop a methodology to decompose the conditional market risk premium and risk premia on higher-order moments of excess market returns into risk premia related to contingent claims on down, up, and moderate market returns. The decomposition exploits information about the risk-neutral market return distribution embedded in option prices, but does not depend on assumptions about the functional form of investor preferences or about the market return distribution. The total market risk premium is highly time-varying, as are the contributions from downside, upside, and central risk. Time-series variation in risk premia associated with each region is primarily driven by variation in risk prices associated with the probability of entering each region at short horizons, but it is primarily driven by variation in risk quantities at longer horizons. Analogous decompositions implied by prominent representative agent models generally fail to match the dynamic risk premium behavior implied by the data. Our results provide a set of new empirical facts regarding the drivers of conditional risk premia and identify new challenges for representative agent models.
Consumers often receive a full or partial refund for product returns or service cancellations. Much of the existing literature studies cash refunds, where consumers get the money back minus a fee upon a product return or service cancellation. However, not all refunds are issued in cash. Sometimes consumers receive credit that can be used for future purchases, oftentimes with an expiration term after which the credit is forfeited. We study the optimal design of credit refund policies. Different from models that consider cash refunds, we explicitly model repeated interactions between the seller and consumers over time. We assume that consumers’ valuation for the product/service varies over time and that there is an exogenous probability for product returns. Several interesting results emerge. First, a credit refund policy facilitates intraconsumer price discrimination for a single type of consumers with stochastic valuation. Second, an optimal policy often involves an intermediate credit expiration term, under which a consumer with a high product valuation always makes a purchase, whereas a consumer with a low product valuation may be induced to make a purchase as the credit approaches expiration, leading to a demand induction effect. Finally, a credit refund policy can be more profitable than a cash refund policy and can lead to a win-win outcome for both the firm and consumers under certain conditions. We also consider several extensions to check the robustness of our findings.
In rare-event simulation, an importance sampling (IS) estimator is regarded as efficient if its relative error, namely, the ratio between its standard deviation and mean, is sufficiently controlled. It is widely known that when a rare-event set contains multiple “important regions” encoded by the so-called dominating points, the IS needs to account for all of them via mixing to achieve efficiency. We argue that in typical experiments, missing less significant dominating points may not necessarily cause inefficiency, and the traditional analysis recipe could suffer from intrinsic looseness by using relative error or, in turn, estimation variance as an efficiency criterion. We propose a new efficiency notion, which we call probabilistic efficiency, to tighten this gap. In particular, we show that under the standard Gartner-Ellis large deviations regime, an IS that uses only the most significant dominating points is sufficient to attain this efficiency notion. Our finding is especially relevant in high-dimensional settings where the computational effort to locate all dominating points is enormous.
It is common for lenders to require innovative firms to report all innovations and take actions, such as patenting, to protect their value. However, many future innovations are difficult to verify, making enforcement of such requirements difficult. This creates a tension for firms between protecting their innovations through patenting and creating assets that can be liquidated by a lender when not fully repaid. We show that the endogeneity of the patenting decision introduces an upper bound on the long-term payments that can be credibly promised to a lender, affecting the feasibility of obtaining financing and the terms of financial contracts.
Using proprietary individual-level trading data around a natural experiment—the release of a smartphone trading app by a large investment advisor—this study investigates how smartphone trading technology affects retail investor behavior and mutual fund performance. App adoption by retail investors leads to an increase in investor attention and trading volume. App adopters’ flows become more sensitive to short-term fund returns and market sentiment, resulting in higher aggregate flow volume among adopters. The funds more exposed to the shock experience a greater decline in abnormal returns, likely attributable to higher fund flow volume and liquidity costs. As a result, both adopters and nonadopters experience a decline in their mutual fund investment returns.
Some firms in the technology sector choose to cooperate with competitors (“co-opetition”) in standard setting organizations (SSOs). These SSOs create technology standards that facilitate rapid market penetration of new technologies such as Wi-Fi, Bluetooth, and 4G. Active participation in the standard setting process requires the exchange of proprietary information with competitors. Although the goal of such information sharing is to further a technology or a product market, firms potentially receive an additional benefit from access to competitor and industry information. We examine whether contributing to SSO committees enhances a firm’s information set and allows managers to better predict future sales. We find that the centrality of a firm’s location within the network of SSO collaborators is positively related to the accuracy of the firm’s sales forecasts. This relation is stronger when firms exchange more information with direct competitors and with larger firms, when forecasting is more difficult ex ante, and when firms forecast over longer horizons. Our findings show that collaborating with competitors in the product market provides an important additional benefit of improving the manager’s information set.
We provide evidence that the yield impact of unconventional monetary policy is not a sufficient statistic to measure the strength of the bank lending channel of unconventional monetary policy. As a laboratory, we study three major positive events in the European sovereign debt crisis—the Greek debt restructuring (private sector involvement (PSI)), outright monetary transactions (OMT), and quantitative easing (QE)—using credit registry and security-level bank balance sheet data from Portugal, a country that was directly exposed to all three events. Even though the price of sovereign debt increased by substantially more after the PSI and OMT announcements, only QE led banks to reduce their net sovereign debt holdings and had statistically and economically significant effects on lending to firms and households. The differential effect on banks’ behavior reflects two forces. First, lower bank capitalization levels during the PSI and OMT episodes as well as disincentives to sell sovereign bonds because of their collateral value in the European Central Bank’s long-term refinancing operation facilities reduce the effectiveness of the recapitalization channel. Second, only QE involved direct asset purchases by the central bank and thereby induced a portfolio rebalancing channel from sovereign debt to loans.
We propose a novel measure of intermediary risk exposure based on the fraction of all trade that is conducted between dealers, called the interdealer trade (IDT) measure. Intuitively, when dealers’ aggregate risk exposure rises, they trade more with each other to redistribute inventory shocks. Consistent with risk exposures relating to expected returns, market-specific IDT measures add incremental return predictability across five different markets. For example, one-standard-deviation increases in the Treasury and foreign exchange (FX) IDT measures, respectively, forecast a 1.8% higher annual excess return on a five-year bond and a 3.7% higher annual excess return on currency-specific FX trades.
Online video platforms face the challenge of balancing the needs of their users with those of their advertisers. Although users typically prefer to have less intrusive ads, advertisers aim to effectively catch user attention. This paper investigates how the provision of ad choice affects the effectiveness of video advertising. We argue that allowing users to choose an ad to view may trigger a “conjecture-formation-and-confirmation” process that motivates users to pay more attention to the selected ad. Two online experiments and four laboratory experiments are conducted to test the theorized underlying mechanism of the ad choice effect. Study 1 finds when users are unfamiliar (versus familiar) with the content of ad options (i.e., they need to make conjectures about ad content), ad choice is more likely to increase user attention to the chosen ad. Study 2 and Study 3 show that the impact of ad choice on user attention is more likely to be positive when users are enabled to make conjectures about ad content, such as when choice options provide more relevant information about ad content. Study 4a and Study 4b provide more direct support for the underlying mechanism by showing that the ad choice effect is attenuated when users cannot form conjectures about ad content at the choice stage. Study 5 further demonstrates that the positive effect of ad choice is robust across different ad settings. Taken together, these studies show ad choice is more likely to boost the effectiveness of video advertising when the “conjecture-formation-and-confirmation” process is triggered.
An understanding of differences in nonmonetary work conditions is fundamental for a complete characterization of individuals’ well-being at work. Thus, to fully characterize gender inequalities in the labor market, scholars have begun to explore gender differences in nonmonetary work conditions. We examine one such condition—meaningful work—using nationally representative survey data linked with worker and employer administrative data. We document a large and expanding gender gap in meaningful work, wherein women experience their jobs as more meaningful than men do. We then explore patterns underlying this difference. We find little correlation between women’s higher experience of meaningful work and either labor market decisions related to first parenthood or women’s underrepresentation in leadership jobs. Instead, the gender gap appears to be highly correlated with the sorting of more women into occupations with a high level of beneficence: the sense of having a prosocial impact. Though both women and men experience such jobs as more meaningful, women do so by a larger margin. Next, we consider the relationship between the gender difference in meaningful work and the gender wage gap, contributing to the discussion on compensating differentials in work amenities. We find that, whereas the gender gap in meaningful work closes a substantial part of the wage gap in lower paid jobs, it does little to close the gap in higher paid jobs in which the gender wage gap is largest.
We use the lens of weak signal asymptotics to study a class of sequentially randomized experiments, including those that arise in solving multiarmed bandit problems. In an experiment with n time steps, we let the mean reward gaps between actions scale to the order 1/n to preserve the difficulty of the learning task as n grows. In this regime, we show that the sample paths of a class of sequentially randomized experiments—adapted to this scaling regime and with arm selection probabilities that vary continuously with state—converge weakly to a diffusion limit, given as the solution to a stochastic differential equation. The diffusion limit enables us to derive refined, instance-specific characterization of stochastic dynamics and to obtain several insights on the regret and belief evolution of a number of sequential experiments including Thompson sampling (but not upper-confidence bound, which does not satisfy our continuity assumption). We show that all sequential experiments whose randomization probabilities have a Lipschitz-continuous dependence on the observed data suffer from suboptimal regret performance when the reward gaps are relatively large. Conversely, we find that a version of Thompson sampling with an asymptotically uninformative prior variance achieves near-optimal instance-specific regret scaling, including with large reward gaps, but these good regret properties come at the cost of highly unstable posterior beliefs.
Overconfident behavior, the excessive willingness to bet on one’s performance, may be driven by optimistic beliefs and/or ambiguity attitudes. Separating these factors is key for understanding and correcting overconfident behavior, as they call for different corrective actions. We present a method to do so, which we implement in two incentivized experiments. The first experiment shows the importance of ambiguity attitudes for overconfident behavior. Optimistic ambiguity attitudes (ambiguity seeking) counterbalanced the effect of pessimistic beliefs, leading to neither over- nor underconfident behavior. The second experiment applies our method in contexts where overconfident behavior is expected to vary: easy versus hard tasks. Our results showed that task difficulty affected both beliefs and ambiguity attitudes. However, although beliefs were more optimistic for relative performance (rank) and more pessimistic for absolute performance (score) on easy tasks compared with hard tasks, ambiguity attitudes were always more optimistic on easy tasks for both absolute and relative performance. Our findings show the subtle interplay between beliefs and ambiguity attitudes: they can reinforce or offset each other, depending on the context, increasing or lowering overconfident behavior.
We examine the impact of family-based human capital stemming from a chairperson’s having siblings vis-à-vis not having siblings on corporate innovation in Chinese family firms. Using hand-collected data, we document that when a firm has a sibling-chairperson, it holds more patents, receives more total citations to its patents, and has greater innovation efficiency and innovation quality than an otherwise equivalent firm with a chairperson having no siblings. The results are economically significant and robust to a battery of robustness checks. Specifically, the findings remain intact after using China’s one-child policy as an exogenous shock to apply a regression discontinuity research design to mitigate endogeneity. Additional analyses suggest that the mechanisms behind the impact of siblings on innovation are consistent with family-based human capital embedded in the sibling relationships such as competition, knowledge spillover, and family firm succession effect among siblings. Furthermore, we show that sibling comanagement and sibling gender diversity matter in corporate innovation. In addition, sibling effect enhances corporate investment efficiency, stock returns, and merger and acquisition performance. Overall, family-based human capital from siblings positively contributes to corporate innovation.
Recent models assume that the anticipation of future consumption can have direct utility consequences. This gives rise to preferences over the timing and structure of information. Using a novel and purposefully simple setup, we study the determinants of preferences for sooner versus later information. Our main results are as follows. We find that the majority of subjects prefer receiving information sooner. This preference, however, is not uniform but depends on the context. When the environment allows subjects to not focus attention on (negative) consumption events, later information becomes more attractive. Finally, variations in prior beliefs do not seem to have strong effects on information preferences.
We first provide evidence of investor inattention to a very simple and well-known capital gains tax exemption in the Brazilian stock market. We then show that inattentive investors exhibit worse trading performance and stronger trading biases even after controlling for several investor-level variables, such as past trading experience. The evidence is consistent with inattention being one of the explanations for the prevalence of behavioral biases.
Traditional asset management strategy has emphasized building barriers to entry or closely guarding unique assets to maintain a firm’s comparative advantage. A new “inverted firm” paradigm, however, has emerged. Under this strategy, firms share data seeking to become platforms by opening digital services to third parties and capturing part of their external surplus. This contrasts with a “pipeline” strategy where the firm itself creates value. This paper quantitatively estimates the effect of adopting an inverted firm strategy through the lens of application programming interfaces (APIs), a key enabling technology. Using both public data and those of a private API development firm, we document rapid growth of the API network and connecting apps since 2005. We then perform difference-in-difference and synthetic control analyses and find that public firms adopting public APIs grew an additional 38.7% over 16 years relative to similar nonadopters. We find no significant effect from the use of APIs purely for internal productivity: the pipeline strategy. Within the subset of firms that adopt public APIs, those that attract more third-party complementors and those that become more central to the network see faster growth. Using variation in network centrality caused by API degradation, an instrumental variables analysis confirms a causal role for APIs in firm market value. Finally, we document an important downside of public APIs: increased risk of data breach. Overall, these facts lead us to conclude that APIs have a large and positive impact on economic growth and do so primarily by enabling an inverted firm strategy.
Informal cross-border trade (ICBT) refers to the illegal activities of cross-border commerce conducted by unregistered small-scale traders. We seek to develop insights to understand the ICBT value chain and offer policy recommendations to successfully integrate it into the formal economy. Using a game-theoretic model, we analyze the operations and key market dynamics of ICBT. We analyze the policy implications of three representative UN directives: enhancing marginalized traders’ access to formal channels, reducing export tax rates for formal traders, and introducing an alternative simplified trade regime (STR) for informal traders. All three policies result in an increase in government proceeds when the inherent profitability of the formal or STR channels is sufficiently high. Furthermore, social welfare increases when the policies effectively balance wholesale price competition within the formal and informal channels. We apply our model to a case study based on Uganda’s agricultural exports over an 11-year horizon. The access enhancement policy is most effective in increasing government proceeds but least effective in improving the welfare of other participants. The tax reduction policy enhances traders’ profitability but sacrifices the welfare of farmers and government proceeds substantially. Finally, the STR acknowledgement policy results in the largest increase in profitability of marginalized traders and farmers but comes at the cost of government proceeds.
We propose a quantitative framework for assessing the financial impact of any form of impact investing, including socially responsible investing; environmental, social, and governance (ESG) objectives; and other nonfinancial investment criteria. We derive conditions under which impact investing detracts from, improves on, or is neutral to the performance of traditional mean-variance optimal portfolios, which depends on whether the correlations between the impact factor and unobserved excess returns are negative, positive, or zero, respectively. Using Treynor–Black portfolios to maximize the risk-adjusted returns of impact portfolios, we derive an explicit and easily computable measure of the financial reward or cost of impact investing as compared with passive index benchmarks. We illustrate our approach with applications to biotech venture philanthropy, a semiconductor research and development consortium, divesting from “sin” stocks, ESG investments, and “meme” stock rallies such as GameStop in 2021.
Papers published in finance and economics journals whose first authors are famous have more citations than papers whose second or third authors are famous. As a paper ages, its citation rate varies most with variation in the fame of the first author and less so with the fame of second and third authors. Author order is alphabetical, so these patterns are unrelated to underlying quality. The magnitudes we find are large; a three-author paper written by the most prolific author in economics and his two research assistants would increase, on average, its percentile rank by 30 percentage points if the prolific author was first rather than second or third. The effect is especially pronounced in three, rather than two, author papers, suggesting that burying a famous author in the “et al.” reduces citations the most.
Using big data on U.S. job postings, we show that firms increase skill requirements when hiring workers in states that cut personal income taxes. We trace a significant driver of this effect to companies’ reallocation of skilled job postings across states based on tax differentials. The tax-induced upskilling is observed within occupations and is more pronounced for high-skill positions within firms. It is accompanied and amplified by concurrent increases in information technology expenditures at local-level establishments. In characterizing the mechanism at play, we show that job upskilling is triggered by tax changes affecting middle- and upper middle-class workers. It is pronounced for high-growth firms, for firms in tradable industries, and in urban areas, but it is mitigated among profitable firms. A narrative-based analysis helps us establish causal inferences.
This paper proposes a novel large-dimensional positive definite covariance estimator for high-frequency data under a general factor model framework. We demonstrate an appealing connection between the proposed estimator and a weighted group least absolute shrinkage and selection operator (LASSO) penalized least-squares estimator. The proposed estimator improves on traditional principal component analysis by allowing for weak factors, whose signal strengths are weak relative to idiosyncratic components. Despite the presence of microstructure noises and asynchronous trading, the proposed estimator achieves guarded positive definiteness without sacrificing the convergence rate. To make our method fully operational, we provide an extended simultaneous alternating direction method of multipliers algorithm to solve the resultant constrained convex minimization problem efficiently. Empirically, we study the monthly high-frequency covariance structure of the stock constituents of the S&P 500 index from 2008 to 2016, using all traded stocks from the NYSE, AMEX, and NASDAQ stock markets to construct the high-frequency Fama-French four and extended eleven economic factors. We further examine the out-of-sample performance of the proposed method through vast portfolio allocations, which deliver significantly reduced out-of-sample portfolio risk and enhanced Sharpe ratios. The success of our method supports the usefulness of machine learning techniques in finance.
Using data from a prominent online platform for launching new digital products, we document that “sampling bias”—defined as the difference between a startup’s target customer base and the actual sample on which early “beta tests” are conducted—has a systematic and persistent impact on the venture’s success. Specifically, we show that products with a female-focused target market launching on a typical day, when nine in 10 users on this platform are men, experience 45% less growth a year after launch than those for whom the target market is more male-focused. By isolating exogenous variation in the composition of beta testers unrelated to the characteristics of launched products on that day, we find that on days when there are unexpectedly more women beta testers on the platform—reducing the amount of sampling bias for female-focused products—the gender performance gap shrinks toward zero. Our results highlight how sampling bias can lead to fewer successfully commercialized innovations for consumers who are underrepresented among early users.
This article introduces a class of Nash games among Stackelberg players (NASPs), namely, a class of simultaneous noncooperative games where the players solve sequential Stackelberg games. Specifically, each player solves a Stackelberg game where a leader optimizes a (parametrized) linear objective function subject to linear constraints, whereas its followers solve convex quadratic problems subject to the standard optimistic assumption. Although we prove that deciding if a NASP instance admits a Nash equilibrium is generally a Σ2p-hard decision problem, we devise two exact and computationally efficient algorithms to compute and select Nash equilibria or certify that no equilibrium exists. We use NASPs to model the hierarchical interactions of international energy markets where climate change aware regulators oversee the operations of profit-driven energy producers. By combining real-world data with our models, we find that Nash equilibria provide informative, and often counterintuitive, managerial insights for market regulators.
High-value payment systems (HVPSs) are typically liquidity intensive because payments are settled on a gross basis. State-of-the-art solutions to this problem include algorithms that seek netting sets and allow for ad hoc reordering of submitted payments. This paper introduces a new algorithm that explores the entire space of payments reordering to improve the liquidity efficiency of these systems without significantly increasing payment delays. Finding the optimal payment order among the entire space of reorderings is, however, an NP-hard combinatorial optimization problem. We solve this problem using a hybrid quantum annealing algorithm. Despite the limitations in size and speed of today’s quantum computers, our algorithm provides quantifiable liquidity savings when applied to the Canadian HVPS using a 30-day sample of transaction data. By reordering batches of 70 payments, we achieve an average of Canadian (C) $240 million in daily liquidity savings, with a settlement delay of approximately 90 seconds. For a few days in the sample, the liquidity savings exceed C$1 billion. Compared with classical computing and with current algorithms in HVPS, our quantum algorithm offers larger liquidity savings, and it offers more reliable and consistent solutions, particularly under time constraints.
This note critically analyzes the conditions for equilibrium existence in a model involving incentive schemes and inventory management. It identifies a technical flaw where equilibrium is asserted to exist under certain parameter values when, in fact, it does not. However, by introducing a reasonable assumption that inventory can only be ordered in sufficiently large increments, the qualitative outcomes and key insights remain intact.
As digitization sweeps across industries and markets, analytics and data-driven decision making (DDD) are becoming increasingly important. The adoption of analytics and DDD has been slower in small-to-medium enterprises (SMEs) compared with large firms, and reliable causal estimates of the impacts of analytics tools for small businesses have been lacking. We derive experiment-based estimates of the impact of an analytics tool on SME outcomes, analyzing the randomized introduction of eBay’s Seller Hub (SH), a data-rich seller dashboard. We find that SH adoption is associated with increased DDD and that access to SH increases e-retailers’ revenues by 3.6% on average, as more items are transacted and service quality increases, without increases in average prices. Our results suggest that analytics and DDD help SMEs establish a competitive advantage. Managerial practices play an important role in reaping the benefits from the analytics dashboard, as more than a third of the SH impact is driven by active performance monitoring. Digital platforms should therefore embed analytics tools to support SMEs to increase entry and revenues. Furthermore, policies to support small businesses’ transition to the data era should address analytics and DDD gaps by ensuring access to both tools like SH and to appropriate managerial training.
We estimate the costs of financial distress prior to default (predefault costs) separately from the loss incurred at default (the loss given default) using a dynamic tradeoff model of capital structure. We show that predefault costs account for a large fraction of total distress costs, approximately 64.1%. At the same time, they vary across industries ranging from 32.1% to 75%. We demonstrate that the expected predefault costs of financial distress vary significantly across industries with a range between 3.2% and 8.3% and are higher for small firms relative to larger ones.
Illegal or unwanted waste disposal methods such as dumping and export are prevalent in practice. To minimize the environmental harm of these methods, policymakers have implemented laws and regulations designed to combat them. Even so, violations are rampant as a high degree of heterogeneity between firms and proprietary information render monitoring imperfect. Decentralized waste disposal chains, a common form of interbusiness organization in this sector, compound this problem as firms also have limited information available on their waste chain partner, creating complex interactions between firm behavior and policy interventions. Against this background, we analyze the effects of domestic and international waste regulations targeting dumping and export, respectively, on firm incentives and compliance. We develop a two-tier waste chain with a producer that generates waste and an operator that treats it. The producer’s waste quality and the treatment operator’s efficiency can be private information. Either party can avoid compliance cost by violating regulations where the producer can arrange for export and the operator can dump locally. Our analysis reveals that primarily focusing on penalizing dumping by treatment operators can worsen environmental harm. Solely focusing on penalizing low-quality waste exports, a common intervention in practice, can also backfire. Instead, penalizing producers for downstream dumping should be given consideration. In addition, the asymmetry in export burden between waste quality levels should be reduced.
Overconfidence is pervasive in subjective probability distributions (SPDs). We develop new methods to analyze judgments that entail both a distribution of possible outcomes in a population (aleatory uncertainty) and imperfect knowledge about that distribution (epistemic uncertainty). In four experiments, we examine the extent to which subjective probability mass is concentrated in a small portion of the distribution versus spread across all possible outcomes. We find that although SPDs roughly match the concentration of the empirical, aleatory distributions, people’s judgments are consistently overconfident because they fail to spread out probability mass to account for their own epistemic uncertainty about the location and shape of the distribution. Although people are aware of this lack of knowledge, they do not appropriately incorporate it into their SPDs. Our results offer new insights into the causes of overconfidence and shed light on potential ways to address this fundamental bias.
We use a game-theoretic model to examine how different types of activist motivation affect strategic interactions between an activist and a firm in the context of a threatened adversarial engagement, in which the activist can benefit from “warm glow” and media publicity as well as from firm compliance with activist demands. The model yields novel predictions about when firms prefer to self-regulate to pre-empt a contested engagement, how vigorously firms defend themselves against the activist’s attack if an engagement occurs, and a new taxonomy of engagements, characterized by offensive and defensive strategies and the likelihood of activist success. The model predicts that when warm glow and campaign-driven wins are important motivations for activists, safe bet and long shot types of engagements are more likely to occur: These tend to be lower expenditure skirmishes where one party has a clear advantage and where a pre-emptive settlement is infeasible. By contrast, firms and activists are more likely to negotiate self-regulation that pre-empts resource-intensive toss-up engagements where each side is evenly matched and expends significant effort. Our findings contribute to strategic management research by developing new insights about how firms respond to different activist motivations and types of engagements. We explore extensions of the model and discuss implications for future empirical and theoretical research on the management of activist relations.
This paper examines the causal relationship between proximity and knowledge diffusion by estimating the elasticity of core-based statistical area (CBSA) pair-level citations to variations in travel time induced by the introduction of new flight routes. The findings reveal that decreasing travel time between U.S. cities by 20% increases knowledge flow by 0.5%, which corresponds to an increase of over 15,000 citations at the aggregate level. Rather than boosting within-firm knowledge transfer, travel time reduction leads to a rise in knowledge spillovers primarily across firm boundaries, particularly among those that form joint ventures, have block holdings in each other, or form supply chain relationships. These effects are stronger among city pairs located farther away from each other, with higher absorptive capacity, in complex technology classes, and for newly developed technologies. Additional mechanism tests suggest that the most likely channel through which travel time reduction impacts knowledge spillover is by influencing the transfer of tacit knowledge via facilitating cross-CBSA inventor flow and information acquisition.
Women are underrepresented in many prestigious positions. Could increasing the share of women in hiring committees boost the rates at which women are hired into these positions? I use a difference-in-differences design to examine the effects of a French law on academic hiring committees that required each gender to represent a share of at least 40% of members. Contrary to the objectives of the law, I show that the reform backfired and significantly lowered women’s probability of being hired. Because the negative effect of the reform is concentrated in committees headed by men, this result seems driven by the reaction of men to the reform. I find little evidence that the reform affects supply-side characteristics, such as the likelihood of women applying. The results suggest that the underrepresentation of women is unlikely to be solved by simply increasing the share of women in hiring committees or interview panels.
Many pharmaceutical drugs have multiple indications, for which they offer a varying degree of benefit for patients. Yet, in the current U.S. pricing system, the price of the drug is the same regardless of the indication for which it is prescribed. This uniform pricing policy can deter the payer from providing coverage for low-value indications, if the price is high relative to the potential benefit for patients, which can reduce patients’ access to the drug. It can also deter the drug manufacturer from investing in obtaining U.S. Food and Drug Administration (FDA) approval for new indications, as the lack of flexibility in pricing can result in too low demand (and thus profits) to recoup fixed investment costs. Indication-based pricing has been proposed as a new pricing mechanism for multi-indication drugs allowing the price to differ according to the indication, to better align the price of the drug to its value. In this paper, we analyze the effect of indication-based pricing in comparison with uniform pricing on the drug manufacturer’s profit and investment incentives, the patient demand and utility, the payer’s coverage incentives, and the payer’s objective. Under uniform pricing, we consider both the cases when the price can versus cannot be adjusted upon introduction of a new indication. We find that the drug manufacturer earns higher profits under indication-based pricing than under uniform pricing. Moreover, indication-based pricing improves incentives for the manufacturer to invest in a new indication and for the payer to cover the drug. If the fixed investment cost is high, indication-based pricing also benefits patients and the payer’s objective. Otherwise, the patient demand, patient utility and payer’s objective may be lower under indication-based pricing. Hence, payers who value the patient utility should carefully consider the tradeoffs involved in implementing this pricing system and where the bottlenecks are under uniform pricing.
Most theories of decision making under risk assume that payoffs and probabilities are separable. In the context of a lottery, the subjective value of a prospective outcome (the payoff) is assumed to be independent of the likelihood that the outcome will occur (the probability). In violation of this assumption, we present eight experiments showing that people anticipate less utility from uncertain outcomes than from certain outcomes, even conditional on their realization. The devaluation of uncertain outcomes is observed across different measures of utility (willingness to spend money or time; choice between different options), different populations (student and online samples), and different manipulations of uncertainty. We show that this result does not simply reflect a misunderstanding of the instructions or people’s aversion toward a “weird” transaction with unexplained features. We highlight the implications of this phenomenon for empirical investigations of risk preferences and conclude with a discussion of the psychological mechanisms that might drive the devaluation of probabilistic outcomes.
We introduce a measure of regret for stock market investors and investigate its cross-sectional asset pricing implications. According to our regret-based framework, investors experience regret due to not achieving the highest possible return from a similar set of stock investments, and equity portfolios with high regret generate 6.84% more annualized alpha than portfolios with low regret. Using investor-trading activity of 78,000 households at a large U.S.-based brokerage firm, we develop an investor-based regret index and show that this household-level regret measure predicts stock returns in a similar way to our proposed regret measure. We also show that regret is not spanned by established risk or behavioral factors that have been documented to be robust predictors of equity returns.
Retailers face increasing competitive pressure to determine how best to deliver products purchased online to the end customer. Grocery retailers often require attended home delivery where the customer must be present to receive the delivery. For attended home delivery to function, the retailer and customer must agree on a delivery time slot that works for both parties. Using online data from a grocery retailer, we observe customer preferences for three delivery service attributes associated with each time slot: speed, precision, and timing. We define speed as the expected time between the placement of an order and its delivery, precision as the duration of the offered time slot, and timing as the availability of choices across times of the day and days of the week. We show that customers not only value speed as an attribute of delivery service but that precision and timing are also key drivers of the customer’s time slot selection process. We also observe substantial customer heterogeneity in the willingness of customers to pay for time slots. Customers that differ in their loyalty to the retailer, basket value, basket size, and basket composition exhibit distinct differences in their willingness to pay. We show that retailers with the capability to tailor their time slot offerings to specific customer segments have the potential to generate approximately 9% more shipping revenue than those who cannot. Our findings inform practitioners seeking to design competitive fulfillment strategies and academics modeling customer behavior in the attended home delivery context.
We study a social dilemma in a single-queue system in which human servers have discretion over the effort with which to process orders that arrive stochastically. We show theoretically that the efficient outcome in the form of high effort can be sustained in the subgame-perfect equilibrium if the interactions are long term (even when each server has a short-term incentive to free-ride and provide low effort). In addition, we show that queue visibility plays an important role in the type of strategies that can sustain a high-effort equilibrium. In particular, we show that limiting feedback about the current state of the queue may be beneficial if the expected duration of interaction is long. We conduct two controlled laboratory experiments to test the theoretical predictions and find that effort increases with the expected duration of an interaction. We also find that visibility has a strong impact on the strategies that human subjects use to provide effort in a dynamic setting. We discuss implications for managers and firms that are trying to improve service systems.
Consumers increasingly use digital advice when making purchasing decisions. How do such tools change consumer behavior and what types of consumers are likely to use them? We examine these questions with a randomized controlled trial of digital expert advice in the context of prescription drug insurance. The intervention we study was effective at changing consumer choices. We propose that, conceptually, expert advice can affect consumer choices through two distinct channels: by updating consumer beliefs about product features (learning) and by influencing how much consumers value product features (interpretation). Using our trial data to estimate a model of consumer demand, we find that both channels are quantitatively important. Digital expert advice tools not only provide consumers with information, but also alter how consumers value product features. For example, consumers are willing to pay 14% less for a plan with the most popular brand and 37% less for an extra star rating when they incorporate digital expert advice on plan choice relative to only having information about product features. Further, we document substantial selection into the use of digital advice on two margins. Consumers who are inherently less active shoppers and those who we predict would have responded to advice more were less likely to demand it. Our results raise concerns regarding the ability of digital advice to alter consumer preferences as well as the distributional implications of greater access to digital expert advice.
We derive analytical expressions for the risk of an investor’s expected utility under parameter uncertainty. In particular, our analysis focuses on characterizing the out-of-sample utility variance of three portfolios: the classic mean-variance portfolio, the minimum-variance portfolio, and a shrinkage portfolio that combines both. We then use our analytical expressions to study a robustness measure that balances out-of-sample utility mean and volatility. We show that neither the sample mean-variance portfolio nor the sample minimum-variance portfolio exhibits maximal robustness individually, and one needs to combine both to optimize portfolio robustness. Accordingly, we introduce a robust shrinkage portfolio that delivers an optimal tradeoff between out-of-sample utility mean and volatility and is more resilient to estimation errors. Our results highlight the importance of considering out-of-sample performance risk in designing and evaluating investment strategies and stochastic discount factor models.
Off-service placement is a common capacity-pooling strategy that hospitals utilize to address mismatches in supply and demand that arise from the day-to-day variation in patient demand. This strategy involves placing patients in a bed in a unit that is designated for another specialty service. Building on prior work that documents the negative first order effects of off-service placement on patients who are placed off service themselves, we quantify the spillover effects of this practice on patients who are actually placed on service. Using an estimation strategy that combines the Heckman correction procedure and a heteroskedasticity-based identification approach, we find that off-service placement has substantial negative spillover effects on the efficiency of care delivered to on-service patients. In particular, we find that a 10 percentage point increase in the level of off-service placement during a patient’s hospitalization is associated with a 10.9% increase in length of stay. Through a series of counterfactual analyses, we propose alternate routing and capacity-planning policies that could meaningfully improve the efficiency of care in the inpatient setting.
We study a variation of the classical Pandora’s problem in which a decision maker (DM) sequentially explores alternatives from a given set and learns their values while trying to acquire the best alternative. The variations in the model we study are (i) alternatives randomly become unavailable during exploration and (ii) the DM’s ability to acquire a remaining alternative is uncertain and depends on a chosen offer price. Such acquisition uncertainties arise in many applications, including housing search, hiring problems, and e-commerce, but greatly complicate the search problem in that optimal policies retain all previously explored alternative values as part of the problem state, as opposed to only the highest explored value as in Pandora’s rule. Our central insight is that despite the complexity that these acquisition uncertainties create, simple greedy policies based on static sequencing and a single threshold value enjoy strong performance guarantees. We develop such a class of policies and show how to compute them using a greedy algorithm whose worst-case run-time scales linearly (up to logarithmic factors) in the number of alternative types. We show that our policies (a) are asymptotically optimal in high multiplicity regimes with many alternatives and (b) obtain at least 1−e−1≈63.2% of the optimal value under a broad set of conditions. Extensive numerical examples support this theory: We illustrate our policies on a variation of Pandora’s problem with disappearing alternatives and housing search on models calibrated on data from the online brokerage Redfin. In these examples, our policies significantly outperform policies based on Pandora’s rule.
Firms seeking business partners and individuals seeking life partners face several challenges in addition to finding available candidates. One of these challenges is uncertainty about a candidate’s compatibility with the match-seeker on various subjective criteria. Another challenge is authentication of objective quality features, particularly in online settings where credentials can be easily misrepresented. Online matching platforms can address these challenges through counseling services that help assess mutual compatibility of match-seekers and authentication services to validate match-seekers’ credentials. Using a stylized model, we show that the platform’s decisions to offer counseling and authentication services are influenced by match-seeker behavior in response to factors such as the platform’s counseling capability, the composition of the matching market, and the interactions between the two services. This leads to optimal strategies for the platform that are not obvious and can even be counterintuitive. For instance, offering better counseling does not necessarily result in higher revenues for the platform. And it may be optimal for the platform to reduce its access fee when it decides to offer an authentication service. We also show that when the platform’s counseling capability is modest, an increase in this capability increases the value of authentication (i.e., counseling complements authentication); however, when the platform’s counseling capability is high, counseling and authentication become substitutes, because narrowing the search to only high-value candidates based on authentication can exclude some compatible candidates. Our analysis and results provide valuable insights for platform operators on the relative value of counseling and authentication, and for researchers studying matching platforms for vertically and horizontally differentiated markets.
This paper studies the impact of betrayal aversion on employees’ effort provision in exchange for a bonus payment that is at the employers’ expense and discretion. In our first study, using a laboratory experiment, we find that the performance of an employee with average betrayal aversion is lower by more than 15% relative to betrayal-neutral employees. Inspired by the findings, we theoretically identify a tradeoff for the effort provision of betrayal-averse employees. That is, higher effort not only increases the level of betrayal when the bonus is not paid, but it also increases the chances to receive a bonus avoiding betrayal. In a second study, we confirm our findings in an online labor market. The data also find support for the identified tradeoff. In a third study, we manipulate employees’ beliefs about the chances of receiving the bonus to provide causal evidence for the tradeoff.
Cooperation between individuals, a critical component of organizational and societal success, typically involves costs and benefits that accrue at different points in time. Using a series of controlled experiments, this paper provides a comprehensive analysis of the determinants of cooperation in an intertemporal context. Our findings demonstrate that cooperation is significantly reduced when the benefits of cooperation are shifted into the future, whereas delaying costs leads to an increase in cooperation. Our analysis of the underlying behavioral mechanisms reveals that the change in the level of cooperation can be explained by three factors: (i) a shift in the beliefs about others’ efforts, (ii) a shift in the willingness to conditionally cooperate, and (iii) an individual’s degree of impatience. We further find that injunctive norms of cooperation are unaffected by the timing of consequences, indicating that changes in behavior are due to a change in norm compliance rather than the norm itself. Implications for management practices are discussed.
We study the comparative advantage of firms with focused and multidivisional organizational forms at attracting valuable human capital. Using the merger and acquisition (M&A) advisory industry as a laboratory, we show that high-performing individuals are more likely to migrate to boutique (focused) banks, especially those who are still on the upward trajectory of their career. Such migration is amplified by the cross-subsidization inside bulge bracket (multidivisional) firms, proxied by poor performance of their non-M&A departments. The transition of skilled labor improves the performance of the boutique sector, potentially contributing to the rise of boutiques over the past two decades. Moreover, M&A deal outcomes differ when having boutique advisors. Our findings suggest that corporate organizational structure and labor migration can jointly shape industry dynamics.
Do credit rating agencies (CRAs) learn from the options market? We examine this question by exploring the relation between options trading activity and credit rating accuracy. We find that as options trading volume increases, credit ratings become more responsive to expected credit risk and exhibit greater ability to predict future defaults. We also find that CRAs rely more on the options market as a source of ratings-related information when firm default risk is higher, options trading is more informative, manager-provided information is of lower quality, and firm uncertainty is higher. Our results are robust to a number of sensitivity tests, including alternative measures of options trading and credit rating accuracy. We reach similar inferences using various approaches to address endogeneity issues, including difference-in-difference analyses and an instrumental variables approach. Overall, our findings are consistent with the view that CRAs incorporate unique information from the options market into their rating decisions which, in turn, improves credit rating accuracy.
With the development of communication technology that makes online transactions easier, there is also an increased risk of disintermediation—sellers and buyers circumventing a platform to transact directly—in online two-sided marketplaces. Such disintermediation may lead to significant revenue loss for online platforms. However, it remains unclear how the characteristics of platforms affect their vulnerability to disintermediation. Using the blockade of Skype in mainland China as a natural experiment, this study examines how online communication technologies affect disintermediation and transaction outcomes in a large U.S. online freelance marketplace. The results show that restricting this alternative communication technology, which platforms struggle to monitor, reduces disintermediation by around 18%. This effect is potentially due to economic frictions in transactions, as the reduction in disintermediation is greater for high-transaction-cost jobs, such as time-sensitive jobs, communication-intensive jobs, and high-skilled jobs, as well as for cost-sensitive users, such as experienced users and personal users as opposed to enterprise users. With these results, platforms can reduce disintermediation risks when making investment and market entry decisions.
This paper compares two estimators—the Border Approach and an Instrumental Variable (IV) estimator—using a unified framework where identifying variation arises from “preference externalities,” following the intuition in Waldfogel (2003). We highlight two dimensions in favor of the IV approach. First, an econometric model of the data-generating process reveals that the border approach requires a set of identification assumptions that are not easily satisfied in practice: the ignorance of some payoff-relevant information and conflicting spatial correlation assumptions. The IV approach, in contrast, exhibits greater internal validity because it is derived from the model that generates the data. Second, the border approach suffers from representative issues when the true effect sizes are different between border and off-border regions. We use a common political advertising example to evaluate these estimators and suggest ways to evaluate or limit the above concerns, such as excluding localities that are a large share of the policy making region and evaluating spatial correlations of observables. We find the border approach’s representative issue to be substantial when the ignorance assumption is most plausible and observe that spatial correlations do not reflect those needed in the unobservables for consistency of the estimator. The IV, in contrast, does not exhibit concerns related to local average treatment effects. We also derive the specific conditions when the border approach can reduce bias relative to OLS.
Errors-in-variables (EIV) biases plague asset pricing tests. We offer a new perspective on addressing the EIV issue: instead of viewing EIV biases as estimation errors that potentially contaminate next stage risk premium estimates, we consider them to be return innovations that follow a particular correlation structure. We factor this structure into our test design, yielding a new regression model that generates the most accurate risk premium estimates. We demonstrate the theoretical appeal as well as the empirical relevance of our new estimator.
Continuity of care, defined as an ongoing therapeutic relationship between a patient and a physician, is a defining characteristic of primary care. However, arranging a consultation with one’s regular doctor is increasingly difficult as practices face physician shortages. We study the effect of declining care continuity on the productivity of physicians by analyzing data of over 10 million consultations in 381 English primary care practices over a period of 11 years. Specifically, we examine whether a consultation with the patient’s regular doctor is more productive than with another doctor in the practice. Using statistical models that account for confounding and selection bias and restricting the sample to consultations with patients who had at least three consultations over the past two years, we find that the time to a patient’s next visit is on average 18.1% (95% confidence interval: 16.9%, 19.2%) longer when the patient sees the doctor they have seen most frequently over the past two years, while there is no operationally meaningful difference in consultation duration. The data show that the productivity benefit of care continuity is larger for older patients, patients with multiple chronic conditions, and patients with mental health conditions. We estimate that the total consultation demand in our sample could have fallen by up to 5.2% had all practices offered continuity of care at the level of the top decile of practices while prioritizing patients expected to yield the largest productivity benefits. We discuss operational and strategic implications of these findings for primary care practices and for third-party payers.
We investigate the participation and effectiveness of paid endorsers in sponsored tweet campaigns. We manipulate the financial pay rate offered to endorsers on a Chinese paid endorsement platform, where payouts are contingent on participation rather than engagement outcomes. Hence, our design can distinguish between variation in participation and variation in outcomes, even if people select to endorse only specific tweets. Also, the lack of compensation for effort allows one to attribute differences in outcomes to precontractual selection rather than postcontractual behavior. The main finding is that endorsers exhibited adverse selection. Several observed and unobserved endorser characteristics associated with a higher propensity to participate had a negative association with being an effective endorser given participation. This adverse selection results in a conundrum when trying to recruit a sizable number of high-quality endorsers. Only 9.5%–11.8% of the endorsers were above the median in both the propensity to participate and the propensity to be effective compared to a benchmark of 25% in the absence of any association. A simulation analysis of various targeting approaches that leverages our data of actual endorsements and outcomes shows that targeting candidate endorsers by scoring and ranking them using models taking into account adverse selection on observables improves campaign outcomes by 13%–55% compared to models ignoring adverse selection.
Invoking a property rights approach, we examine how competition between advertising exchanges influences the targeting options that these exchanges make available to advertisers. When advertisers have strong property rights over data regarding consumers’ active purchase interests, competition between ad exchanges leads to too little sharing of data. This may harm consumers, who receive too few pertinent ads, and advertisers themselves can also be harmed due to a situation resembling a prisoner’s dilemma. We find that reallocating property rights to consumers, that is, giving them the right to opt out of tracking may also benefit consumers who allow tracking by altering the incentives of ad exchanges to offer improved targeting options. In addition, we show that initiatives by Apple and Google to limit third-party tracking and to introduce alternative tracking systems such as Google’s Topics might benefit consumers by weakening the data property rights of advertisers. Because more data are shared by default under such systems, this can be true even if these systems are less accurate than third-party tracking systems.
Data brokers use black-box methods to profile and segment individuals for ad targeting, often with mixed success. We present evidence from 5 complementary field tests and 15 data brokers that differences in profiling accuracy and coverage for these attributes mainly depend on who is being profiled. Consumers who are better off—for example, those with higher incomes or living in affluent areas—are both more likely to be profiled and more likely to be profiled accurately. Occupational status (white-collar versus blue-collar jobs), race and ethnicity, gender, and household arrangements often affect the accuracy and likelihood of having profile information available, although this varies by country and whether we consider online or offline coverage of profile attributes. Our analyses suggest that successful consumer-background profiling can be linked to the scope of an individual’s digital footprint from how much time they spend online and the number of digital devices they own. Those who come from lower-income backgrounds have a narrower digital footprint, leading to a “data desert” for such individuals. Vendor characteristics, including differences in profiling methods, explain virtually none of the variation in profiling accuracy for our data, but explain variation in the likelihood of who is profiled. Vendor differences due to unique networks and partnerships also affect profiling outcomes indirectly due to differential access to individuals with different backgrounds. We discuss the implications of our findings for policy and marketing practice.
Using microdata from the American Red Cross (ARC) and the Federal Election Commission (FEC) in two natural experiments, we provide evidence that political giving and charitable giving are substitutes. In the first natural experiment, we estimate the effects of a positive shock to charitable donations to the ARC: foreign natural disaster events. We find that although charitable donations to ARC increase by 34.9% in the six weeks following a disaster, political donations decline by 18.8% in the same period. Put differently, each 1% increase in the charitable giving to ARC is accompanied by a 0.53% drop in political donations. At the average county-week–level donations, the implied effect of a $1 increase in charitable giving is a $0.42 decline in political donations. In the second natural experiment, we estimate the effects of a positive shock to political giving: advertisements for political campaigns. Exploiting geographic discontinuities in advertising markets, we find that political advertisements increase political giving, whereas they decrease charitable donations to ARC. Our estimates imply that each 1% increase in the political giving is accompanied by a 0.59% drop in charitable donations to ARC. At the average county-week–level donations, the implied effect of a $1 increase in political giving is a $0.33 decline in charitable donations. The crowding-out elasticities suggest that political giving and charitable giving are relatively close substitutes. We provide a number of robustness checks, and we discuss potential causal mechanisms.
We study lending agreements and derivative positions of U.S. oil and gas producers, showing that loan covenants are important determinants of hedging policies. Hedging covenants appear in more than 85% of sample loan agreements, with explicit minimum hedging requirements in more than half. Covenants are more common when expected default costs are larger. The well-documented positive relation between borrowing and hedging is largely attributable in our sample to binding covenants, as the relation is much weaker in their absence. These results suggest that understanding firms’ hedging choices requires the consideration of lender interests along with those of owners and managers.
We show that macroprudential policies dampen the impact of global financial conditions on local bank credit cycles. For identification, we exploit variation in the U.S. volatility index (VIX) and household and business credit registers in an emerging market economy in which banks depend on foreign funding and macroprudential measures vary over the full cycle. Our results suggest that when the VIX is low, tighter macroprudential policies reduce household lending, notably for riskier (foreign currency (FX) and high debt-service-to-income) loans and by banks dependent on foreign funding. Moreover, they increase (less regulated) local currency lending to real estate firms. Such periods are associated with less subsequent total lending to households and firms and with a lower share of FX loans at the local level. Consistently, when the VIX is low, tighter macroprudential policies dampen house prices and economic activity.
We study the effects of takeover feasibility on asset prices and returns in a unified framework. We show theoretically that takeover protections increase equity risk, stock returns, and bond yields by removing a valuable put option to sell the firm, notably for firms approaching distress. We investigate these claims empirically and find that distressed firms experience a significant decrease in value and increase in returns and market betas after the passage of antitakeover laws, in line with our predictions. At issue bond yields are also higher when an antitakeover law is in effect. Consistent with the model, the effects of antitakeover laws on stock returns, respectively, bond yields, are greater when shareholders, respectively, bondholders, have greater bargaining power.
Political influence on bank credit allocation is often viewed as being necessary to address social problems like income inequality. We hypothesize that such influence elicits bank capital responses. Our hypothesis yields three testable predictions for which we find supporting evidence. First, when banks observe election outcomes that suggest greater impending political credit-allocation influence, they reduce capital to increase fragility and deter political influence. Second, banks subject to greater political influence nonetheless increase lending that politicians favor, and household consumption consequently increases. Third, these banks exhibit poorer post-lending performance. Our study has implications for the interaction between politics, household consumption, and bank risk through a specific channel—the interplay between credit-allocation regulation and bank capital structure.
Revealed preferences between alternatives can be systematically reversed across a variety of elicitation procedures (e.g., choice, valuation, matching, joint/separate evaluation). These puzzling findings have been usually invoked to challenge the procedure invariance principle. Yet procedure-dependent preferences can be endogenous. This paper presents a unifying theory of contextual deliberation to account for seemingly disparate phenomena of preference reversals. When attribute importance is ex ante imperfectly known, people can engage in costly information retrieval/acquisition activities (i.e., deliberation) prior to making decisions. Elicitation procedures can influence revealed preferences through affecting the incentive for deliberation. Therefore, contextual deliberation can endogenously yield procedure-dependent preference reversals, offer a common microfoundation for extant psychological accounts (e.g., the prominence hypothesis, the evaluability hypothesis), and coherently organize apparently unrelated/inconsistent findings in the literature. We also run five experiments and document new findings that are inconsistent with extant hypotheses but can be reconciled by contextual deliberation.
This paper revisits the descending clock “reverse” auction design used in the U.S. Federal Communications Commission’s 2016–2017 “incentive auction.” We use extensive computational simulations to investigate the quantitative significance of various aspects of the design, leveraging a reverse auction simulator and realistic models of bidder values.
As part of a term-long anonymous assessment, thousands of undergraduate university students were divided into groups, each led by a randomly selected peer. The leader’s gender had no effect on the assessment’s outcomes, but female students led by a female peer achieved 0.26 standard deviations (SD) higher course grades when the gender of the leader was revealed. They also outperformed by 0.22 SD their counterparts in groups in which the leader’s gender was revealed to be male. The mechanism involved attempting more difficult practice questions. Our structural estimates suggest this operated via a stereotype threat reversal that reduced anxiety.
We examine the influence of physical proximity on between-start-up knowledge spillovers at one of the largest technology coworking hubs in the United States. Relying on the exogenous assignment of office space to the hub’s 251 start-ups, we find that proximity positively influences knowledge spillovers as proxied by the likelihood of adopting an upstream web technology already used by a peer start-up. This effect is largest for start-ups within close proximity of each other and quickly decays; start-ups more than 20 meters apart on the same floor are indistinguishable from start-ups on different floors. The main driver of the effect appears to be social interactions. Although start-ups in close proximity are most likely to participate in social coworking space events together, knowledge spillovers are greatest between start-ups that socialize but are dissimilar. Ultimately, start-ups that are embedded in environments that have neither too much nor too little diversity perform better but only if they socialize.
We study the impact of firm-specific business disruptions on the performance of small emerging market firms and test the effectiveness of building in redundancies to buffer against disruptions. Managerial disruptions result in the absence of the entrepreneur-owner, whereas operational disruptions lead to a shortage of critical resources, for example, inventory or electricity. We propose the use of relational redundancy—that is, the availability of a trusted and capable person with whom the entrepreneur-owner has an existing relationship, who can manage the business in his or her absence—to recover from managerial disruptions. We also examine whether resource redundancy—for example, maintaining safety stock or electricity backup—helps recover from operational disruptions. In the absence of publicly available data, we hand-built a panel data set by interviewing 646 randomly selected small firms over four time periods in Kampala, Uganda. We find that disruptions are highly prevalent and have a statistically and economically significant effect on firm performance. When a firm faces multiple exogenous and severe disruptions in a six-month period, its monthly sales decrease by 13.8% (p = 0.013), and its sales growth decreases by 18.8 percentage points (p = 0.070). Importantly, we find that both managerial and resource redundancies can help firms build resilience against the negative impact of disruptions. In some cases, firms with high levels of redundancy are able to completely overcome the negative effect of disruptions on sales and sales growth. We discuss implications for entrepreneurs, policymakers, and large multinationals that buy from or sell to small emerging market firms.
We present a nonparametric method to recover a bound on ex ante dispersion of beliefs (DBB) from asset prices with minimal assumptions. DBB constrains the dispersion among all possible distributions in an economy, consistent with observed prices and subject to a good-deal bound. In model-based economies, DBB effectively tracks belief heterogeneity and serves as a diagnostic tool for evaluating model calibrations. Empirically, DBB relates to common proxies of belief dispersion, offering a real-time, market-implied disagreement measure. Our versatile approach applies to both complete and incomplete markets represented by any asset class.
Working at home benefits entrepreneurs by lowering fixed costs and allowing them to engage in joint market and household production. We evaluate a large-scale reform in Singapore, the Home Office Scheme, that allowed business creation at one’s residential property and study whether home-based entrepreneurship spurs entrepreneurial activities. The difference-in-differences estimate shows that the reform led to a significantly higher level of business creation and the firms newly created in response to the reform had a higher survival rate. The effect is more pronounced for low-income female individuals and industries with high start-up capital, implying that financial constraints and nonpecuniary benefits likely drive the effect. The reform also encourages entrepreneurs to become serial entrepreneurs, and they open a larger business with a similar survival rate for their second firm. Overall, our findings suggest that the program effectively attracted more entry into self-employment without significantly lowering the average quality of the pool.
We examine how managers’ disclosure decisions vary in response to monetary policy shocks. Specifically, we examine the extent to which firms issue Forms 8-K, press releases, and management forecasts following unexpected changes to the target federal funds rate. We find consistent evidence that unexpected decreases (increases) in the Federal Open Market Committee target rate lead to larger increases (decreases) in disclosure by firms that are more sensitive to monetary policy shocks. Furthermore, the disclosure response to target rate surprises is concentrated in Form 8-K items that require a materiality-based decision and hold when we examine a sample of M&A-participating firms. Extended tests show that our results are not attributable to changes in the cost of capital leading to increased disclosure because firms are simultaneously raising capital or hyping their stock. Overall, our results are consistent with arguments raised in recent research papers that changes in cost of capital influence firms’ materiality thresholds, which can affect their decision to disclose.
We explore whether employee interests affect the evaluation of acquisition offers by target boards of directors. Exploiting changes in state unemployment insurance (UI) as sources of exogenous variation in worker unemployment costs, we find that lower unemployment costs increase acquisition activity. The adoption of state constituency statutes strengthens this relation. Boards of target firms having high labor intensity, low short-term institutional ownership, headquartered in low population or high social capital counties, and with female independent directors, more often strongly weight employee interests. Higher UI levels are also associated with larger postacquisition layoffs. Our evidence supports theories rationalizing target boards’ consideration of employee interests.
We measure firms’ beliefs about climate regulation, plans for future abatement, and current emissions mitigation from responses to the Carbon Disclosure Project. These measures vary strikingly around the Paris announcement. A dynamic model of a representative firm facing a future carbon levy, trading off abatement and capital growth, and facing convex adjustment costs cannot fit the data. A two-firm model with crossfirm reputational externalities, heterogeneous beliefs over climate regulation, and leader-follower interactions does. Out of sample, the model predicts firms’ reactions when the United States exits the Paris agreement. Firms’ beliefs about climate regulation strongly affect abatement, and crossfirm interactions amplify regulatory impacts.
We develop measures of technology decoupling and dependence between the United States and China based on combined patent data. The first two decades of the century witnessed a steady increase in technology integration (or less decoupling), but China’s dependence on the United States increased (decreased) during the first (second) decade. Firms covered by China’s Strategic Emerging Industries policies became less decoupled with the United States, gained cash flows, and gained valuation, but they saw no improvement in either innovation output/quality or productivity. Post-U.S. sanctions, firms in sanctioned sectors and their downstream suffered in performance but also became less decoupled with the United States. However, firms in the upstream of the sanctioned sectors improved productivity and produced more high-quality innovations.
Audit committee (AC) responsibilities have increased over time, prompting concerns that overloading the AC with too many duties may impair the AC’s ability to oversee financial reporting. Using new AC charter-based proxies to measure AC responsibilities, we find that an emphasis on the AC overseeing financial risk management (which is a noncore AC duty) is associated with worse financial reporting quality, as proxied by restatements—consistent with the argument of AC overload by distraction. This overload effect is attenuated when an AC has more directors to share duties or when the AC retains an expert auditor who can serve as a substitute for AC oversight. This overload effect is accentuated when AC members are busy with multiple board appointments or when the external auditor is busy with other audits. We also find that AC financial risk oversight is associated with more AC meetings and greater turnover of AC directors, consistent with the notion of overload. In sharp contrast, we find that greater AC oversight over internal controls (which is a core AC duty) is associated with improved financial reporting quality. Overall, we document that the nature of AC duties impacts the AC’s ability to promote financial reporting quality and that noncore duties may overload ACs.
Output fluctuations in nontraded sectors are an important country-specific risk factor because nontraded outputs are only consumed domestically. In interest rate markets, countries with higher nontraded output growth risks are associated with stronger motives for precautionary savings and lower interest rates. In currency markets, strategies with higher exposures to nontraded output growth risks offer higher average excess returns. Economic sizes play an important role in exacerbating the pricing impact of nontraded output growth risks as it requires larger international trades to mitigate nontraded output risks of larger economies. However, because economic sizes are functions of outputs in equilibrium, output growth risks also explain this role of economic sizes in international asset pricing.
While conventional wisdom suggests that financial supervision is costly for bank shareholders, agency theory suggests that supervisors’ audits can reduce shareholder monitoring costs. I study this trade-off in the context of an unexpected decrease in off-site surveillance intensity by the U.S. Federal Reserve. Banks subject to reduced surveillance experience a 1% loss in bank Tobin’s q and a 7% loss in equity market-to-book. These banks engage in more earnings management, and appear to compensate lower regulatory surveillance with costly internal audits. My results document a novel substitution effect between public monitoring by regulators and private monitoring by shareholders.
We find the Small Business Administration’s disaster-relief home loan program denies significantly more loans in areas with larger shares of minorities, subprime borrowers, and higher income inequality. We find that risk-insensitive loan pricing, a feature present in many regulated and government-run lending programs, is an important driver of these disparities in access to credit. The differences in denial rates are disproportionately high compared with private-market lending and government-insured risk-sensitive loan pricing programs. Thus, despite ensuring “fair” prices, the use of risk-insensitive pricing may lead to “unfair” access to credit.
We are witnessing an increasing use of data-driven predictive models to inform decisions in high-stakes situations, from lending and hiring to university admissions. As decisions have implications for individuals and society, there is increasing pressure on decision makers to be transparent about their decision policies. At the same time, individuals may use knowledge, gained by transparency, to invest effort strategically in order to maximize their chances of receiving a beneficial decision. Our goal is to find decision policies that are optimal in terms of utility in such a strategic setting. First, we characterize how strategic investment of effort by individuals leads to a change in the feature distribution. Using this characterization, we show that, in general, optimal decision policies are hard to find in polynomial time, and there are cases in which deterministic policies are suboptimal. Then, we demonstrate that, if the cost individuals pay to change their features satisfies a natural monotonicity assumption, we can narrow down the search for the optimal policy to a particular family of decision policies with a set of desirable properties, which allow for a highly effective polynomial time-heuristic search algorithm using dynamic programming. Finally, under no assumptions on the cost, we develop an iterative search algorithm that is guaranteed to converge to locally optimal decision policies. Experiments on synthetic and real credit card data illustrate our theoretical findings and show that the decision policies found by our algorithms achieve higher utility than those that do not account for strategic behavior.
AT&T was the largest U.S. firm for most of the 20th century. Telephone operators once comprised more than 50% of its workforce, but in the late 1910s, it initiated a decades-long process of automating telephone operation with mechanical call switching—a technology invented in the 1880s. We study what drove AT&T to do so and why it took nearly a century. Interdependencies between call switching and nearly every other activity in AT&T’s business presented obstacles to change: Telephone operators were the fulcrum of a complex production system that had developed around them, and automation only began after the firm and new technology were adapted to work together. Even then, automatic switching was only profitable in larger markets—hence, diffusion expanded when the technology improved or service areas grew. The example suggests even narrowly defined tasks can be difficult to automate if they interact with many others.
People often need to predict the outcomes of future events. We investigate the influence of order on such forecasts. Six preregistered studies (n = 7,955) show that people are more likely to forecast improbable outcomes (e.g., that an “underdog” will win a game) for predictions they make later versus earlier within a sequence of multiple predictions. This effect generalizes across several contexts and persists when participants are able to revise their predictions as well as when they are incentivized to make correct predictions. We propose that this effect is driven by people’s assumption that improbable outcomes are bound to occur at some point within small sets of independent events (i.e., “belief in the law of small numbers”). Accordingly, we find that the effect is attenuated when the statistical independence of events is made salient to forecasters both through the nature of the predictions themselves (i.e., when the events are from distinct domains) and through directly informing them about statistical independence. These findings have notable practical implications, as policy makers and businesses have the ability to control the order in which people evaluate and predict future events.
We provide firm-level evidence on the real effects of shadow banking in terms of technological innovation. Firm-to-firm entrusted loans, the largest part of the shadow banking sector in China, enhance the borrowers’ innovation output. The effects are more prominent when the borrowers are subject to severer financial constraints, information asymmetry, and takeover exposures. A plausible underlying channel is capital reallocations from less productive but easily financed lender firms to more innovative but financially less privileged borrower firms. Our paper suggests that shadow banking helps correct bank credit misallocations and thus, serves as a second-best market design in financing the real economy.
This paper tests the efficiency of a set of sports betting markets using detailed betting line movement from opening until closing for four different sportsbooks for each of 3,681 Major League Baseball games. The reliability of the markets’ forecasts are assessed at several lead times. The forecasts are mostly reliable, but there are simple betting strategies that would have yielded significant profit. In addition, forecasts do not always improve monotonically as the games get closer, despite more information being available; forecasts at weekend day games’ start times are significantly worse than forecasts 90 minutes earlier. Furthermore, analysis of the sequences of forecasts within individual games reveals that these betting markets do not incorporate information optimally. There is sufficient evidence to reject weak form market efficiency; specifically, betting lines tend to overreact, exhibiting significant negatively autocorrelated changes that could be exploited by sophisticated bettors.
Project leaders are responsible for planning, controlling, and revising projects. As a project unfolds, the leader evaluates the project’s progress by comparing ongoing costs and scope to a baseline plan and considers potential revisions. We offer a general model of managerial mental accounting, which includes loss aversion, reference point updating, and narrow framing, and examine how it impacts downstream decisions. Our model predicts insufficient adjustments of project scope and cost at revision, resulting in reduced financial profit. We show that the choice of measure to quantify the project progress—planned, actual, or earned—affects the updating of reference points, and hence the downstream decisions. Thus, progress measures could be wisely employed to mitigate insufficient adjustments. It turns out that measuring progress via planned scope is often advantageous, whereas utilizing earned value for cost is never advisable.
Workers in knowledge-intensive industries often complain of having too many meetings, but organizations still give little thought to deciding when or how often to meet. We investigate the efficiency and robustness of various coordination scheduling rules. We consider workers who are engaged in a common activity (e.g., software programming) that can be divided into largely independent, parallel production tasks, but that necessitates periodic coordination. Coordination enables workers to address issues they have encountered in their independent work but takes time away from production. Using a stylized game-theoretic model, we show that small teams allow a more fluid, that is, worker-driven, approach to scheduling coordination, such as preemptive coordination (or production), under which any worker can impose coordination (or production). In larger teams this becomes inefficient. Several approaches can mitigate this effect. One option is to allocate the decision rights to produce or coordinate to the most productive worker. A more general version is to implement a voting-based scheme, where a minimum number of workers from a predetermined subset choose to coordinate. A third approach is to modify the preemptive coordination and production rules by adding time-based controls, to reserve some minimal amount of productive time or to enforce coordination after some point. Finally, a fixed-interval meeting schedule works well for very large teams. Our research helps formalize the tension between meeting (coordinating) and producing and indicates how to adapt team coordination scheduling rules to the degree of worker heterogeneity and team size.
Do consumers search the brands they know more or less frequently than the brands with which they are unfamiliar? In this paper, we attempt to answer this question using data from an experiment with two novel features: (i) survey information on consumers’ prior brand ownership, familiarity with each brand, and prior experience using different product features and (ii) eye-tracking data capturing search behavior at a very granular level. We find that consumers are generally more likely to search and buy brands they own and with which they are familiar, highlighting the importance of accounting for prior information. For this reason, we develop a search model in which both the information obtained during the search process and the information possessed by consumers prior to search are allowed to influence search and purchase decisions. Our model contributes to prior work by modeling search at the brand and attribute levels within a Bayesian learning framework. Using this model, we then quantify the impact of prior information on consumer choices as well as document the estimation bias arising when prior information is absent from the model. Finally, through a series of counterfactuals, we explore the managerial value of prior information data.
In this paper, we examine the usefulness of time commitment as a voting resource for decentralized governance when the identity of voters cannot be verified. In order to do so, we take a closer look at two issues that confront token-based voting systems used by blockchain communities and organizations: voter fraud through the creation of multiple identities (Sybil attack) and concentration of voting power in the hands of the wealthy (plutocracy). Our contribution is threefold: first, we lay analytical foundations for the formal modeling of the necessary and sufficient conditions for a voting system to be resistant to a Sybil attack; second, we show that tokens as the only instrument for weighting votes cannot simultaneously achieve resistance to both Sybil attacks and a plutocracy in the voting process; and third, we design a voting mechanism, bond voting, that is Sybil resistant and offers a second instrument (time commitment) that is effective for countering plutocracy when large token holders also have a relatively high opportunity cost of locking tokens for a vote. Overall, our paper emphasizes the importance of time-based suffrage in decentralized governance.
Using a real-effort experiment, we show that people project their current tastes onto others when forecasting others’ behavior, even when their tastes are exogenously manipulated and transparently different. In the first part of our experiment, “workers” stated their willingness to continue working on a tedious task. We varied how many initial tasks workers completed before eliciting their willingness to work (WTW); some were relatively fresh when stating their WTW, whereas others were relatively tired. Later, a separate group of “predictors”—who also worked on the task—guessed the WTW of workers in each state. We find: (i) tired workers were less willing to work than fresh workers; (ii) predictors (in aggregate) accurately guessed the WTW of workers when they were in the same state as the workers about whom they were predicting; but (iii) when fresh predictors were guessing about tired workers, they substantially overestimated their WTW; and (iv) when tired predictors were guessing about fresh workers, they underestimated their WTW. Using an additional treatment, we find that workers also mispredicted their own future WTW, and we compare the magnitudes of intrapersonal and interpersonal projection bias.
I develop two empirical models to quantify agency friction caused by moral hazard in top management teams. They capture shareholders’ distinct perspectives on compensating top managers: the Individual Model, in which managers in a firm unilaterally shirk, and the Team Model, in which managers choose effort jointly. The Team Model rationalizes observed compensation better than the Individual Model, underlining the importance of managerial coordination and team-based incentives. This paper also offers novel estimates of agency friction in a team production setting. Risk premium explains a large portion of the pay differential across and within firms. Firm value would have dropped between 4% and 15% if managers shirked. Additional counterfactual estimation suggests that shareholders could reduce the total compensation by up to $17 million if they were to switch from the individual perspective to the team perspective.
We study indefinitely lived assets in experimental markets and find that the traded prices of these assets are on average about 40% of the risk-neutral fundamental value. Neither uncertainty about the value of total dividend payments nor horizon uncertainty about the duration of trade can account for this low traded price. An Epstein-Zin recursive preference specification that models the dynamic realization of dividend payments, combined with either probability weighting or subjects’ heterogeneous risk attitudes, can rationalize the low traded prices observed in our indefinitely lived asset market.
Cyber risk is an important emerging source of risk in the economy. To estimate its impact on the asset market, we use machine learning techniques to develop a firm-level measure of cyber risk. The measure aggregates information from a rich set of firm characteristics and shows superior ability to forecast future cyberattacks on individual firms. We find that firms with higher cyber risk earn higher average stock returns. When these firms underperform, cybersecurity experts tend to have higher concerns about cyber risk, and cybersecurity exchange-traded funds outperform. Further tests strengthen the identification of the cyber risk premium.
As the prevalence of user-generated reviews has been growing, the pervasiveness of fraudulent reviews has been increasing as well. In an effort to alleviate the consequences of fraudulent reviews, platforms have been using machine-learning algorithms for fraudulent review detection. However, the current business practice of simply removing fraudulent reviews might not be sufficient, as even their temporary presence might forge spillover effects propagating through other shopping tools. In particular, we examine and discover the persistence of long-lasting significant adverse impact of fraudulent reviews through their propagation to recommender systems, even long after successfully detecting and removing all fraud incidents. We conduct additional analyses further examining the intensity and evolution of the spillover effect over time across different dimensions, such as the cost of the fraudulent activity, the effectiveness and timeliness of the detection algorithms, and the quality of products. The results illustrate the gravity of the identified effect and highlight that the current business practice of just removing the fraudulent reviews is insufficient in the presence of recommender systems, albeit potentially sufficient in their absence in the long run. Such findings are timely as they might inform current regulatory and policy discussions. Finally, we also design a simple remedy that can be easily introduced to collaborative filtering algorithms to debias the recommender systems and demonstrate its effectiveness in alleviating the impact of the identified spillover effect.
This study tests the rationality of the decisions to purchase information, the informational efficiency of prices, and the optimality of the resulting allocations in decentralized markets with a series of laboratory experiments. The theory predicts that markets with dispersed information and natural buyers and sellers converge to a fully revealing equilibrium. It is profitable to pay for information, and as such, the Grossman-Stiglitz paradox does not emerge. Statistically significant improvements in both price efficiency and allocative efficiency are documented across trading periods. In contrast to the theory, which predicts that the higher the number of informed agents, the stronger the individual incentives to invest in information, the and the more data present the opposite result. The price of information is higher when there are fewer informed agents. The result can be explained by boundedly rational decision making.
In several applications of the stochastic multiarmed bandit problem, the traditional objective of maximizing the expected total reward can be inappropriate. In this paper, we study a new objective in the classic setup. Given K arms, instead of maximizing the expected total reward from T pulls (the traditional “sum” objective), we consider the vector of total rewards earned from each of the K arms at the end of T pulls and aim to maximize the expected highest total reward across arms (the “max” objective). For this objective, we show that any policy must incur an instance-dependent asymptotic regret of Ω(log T) (with a higher instance-dependent constant compared with the traditional objective) and a worst case regret of Ω(K1/3T2/3). We then design an adaptive explore-then-commit policy featuring exploration based on appropriately tuned confidence bounds on the mean reward and an adaptive stopping criterion, which adapts to the problem difficulty and simultaneously achieves these bounds (up to logarithmic factors). We then generalize our algorithmic insights to the problem of maximizing the expected value of the average total reward of the top m arms with the highest total rewards. Our numerical experiments demonstrate the efficacy of our policies compared with several natural alternatives in practical parameter regimes. We discuss applications of these new objectives to the problem of conditioning an adequate supply of value-providing market entities (workers/sellers/service providers) in online platforms and marketplaces.
We use the internal trading records of a major Bitcoin exchange leaked by hackers to detect and characterize wash trading—a type of market manipulation in which a single trader clears the trader’s own limit orders to “cook” transaction records. Our finding provides direct evidence for the widely suspected “fake volume” allegation against cryptocurrency exchanges, which has so far only been backed by indirect estimation. We then use our direct evidence to evaluate various indirect techniques for detecting the presence of wash trades and find measures based on Benford’s law, trade size clustering, lognormal distributions, and structural breaks to be useful, whereas ones based on power law tail distributions to give opposite conclusions. We also provide suggestions to effectively apply various indirect estimation techniques.
Exploration is often necessary in online learning to maximize long-term rewards, but it comes at the cost of short-term “regret.” We study how this cost of exploration is shared across multiple groups. For example, in a clinical trial setting, patients who are assigned a suboptimal treatment effectively incur the cost of exploration. When patients are associated with natural groups on the basis of, say, race or age, it is natural to ask whether the cost of exploration borne by any single group is “fair.” So motivated, we introduce the “grouped” bandit model. We leverage the theory of axiomatic bargaining, and the Nash bargaining solution in particular, to formalize what might constitute a fair division of the cost of exploration across groups. On one hand, we show that any regret-optimal policy strikingly results in the least fair outcome: such policies will perversely leverage the most “disadvantaged” groups when they can. More constructively, we derive policies that are optimally fair and simultaneously enjoy a small “price of fairness.” We illustrate the relative merits of our algorithmic framework with a case study on contextual bandits for warfarin dosing where we are concerned with the cost of exploration across multiple races and age groups.
Do pharmacy benefit managers (PBMs) reduce spending on prescription drugs? Reduced-form evidence suggests that PBMs enforce a tradeoff between net-of-rebate prices and access to drugs within each market. However, net-of-rebate prices grow consistently over time and appear unresponsive to competitor entry. We argue that inertia in drug demand can reconcile these facts. To formally analyze the roles played by PBMs and demand inertia, we build a dynamic structural model of drug pricing and estimate it using net-of-rebate prices of three major statins from 1996 to 2013. Counterfactuals suggest that, relative to a market with price-setting by drug manufacturers and patients who face coinsurance, PBMs reduce overall spending by 28%, without greatly limiting patient access. Without demand inertia, the presence of PBMs would cause prices to fall significantly as competitors enter.
How much can we learn finance directly from text data? This paper presents a new framework for learning textual data based on the factor augmentation model and sparsity regularization, called the factor-augmented regularized model for prediction (FarmPredict), to let machines learn financial returns directly from news. FarmPredict allows the model itself to extract information directly from articles without predefined information, such as dictionaries or pretrained models as in most studies. Using unsupervised learned factors to augment the predictors would benefit our method with a “double-robust” feature: that the machine would learn to balance between individual words or text factors/topics. It also avoids the information loss of factor regression in dimensionality reduction. We apply our model to the Chinese stock market with a large proportion of retail investors by using Chinese news data to predict financial returns. We show that positive sentiments scored by our FarmPredict approach from news generate on average 83 basic points (bps) stock daily excess returns, and negative news has an adverse impact of 26 bps on the days of news announcements, where both effects can last for a few days. This asymmetric effect aligns well with the short-sale constraints in the Chinese equity market. The result shows that the machine-learned prediction does provide sizeable predictive power with an annualized return of 54% at most with a simple investment strategy. Compared with other statistical and machine learning methods, FarmPredict significantly outperforms them on model prediction and portfolio performance. Our study demonstrates the far-reaching potential of using machines to learn text data.
Given an input graph Gin=(V,Ein), we consider the problem of designing a sparse subgraph G=(V,E) with E⊆Ein that supports a large matching after some nodes in V are randomly deleted. We study four families of sparse graph designs (namely, clusters, rings, chains, and Erdős–Rényi graphs) and show both theoretically and numerically that their performance is close to the optimal one achieved by a complete graph. Our interest in the stochastic sparse graph design problem is primarily motivated by a collaboration with a leading e-commerce retailer in the context of its middle-mile delivery operations. We test our theoretical results using real data from our industry partner and conclude that adding a little flexibility to the routing network can significantly reduce transportation costs.
Standard models of decision making under risk and uncertainty are deterministic. Inconsistencies in choices are accommodated by separate error models. The combination of decision model and error model, however, is arbitrary. Here, I derive a model of decision making under uncertainty in which choice options are mentally encoded by noisy signals, which are optimally decoded by Bayesian combination with preexisting information. The model predicts diminishing sensitivity toward both likelihoods and rewards, thus providing cognitive microfoundations for the patterns documented in the prospect theory literature. The model is, however, inherently stochastic, so that choices and noise are determined by the same underlying parameters. This results in several novel predictions, which I test on one existing data set and in two new experiments.
Nested simulation concerns estimating functionals of a conditional expectation via simulation. In this paper, we propose a new method based on kernel ridge regression to exploit the smoothness of the conditional expectation as a function of the multidimensional conditioning variable. Asymptotic analysis shows that the proposed method can effectively alleviate the curse of dimensionality on the convergence rate as the simulation budget increases, provided that the conditional expectation is sufficiently smooth. The smoothness bridges the gap between the cubic root convergence rate (that is, the optimal rate for the standard nested simulation) and the square root convergence rate (that is, the canonical rate for the standard Monte Carlo simulation). We demonstrate the performance of the proposed method via numerical examples from portfolio risk management and input uncertainty quantification.
We use the spread of COVID-19 in Italy, the first Western country hit by the pandemic, to investigate the role of structured management practices in responding to a large shock. We exploit a survey eliciting expected sales growth for 2020 to set up a difference-in-difference analysis with repeated cross sections, leveraging the fact that the data collection began prior to the pandemic and continued throughout its spread. We find a sizable effect of such practices on firm performance: a one-standard-deviation increase in the management score increases expected sales growth by 2.3%, against an average drop of 8.3%. Results are confirmed with actual sales growth. Firms with more structured practices were more likely to implement a comprehensive set of changes, including a more intense use of remote work.
In recent years, there has been an increasing trend in supply chains to employ a “control tower” approach to improve supply chain performance. One such strategy is customer-managed inventory (CMI) in which the customer (the downstream party) controls the inventory of the supplier (the upstream party). However, the optimal design of the inventory control policy and the required incentive structure for CMI has not been fully explored in the related literature. In this paper, we develop a two-echelon model consisting of a supplier and a customer that captures the interaction between both decision makers. Analysis of this model indicates that achieving the potential benefits of CMI requires suitable incentive mechanisms to be put in place. The two-echelon inventory system model, however, does not give closed-form solutions, which are required to generate an optimal solution to the inventory control and incentive problem. To address this challenge, we propose an approximate model, which decouples the two-echelon model into two newsvendor-type models with each party operating a single-echelon system. This approximate model requires the introduction of additional cost parameters so that the decoupled model captures the cost implications of supplier shortages in the original two-echelon model. By developing appropriate values for these cost parameters, the approximate model yields near optimal results. Analysis of the solution reveals important operational factors that determine which environments are conducive for CMI and conditions for Pareto improvement of all stakeholders.
Smartphones have enabled the delivery of life-improving information services to base-of-the-pyramid (BOP) consumers. However, little is known about how the poor interact with the digital world. Through a novel app we developed to investigate real-time smartphone usage, we identify an unnoticed barrier to digital information access by the poor—data shortages. By analyzing over 9.4 million minutes of smartphone usage data from 929 residents of a Mumbai settlement, we find that entertainment consumes 61% of their phone time. Our data reveal that under universally adopted monthly data plans, low-income individuals binge on YouTube and social media, resulting in data shortages and information isolation in the late-plan period. We offer a practical operational solution to this problem—shorter data-replenishment cycles—which serve as a commitment device to curb binge usage. We randomly assign participants to a “capped plan”—with daily data usage caps—or a standard (monthly) plan. Assignment to the capped plan increases late-plan access of invites to health camps sent via WhatsApp, increases attendance at these in-person camps by 27%, and reduces social media binge usage. Most participants (particularly those with low self-control and high fear of missing out) prefer the capped plan, even when costlier—clearly signaling demand. Because capped plans are inherently cheaper to provide, offering them could enable providers to increase BOP customer value and expand access. Our results suggest an opportunity to amplify the impact of life-improving services targeted at the poor by leveraging users’ interactions with smartphone technology.
Since the launch of ChatGPT in December 2022, large language models (LLMs) have been rapidly adopted by businesses to assist users in a wide range of open-ended tasks, including creative work. Although the versatility of LLM has unlocked new ways of human-artificial intelligence collaboration, it remains uncertain how LLMs should be used to enhance business outcomes. To examine the effects of human-LLM collaboration on business outcomes, we conducted an experiment where we tasked expert and nonexpert users to write an ad copy with and without the assistance of LLMs. Here, we investigate and compare two ways of working with LLMs: (1) using LLMs as “ghostwriters,” which assume the main role of the content generation task, and (2) using LLMs as “sounding boards” to provide feedback on human-created content. We measure the quality of the ads using the number of clicks generated by the created ads on major social media platforms. Our results show that different collaboration modalities can result in very different outcomes for different user types. Using LLMs as sounding boards enhances the quality of the resultant ad copies for nonexperts. However, using LLMs as ghostwriters did not provide significant benefits and is, in fact, detrimental to expert users. We rely on textual analyses to understand the mechanisms, and we learned that using LLMs as ghostwriters produces an anchoring effect, which leads to lower-quality ads. On the other hand, using LLMs as sounding boards helped nonexperts achieve ad content with low semantic divergence to content produced by experts, thereby closing the gap between the two types of users.
In this rejoinder, we address the concern about equilibrium existence in our previous work published in 2013. By making the simple adjustment of discretizing inventory ordering amounts to prevent inventory orders in arbitrarily small increments, we show that the results from that paper hold, thus maintaining the robustness of the insights obtained.
We build on recent studies on daughter-to-father influence to explore how male founders’ fatherhood of daughters impacts female representation in their ventures. We find that, conditional on the total number of children, fathering an additional daughter versus a son is associated with a 4% (11%) increase in female director (employee) representation. This daughter-to-father effect gradually matures as daughters grow up and socialize in schools and workplaces, and it increases as daughters age, suggesting that male founders vicariously learn from their daughters about the constraints women face throughout the daughters’ life cycles. Heterogeneity analyses (regarding founder cohort, divorce status, and social class), combined with qualitative evidence, further substantiate the plausibility of vicarious learning as a potential yet understudied mechanism underlying daughter effects. In addition, daughter effects on employee recruitment are concentrated in microbusinesses (number of employees is ≤10) where the founder is close in decision authority to all employees. These findings add important nuances to our understanding of daughter effects in organizational contexts and extend theory of gender homophily in organizations.
We measure the ability of professional investment managers in timing cashflow versus discount-rate news, the two components of market returns. We find that the average U.S. equity mutual fund exhibits cashflow-timing skills of 1.77%/year, but discount-rate timing of −0.87%/year; furthermore, cashflow-timing skills, but not discount-rate timing skills, strongly persist over future quarters. Our evidence indicates that misspecification of market-timing abilities accounts for the failure of prior research to locate talented timing funds. Importantly, we find that value funds outperform growth funds in timing cashflow news, which provides new evidence on the unique skills of value-oriented mutual funds.
We use deep neural networks to estimate an asset pricing model for individual stock returns that takes advantage of the vast amount of conditioning information, keeps a fully flexible form, and accounts for time variation. The key innovations are to use the fundamental no-arbitrage condition as criterion function to construct the most informative test assets with an adversarial approach and to extract the states of the economy from many macroeconomic time series. Our asset pricing model outperforms out-of-sample all benchmark approaches in terms of Sharpe ratio, explained variation, and pricing errors and identifies the key factors that drive asset prices.
This study investigates whether investors can reap economic benefits from analyzing differences in analyst quality. Although high-quality analysts’ average forecast is more accurate than the consensus forecast for firms with a large analyst following, the benefits of using high-quality analysts’ average forecasts are not economically significant. In contrast, the value of analyst quality differentiation exists in the second moment of forecasts. High-quality analysts’ forecast dispersion gives investors an advantage in dealing with uncertainty by predicting return volatility and providing opportunities for economically significant returns using option straddle and post-earnings announcement drift investment strategies.
Exploiting the staggered arrival of fiber-optic submarine cables, we show that high-speed internet promotes the role of banks and credit in Africa. Variation within country and across multicountry bank networks indicates that high-speed internet induced a 22% expansion in credit supply. We investigate the role of plummeting telecommunication costs in promoting the bank adoption of new financial technologies and study a specific technology used in the interbank market, the real-time gross settlement system (RTGS). We find that upon connecting to high-speed internet, banks adopt the RTGS more extensively, reduce inside liquidity, and increase interbank transactions and lending. We also observe that high-speed internet particularly strengthens firms in countries with weak preexisting interbank markets.
Existing studies suggest that platform access restriction may cause restricted complementors to switch to competing platforms, which will increase complement quantity on competing platforms. We re-examine this prediction by accounting for the impact of economies of scope on complementor responses to platform access restriction. We argue that restricting a complementor’s access on a platform may prevent it from achieving economies of scope from multi-homing, thereby incentivizing it to abandon both the restricted and (unrestricted) competing platforms. Using rideshare data in New York City, we compare the numbers of trips made by Lyft and Uber drivers, respectively, before and after Lyft restricted drivers’ access on its platform. We find that Lyft’s access restriction reduced trip numbers not only on Lyft but also on Uber. In addition, both Lyft’s and Uber’s trip numbers decreased not only during the restricted low-demand periods (e.g., non-rush hours) but also during the unrestricted high-demand periods (e.g., rush hours). In contrast, after a substantial number of multi-homing drivers left both platforms following Lyft’s access restriction, a subsequent access restriction by Uber led to an increase in trip numbers on Lyft. These results highlight the importance of accounting for interdependencies across complementor activities when designing platform governance policies.
Tracking the movement of top managers across firms, we document the importance of manager-specific fixed effects in explaining heterogeneity in firm exposures to systematic risk. In equilibrium, manager fixed effects on systematic risk are positively related with manager fixed effects on stock returns. These differences in systematic risk are partially explained by managers’ corporate strategies, such as their preferences for internal growth and financial conservatism. The early career experiences of managers starting their first job in a recession also contribute to differential loadings on systematic risk. These effects are more pronounced when managers wield more influence, as in smaller firms and firms that do not have an independent board. Overall, our results suggest that managers play an important role in shaping a firm’s systematic risk.
We document that regulatory enforcement actions for financial misrepresentation cluster in industry-specific waves and that wave-related enforcement has information spillovers on industry peer firms. Waves and spillovers have significant effects on share prices. Early-wave target firms have the largest short-run losses in share values and the largest information spillovers on industry peer firms. Late-wave targets’ short-run losses are smaller, but not because they involve less costly instances of misconduct. Rather, late-wave targets are subject to more information spillovers from earlier in the wave. These results indicate that prices incorporate changes in the likelihood that a firm will face wave-related enforcement action for financial misconduct. Short-window share-price losses understate the total share-price impact, particularly for firms whose financial misrepresentation is revealed late in an enforcement wave.
Closed-end funds are thought to have negligible fire sale risk as they have stable funding. However, I show that embedded covenants can generate price pressure in collateralized loan obligation (CLO) funds, even though such funds are closed end. Loans held by constrained CLOs report significantly lower cumulative returns than loans held by unconstrained CLOs. This can be explained by contractual arbitrage, a practice by which CLOs exploit loopholes in the design of covenants to mechanically loosen their covenants and avoid covenant breaches. Covenant breaches are associated with significant pecuniary and nonpecuniary costs, affecting CLO compensation, reputation, and career prospects. I show that when covenants breaches are imminent, managers fire sell distressed loans. Hence, I demonstrate a channel by which closed-end funds can also create fire sale risk, akin to their open-end counterparts.
Unemployment risk influences workers’ incentives to invest in firm-specific human capital. This paper investigates the impact of unemployment risk on chief executive officers (CEOs)’ risk-taking incentive compensation. Exploiting state-level changes in unemployment benefits, we find that after unemployment insurance benefits become more generous, boards increase the convex payoff structure of CEO pay to encourage risk taking. The increase in CEOs’ convexity payoff is stronger in firms with more independent and diverse boards, higher ownership of long-term shareholders, and in industries requiring highly skilled labor. Our findings suggest that boards internalize workers’ interests in firms’ risk-taking decisions and executive compensation is one mechanism used.
Highly skilled workers are not only a crucial and relatively scarce input into firms’ productive and innovative processes, but are also a critical resource determining competitive advantage. An increasingly high proportion of these workers in the United States were born abroad and permitted to work on skilled worker visas. How do multinational firms respond when artificial constraints, namely, policies restricting skilled immigration, are placed on their ability to hire scarce human capital? This paper combines visa microdata and comprehensive data on U.S. multinational firm activity to demonstrate that firms respond to restrictions on H-1B immigration by increasing foreign affiliate employment at the intensive and extensive margins, particularly in China, India, and Canada. The most impacted jobs were R&D-intensive ones, but there is some evidence that non-R&D employment was also affected. The paper highlights a means by which firms can circumvent constraining policies and mitigate country-level risk, and it also suggests that, for the average multinational company (MNC), this means is imperfect; for every visa rejection, they hire 0.4 employees abroad. The most globalized MNCs are the most likely to respond to these restrictions by offshoring, highlighting that firm capabilities—in the form of prior internationalization—shape the decision and ability to offshore in response to skilled immigration restrictions; indeed, these firms hire 0.9 employees abroad for every visa rejection. More broadly, the paper provides evidence of a push factor for internationalizing knowledge activity: artificial constraints on resources result in firms circumventing restrictive policies in ways that may not be anticipated by policy makers.
This paper provides empirical evidence on predictable time variations in out-of-sample bond return predictability. Bond return predictability is associated with periods of high (low) economic activity (uncertainty), which implies that violations of the expectations hypothesis are state dependent and linked to features of the business cycle. These state dependencies in predictability, established by introducing a new multivariate test for equal conditional predictive ability, can be used in real time to improve out-of-sample bond risk premia estimates and investors’ economic utility through a novel dynamic forecast combination scheme that uses predicted forecasting performance to identify the best set of methods to include in the combined forecast. Dynamically combined forecasts exhibit strong countercyclical behavior and peak during recessions.
There have been several cases in recent years where credit default swap (CDS) buyers and sellers intervene in the restructuring of a distressed firm. We show theoretically that this can increase firm value. Intervention by CDS buyers solves the commitment problem between equity and debt holders but increases the probability of inefficient liquidation. Intervention by CDS sellers reduces the issue of excessive liquidation while keeping the benefits of CDS buyer intervention. Having both types of intervention decouples the commitment problem from the liquidation problem. Under certain assumptions, the so-called empty creditor problem can be solved, and firm value reaches first best.
We investigate the potential and limits of privacy-preserving corporate blockchain applications for information provision. We provide a theoretical model in which heterogeneous firms choose between adopting a blockchain application or relying on traditional third-party intermediaries to inform the capital market. The blockchain’s ability to generate information depends on each firm’s data profile and all firms’ endogenous adoption decisions. We show that blockchain technology can improve the information environment and outperform traditional institutions with firms’ adoption decisions serving as a credible value signal and the application uncovering firm values by analyzing all participating firms’ data. However, we also characterize an adverse mixed-adoption equilibrium in which neither of the two channels realizes its full potential and information provision declines not only for individual firms, but also in aggregate. The equilibrium is a warning sign that has broad implications for policymakers’ regulatory effort and investors’ assessment of corporate blockchain applications.
In most platform environments, the exclusive provision of premium content from leading creators (superstars) is used as a strategy to boost user participation and secure a competitive edge vis-à-vis rivals. In this article, we study the impact of superstar exclusive content provision on platform competition and complementors’ homing decisions. Two competing platforms facilitate interactions between consumers and suppliers, of which the latter are identified by the superstar and a fringe of complementors (e.g., independent developers, amateurs). When platform competition is intense, more consumers become affiliated with the platform favored by superstar exclusivity. This mechanism is self-reinforcing as it generates an entry cascade of complementors, and some complementors single home on the favored platform. We find that cross-group externalities are key in shaping market outcomes. First, exclusivity benefits complementors and might make consumers better off when cross-group externalities are large enough. Second, contrary to conventional wisdom, vertical integration (platform-superstar) may make exclusivity less likely than vertical separation under reasonable conditions. Finally, we discuss implications for the strategies of platform owners, managers of superstars and complementors, and antitrust enforcers.
This paper studies an emerging subscription model called ship-then-shop. Leveraging its predictive analytics and artificial intelligence (AI) capability, the ship-then-shop firm curates and ships a product to the consumer, after which the consumer shops (i.e., evaluates product fit and makes a purchase decision). The consumer first pays the up-front ship-then-shop subscription fee prior to observing product fit and then pays the product price afterward if the consumer decides to purchase. We investigate how the firm balances the subscription fee and product price to maximize its profit when consumers can showroom. A key finding is the ship-then-shop firm’s nonmonotonic surplus extraction strategy with respect to its prediction capability. As prediction capability increases, the firm first switches from ex ante to ex post surplus extraction (by lowering fees and raising prices). However, if the prediction capability increases further, the firm reverts to ex ante surplus extraction (by raising fees and capping prices). We also find that the ship-then-shop model is most profitable when (i) the prediction capability is advanced, (ii) the search friction in the market is large, or (iii) the product match potential is large. Finally, we show that the marginal return of AI capability on the firm’s profit decreases in search friction but increases in product match potential. Taken together, we provide managerially relevant insights to help guide the implementation of the innovative subscription model.
Through repeated interactions, firms today refine their understanding of individual users’ preferences adaptively for personalization. In this paper, we use a continuous-time bandit model to analyze firms that recommend content to multihoming consumers, a representative setting for strategic learning of consumer preferences to maximize lifetime value. In both monopoly and duopoly settings, we compare a forward-looking recommendation algorithm that balances exploration and exploitation to a myopic algorithm that only maximizes the quality of the next recommendation. Our analysis shows that, compared with a monopoly, firms competing for users’ attention focus more on exploitation than exploration. When users are impatient, competition decreases the return from developing a forward-looking algorithm. In contrast, development of a forward-looking algorithm may hurt users under monopoly but always benefits users under competition. Competing firms’ decisions to invest in a forward-looking algorithm can create a prisoner’s dilemma. Our results have implications for artificial intelligence adoption and for policy makers on the effect of market power on innovation and consumer welfare.
We examine how asymmetric information in financial markets affects voluntary research and development (R&D) disclosure, considering scientific publications as a disclosure channel. Difference-in-differences regressions around brokerage house mergers and closures, which increase information asymmetry through reductions in analyst coverage, indicate a quick and sustained increase in scientific publications from treated firms relative to the number of publications from control firms. The treatment effects are concentrated among firms with higher information asymmetry and lower investor demand, firms with greater financial constraints, and firms with lower proprietary costs. We do not find evidence of changes in financial disclosure, nor do we find changes in patenting. Results from ordinary least squares regressions show that scientific publications by firms are positively associated with investor attention toward those firms. We complement these results with qualitative evidence from conference calls. Our results highlight the limitations and trade-offs R&D firms face in their financial market disclosure policies.
On many multisided app platforms, the supply-side monetizes their work with targeted advertising. The targeting of ads has raised concerns over user privacy and has led to calls for platform firms and regulators to bar this practice. Important for this debate is to understand the consequences that a ban on targeted advertising would have for app development. To inform, we exploit that Google, in 2019, banned targeted advertising in Android children’s games. This setting represents an ideal real-world laboratory and permits a quasi-experimental research design. Our overall finding is that the ban on targeted advertising caused substantial app abandonment. The ban reduced the release of feature updates, particularly for games of young, undiversified, and advertisement-dependent firms. Only games of exceptionally high quality and demand showed an increase in development. Corroborating this picture, affected games were more likely to be delisted. Developers shifted their efforts toward their unaffected games and released fewer new games on average. Further tests substantiate that targeted advertising represented a crucial form of monetization for affected games and that the ban obliterated ad revenues used for app development. Our findings have several implications. To avoid a loss in app innovation, platform firms should consider implementing measures to reduce the burden on developers, especially by creating alternative monetization opportunities. Consumers and policymakers should be aware that targeted advertising plays a crucial role for app development and can use our estimates for designing policies. Thus, consumers’ demand for privacy can conflict with platform firms’ goal to foster app innovation.
Start-ups are increasingly using social media to signal quality and provide information to potential investors. However, the effectiveness of social media on venture capital (VC) financing is likely to be heterogeneous, differing by demographic and network characteristics of the founders. In this paper, we examine whether social media use can improve funding outcomes for firms founded by women and by other people also lacking connections to the investor network, two groups that face greater difficulties in securing VC financing. Using Twitter data and data on VC investment in start-ups from Crunchbase, we explore the interaction effect between Twitter usage and gender and between Twitter usage and the network constraint measure. Overall, we show that social media can mitigate some disparities in financing experienced by these firms through improving information access. We find that this effect is stronger for first-time entrepreneurs than for experienced ones, stronger for attracting new investors than repeat ones, and stronger in more competitive markets. Collectively, these results suggest that social media could primarily help women and less connected individuals obtain financing by alleviating information asymmetry between founders and investors.
Using a dataset on syndicated loan primary market pricing adjustments, we examine whether relationship banks’ information advantage facilitates price discovery in loan issuances. We find that the lead bank makes fewer adjustments to the initial pricing terms of a syndicated loan and shortens the syndication time when it has a stronger relationship with the borrower. A stronger relationship also reduces loan underpricing. A relationship lead bank relies less on information from syndicate members. Exogenous shocks to relationships caused by bank mergers and closures confirm our findings. We contribute to the literature by showing that relationship lending improves loan pricing efficiency.
Do people have well-defined social preferences waiting to be applied when making decisions? Or do they have to construct social decisions on the spot? If the latter, how are those decisions influenced by the way in which information is acquired and evaluated? These temporal dynamics are fundamental to understanding how people trade off selfishness and prosociality in organizations and societies. Here, we investigate how the temporal dynamics of the choice process shape social decisions in three studies using response times and mouse tracking. In the first study, participants made binary decisions in mini-dictator games with and without time constraints. Using mouse trajectories and a starting time drift diffusion model, we find that, regardless of time constraints, selfish participants were delayed in processing others’ payoffs, whereas the opposite was true for prosocial participants. The independent mouse trajectory and computational modeling analyses identified consistent measures of the delay between considering one’s own and others’ payoffs (self-onset delay, SOD). This measure correlated with individual differences in prosociality and predicted heterogeneous effects of time constraints on preferences. We confirmed these results in two additional studies, one a purely behavioral study in which participants made decisions by pressing computer keys, and the other a replication of the mouse-tracking study. Together, these results indicate that people preferentially process either self or others’ payoffs early in the choice process. The intrachoice dynamics are crucial in shaping social preferences and might be manipulated via nudge policies (e.g., manipulating the display order or saliency of self and others’ outcomes) for behavior in managerial or other contexts.
Market skewness risk is priced, but the components of its premium are not fully understood. We propose new trading strategies decomposing the skewness risk premium into jump and leverage effect components, and we analyze the skewness risk premia in the market for S&P 500 index options. We find that the skewness premium is higher when markets are closed than during trading hours, consistently with uncertainty resolution patterns by non-U.S investors; that it increases after left-tail market events; and that it is distinct from the variance premium. Moreover, during trading hours, the skewness premium is dominated by priced jump risk.
This paper evaluates liquidity requirements and public liquidity injections in the context of financial crises, using a model that includes near-money assets. Some key effects are transmitted through liquidity premia and the price of liquid assets, producing a heterogeneous impact that depends on an agent’s holdings of liquid assets. In addition, some agents prefer policies that do not fully relax their liquidity constraints because these policies are associated with higher prices. Liquidity requirements and liquidity injections have the opposite impact on liquidity premia and combining them leads to Pareto improvements that cannot be achieved by each policy separately.
We examine the extent to which the labor market facilitates the diffusion of tax-planning knowledge across firms. Using a novel data set of tax department employee movements between S&P 1500 firms, we find that firms experience an increase in their tax planning after hiring a tax employee from a tax-aggressive firm. This finding is robust to various research designs and specifications. Consistent with tax-planning knowledge driving the result, we find that the tax-planning benefits are more substantial when the employee is involved in a director-level role and has more experience. Further tests suggest that tax-planning knowledge is highly specific in nature: the increase in tax avoidance is larger when the hiring and former firms are similar (i.e., operating in the same sector or having similar foreign operations), and firms are more likely to hire tax department employees from firms with similar characteristics. Finally, we do not find that the prior firm’s tax planning changes after the employee leaves the firm, suggesting that the tax-planning knowledge simply spreads to the hiring firm and does not leave the prior firm. Our study documents the first order role of the labor market in the diffusion of tax-planning knowledge across firms, and our findings suggest that tax department human capital is a central determinant of tax-planning outcomes.
This paper proposes a methodology to price bonds jointly issued by a group of countries—Eurobonds in the euro-area context. We consider two types of bonds; the first is backed by several and joint guarantees (SJGs), and the second features several but not joint guarantees (SNJGs). The pricing of SJG and SNJG bonds reflects different assumptions regarding the pooling of debtors’ fiscal resources. We estimate fiscal limits for the six largest euro-area economies over 2008–2021 and deduce counterfactual Eurobond prices. For the five-year maturity, SNJG bond yield spreads would have been about three times larger than SJG ones over the estimation sample. Hence, issuing SJG bonds could result in gains at the aggregate level. Notwithstanding, our model also predicts that gains may temporarily vanish in periods of acute fiscal stress. We finally envision postissuance redistribution schemes, whereby gains stemming from the issuance of SJG bonds could be shared among participating countries; we argue that these schemes may alleviate the reduction in market discipline resulting from common bonds issuance.
Endogenous response time data are increasingly becoming available to applied researchers of economic choices. However, the usefulness of such data for preference estimation is unclear. Here, we adapt a sequential sampling model—previously validated to jointly explain subjects’ choices and response times in laboratory experiments—to model users’ responses to video advertisements on mobile devices in a field setting. Our estimates of utility correlate positively with out-of-sample measures of ad engagement, thus providing external validation of the value of incorporating endogenous response time information into a choice model. We then use the model estimates to assess the effectiveness of manipulating attention toward an advertisement at the beginning of a decision. Counterfactual simulations predict that making an ad “nonskippable” (requiring users to watch some portion of the ad)—as is the practice of some online platforms (e.g., YouTube)—generates only modest increases in click-through rates and revenue.
The rapid adoption of artificial intelligence (AI) technologies by many organizations has recently raised concerns that AI may eventually replace humans in certain tasks. In fact, when used in collaboration, machines can significantly enhance the complementary strengths of humans. Indeed, because of their immense computing power, machines can perform specific tasks with incredible accuracy. In contrast, human decision makers (DMs) are flexible and adaptive but constrained by their limited cognitive capacity. This paper investigates how machine-based predictions may affect the decision process and outcomes of a human DM. We study the impact of these predictions on decision accuracy, the propensity and nature of decision errors, and the DM’s cognitive efforts. To account for both flexibility and limited cognitive capacity, we model the human decision-making process in a rational inattention framework. In this setup, the machine provides the DM with accurate but sometimes incomplete information at no cognitive cost. We fully characterize the impact of machine input on the human decision process in this framework. We show that machine input always improves the overall accuracy of human decisions but may nonetheless increase the propensity of certain types of errors (such as false positives). The machine can also induce the human to exert more cognitive efforts, although its input is highly accurate. Interestingly, this happens when the DM is most cognitively constrained, for instance, because of time pressure or multitasking. Synthesizing these results, we pinpoint the decision environments in which human-machine collaboration is likely to be most beneficial.
One million international students study in the United States each year, and the majority of them compete in global labor markets after graduation. I conducted a large-scale field experiment and a companion employer survey to study how employers in China value U.S. college education. I sent more than 27,000 fictitious online applications to business and computer science jobs in China, randomizing the country of college education. I find that U.S.-educated applicants are on average 18% less likely to receive a callback than applicants educated in China, with applicants from very selective U.S. institutions underperforming those from the least selective Chinese institutions. The United States-China callback gap is smaller at high-wage jobs, consistent with employers fearing U.S.-educated applicants have better outside options and would be harder to hire and retain. The gap is also smaller at foreign-owned firms, consistent with Chinese-owned firms knowing less about American education. Controlling for high school quality, test scores, or U.S. work experiences does not attenuate the gap, suggesting that the gap is not driven by employer perceptions of negative selection. A survey of 507 hiring managers at college career fairs finds consistent and additional supporting evidence for the experimental findings.
We introduce a model of advice in which firms steer advisors through nonlinear incentive schemes. In addition to developing an isomorphism to pricing with mixed bundling, we obtain three main insights. First, firms optimally use nonlinear bonuses to economize on the rent paid to advisors. Second, equilibrium bonus payments induce advisors to make biased recommendations that are artificially contingent on each other, resulting in an inefficient allocation. Third, if advisor liability is stepped up, firms respond by increasing the size of the bonus, leaving advisor bias unchanged. These results shed light on prevailing compensation practices for advisors and support direct regulatory interference.
With the help of more than 700 reviewers, we assess the reproducibility of nearly 500 articles published in the journal Management Science before and after the introduction of a new Data and Code Disclosure policy in 2019. When considering only articles for which data accessibility and hardware and software requirements were not an obstacle for reviewers, the results of more than 95% of articles under the new disclosure policy could be fully or largely computationally reproduced. However, for 29% of articles, at least part of the data set was not accessible to the reviewer. Considering all articles in our sample reduces the share of reproduced articles to 68%. These figures represent a significant increase compared with the period before the introduction of the disclosure policy, where only 12% of articles voluntarily provided replication materials, of which 55% could be (largely) reproduced. Substantial heterogeneity in reproducibility rates across different fields is mainly driven by differences in data set accessibility. Other reasons for unsuccessful reproduction attempts include missing code, unresolvable code errors, weak or missing documentation, and software and hardware requirements and code complexity. Our findings highlight the importance of journal code and data disclosure policies and suggest potential avenues for enhancing their effectiveness.
This paper studies how competition affects the role of reputation in encouraging sellers to exert effort. More competition disciplines sellers, but at the same time, it erodes reputational premia. This paper identifies whether one effect dominates the other using data from Airbnb. I exploit the introduction of a short-term rental regulation effective in San Francisco in 2017 that halved the number of short-term listings on the platform. I focus on hosts who are present on the platform before and after the regulation, and I identify a negative causal effect of the number of competitors on ratings about hosts’ effort. I extend this result with two other measures of effort: hosts’ response rate and response time. I confirm that hosts exert less effort when the number of competitors increases. The rate of responses within 24 hours decreases, and response time increases.
Motivated by the Financial Accounting Standards Board’s project on the disaggregation of income statement expenses, we study a Korean rule change that allowed firms to withhold a previously mandated disaggregation of cost of sales (CoS). We find that after withholding, firms’ profitability increases by 1.6 percentage points. Our industry-focused results suggest that withholding affects profitability by reducing the transfer of competitive information to peer firms. We then document a range of evidence consistent with the idea that firms withhold disaggregated CoS to protect cost innovations from rivals. First, we construct a novel measure of firms’ cost-innovative potential and show that it predicts withholding and subsequent profitability gains under the voluntary disclosure regime. Second, we document efficiency gains following the withholding of disaggregated CoS. Third, our survey experiment of 1,257 U.S. public firm managers shows that they would reduce investments in process/cost innovations if they were required to disaggregate CoS. Our study highlights to standard setters and academics that CoS disaggregation entails operational consequences for firms.
In this paper, we solve a class of two-stage distributionally robust optimization problems that have the property of supermodularity. We exploit the explicit worst case expectation of supermodular functions and derive the worst case distribution for the robust counterpart. This enables us to develop an efficient method to obtain an exact optimal solution to these two-stage problems. Further, we provide a necessary and sufficient condition for checking whether any given two-stage optimization problem has the supermodularity property. We also investigate the optimality of the segregated affine decision rules when problems have the property of supermodularity. We apply this framework to several classic problems, including the multi-item newsvendor problem, the facility location problem, the lot-sizing problem on a network, the appointment-scheduling problem, and the assemble-to-order problem. Whereas these problems are typically computationally challenging, they can be solved efficiently under our assumptions. Finally, numerical examples are conducted to illustrate the effectiveness of our approach.
Financial companies often use disclosure statements (DSs) to categorize uncertain contingencies into different sections (e.g., a health insurance plan includes sections such as “hospital coverage” and “dental coverage”). We model DSs as information partitions of the state space, which influence how a consumer perceives her choice problem and how she makes her demand decisions. We study a consumer with weakly separable preferences, which allows us to define event-risk attitude to the DS’s categories. Our main results show that aggregating the more expensive events into a coarser DS decreases a firm’s profit if and only if the consumer is event-risk averse. The opposite result holds for event-risk loving. Our findings can be extended to a more general class of consumer preferences and provide insights into the optimal design of DSs.
This paper investigates the problem of information sharing between a retail platform and a manufacturer in a supply chain. We develop a stylized model salient to which is that the product’s quality is a priori unknown to customers, who can infer it from customer-generated reviews. The platform, in turn, has access to private information concerning the relationship between quality and demand, and the manufacturer can choose to acquire said information to help determine the quality of its product accordingly. Our analysis yields three main insights. First, information sharing in and of itself induces the manufacturer to improve quality. Second, under a wholesale price contract, information sharing and product reviews together have a negative effect on product quality: When each firm is able to adjust its price in response to the quality signal, it benefits the manufacturer and hinders the platform. Consequently, the presence of reviews discourages the platform from sharing information, and the manufacturer tends to produce a lower-quality product. Finally, the negative effect of product reviews on the supply chain can be mitigated when the platform can share less accurate information or when the platform and manufacturer make a commission contract, rather than a wholesale price contract.
Classic theories show that choices can be represented by a stable utility function when they satisfy consistency axioms, such as transitivity and the independence of irrelevant alternatives. Empirical choice data, however, display several contextual choice effects that violate these axioms. We study a choice model with a fixed underlying utility function and explain contextual choices with a novel type of information friction: the agent’s perception of the options is affected by attribute-specific uncertainty. Under this friction, the agent learns useful information when the agent sees more options. Therefore, the agent chooses contextually, exhibiting intransitivity, joint–separate evaluation reversal, the compromise effect, the phantom decoy effect, the attraction effect, and the similarity effect. Because the uncertainty is attribute-specific and common across alternatives, the classic axioms hold when the alternatives dominate one another in attributes.
We study the link between a previously neglected form of intangible firm asset—country reputation—and corporate sales. By exploiting variation in nationalities of foreign victims in local terror attacks, we detect unanticipated distortions in reputations of local countries in foreign countries and we pin down reductions in sales of local country firms in foreign markets. The reductions in sales are economically and statistically significant, persistent, and more pronounced after attacks with high levels of foreign media coverage. Local country firms, whose names resemble names from their countries of origin, experience greater deteriorations in their sales. The distortions in country reputations are associated with depreciations in overall firm value, sales growth, and profitability.
We investigate whether and through which channel credit rating downgrades induce corporate restructurings. For a comprehensive sample, we find a strong and robust link between rating actions and subsequent restructuring activity and hypothesize these could be disciplinary (ex post efficient) or meant to counter tighter financial constraints (ex post inefficient). Based on the self-reported restructuring types and the choice of assets sold, we find evidence for rating-induced asset sales aimed to relax financial constraints. We find no evidence of firms addressing inefficiencies in their assets allocations as a result of rating actions.
A key issue for the design of online marketplaces is addressing leakage. Buyers may use the marketplace to discover a seller or to obtain certain conveniences, but the seller may then want to take transactions off the marketplace to avoid transaction fees. Assuming buyers are heterogenous in their switching cost or inconvenience cost of purchasing directly, we provide a model in which there is partial leakage in equilibrium. We use the model to analyze the trade-offs associated with different strategies the marketplace can use to attenuate the effects of leakage: investing in transaction benefits, limiting communication, charging referral fees, using price-parity clauses, introducing seller competition on the marketplace, and hiding sellers that try to induce too much leakage.
We develop a dynamic portfolio choice model with illiquid alternative assets to analyze the “endowment model,” widely adopted by institutional investors, such as pension funds, university endowments, and sovereign wealth funds. In the model, the alternative asset has a lockup but can be liquidated at any time by paying a proportional cost. We model how investors can engage in liquidity diversification by investing in multiple illiquid alternative assets with staggered lockup expirations and show that doing so increases alternatives allocations and investor welfare. We show how illiquidity from lockups interacts with illiquidity from secondary market transaction costs resulting in endogenous and time-varying rebalancing boundaries. We extend the model to allow crisis states and show that increased illiquidity during crises causes holdings to deviate significantly from target allocations.
Becker and Murphy proposed that task specialization raises productivity but is limited by the costs of coordinating workers. We propose that automation enables workers to specialize without coordination costs. To the extent that the cost of effort exhibits increasing differences, workers increase effort in nonautomated tasks and productivity. The proposition is supported by a field experiment among supermarket cashiers. Conventionally, supermarket cashiers perform two tasks: scanning purchases and collecting payment. Cashiers exhibited increasing differences in the cost of effort: when they scanned faster, they took longer to collect payments. We rotated cashiers between the conventional job design and one in which they specialized in scanning. The new job design increased cashier productivity in scanning by more than 10%. The faster scanning was not due to customer sorting or cashier learning. The proposition is also validated by a survey of taxi drivers. Drivers who reported that difficulties in finding their way affected their driving were more likely to use map apps.
We study whether and how the availability of incremental time associated with retirement and unemployment affects the types of products households buy. For this, we develop a new theoretical model of the composition and size of the grocery shopping basket subject to money and time constraints. We construct a novel household panel data set that combines purchase records for grocery goods with information on labor market status and other demographics. We use the data to document that, in line with predictions from the model, consumers buy more varieties and generally shift their spending into products that take more time to turn them into consumption experiences.
Deliberation among individuals online plays a key role in shaping the opinions that drive votes, purchases, donations, and other critical offline behavior. Yet, the determinants of opinion change via persuasion in deliberation online remain largely unexplored. Our research examines the persuasive power of ethos—an individual’s “reputation”—using a seven-year panel of over a million debates from an argumentation platform containing explicit indicators of successful persuasion. We identify the causal effect of reputation on persuasion by constructing an instrument for reputation from a measure of past debate competition and by controlling for unstructured argument text using neural models of language in the double machine-learning framework. We find that an individual’s reputation significantly impacts their persuasion rate above and beyond the validity, strength, and presentation of their arguments. In our setting, we find that having 10 additional reputation points causes a 31% increase in the probability of successful persuasion over the platform average. We also find that the impact of reputation is moderated by characteristics of the argument content, in a manner consistent with heuristic information processing under cognitive overload. We discuss managerial implications for platforms that facilitate deliberative decision making for public and private organizations online.
I examine the effect of relative performance evaluation (RPE) on firm performance and risk-taking behavior. Agency theory suggests that, for firms that experience common shocks, RPE in executive compensation plans improves risk sharing and strengthens incentive alignment by providing more information about managerial effort than firm performance alone. In practice (i.e., in the presence of a finite number of peers), RPE effectiveness depends on the extent of firms’ common risk exposure and the amount of common risk captured by RPE peers. I develop a simple model that predicts that the benefit of using RPE exceeds the cost of adding noise from peers when the amount of common risk removed is large. Consistent with predictions, I find empirically that RPE firms perform better than similar non-RPE firms when there is high common risk exposure and the common risk removed by the selected peers is high. I also find that, in these circumstances, the use of RPE is associated with greater firm risk and a lower likelihood of underinvesting. Overall, my study provides evidence that RPE is associated with better risk sharing and stronger incentive alignment when (1) RPE firms are exposed to high common risk and (2) RPE peers are effective in removing common risk for performance evaluation.
This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity—largely through workers converting more calls into sales and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking.
Does removing constraints on the time and place of work benefit the utility of workers and firms? We design a randomized experiment of a sample of workers in a large Italian company; workers are randomly divided into a treated group that engages in flexible place and time of work (which we call “smart working”) one day per week for nine months and a control group that continues to work traditionally. By comparing the treated and control groups, we find causal evidence that the flexibility of smart working increases the productivity of workers. We estimate a decrease of one day of leave on average per month, and we exclude a significant decline in objective productivity in any month. We also find sizeable improvements in well-being and work–life balance, and we observe that men also increase the time dedicated to household and care activities.
This paper extends the methodology of statistically extracting latent factors in settings with return-predictive firm characteristics as conditional covariances (betas) between returns and factors. The main feature is that the pricing errors (alphas) are specified to be orthogonal to the affine-transformed firm characteristics as the betas with one component of pricing errors lying outside the space spanned by the firm characteristics. The specification is shown to make substantial differences with the extant literature as the zero pricing error hypothesis is strongly rejected for various models with commonly used firm characteristics.
Recent years have witnessed significant advancements in matching technologies used to improve the matching between workers and employers requesting job tasks on a gig-economy platform. Although conventional wisdom suggests that technologies with higher matching quality benefit the platform by assigning better-matched jobs to workers, we discover a possible unintended revenue-decreasing effect. Our stylized game-theoretic model suggests that, although a technology’s matching enhancement effect can increase a platform’s revenue, the jobs assigned by the better matching technology can also unintentionally reveal more information about uncertain labor demand to workers, especially when demand is low, and thus unfavorably change workers’ participation decisions, resulting in a revenue loss for the platform. We extend our model to cases in which (1) the share of revenue between workers and platform is endogenous, (2) the matching quality can be improved continuously, (3) the opportunity cost of workers is affected by competition between platforms, and (4) workers compete for job tasks. We find consistent results with additional insights, including the optimal matching quality that a platform should pursue. Furthermore, we examine two approaches to mitigate the potential negative effect of using an advanced matching technology for the platform and find that under certain conditions, the platform can benefit from revealing labor demand or competition information directly to workers. Our results shed light on both the intended positive and unintended negative effects of improvements in matching quality and highlight the importance of thoughtful development, management, and application of matching technologies in the gig economy.
Maintaining good ratings increases the profits of sellers on online platforms. We analyze the role of strategic pricing for ratings management in a setting where a monopolist sells a good of unknown quality. Higher prices reduce the value for money, which on average worsens reviews. However, higher prices also induce only those consumers with a strong taste for the product to purchase, which on average improves reviews. Our model flexibly parametrizes the two effects. This parametrization can rationalize the observed heterogeneity in the relationship between reviews and prices. Based on an analytic characterization of the optimal dynamic pricing strategy, we study a platform’s choice of the sensitivity of its rating system to incoming reviews. The optimal sensitivity depends on the effect of prices on reviews and on how the platform weighs consumers and sellers in its objective. Although sellers always benefit from more sensitivity, consumers may suffer from higher prices and from slower learning from reviews due to endogenously emerging price and rating cycles.
When actions by one agent force another to deviate from their agreements with a third, “victim” turns into “injurer” in the chain’s subsequent steps. Should the chain’s initiator be responsible only for the direct harm they cause or also bear some of the indirect losses they trigger? Through an axiomatic approach, we characterize the class of fixed-fraction rules, which strike a balance between incentives for accident prevention on the one hand and fairness in terms of how liabilities are assigned on the other. Their simple design make the rules ideal for practical implementation through smart contracts, enabling automated conflict resolution.
In manufacturing and service operations, flexibility is beneficial for matching supply with demand, but it comes at a cost. In modern digital workplaces, agents have different skills associated with corresponding and variable task preferences. Some are inclined to give up part of their payment to avoid unfavorable matches, and the platform manager, in turn, gains extra freedom in allocating tasks by possibly charging servers for favorable assignments. Innovative marketplaces facilitate task discretion and seek novel and beneficial implementations in the platform design. This naturally leads to the problem of exploring and optimizing the task allocation process. We introduce an innovative mechanism for task assignment in the workplace and compare it to the traditional mechanism in which task routing is solely the platform’s decision. To improve the welfare of all users, agents are allowed some task discretion in exchange for a fee. In a multiserver system, a server may request flexibility and, considering the other agents’ behavior, choose the costly to the platform option, which may or may not be beneficial for the platform when the agents’ and the platform’s preferences are misaligned. We model different working environments and server preferences via different distributions and study how the agents’ preferences, task costs, and flexibility fee affect the equilibrium assignment. In every case, through pricing and offering flexibility, the platform can do at least as well as a scheme with no flexibility. An important conclusion is that pricing task discretion can often improve the agents’ welfare and the labor platform’s profit.
This paper studies whether credit ratings can alleviate the hold-up problem in the loan market. We exploit a refinement of the rating information produced by a certifier that rates private bank-dependent firms in France on a vast scale. The refinement causes some firms to receive a positive rating surprise that is not due to an improvement in firm fundamentals. We show that affected firms become less reliant on lenders that have greater ability to extract informational rents. These firms receive greater and less expensive bank credit from new and less informed lenders and invest more. We deduce that credit ratings reduce the monopoly power of informed banks, helping firms to expand their access to bank credit.
We provide new insights on how hedge funds use options and short-selling channels to trade on their negative information. Bearish information in hedge fund option positions is economically large, distinct from information in short interest, and it is the combination of option positions and short interest that provides the strongest information signal. A portfolio of stocks with high short interest and bearish hedge fund option positions predicts negative abnormal returns that are more than four times as large as the portfolio with high short interest and bullish options positions. The information in hedge fund option positions increases during periods of market stress, whereas that in short interest does not. This increase is concentrated in capital-constrained hedge funds, suggesting that options provide a channel for capital-constrained hedge funds to exploit their information advantage.
One advantage of online retail is that a large number of products can be displayed at low cost. However, online retailers must decide which of these products to carry in inventory (stock items) and which to order from suppliers when a customer places an order (nonstock items). In this paper, we empirically investigate how carrying a product in inventory affects its online sales. We use data from a European furniture and interior design retailer consisting of daily sales transactions and inventory data covering 18 months. We use a quasi-natural experiment—random transitions of products in and out of inventory at the retailer’s central warehouse—to estimate the causal effect of carrying inventory on sales. Our results show a strong and statistically significant increase in sales of, on average, 65% associated with having the product available in stock. More interestingly, this effect differs between products and is moderated by the price of the product: sales of more expensive products are less sensitive to the product being in stock. We use these results to draw insights on which types of items to carry in inventory.
A one-factor model based on long-run consumption growth explains the risk premiums on corporate bond portfolios sorted on credit rating, credit spreads, downside risk, idiosyncratic volatility, long-term reversals, maturity, and sensitivity to the financial intermediary capital factor. The estimated risk-aversion coefficient is lower when we use the consumption growth of wealthy households over a longer horizon as a risk factor, and a model with a 20-quarter horizon yields a risk-aversion coefficient of 15, a value similar to the one estimated from equity portfolios.
In 2020, Berlin introduced a rigorous rent-control policy responding to soaring prices by capping rents: the Mietendeckel (rent freeze). The German Constitutional Court revoked the policy only one year later. Although successful in lowering rents during its duration, the consequences for Berlin’s rental market and close-by markets are per se not clear. This article evaluates the short-term causal supply-side effects in terms of prices, quantities, and landlords’ strategic behavior. We develop a theoretical framework capturing the key features of first-generation rent control policies and Berlin-specific aspects. Using a rich pool of detailed rent advertisements, predictions are tested, and further empirical causal inference techniques are applied for comparing price trajectories of dwellings inside and outside the policy’s scope. Mechanically, advertised rents drop significantly upon the policy’s enactment. A substantial rent gap along Berlin’s administrative border emerges, and rapidly growing rents in Berlin’s (unregulated) adjacent municipalities are observed. Landlords started adopting a hedging strategy insuring themselves against the risk of contractually long-term fixed low rents following a potentially unconstitutional law. Whereas this hedge was beneficial for landlords, the risk was completely borne by tenants. Moreover, the number of available properties for rent dropped significantly, a share of which appears to be permanently lost for the rental sector. This hampers a successful housing search for first-time renters and people moving within the city. Overall, negative consequences for renters appear to outweigh positive ones.
We hypothesize that after a relaxation of short selling constraints, an escalation in short selling activity will heighten incentives for short sellers to accelerate price discovery by revealing their negative information. Consistent with this conjecture, we find that the overall sentiment of media coverage tilts significantly more negative for pilot relative to control firms following exogenous relief of short sale constraints. We find a more pronounced effect for media-initiated articles relative to firm-initiated press releases. Further, following abnormal increases in short interest, there is a significantly greater increase in negative news flow for pilot relative to nonpilot firms. Finally, we find that stock returns of firms with lower short selling constraints become significantly more sensitive to negative news reports.
We provide novel evidence that equity investors react to currency shocks with a delay. Using the cross-section of currency returns and the relative presence of U.S. firms in foreign economies, we compute a foreign operations-related exchange shock (FOREXS) measure. We find FOREXS to predict firms’ future cash flows and stock returns, driving much of the previously documented underreaction to foreign information. An FOREXS-based long-short strategy yields a 6.74% annualized abnormal return. FOREXS predictive power comes from firms’ incomplete hedging and investors’ limited attention, highlighting the challenges involved when processing information from a different asset class.
Using a unique data set with detailed information on Danish households and their mortgages, we show that young and old households are more likely to use interest-only (IO) mortgages compared with middle-aged households. Young households use IO mortgages because they expect higher future income and old households because IO mortgages allow them to circumvent an otherwise binding liquidity constraint. Through different channels, IO mortgages thus facilitate consumption smoothing for young and old households. Our detailed data also allow us to examine how households with IO mortgages differ from households with repayment mortgages in terms of leverage, debt and asset composition, and pension contributions.
This paper studies network market problems in which firm(s)/platform(s) sets quality in addition to price. A well-established result in the network economics literature is that a profit-maximizing firm concerns only how quality is valued by the marginal consumer but not by inframarginal consumers, aka the Spence effect/distortion. For markets with strong network effects under which multiple market-tipping equilibria exist, I show that the validity of the previous result depends on the choice of the equilibrium selection criterion. Precisely, I show that all criteria commonly used in this literature give rise to the Spence effect, whereas the well-justified risk dominance criterion in game theory and its generalizations do not. Novel quality strategies are derived based on the latter criteria.
As coined by the SEC, Digital Engagement Practices (DEPs) are visual cues and design features to engage investors on mobile trading platforms. With the rise of mobile stock trading, regulators and other capital market participants are concerned that these DEPs may cause retail investors to trade in ways they otherwise would not. Using an experiment, we examine the impact of two common DEPs in mobile trading platforms—color (i.e., the use of green or red to indicate performance) and allowing investors to swipe versus click and confirm to execute trades. Drawing from prior research, we predict that the color associated with firm information will interact with how investors execute their trades to affect investment decisions. Consistent with expectations, results show that investors make the largest investment in a firm when they swipe to trade and firm information is colored green, relative to when they click and confirm to trade or firm information is colored red, holding firm economics constant. Swiping to trade causes investors to focus relatively more on the upside of investing, but only when firm information is colored green and not when it is colored red. The color red leads to a muted effect of swiping to trade as investors experience more negative affect and focus relatively more on the downside of investing. Our study answers calls from regulators and academics to examine the effects of technology on information evaluation in investment decisions, and has important practical implications for investors.
Do gender diversity quotas enhance firm value? This broad question has sparked significant debate because, whereas diversity is generally thought to improve board functioning, prior research in international settings suggests mandating diversity may have adverse consequences if it moves firms away from market equilibrium. Exploiting the political events leading up to the passage of California Senate Bill (SB) 826 as an exogenous shock to assess the market implications of board gender composition, we document either significantly positive or insignificant two-day abnormal returns for California firms across a variety of model specifications. Importantly, this finding contradicts the strong claims of contemporary studies of SB 826 that the California mandate was value-destructive. We reconcile these findings by providing critical evidence that contemporaneous results lack robustness to alternative model specifications and provide an incomplete picture by failing to comprehensively examine a broader set of event dates or account for mixed messaging on the final event date. We also provide some evidence of significant negative abnormal returns to litigation milestones that threaten to overturn SB 826 with the magnitude increasing in the size of board gender deficits. Finally, we provide evidence that there is no observable decline in newly appointed female director quality following the implementation of SB 826 relative to either preregulation or male director appointments. Collectively, our results suggest nonnegative (if not positive) consequences to California firms arising from SB 826 and directly counter assertions that mandated gender diversity is detrimental to firm value.
Retailers are increasingly selling returned products as open-box along with their new product offerings. Whereas some retailers position open-box products side-by-side with their new counterparts in the assortment (i.e., side-by-side strategy), other retailers position them separately in a different part of the retail space/website (i.e., separate strategy). We conduct multimethodology research to investigate the effectiveness of these two visual merchandising strategies. First, using a choice-based conjoint design in a controlled behavioral experiment, we identify how the two strategies affect the perceived value of new products. Next, using proprietary data from a retailer that has implemented both strategies across different stores, we empirically test the impact of each strategy on new product sales and returns under real market conditions. Finally, we extend our empirical analysis to prescribe how retailers can identify the right visual merchandising strategy for a given product profile. Overall, our results reveal a novel trade-off between the two visual merchandising strategies. With respect to retail revenue, the separate strategy outperforms the side-by-side strategy as we find that new product sales are higher with the separate strategy. However, with respect to retail cost, the side-by-side strategy outperforms the separate strategy as we find that new product returns are lower with the side-by-side strategy. Our extended analysis reveals that this tradeoff varies by product profile. Using counterfactual analysis, we further demonstrate that customizing the visual merchandising strategy by product mix could increase profit by 3.2% at the focal retailer.
In April 2020, the U.S. government sent economic impact payments (EIPs) directly to households as part of its measures to address the COVID-19 pandemic. We characterize these stimulus checks as a wealth shock for households and examine their effect on retail trading in Bitcoin. We find a significant increase in Bitcoin buy trades of size $1,200, which is the modal EIP amount. We find similar increases in trading for other countries that paid out stimulus checks. We estimate that the EIPs have a significant impact on the U.S. dollar–Bitcoin trading pair, increasing buy volume by 3.8% and the price by 0.6%. We also find that demand for Bitcoin is highly price inelastic compared with the demand for stocks. We suggest the demographic characteristics that make people more resilient to the COVID-19 economic shock—single, computer literate, and educated—are also characteristics of people who are more interested in Bitcoin.
This article studies the consumption of advertising when attention is costly. Our objective is to understand the advertiser’s optimal decision for informational and noninformational content when facing consumers with heavy distractions common in the digital age. We take an equilibrium approach in which an ad is consumed if and only if the content of the ad is worth the viewer’s attention. We classify many advertising decisions observed in practice. First, an advertiser can structure content to induce curiosity for continued viewing by showing the information that is relatively less likely to resonate with the broad audience. This tactic is evident in clickbait ads and “mystery ads.” Second, we find that digital ads, which tend to be skippable, have lower amounts of noninformational content (e.g., entertainment) relative to nonskippable ads. This finding can explain the industry perception that ads on digital media are of lower copy quality than traditional ads.
We consider a dynamic pricing problem in which a firm sells one item to a single buyer to maximize expected revenue. The firm commits to a price function over an infinite horizon. The buyer arrives at some random time with a private value for the item. He is more impatient than the seller and strategizes over the timing of the purchase in order to maximize his expected utility, which implies either buying immediately, waiting to benefit from a lower price, or not buying. We study the value of the seller’s ability to observe the buyer’s arrival time in terms of her expected revenue. When the seller can observe the buyer’s arrival, she can make the price function contingent on the buyer’s arrival time. On the contrary, when the seller can’t, her price function is fixed at time zero for the whole horizon. The value of observability (VO) is defined as the worst-case ratio between the expected revenue of the seller when she observes the buyer’s arrival and that when she does not. First, we show that, for the particular case in which the buyer’s valuation follows a monotone hazard rate distribution, the upper bound of VO is exp(1). Next, we show our main result: in a setting very general on valuation and arrival time distributions: VO is at most 4.911. To obtain this bound, we fully characterize the solution to the observable arrival problem and use this solution to construct a random and periodic price function for the unobservable case. Finally, we show by solving a particular example to optimality that VO has a lower bound of 1.136.
Recent financialization in commodity markets makes it easier for institutional investors to trade a portfolio of commodities via various commodity-indexed products. We present several pieces of novel causal evidence that daily exposure to such index trading results in price overshoots and reversals, as reflected in negative daily return autocorrelations, only among commodities in that index. This is because index trading propagates nonfundamental noise to all indexed commodities. We present direct evidence for such noise propagation using commodity news sentiment data.
This paper studies the effects of default risk on expected equity option returns. In the cross-section, expected delta-hedged equity option returns have a negative relation with default risk measured by credit ratings or default probability. In the time series, credit rating downgrades (upgrades) lead to a decrease (increase) in the firm’s delta-hedged option return. Our results are consistent with a stylized capital structure model in which the negative relation between option returns and default risk is driven by firm leverage and asset volatility.
Using an administrative payroll data set for 2.6 million retail workers, we find that the staggered rollout of a major e-commerce firm’s fulfillment centers reduces traditional retail workers’ income in geographically proximate counties by 2.4%. Wages of hourly workers, especially part-time hourly workers, decrease significantly driven by a drop in the number of hours worked. We observe a U-shaped pattern in which both young and old workers experience a sharper decrease in wage income. Consequently, some workers experience an increase in credit card delinquency. Using data for 3.2 million stores, we find that sales (employment) at proximate stores decrease by 4% (2.1%). Exits, especially of young and small stores, increase, and entry decreases. Our results highlight how creative destruction led by e-commerce impacts local labor markets.
We propose a mean field game model to study the question of how centralization of reward and computational power occur in Bitcoin-like cryptocurrencies. Miners compete against each other for mining rewards by increasing their computational power. This leads to a novel mean field game of jump intensity control, which we solve explicitly for miners maximizing exponential utility and handle numerically in the case of miners with power utilities. We show that the heterogeneity of their initial wealth distribution leads to greater imbalance of the reward distribution, and increased wealth heterogeneity over time, or a “rich get richer” effect. This concentration phenomenon is aggravated by a higher Bitcoin mining reward and reduced by competition. Additionally, an advantaged miner with cost advantages such as access to cheaper electricity, contributes a significant amount of computational power in equilibrium, unaffected by competition from less efficient miners. Hence, cost efficiency can also result in the type of centralization seen among miners of cryptocurrencies.
Does financial reform improve public good provision? We examine state-level adoption of municipal bankruptcy law. After reform, municipalities’ borrowing costs decrease and bonds’ issuance increase, particularly for bonds financing hospitals; hospitals’ investments increase, particularly when using such bonds; local firms’ investment and performance increase, particularly in the construction sector. Ex ante, reform occurs earlier in states with weaker unions, stronger bondholders’ interests, and better courts. Similar factors explain congressional voting on municipal bankruptcy law. These results support the hypothesis that financial reform destroys labor union rents and expands investment, highlighting a novel spillover channel from public finance to the real economy.
We explore the statistical and economic importance of restrictions on the dynamics of risk compensation from the perspective of a real-time Bayesian learner who predicts bond excess returns using dynamic term structure models (DTSMs). The question on whether potential statistical predictability offered by such models can generate economically significant portfolio benefits out-of-sample is revisited while imposing restrictions on their risk premia parameters. To address this question, we propose a methodological framework that successfully handles sequential model search and parameter estimation over the restriction space in real time, allowing investors to revise their beliefs when new information arrives, thus informing their asset allocation and maximizing their expected utility. Empirical results reinforce the argument of sparsity in the market price of risk specification since we find strong evidence of out-of-sample predictability only for those models that allow for level risk to be priced and, additionally, only one or two of these risk premia parameters to be different than zero. Most importantly, such statistical evidence is turned into economically significant utility gains, across prediction horizons, different time periods and portfolio specifications. In addition to identifying successful DTSMs, the sequential version of the stochastic search variable selection scheme developed can be applied on its own and offer useful diagnostics monitoring key quantities over time. Connections with predictive regressions are also provided.
The overstatement of asset collateral values reduces bank capital requirements. We identify this novel form of regulatory arbitrage by studying housing overappraisals, the difference between housing collateral values computed by appraisers and actual transaction prices. We leverage granular loan-level data from Spain and a kink in the scheme of residential mortgage risk weights to show that tighter regulatory requirements cause larger overappraisals. This bias depends on the relationship between appraisers and banks; appraisers inflate mortgage collateral values only for their major customer banks. On average, overappraisals lower risk-weighted mortgages by 9%. Mortgage overappraisals allow banks to free up regulatory capital to support additional risk-taking in the corporate loan market.
We study the behavior of an individual avoiding peer information from a natural field experiment of charity crowdfunding. The unique experimental design enables us to employ an instrumental variable strategy to identify how the behavior influences individual giving to and promotion of charity campaigns. We find that, even with free access, 89% of individuals chose not to seek peer information. These individuals were less likely, whereas their peers were more likely to give and help promote in the past. The behavior would reduce the total distribution of campaigns by 8.5% and the total donation amount by 7.7%. A stylized model is used to illustrate how the pressure from peer comparison drives the individuals not to seek the information and how this behavior could influence giving and promoting behaviors of a group of marginal individuals.
Does the arrival of culturally diverse workers affect the work style of incumbent workers? We examine how the large influx of Russian hockey players in the National Hockey League after 1989 affected North American–born players. The Soviet style of hockey was largely based on skilled skating, constant movement, circling, and passing. In contrast, the North American play was more individualistic and linear, with higher emphasis on physical strength and aggressive behavior. Using 50 years of data at the player-game level, we show that (i) the number of penalty minutes per game increases steadily from 1970 to 1989, but decreases thereafter; (ii) although Russian players get systematically fewer penalty minutes in and after 1989, the trend reversal is driven by North American–born players; and (iii) the number of penalty minutes per game of North American–born players decreases systematically with the number of Russian players on their team and on the opposing team. Evidence shows that the hockey style brought about by Russian players was adopted and diffused within and across North American teams and players.
We study policy optimization for the feature-based newsvendor, which seeks an end-to-end policy that renders an explicit mapping from features to ordering decisions. Most existing works restrict the policies to some parametric class that may suffer from suboptimality (such as affine class) or lack of interpretability (such as neural networks). Differently, we aim to optimize over all functions of features. In this case, the classic empirical risk minimization yields a policy that is not well-defined on unseen feature values. To avoid such degeneracy, we consider a Wasserstein distributionally robust framework. This leads to an adjustable robust optimization, whose optimal solutions are notoriously difficult to obtain except for a few notable cases. Perhaps surprisingly, we identify a new class of policies that are proven to be exactly optimal and can be computed efficiently. The optimal robust policy is obtained by extending an optimal robust in-sample policy to unobserved feature values in a particular way and can be interpreted as a Lipschitz regularized critical fractile of the empirical conditional demand distribution. We compare our method with several benchmarks using synthetic and real data and demonstrate its superior empirical performance.
Economic theory predicts that bond insurance lowers issuers’ financing costs by resolving asymmetric information and mitigating credit risk. With comprehensive data over the last 36 years, we find increasingly diminished empirical support for these models. The value of insurance in resolving asymmetric information beyond that resolved by credit ratings and other observable bond characteristics is economically minimal. The average gross value of insurance ranges from 4 to 14 bps when bond insurers offer Aaa-rated coverage. However, this gross value becomes insignificant after 2008 when Aaa-rated insurance no longer exists. Evidence suggests that the lack of insurance benefit in the postcrisis period is attributable to the deteriorated creditworthiness of insurance companies. Examining noninterest saving explanations for the continued use of insurance in the no-Aaa insurance market, we find evidence that issuers purchase insurance out of habit (with insurance value most diminished for habitual purchasers with low governance quality) and for the convenience it affords in default, but no evidence that insurance improves secondary market liquidity.
We exploit a unique data set from the Israeli Professional Football Leagues that provides high-frequency direct measures of players’ effort to estimate effort peer effects in a high-skill collaborative team task. Using two complementary identification strategies, we find robust evidence of substantial positive peer effects. Our findings highlight that effort spillovers play an important role in team production and that even a change in just one worker’s effort can substantially influence team effort and thus performance. Moreover, we present suggestive evidence that behavioral considerations are a relevant mechanism for creating peer effects even in highly skilled teams of workers.
We explore the consequences of limiting executive pay on voluntary executive turnover by exploiting a Chinese government policy restricting executive pay at a subset of firms. Affected firms experience an increase in voluntary executive turnover, with executives increasingly moving to firms not bound by the pay policy. Executives that leave are of higher quality as suggested by their stronger stock and accounting performance in the year prior to the turnover. Affected firms suffer firm value losses, especially when talented executives leave. Our findings demonstrate the importance of compensation contracts in retaining executives and suggest a need for regulatory caution, since mandated constraints aimed at trying to “fix” executive pay can hurt firm value and ultimately, hurt shareholders.
We study how social connectedness affected active mutual fund manager trading behavior in the first half of 2020. In the first quarter during which the coronavirus disease 2019 (COVID-19) outbreak occurred, fund managers located in or socially connected to COVID-19 hotspots sold more stock holdings compared with a control group of unconnected managers. The economic impact of social connectedness on stock holdings was comparable with that of COVID-19 hotspots and was elevated among “epicenter” stocks most susceptible to the pandemic shock. In the second quarter, social interaction had an overall negative effect on fund performance, but this effect depended on manager skill; unskilled managers who were connected to the hotspots underperformed, whereas skillful managers suffered no deleterious effect. Our evidence suggests that social connections can intensify salience bias for all but the most skilled institutional investors, and policy makers should be wary of the destabilizing role of social networks during market downturns.
We study interdepartment knowledge sharing in an investment research setting where the benefits are potentially significant for the brokerage and the capital market, but so are the frictions impeding it. Using hand-collected data on equity analyst access to in-house debt research expertise, we find significant benefits to equity analysts in the form of improved ability to forecast cash flows and to anticipate credit rating downgrades. Moreover, we find evidence that access to management and research expertise underlie in-house debt analysts’ capacity to generate information beneficial to equity analysts. Finally, these benefits exist only in the presence of a collaborative brokerage culture or debt-equity analyst collocation, consistent with these factors promoting knowledge sharing in the investment research industry.
I study the impact of financial stress on the incidence of harassment and discrimination using Equal Employment Opportunity (EEO) charges brought forward by U.S. Postal Service workers. An analysis of more than 800,000 EEO charges filed between 2004 and 2019 demonstrates that financial stress experienced in the second week of the pay cycle increases EEO incidents by about 5% compared with the first week. Further analyses suggest that the uncovered effects are driven by changes in the number of incidents rather than in their reporting.
Disputes over transactions on two-sided platforms are common and usually arbitrated through platforms’ customer service departments or third-party service providers. This paper studies crowd-judging, a novel crowdsourcing mechanism whereby users (buyers and sellers) volunteer as jurors to decide disputes arising from the platform. Using a rich data set from the dispute resolution center at Taobao, a leading Chinese e-commerce platform, we aim to understand this innovation and propose and analyze potential operational improvements with a focus on in-group bias (buyer jurors favor the buyer, likewise for sellers). Platform users, especially sellers, share the perception that in-group bias is prevalent and systematically sways case outcomes as the majority of users on such platforms are buyers, undermining the legitimacy of crowd-judging. Our empirical findings suggest that such concern is not completely unfounded: on average, a seller juror is approximately 10% likelier (than a buyer juror) to vote for a seller. Such bias is aggravated among cases that are decided by a thin margin and when jurors perceive that their in-group’s interests are threatened. However, the bias diminishes as jurors gain experience: a user’s bias reduces by nearly 95% as experience grows from zero to the sample median level. Incorporating these findings and juror participation dynamics in a simulation study, the paper delivers three managerial insights. First, under the existing voting policy, in-group bias influences the outcomes of no more than 2% of cases. Second, simply increasing crowd size through either a larger case panel or aggressively recruiting new jurors may not be efficient in reducing the adverse effect of in-group bias. Finally, policies that allocate cases dynamically could simultaneously mitigate the impact of in-group bias and nurture a more sustainable juror pool.
Quality disclosures regarding medical outcomes such as patient mortality are common healthcare policy instruments. Mixed evidence exists on whether quality disclosures improve outcomes for disclosed ailments. Moreover, disclosure policies can generate spillovers and impact ailments not targeted by the policy. We examine the effects of quality disclosure regulation on mortality improvements in disclosed and nondisclosed ailments. We use patient records for California hospitals for 1995–2014 and construct three groups of ailments: those that were the target of disclosure regulation, complementary ailments that were not the target of disclosure regulation but are medically related to the disclosed ailments, and medically unrelated ailments. Using a difference-in-differences design, we find that quality disclosure regulation is associated with declines in mortality risk of disclosed ailments ranging from 11.5% to 23%. Quality disclosure regulation is also associated with improvements for complementary ailments that are not the target of the disclosure policy. Such positive spillover effects yield an estimated 15.2% decline in mortality risk. Consistent with demand-side pressures driving improvements, market shares become sensitive to disclosed quality measures after disclosure regulation. Our findings of direct and spillover effects have implications for disclosure regulation.
We study the effects of grade nondisclosure (GND) policies implemented within master of business administration (MBA) programs at highly ranked business schools. GND precludes students from disclosing their grades and grade point averages (GPAs) to employers. In the labor market, we find that GND weakens the positive relation between GPA and employer desirability. During the MBA program, we find that GND reduces students’ academic effort for a given course by approximately 4.9% relative to comparable students not subject to the policy. Consistent with our model, in which abilities are potentially correlated and students can substitute effort toward other activities in order to signal GPA-focused abilities, students participate in more extracurricular activities and enroll in more difficult courses under GND. Finally, we show that students’ tenure with their first employers after graduation decreases under GND.
Based on a representative sample of consumer financial transaction data, this paper studies the consumption and savings response to a 2015 permanent increase in the marginal income tax of the high-income taxpayers. Using difference-in-differences regressions, controlling for individual and time fixed effects, we show robust results that the affected consumers experienced little change in their spending. The pattern is prevalent across consumer demographics and is found even among consumers who are sophisticated, have high levels of debt, or register little income changes. Furthermore, the tax increase financed fiscal redistribution leads to a long-lasting increase in the consumption of the lower-income population.
Models of statistical discrimination typically assume that employers make rational inference from (education) signals. However, there is a large amount of evidence showing that most people do not update their beliefs rationally. We use a model and two experiments to show that employers who are conservative, in the sense of signal neglect, discriminate more against disadvantaged groups than Bayesian employers. We find that such non-Bayesian statistical discrimination deters high-ability workers from disadvantaged groups from pursuing education, further exacerbating initial group inequalities. Excess discrimination caused by employer conservatism is especially important when signals are very informative. Out of the overall hiring gap in our data, around 40% can be attributed to Bayesian statistical discrimination, a further 40% is due to non-Bayesian statistical discrimination, and the remaining 20% is unexplained or potentially taste-based.
This paper examines mutual funds’ dual role as both clients and shareholders of broker banks. Mutual funds are more likely to hold and significantly overweight stocks of their broker banks. In line with the portfolio decisions, fund voting is biased toward broker management in contentious proposals. Such voting bias is inconsistent with maximizing broker banks’ shareholder value yet significantly affects voting outcomes and the presence of contentious proposals at the banks’ shareholder meetings. Furthermore, we show that although client funds are rewarded with preferential initial public offering allocations from connected brokers for their voting support, fund managers’ engagement in reciprocal practices is ultimately determined by the economic tradeoffs they face. Our study not only uncovers a new mechanism—being brokers’ friendly shareholders—through which the two parties maintain their quid pro quo relationships but also raises a broader concern about governance of important financial institutions.
We conducted a randomized, controlled trial with small- and medium-sized enterprises in Kenya to estimate the causal impact of an electronic payment (e-payment) technology on business finance. Using an encouragement design, we exogenously increased e-payment usage among a random subset of firms by relaxing adoption transaction costs and information barriers. Sixteen months after the intervention, we find that the e-payment technology increased access to mobile loans (in the number of loans as well as in the amount borrowed) by at least 50% (0.17 standard deviation), likely because of the reduction of information asymmetries brought by an increase in digital transactions. We find no effect of the e-payment technology on sales and profits, but we do find a reduction of sales volatility and precautionary investment, especially for smaller firms. This suggests that mobile loans help smaller firms cope with short-term negative shocks. We provide a stylized model of business finance that rationalizes these findings.
Using an hourly data set on retail investor individual security positions from Robinhood Markets, we find no evidence that environmental, social, and governance (ESG) disclosures inform retail investors’ buy and sell decisions. The response on ESG press release days by retail investors is indistinguishable from nonevent days. In contrast, these same investors make economically meaningful changes to their portfolios in response to non-ESG press releases, especially those that pertain to earnings announcements. We use stock return tests to show that there is economic content in ESG press releases, and we conduct subsample analyses showing that retail investors do not respond to the most salient and economically transparent ESG disclosures. Collectively, these tests suggest that a lack of economic content, a lack of visibility, and difficulty with investment integration are unlikely to explain our findings.
Personal data have become a key input in internet commerce, facilitating the matching between millions of customers and merchants. Recent data regulations in China, Europe, and the United States restrict internet platforms’ ability to collect and use personal data for personalized recommendation and may fundamentally impact internet commerce. In collaboration with the largest e-commerce platform in China, we conduct a large-scale field experiment to measure the potential impact of data regulation policy and to understand the value of personal data in internet commerce. For a random subset of 555,800 customers on the Alibaba platform, we simulate the regulation by banning the use of personal data in the home page recommendation algorithm and record the matching process and outcomes between these customers and merchants. Compared with the control group with personal data, we observe a significantly higher concentration in the algorithmic recommendation of products in the treatment group and a very sharp decrease in the matching outcomes as measured by both customer engagement (click-through rate and product browsing) and market transaction (sales volume and amount). The negative effect is disproportionate and more pronounced for niche merchants and customers who would benefit more from e-commerce. We discuss the potential economic impact of data regulation on internet commerce as well as the role of personal data in generating value and fostering long-tail innovations.
Simple and affordable testing tools are often not accurate enough to be operationally relevant. For coronavirus disease 2019 (COVID-19) detection, rapid point-of-care tests are cheap and provide results in minutes, but they largely fail policy makers’ accuracy requirements. We propose an analytical methodology, based on robust optimization, that identifies optimal combinations of results from cheap tests for increased predictive accuracy. This methodological tool allows policy makers to credibly quantify the benefits from combination testing and thus, break the trade-off between cost and accuracy. Our methodology is robust to noisy and partially missing input data and incorporates operational constraints—relevant considerations in practice. We apply our methodology to two data sets containing individual-level results of multiple COVID-19 rapid antibody and antigen tests, respectively, to generate Pareto-dominating receiver operating characteristic curves. We find that combining only three rapid tests increases out-of-sample area under the curve by 4% (6%) compared with the best-performing individual test for antibody (antigen) detection. We also find that a policy maker who requires a specificity of at least 0.95 can improve sensitivity by 8% and 2% for antibody and antigen testing, respectively, relative to available combination testing heuristics. Our numerical analysis demonstrates that robust optimization is a powerful tool to avoid overfitting, accommodate missing data, and improve out-of-sample performance. Based on our analytical and empirical results, policy makers should consider approving and deploying a curated combination of cheap point-of-care tests in settings where “gold standard” tests are too expensive or too slow.
Because of many firms’ lack of collateral, banks have imposed loan limits as a form of risk-control regulations of bank financing. In response, loan insurance has emerged as a useful instrument to lift loan limits. To study the joint value of bank loans and loan insurance, this article investigates a supply chain composed of one supplier and one capital-constrained buyer who takes out a bank loan and potentially purchases loan insurance. The analysis reveals that both the supplier and the buyer can benefit from the bank financing with loan insurance in an insurance-cooperation region wherein the supplier is willing to reduce the wholesale price to entice the buyer to purchase insurance. Further still, in the insurance-cooperation region, worse contract terms of the bank loan benefit the supplier, whereas worse policy terms of loan insurance help the buyer. Such benefits can lead to a higher order quantity than the capital-abundant one and partially coordinate the entire supply chain. However, when production cost is low, the supplier and the buyer can encounter a Stackelberg prisoner’s dilemma, in which both firms no longer cooperate with each other, and both are worse off. Nevertheless, a government subsidy for the insurance premium can not only reduce the Stackelberg prisoner’s dilemma, but also improve the social welfare although it is not always optimal for the government to provide that subsidy. The main qualitative results are robust in a variety of extensions.
The editors of Management Science acknowledge all our 2023 reviewers and guest associate editors who generously provided expert counsel and guidance on a voluntary basis. We are grateful for their contributions; without them, the journal could not function. Here, we list those reviewers who contributed four or more reviews in 2023.
The common ownership of firms can have anticompetitive effects by incentivizing collusive outcomes that maximize joint surpluses of the commonly held firms or procompetitive effects through enhanced knowledge spillovers. Using a difference-in-differences regression methodology that exploits mergers between financial institutions as exogenous shocks to common ownership, our baseline results suggest that higher common ownership leads to greater product market fluidity (a text-based metric of competition) and generally leads to more product development and higher investments. These findings suggest that, on average, common ownership spurs dynamism in product spaces rather than tacit collusion between cross-held competitors. This is especially true in economic environments in which it is easier to take advantage of knowledge spillovers. However, common ownership can also inhibit product market competition and dynamism, especially in industries more prone to quasi-monopoly outcomes in product spaces. Implementing a one-size-fits-all regulatory policy limiting common ownership may be harmful in industries with strong spillover opportunities.
Substantial funding is provided to the healthcare systems of low-income countries. However, an important challenge is to ensure that this funding is used efficiently. This challenge is complicated by the fact that a large share of healthcare services in low-income countries is provided by nonprofit health centers that often lack (i) effective governance structures and (ii) organizational know-how and adequate training. In this paper, we argue that the bundling of performance-based incentives with auditing and feedback (A&F) is a potential way to overcome these obstacles. First, the combination of feedback and performance-based incentives—that is, feedback joined with incentives to act on this feedback and achieve specific health outcomes—helps address the knowledge gap that may otherwise undermine performance-based incentives. Second, coupling feedback with auditing helps ensure that the information underlying the feedback is reliable—a prerequisite for effective feedback. To examine the effectiveness of this bundle, we use data from a randomized governance program conducted in the Democratic Republic of Congo. Within the program, a set of health centers was randomly assigned to a “governance treatment” that consisted of performance-based incentives combined with A&F, whereas others were not. Consistent with our prediction, we find that the governance treatment led to (i) higher operating efficiency and (ii) improvements in health outcomes. Furthermore, we find that funding is not a substitute for the governance treatment; health centers that only receive funding increase their scale but do not show improvements in operating efficiency or health outcomes.
In this paper, we explore an employee workaround widespread in K–12 schools: compensating for insufficient funding with partnerships to nonprofit organizations (NPOs). We take an equity perspective and ask the following questions. (i) How do partnering workarounds differ across schools with different levels of socioeconomic advantage? (ii) What can education and NPO leaders do to ensure these workarounds do not exacerbate educational inequities? To answer these questions, we use Little’s Law: a school’s long-run average number of resource-supplementing partnerships (L) is a function of its average annual partnership formation rate (λ) and partnership cycle time (W). We collect and analyze interview (n = 62) and survey (n = 140) data from six strategically sampled schools with different levels of socioeconomic advantage to compare differences in λ and W and understand the implications for educational equity. We find wealthier schools have higher λ, making them more productive. We also find schools have equal W independent of wealth, but this problematically amplifies differences in λ. The difference in partnering productivity translates to educational inequities. We find that poorer schools report about 35% greater utility per partnership, but that is not enough to offset the disadvantage of fewer partnerships. Moreover, we find that differences in partnering productivity are particularly large for curricular partnerships, which are harder for poorer schools to form because of the high demand for wrap-around partnerships. Our findings contribute new knowledge on workarounds which supplement organizational resources and on the relationship between workarounds and equity.
U.S. generally accepted accounting principles (GAAP) allows banks to offset derivative assets against derivative liabilities with the same counterparty and report only the net amount on the balance sheet. Derivative offsetting under international financial reporting standards (IFRS) is much more restrictive, resulting in the single largest difference in balance sheet presentation between U.S. GAAP and IFRS. Two important factors dominate the standard-setting discussion on this issue: (1) whether these derivatives are informative about bank risk and (2) whether disclosing them substitutes for recognition on the balance sheet. Using a hand-collected global sample of banks, we first show that offsetable derivatives are positively associated with bank risk, based on multiple risk measures, suggesting that these derivatives are informative about bank risk. Next, exploiting the differential accounting treatment across U.S. GAAP and IFRS banks, we find that disclosure versus recognition of offsetable derivatives matters for the risk assessment of equity investors but not for that of (more sophisticated) credit default swap investors. Additional tests corroborate the inference that investor sophistication helps explain the differential investor assessment of recognized versus disclosed offsetable derivatives. Collectively, our findings suggest that offsetable derivatives convey information about bank risk and that, for less sophisticated investors, disclosing them may not substitute for recognizing them.
We study a shipment consolidation problem commonly faced by companies that outsource logistics operations and operate in a commit-to-delivery mode. It involves delivering a given set of orders to their destinations by their committed due times using multiple shipping methods at the minimum total shipping and inventory cost. The shipping cost is generally nonlinear in shipping quantity and can be represented by a subadditive piecewise linear function. We investigate two shipping scenarios, one involving long-haul shipping only and the other involving joint long-haul and short-haul shipping. We develop analytical results and solution algorithms for the shipment consolidation problem under each shipping scenario. The problem under the first shipping scenario is shown to be strongly NP-hard. We find that a simple policy, called the First-Due-First-Delivered (FDFD) policy, which assigns orders with earlier delivery due times to shipping methods with earlier destination arrival times, is very effective. This policy enables us to develop a polynomial time algorithm, which not only solves the problem under the concave shipping cost structure optimally but also achieves a performance guarantee of 2 for the problem under the general subadditive shipping cost structure. For the problem under the second shipping scenario, we extend the FDFD policy for long-haul shipping and derive another policy, called the No-Wait policy, for short-haul shipping. We use these policies to develop a polynomial time algorithm and analyze its performance guarantee. Our computational experiments show that the algorithm significantly outperforms a commercial optimization solver, and its performance is robust across different parameter settings that reflect various practical situations.
Unusual events trigger persistent spikes in uncertainty. Standard models cannot match these dynamic patterns. This paper presents a unified framework, motivated by the literature on inattention. Agents choose whether and how to prepare for different possible states of the world by collecting information. Agents optimally ignore sufficiently unlikely events, so the occurrence of such events does not resolve, but rather increases, uncertainty. Uncertain agents have dispersed beliefs, making it harder to focus future preparation. Thus, uncertainty begets uncertainty for an inattentive agent, endogenously persisting. In a financial application, this framework matches patterns in volatility, volume of trade, belief dispersion, and spreads.
Using a quantitative, calibrated life-cycle model, I show that countercyclical earnings risk affects individual consumption growth, reduces the share of wealth in stocks, and affects savings behavior. Using the Panel Study of Income Dynamics survey, I construct an empirical measure of countercyclical earnings risk and find evidence consistent with the model’s predictions. Specifically, larger downside earnings risk decreases consumption growth, increases left skewness in consumption growth, and reduces the share of wealth in stocks. Furthermore, the consumption effects are substantially more significant for stockholders than for nonstockholders, which arises through heterogeneity in the elasticity of intertemporal substitution.
Laws mandating vaccination against Covid-19 as a condition of employment in health and care sectors were commonplace during the pandemic. Using weekly data at the local authority level, we examine the impact of the vaccine mandate for elderly care homes in England on vaccine take-up, staffing levels, and mortality. Our identification strategy involves (1) comparing take-up and staffing in English elderly care homes relative to other social care settings unaffected by the mandate; (2) comparing take-up and staffing in English elderly care homes relative to those in Wales where the mandate also did not apply; and (3) comparing Covid-19 mortality among English elderly care home residents relative to mortality in domestic homes in England and to care homes in Wales. Our results suggest that the mandate substantially decreased the proportion of care home workers who remained unvaccinated (equivalent to between 28,000 and 41,000 fewer unvaccinated staff), but this came at the cost of a reduction in staffing levels of between 3% and 4% (equivalent to 14,000 to 18,000 staff). We observe this effect most strongly in areas of low unemployment. Our results do not provide evidence that the vaccine mandate was successful in its primary aim of reducing care home deaths. Relatively wide confidence intervals mean inferences regarding mortality are more uncertain than for vaccination uptake and staffing.
We measure the willingness to compete of entrepreneurs and salaried workers in an experiment. Participants can choose between a piece rate and a tournament scheme in either private or public. We find that in the private condition, entrepreneurs are less competitive than salaried workers, but in the public condition, this ordering is reversed. Survey data suggest that perceived norms of appropriate behavior, along with beliefs about the instrumental value of competitiveness for professional success, can explain why entrepreneurs are more competitive when decisions are publicly observable. We also find that the latter condition improves the quality of experimental decisions.
People often need to plan how to allocate their attention across different tasks. In this paper, we run two experiments to study a stylized version of this attention-allocation problem between strategic tasks. More specifically, we present subjects with pairs of 2 × 2 games, and for each pair, we give them 10 seconds to decide how they would split a fixed time budget between the two games. Then, subjects play both games without time constraints, and we use eye-tracking to estimate the fraction of time they spend on each game. We find that subjects’ planned and actual attention allocation differ and identify the determinants of this mismatch. Further, we argue that misallocations can be relevant in games in which a player’s strategy choice is sensitive to the time taken to reach a decision.
This paper studies a canonical general scheduling model that captures the fundamental trade-off between processing jobs and performing diagnostics (testing). In particular, testing reveals the required processing time and urgency of need-to-schedule jobs to inform future scheduling decisions. The model captures a range of important applications. Prior work focused on special cases (e.g., jobs with independent and identically distributed processing time) to devise optimal policies. In contrast, the current paper studies the most general form of the model and describes two simple heuristics to solve it; adaptive weighted shortest processing time is an adaptive generalization of Smith’s rule that optimally solves several important extensions of previously studied models, whereas index policy optimally solves a closely related stochastic optimization bandit problem. The latter achieves an approximation guarantee that quickly approaches a constant factor that is bounded by two as the number of jobs grows and approaches optimally when the testing time decreases. Extensive numerical experiments suggest that our policies effectively solve the general setting (under 0.1% from optimal on average and under 10% from optimal in rare, worst-case instances).
We examine a large transaction-level data set of an Italian omnichannel furniture retailer to study channel-specific effects of fulfillment lead time on demand. This omnichannel retailer sells the same products and has the same product fulfillment across three channels: showroom, online, and catalog. The showroom channel carries no inventory but allows customers to touch and feel the products. The online channel provides a website for consumers to browse and order the products. The catalog channel sends a product catalog to all the households in Italy, so they can place an order over the phone. We find that the showroom channel makes consumers less sensitive to fulfillment lead time than both online and catalog channels. In particular, a 10% increase in lead time (1.83 days from the sample mean of 18.26 days) causes a 0.29% reduction in the daily sales volume (approximately 29 units from the sample mean of 10,140 units per day) at the showroom, which is less than the reduction of 1.85% and 0.92% in the online and the catalog channels, respectively. This finding contradicts the common practical and theoretical assumption about homogeneous lead time sensitivity across channels. In addition, we find that niche products and experience goods accentuate the difference of lead time sensitivity between showroom and nonphysical channels. Through an online experiment, we show that higher product information precision, such as that provided in a showroom, can alleviate customers’ anxiety around product uncertainty, thus reducing lead time sensitivity and increasing purchase intent. We further build two analytical models to show the implications of our empirical results for showroom network design and inventory rationing decisions. Our study highlights the previously ignored fulfillment time sensitivity aspect of the physical store’s value.
Mandatory filings for UK hedge funds suggest that managers having worked at the same prior employer invest more similarly in terms of distances of returns. If they overlapped in employment, increasing the chance of social ties, investments become even more similar. The joint effect accounts for up to two thirds of the difference in investing behavior. Results are robust to fund- and manager-level controls as well as to identification concerns. With controls, the same-employer effect is concentrated in the systematic component (beta), whereas the overlap effect is concentrated in the idiosyncratic components (alpha and residuals). Managerial ties make any two funds more similar in their stock holdings. Moreover, portfolios of connected funds outperform their peers in terms of alpha, return volatility, and Sharpe ratio.
The benefits of vicarious learning are usually conceptualized in terms of a mechanism for learners to utilize the superior knowledge of others. Building on the fact that vicarious learning typically co-occurs and interacts with individual learning-by-doing, we propose an alternative mechanism—one in which vicarious learning is useful because it corrects for certain well-known limitations of individual learning-by-doing. Using computational agent-based models, we show that, under this mechanism, vicarious learning can be beneficial, even without any ex ante differential knowledge to exploit. Our analysis contributes to a deeper understanding of the microfoundations of vicarious learning, which is a vital component of organizational learning. We draw implications for empirical analysis and managerial practice.
This paper studies a dynamic principal–agent setting in which the principal needs to dynamically schedule an agent to work or be suspended. When the agent is directed to work and exert effort, the arrival rate of a Poisson process is increased, which increases the principal’s payoff. Suspension, on the other hand, serves as a threat to the agent by delaying future payments. A key feature of our setting is a switching cost whenever the suspension stops and the work starts again. We formulate the problem as an optimal control model with switching and fully characterize the optimal control policies/contract structures under different parameter settings. Our analysis shows that, when the switching cost is not too high, the optimal contract demonstrates a generalized control-band structure. The length of each suspension episode, on the other hand, is fixed. Overall, the optimal contract is easy to describe, compute, and implement.
A significant proportion of the world’s population has no access to grid-based electricity and so relies on off-grid lighting solutions. Rechargeable lamp technology is gaining popularity as an alternative off-grid lighting model in developing countries. In this paper, we explore consumer behavior and the operational inefficiencies that result under this model. Specifically, we are interested in (i) measuring the impact of inconvenience (of traveling to recharge the lamp) along with the impact of liquidity constraints (because of poverty) on lamp usage and (ii) evaluating the efficacy of strategies that address these factors. We build a structural model of consumers’ recharge decisions that incorporates several operational features of the low-income regions. We conducted large-scale field experiments in Rwanda in partnership with a local rechargeable lamp operator and use the resultant data to estimate and test our model. We find that the complete removal of inconvenience and liquidity constraints from the current business model results in 73% and 126% increases in both recharges and revenue, thereby suggesting that these constraints are major sources of inefficiency. By implementing simple operations-based strategies—such as starting more recharge centers, visiting consumers periodically to collect their lamps for recharge, and allowing consumers to partially recharge their lamps and pay flexibly for the recharge—more than half the benefit of completely eliminating the inefficiencies can be attained. By contrast, the price- and capacity-based strategies that vary the economic variables (i.e., the amount paid per recharge and the amount of light obtained in return) but not the operational model perform far worse than the aforementioned strategies. Overall, our analysis emphasizes the importance of managing operations effectively even in markets with cash-constrained consumers, in which firms may have a natural tendency to focus more on reducing prices.
Investment mandates of fixed income funds constrain managers’ portfolio decisions, often employing credit ratings to classify asset risk. We categorize U.S. and European fixed income funds’ mandates using textual analysis and measure the use of ratings. Over the past two decades, despite the weaknesses of ratings revealed in the global financial crisis, ratings use has increased significantly. Since 2010, the fraction of funds not using ratings in any way has fallen by almost half in both the United States and Europe. By 2020, 94% of U.S. funds and 65% of European funds used ratings. These patterns fit agency-based models of investment mandates and point to a lack of practically useful alternatives.
In this paper, we identify product market competition as a driver of privatization. Using product market shocks caused by trade liberalization of China, which has the world’s largest state sector, we find that subjecting state-owned enterprises (SOEs) to higher competition leads to an increase in private ownership. This response is strengthened when SOEs operate in industries with large technology or productivity gaps from those in the frontier economies or when SOEs impose large fiscal burdens on local governments. Our findings are consistent with politicians’ incentives to boost economic growth for better career development and to shed burdens when rents decrease.
We develop a theory of credible skepticism in organizations to explain the main tradeoffs in organizing data generation, analysis, and reporting. In our designer-agent-principal game, the designer selects the information privately observed by the agent who can misreport it at a cost, whereas the principal can audit the report. We study three organizational levers: tampering prevention, tampering detection, and the allocation of the experimental-design task. We show that motivating informative experimentation while discouraging misreporting are often conflicting organizational goals. To incentivize experimentation, the principal foregoes a flawless tampering detection/prevention system and separates the tasks of experimental design and analysis.
Because of a growing population and shrinking arable land, the world is facing a global food crisis. One important solution could be to subsidize farmers to sustain their production so that they can produce more food for consumers and earn more money for themselves. An efficient subsidy program should also aim to reduce income inequality among farmers, as measured by the Gini coefficient of farmers’ income. In this paper, we examine and compare the effects of input and output farm subsidy programs. The input subsidy reduces the farmers’ input purchasing costs, whereas the output subsidy reduces the farmers’ output processing costs. By considering a continuum of infinitesimal price-taking farmers who are heterogeneous in their average yield rates, our equilibrium analysis of a game-theoretical model yields three results. First, both subsidy schemes reduce the aggregate income inequality measured by the Gini coefficient. However, they create the following “opposite” effects: the input subsidy decreases the income gap among farmers (under mild conditions), whereas the output subsidy increases it. Second, farmers with low yield rates prefer the input subsidy, whereas farmers with high yield rates prefer the output subsidy. Third, the output subsidy scheme is more effective in improving the total farmer income than the input subsidy scheme, whereas the input subsidy scheme is more effective in reducing income disparities and improving consumer surplus than the output subsidy scheme. Our results provide new insights for policymakers who are crafting subsidy schemes.
Companies that are investing in blockchain technology to enhance supply chain transparency face challenges in fostering collaborations with others and deciding what information to share. Transparency over the actions of supply chain partners can improve operational decisions, but sharing own data on the blockchain can put firms at a competitive disadvantage. In this paper, we investigate the resulting questions of when blockchain should be adopted in a supply chain and how it should be designed by analyzing two ways that it can enhance supply chain transparency: making the manufacturer’s sourcing cost transparent to the buyers (i.e., vertical cost transparency) and making the ordering status of buyers transparent to each other (i.e., horizontal order transparency). Given such transparency, firms can design a smart contract that automates transactions contingent on the revealed information and enables them to realize better equilibrium outcomes. We find that blockchain increases supply chain profit only when the manufacturer’s capacity is large and decreases supply chain profit otherwise. If the capacity is sufficiently large to eliminate the buyers’ competition, blockchain leads to a win–win–win and the incentives of all participants are naturally aligned. If the capacity is only moderately large, the manufacturer needs to compensate the buyers to facilitate a blockchain implementation. However, if the capacity is small, horizontal order transparency enabled by the blockchain mitigates the buyers’ overorder incentive to compete for the manufacturer’s capacity and increases double marginalization. For such cases, we show that a blockchain that only enables vertical cost transparency should (and can) still be adopted in a range of small capacity cases, and we propose an access control layer for the logistics data to implement such a blockchain.
We conducted a large-scale personalized field experiment to examine how match quality, recognition, and social impact influence domain experts’ contributions to Wikipedia. Forty-five percent of the experts expressed willingness to contribute in the baseline condition, whereas 51% (a 13% increase over the baseline) expressed interest when they received a signal that an article matched their expertise. However, none of the treatments had a significant effect on actual contributions. Instead experts contributed longer and better comments when the actual match between a recommended Wikipedia article and an expert's expertise, measured by cosine similarity, was higher, when they had higher reputation, and when the original article was longer. These findings suggest that match quality between volunteers and tasks is critically important in encouraging contributions to digital public goods and likely to volunteering in general.
We examine the impact of government interventions on the spread of COVID-19 and consumer spending. We do this by first estimating models of COVID-19 spread, consumer spending, and social distancing in the United States during the early stages of the COVID-19 pandemic. Social distancing has a large effect on reducing COVID-19 spread and is responsive to national and local case numbers. Nonmask government interventions reduce COVID-19 spread, whereas the effectiveness of mask mandates is much smaller and statistically insignificant. Mask mandates tend to increase social distancing, as do nonmask governmental restrictions as a whole. Social distancing hurts spending in the absence of a mask mandate but has a negligible effect on spending if there is a mask mandate. Mask mandates have a direct effect of increasing spending in counties with high levels of social distancing while reducing spending in counties with low levels of social distancing. We use these three estimated models to calculate the effect of mask mandates and other governmental interventions on COVID-19 cases, deaths, and consumer spending. Implemented mask mandates decreased COVID-19 cases by a statistically insignificant 774,000 cases, saving 28,000 lives, over a four-month period, but led to $76B–$155B of additional consumer spending. Other nonmask governmental interventions that were implemented reduced the number of COVID-19 cases by 34M, saving 1,230,000 lives, while reducing consumer spending by approximately $470B–$703B over our 4-month period of the study. Thus, these restrictions were cost effective as long as one values each saved life at $387,000–$608,000 or more.
Little is known about how people-centric factors affect the shape of service time distributions despite distributional statistics (variance or quantiles) being key drivers of system performance in many service industries. We investigate the impact of one people-centric factor—worker fatigue—on the average, variance, and quantiles of service times in paramedic operations. Our analysis uses data on the performance of 368,634 paramedic teams in the London Ambulance Service over 10 years. We measure fatigue by the number of prior jobs a paramedic crew has completed during a shift and estimate its impact on the time it takes the crew to respond to incidents and bring patients to hospitals. Using a recentered influence function regression approach with multiple fixed effects, we find that the average time to hospital increases by 5% throughout the course of an average shift. In addition, the workers become less consistent with fatigue; service time variance increases by 39% during a normal shift. Furthermore, we find that in addition to an upward shift in mean service times, both the upper and lower tails of the distribution have more weight for fatigued paramedics. These effects are driven mostly by the performance of paramedics at the scene, rather than their driving to or from the incident. The distributional effects of fatigue are only slightly mitigated by increased experience or reduced system workload. Our work demonstrates that the impact of people-centric factors can be highly nonuniform across the service time distribution.
We exploit the quasi-random assignment of federal bank examiners to syndicated loans to study the effect of supervision on corporate lending. Following supervisory rating downgrades, banks decrease credit commitments and downgrade internal risk assessments. Borrowers face larger commitment reductions whenever banks have low ex ante screening and monitoring incentives or whenever examiners’ assessments contain more information than banks’ assessments, suggesting that examinations complement bank monitoring. Although public firms can offset the loss of bank credit by tapping external capital markets, smaller and more opaque private firms draw on internal cash balances instead and reduce investment and sales growth.
We study the classic model of joint pricing and inventory control with lost sales over T consecutive review periods. The firm does not know the demand distribution a priori and needs to learn it from historical censored demand data. We develop nonparametric online learning algorithms that converge to the clairvoyant optimal policy at the fastest possible speed. The fundamental challenges rely on that neither zeroth-order nor first-order feedbacks are accessible to the firm and reward at any single price is not observable due to demand censoring. We propose a novel inversion method based on empirical measures to consistently estimate the difference of the instantaneous reward functions at two prices, directly tackling the fundamental challenge brought by censored demands. Based on this technical innovation, we design bisection and trisection search methods that attain an O˜(T) regret for the case with concave reward functions, and we design an active tournament elimination method that attains O˜(T3/5) regret when the reward functions are nonconcave. We complement the O˜(T3/5) regret upper bound with a matching Ω˜(T3/5) regret lower bound. The lower bound is established by a novel information-theoretical argument based on generalized squared Hellinger distance, which is significantly different from conventional arguments that are based on Kullback-Leibler divergence. Both the upper bound technique based on the “difference estimator” and the lower bound technique based on generalized Hellinger distance are new in the literature, and can be potentially applied to solve other inventory or censored demand type problems that involve learning.
Emotional involvement is known to be necessary but not sufficient for good decision making in the face of uncertainty. It has been conjectured that emotional engagement in anticipation of risky outcomes constitutes “good” emotions. We introduce a new methodology to determine whether anticipatory emotional engagement is beneficial in the context of trading in financial markets. We focus on heart rate changes because they occur at a sufficiently high frequency to discern timing relative to events in the marketplace. After conservatively adjusting for multiple hypothesis testing, we find that participants whose heart rate changes anticipate their order submissions at inflated prices earn significantly more, whereas participants whose heart rate responds to their trades earn significantly less. By investigating cointegration between skin conductance response and the dynamics of individual portfolio values, we confirm the importance of emotional involvement in determining who makes or loses money.
Consumers encounter costs and inconvenience in the purchase and consumption of goods. Time, effort, training, and expertise, also referred to as access costs, are examples of factors that constrain the ability of consumers to derive utility from offerings beyond the price of a good. Economies of scope exist when household production of these factors share commonalities among choice alternatives, making it more likely that some products would be purchased and consumed in conjunction with others. For example, consumers planning out-of-town travel might consider visiting multiple tourist attractions during their trip, and course offerings might appeal more to students who are already in the mindset of taking courses. We develop a model with economies of scope that captures a leveraging effect where the purchase of one good makes the purchase of other goods more likely. Our model offers an alternative explanation of complementary purchases that are driven by cost reductions rather than utility enhancements. The model is applied to four conjoint datasets where we find that economies of scope and access costs play an important role in consumer purchases and have high economic value relative to the role played by price.
Connected healthcare is a form of health delivery that connects patients and providers through connected health devices, allowing providers to monitor patient behavior and proactively intervene before an adverse event occurs. Unlike the costs, the benefits of connected healthcare in improving patient behavior and health outcomes are usually difficult to determine. In this study, we examine the efficacy of a connected health system that aimed to reduce readmissions through improved medication adherence. Specifically, we study 975 patients with heart disease who received electronic pill bottles that tracked medication adherence. Patients who were nonadherent received active social support that involved different types of feedback, such as text messages and calls. By integrating data on adherence, intervention, and readmission, we aim to (1) investigate the efficacy of connected healthcare in promoting medication adherence, (2) examine the relationship between medication adherence and readmission, and (3) develop a dynamic readmission risk-scoring model that considers medication adherence and use the model to better target nonadherent patients. Our findings suggest that patients are more likely to become adherent when they or their partners receive high levels of intervention that involve personalized feedback and when the intervention is escalated quickly and consistently. We also find that long-term adherence to three common heart medications is strongly associated with reduced readmission risk. Lastly, using counterfactual simulation, we apply the dynamic readmission risk-scoring model to our setting and find that, when using an intervention strategy that prioritizes high-risk patients, we obtain 10% fewer readmissions while using the same effort level from the patient support team.
We study a dynamic moral hazard setting where the manager has private evidence that predicts the firm’s cash flows. Bad-news disclosure is rewarded by a lower borrowing cost relative to the no-evidence case, whereas no disclosure leads to higher borrowing costs. For a given capital structure, disclosure reduces the firm’s default risk by lowering its pay-for-performance sensitivity. However, for a set of low-profitability firms, the anticipation of future disclosure of information by managers lowers both firm value and managerial rents at the financing stage because of a reduction in the firm’s initial liquidity. The model can reconcile the empirical evidence on the effects of providing earnings guidance, especially for loss firms.
We use the dynamic conditional correlation (DCC) model to estimate daily frequency mutual fund betas. Compared with traditional estimates, daily betas better capture changes in fund risk stemming from daily fund trading activity. Based on these beta estimates and a two-stage estimation procedure, we find significant evidence of market timing ability among actively managed U.S. equity funds that is not apparent via standard approaches. Unlike traditional measures, our timing estimates correlate positively with fund performance. Market timing is especially evident during down markets, with successful timers exhibiting low downside risk. Timing ability persists across time and attracts investor flows.
Decisions with risky consequences at multiple points in time are driven not only by risk attitudes and time preferences but also by attitudes toward intertemporal correlation (i.e., the correlation between outcomes at different points in time). This paper proposes a model-free method to measure degrees of intertemporal correlation aversion. We disentangle attitudes toward positive and negative intertemporal correlation, which can differ if expected intertemporal utility is violated. In an experiment, subjects on average exhibited correlation aversion both for lotteries with positive correlation and for lotteries with negative correlation. That is, they disliked positive correlations and liked negative correlations. At the individual level, we found heterogeneity and remarkably, many subjects being insensitive to intertemporal correlations. Moreover, for most subjects, expected intertemporal utility was violated because attitudes toward positive and negative correlation differed.
We examine whether the gender gap in competitiveness in stereotypically male tasks persists when the decision to compete is made by someone else. In a within-subject laboratory experiment, decision makers make competition entry decisions for both themselves and a randomly selected other. Although we replicate the standard gender gap in decisions for oneself, we do not find a gender gap when the decision to compete is made by someone else. This is driven by men being significantly more willing to enter themselves than others into competitions. Men’s and women’s willingness to enter others into competitions is statistically indistinguishable from women’s willingness to enter themselves into competitions. Subgroup analyses reveal that competitive preferences are significantly greater for Asian than non-Asian subjects. Our results have implications for the effectiveness of nominations and mentorship to close gender gaps in labor market outcomes that are affected by competitive preferences.
This paper presents a novel decomposition approach for measuring deterrence motives in dynamic oligopoly games. Our approach yields a formalized, scale-free, and interpretable measure of deterrence motives that informs researchers about the proportion for which deterrence motives account of all entry motives. In addition, the decomposition leads to a set of conditions for counterfactual analysis where hypothetical scenarios with deterrence motives eliminated can be explored. We illustrate the use of our measure and counterfactual by conducting an empirical case study about the dynamics of coffee chain stores in Toronto, Canada. The inferred deterrence motives suggest that a noticeable proportion of entry motives can be attributed to deterrence; it can be as high as 43% for the increasingly dominant coffee chain, Starbucks, in certain types of markets. Finally, counterfactual analysis confirms that deterrence motives are indeed associated with Starbucks’ aggressive presence as the number of its outlets and its market share are markedly lower once these motives are eliminated.
The stock option-implied volatility skew reflects both the structural risk characteristics of the underlying company and the short-term information flow about the stock price movement. This paper builds a semistructural, cross-sectional option pricing model to separate the structural risk contributions from the information flow. The model identifies two structural risk sources that contribute to the cross-sectional variation of the skew: the company’s business cyclicality and its default risk. The model can explain as much as 44% of the cross-sectional variation in implied volatility skew and is particularly informative during and after recessions. The remaining skew variation reflects mainly short-term information flow and can be used to construct stock portfolios with much better investment performance and without hidden structural risk exposures.
Fragmenting clearing across multiple central counterparties (CCPs) is costly because global dealers cannot net positions across CCPs. They have to collateralize both the short position in one CCP and an offsetting long position in another CCP. This, coupled with a structural net order imbalance across CCPs, can cause prices to persistently differ across them (“the CCP basis”). Tests based on unique CCP data for interest-rate derivatives (IRDs) yield broad empirical support for this intuition and suggest that the clearing friction costs sellers clearing in LCH, the largest European CCP for IRDs, $80 million daily.
We use a novel data set to examine the impact of exposing institutional orders to electronic liquidity providers (ELPs). We present empirical evidence that marketable pieces of large parent orders are routed to ELPs seemingly to avoid paying liquidity fees on exchanges. This routing decision results in lower net effective spreads for these child orders but leads to higher execution shortfall for the parent order. We provide evidence suggestive of a causal relation by utilizing the parent orders of investors that disallow the broker to route their child orders to ELPs. Our analysis suggests that ELPs detect the presence of the parent order very quickly given the transparent bilateral relation with the broker.
Early stage technology startups rely critically on talented scientists and engineers to commercialize new technologies. And yet these startups compete with established technology firms to hire the best workers. Theories of ability sorting predict that high-ability workers will choose jobs in established firms that offer greater complementary assets and higher pay, leaving low-ability workers to take lower paying and riskier jobs in startups. We propose an alternative view in which heterogeneity in both worker ability and preferences enable startups to hire talented workers who have a taste for a startup work environment even at lower pay. Using a longitudinal survey that follows 2,394 science and engineering PhDs from graduate school into their first industrial employment, we overcome common empirical challenges by observing ability and stated preferences prior to entry into the labor market. We find that both ability and career preferences strongly predict startup employment with high-ability workers who prefer startup employment being the most likely to work in a startup. We show that this partly reflects dual selection effects whereby worker preferences result in a large pool of startup job applicants and startups make job offers to the most talented workers. Additional analyses confirm that startup employees earn approximately 17% lower pay. This gap is greatest for high-ability workers and persists over workers’ early careers, suggesting that they accept a negative compensating differential in exchange for the nonpecuniary benefits of startup employment. Data on job attributes and stated reasons for job choice further support this interpretation.
Classic inventory theory suggests that inventory plays a vital role in matching demand and supply. This paper provides both country- and firm-level evidence that inventory can be used as a financial instrument to take advantage of arbitrage opportunities in financial markets with limited capital mobility. Using data from China customs’ metal imports and firm-level inventory from metal processing industries, we show that firms can utilize the inventory of an imported product to carry lower cost capital into a country with strict capital controls and, thus, gain higher financial returns. Specifically, at the country level, we find that the level of imported metal commodities is positively associated with the expected returns from financial arbitrage. At the firm level, we find that a higher expected return from financial arbitrage will incentivize a firm to increase its inventory level through short-term borrowing. In addition, our evidence supports the notion that firms with higher liquidation value, larger size, and higher sales growth (i.e., firms with higher borrowing capacity) may be more active in using inventory as an instrument to seek higher financial gains. We also utilize a unique regulatory policy shock as a quasi-natural experiment to enhance the causality interpretation of our empirical findings.
Several features of automotive procurement distinguish it from the prototypical supply chain in the academic literature: pass-through pricing that reimburses suppliers for raw material costs, market frictions that prohibit cost transparency and imbue suppliers with pricing power, and contractual commitments that span multiple production periods. In this context, we formalize a procurement model by considering an automaker that buys components from an upstream supplier to assemble cars over several production periods in an environment where period demands and raw material costs are both stochastic. Our paper clarifies how information asymmetry and market factors that amplify or weaken this asymmetry affect the firms’ procurement protocol preferences. Then, using proprietary contract and supplier data from BMW, we empirically validate this model and show that it reflects BMW’s reality: the factors that should theoretically go into automotive procurement decisions do so. Our analysis also reveals that existing contracting protocols in this context are not optimal for procurement under asymmetric information, and so we propose an alternative contracting method. We calibrate our model and estimate an automaker’s performance improvement from this optimal contract over the status quo.
Hedge funds earn positive ex post abnormal returns and avoid negative abnormal returns on their equity portfolios when trading in the opposite direction of highly diversified low-turnover institutional investors (quasi indexers). This pattern seems to be driven by the preferences of quasi indexers for high-market-beta stocks together with the ability of hedge funds to identify subsets of especially profitable trades. It remains pronounced when accounting for other determinants of hedge fund trades, such as stock liquidity, market anomalies, and major corporate events. Trading against other institutional investors or noninstitutions does not result in abnormal performance for hedge funds.
We explore the nature and robustness of the attraction effect. The attraction effect can be seen as a persistent bias or as the result of heuristics that may not persist upon reflection. We provide robust experimental evidence that the attraction effect first rises and then falls over time when participants are incentivized to make a quick choice they can later revise. Participants in two experiments under continuous time pressure make choices among options with the aim to maximize an objective, measurable value. We find that participants disproportionately favor the asymmetrically dominant option in the first seconds and then revise their choices until the effect disappears or is significantly reduced. The effect survives only in the special and often studied case of indifference among options. We develop a tractable extension to the multiattribute linear ballistic accumulator model to allow for choice revisions. That model explains how choice revisions reduce context effects. We estimate its parameters at the individual level and document differences between fast and slow participants that also play a role in explaining the rise-and-fall pattern in the attraction effect. We extend the analysis to similarity and compromise effects. We find a very small similarity effect, which does not exhibit any dynamics, and a significant reverse compromise effect displaying a rise-and-fall pattern. Our findings, although limited to objective-value tasks, are consistent with context effects being short-term heuristics that can be superseded by more reflective cognitive strategies when decision makers have time and incentive to do so.
Marketing researchers are often interested in estimating causal effects when a randomized experiment is infeasible. The synthetic control (SC) method has emerged as a powerful tool in these quasiexperimental settings. It is important to verify the SC parallel pretrends assumption, the testable part of the identifying assumption, because its violation may lead to biased estimates. However, no formal test exists, so researchers have to rely on visual inspection. Even with a formal test, researchers still need to know how to balance the bias-efficiency trade-off for the estimate. We fill this void and advance the two-step synthetic control (TSSC) approach that comprises a formal test for the SC pretrends assumption in the first step and recommends an appropriate method that balances the dual goal of reducing bias and increasing efficiency in the second step. Simulations show that the TSSC approach performs favorably in the bias-variance (bias-efficiency) trade-off. Applying the TSSC approach, we find that New York State’s repeal of the tampon tax caused a positive and significant (2.08%) increase in weekly tampon sales. Using theory, simulations, and empirics, we demonstrate the importance, validity, and usefulness of the TSSC approach.
We deal with the problem of managing a project or a complex operational process by controlling the execution pace of the activities it comprises. We consider a setting in which these activities are clearly defined, are subject to precedence constraints, and progress randomly. We formulate a discrete-time, infinite-horizon Markov decision process in which the manager reviews progress in each period and decides which activities to expedite to balance expediting costs with delay costs. We derive structural properties for this dynamic project expediting problem. These enable us then to devise exact solution methods that we show to reduce computational burden significantly. We illustrate how our method generalizes and can be used to tackle a wide range of so-called stochastic shortest-path problems that are characterized by an intuitive property and can capture other applications, including medical decision-making and disease-modeling problems. Moreover, we also deal with the state identification issue for our problem, which is a challenging task in and of itself, owing to precedence constraints. We complement our analytical results with numerical experiments, demonstrating that both our solution and state identification methods significantly outperform extant methods for a supply chain example and for various randomly generated instances.
The Hospital Value-Based Purchasing (VBP) Program is Medicare’s implementation of yardstick incentives applied to hospitals in the United States. Under the VBP Program, 2% of all Medicare payments to hospitals, estimated to be U.S. $1.9 billion in fiscal year 2021, are withheld and redistributed based on their relative performance in the quality of delivered care. We develop a dynamic mean-field equilibrium model in which hospitals are engaged in repeated competition under yardstick incentives. Using structural estimation methods, we recover key parameters that govern hospitals’ decisions to invest in quality improvement, including the financial and nonfinancial costs and uncertain outcomes of investment. By dynamically solving for hospitals’ individually optimal investment policies, we estimate the trajectory of quality improvements for each hospital, including its investment decisions and quality levels throughout the implementation of the VBP Program. Our counterfactual analyses explore the benefits, on the one hand, of modifying the overall size of the yardstick incentives and on the other hand, of implementing a more focused program tailored to hospital type. We find that increasing the size of the incentives from 2% to 4% would have resulted in an additional quality investment of U.S. $1.2 billion from 2011 to 2018, leading to a 3.3% reduction in the average rate of central line-associated bloodstream infections (CLABSIs). Applying yardstick incentives to the tailored hospital peer groups, even without changing the size of the incentives, can lead to an average reduction of 1.4% in the rate of CLABSI among groups of hospitals associated with the highest costs of quality investment.
We investigate how market participants price and manage counterparty credit risk using confidential trade repository data on single-name credit default swap (CDS) transactions. We find that counterparty risk has a modest impact on the pricing of CDS contracts but a large impact on the choice of counterparties. For contracts ineligible for central clearing, we show that market participants are significantly less likely to trade with counterparties whose credit risk is highly correlated with the credit risk of the reference entities and with counterparties whose credit quality is low. For clearable contracts, we find that nondealers are more likely to clear during a crisis and less likely to clear when the reference entity is a large U.S. dealer or a sovereign.
Decision makers often want to target interventions so as to maximize an outcome that is observed only in the long term. This typically requires delaying decisions until the outcome is observed or relying on simple short-term proxies for the long-term outcome. Here, we build on the statistical surrogacy and policy learning literatures to impute the missing long-term outcomes and then approximate the optimal targeting policy on the imputed outcomes via a doubly robust approach. We first show that conditions for the validity of average treatment effect estimation with imputed outcomes are also sufficient for valid policy evaluation and optimization; furthermore, these conditions can be somewhat relaxed for policy optimization. We apply our approach in two large-scale proactive churn management experiments at The Boston Globe by targeting optimal discounts to its digital subscribers with the aim of maximizing long-term revenue. Using the first experiment, we evaluate this approach empirically by comparing the policy learned using imputed outcomes with a policy learned on the ground-truth, long-term outcomes. The performance of these two policies is statistically indistinguishable, and we rule out large losses from relying on surrogates. Our approach also outperforms a policy learned on short-term proxies for the long-term outcome. In a second field experiment, we implement the optimal targeting policy with additional randomized exploration, which allows us to update the optimal policy for future subscribers. Over three years, our approach had a net-positive revenue impact in the range of $4–$5 million compared with the status quo.
This paper investigates how organizational misconduct is perpetuated through intergenerational transmission. We theorize that early exposure to a subculture of misconduct imprints newcomers with the belief that misconduct is normal, which is then carried by these individuals into managerial positions and passed down to their subordinates. We test this using longitudinal administrative data from the Chicago Police Department from 1980 to 2017. We exploit a lottery that assigns applicants to training cohorts to demonstrate that officers exposed early on to a subculture of misconduct not only engage in more misconduct over their entire careers, but also increase the misconduct of their subordinates after they become managers. We also find that this intergenerational dynamic is stronger when subordinate officers were exposed to a subculture of misconduct themselves, are earlier in their tenure, and have not yet received their annual review from their manager. Taken together, these findings reveal a bottom-up dynamic whereby beliefs about misconduct are developed in an organization’s lowest ranks, carried by these individuals over time, and passed down to future generations. This study expands our understanding of how organizational misconduct is perpetuated as well as offers important policy implications for addressing the problem of police misconduct.
Despite mixed evidence for the relationship between demographic diversity and creativity, we propose that observers hold a lay belief that demographic diversity increases creativity and apply this lay belief in judgments about teams and their creative work. Across eight preregistered studies (n = 5,530), we find that observers judge teams diverse in terms of race and gender to be more creative than teams homogeneous in terms of race and gender, including in incentive-compatible predictions made about real teams competing in a creativity challenge. We also find that products attributed to demographically diverse teams are evaluated as more creative compared with identical products attributed to demographically homogenous teams. Mediation analyses provide evidence consistent with the notion that people perceive demographic diversity (i.e., social category differences) to be correlated with cognitive diversity (i.e., difference of perspectives), and this belief contributes to attributions of greater creativity to diverse teams and the ideas they generate. We can also turn off the perceived association between demographic diversity and creativity by directly manipulating people’s perceptions of team cognitive diversity. Furthermore, we find evidence of a curvilinear relationship between the proportion of racial minorities or women in a group and judgments of the group’s creativity. Together, our results suggest that the popular uptake of the belief that diversity boosts creativity may impact how creativity is identified in organizational contexts.
This paper presents one of the first marketing applications of molecular genetics. We report evidence that salespeople’s genetic variants linked to educational attainment predict sales performance. Both genetics and selling effort contribute to sales performance, whereas genetics contribute more than personality traits. We further show that adaptive learning, as captured in salespeople’s customer orientation and opportunity recognition skills, may explain the gene-sales relationship. We discuss the implications of these findings for sales management and the value of genetic research for the marketing field.
Cybersecurity breaches pose a substantial concern in the digital era. We investigate how customers respond to multiple unexpected data breaches of their information in India. Difference-in-differences estimates show that digital payments declined by 9% relative to cash payments immediately after an unexpected data breach in a food delivery platform, but the gap disappeared three months later. Customer entry and exit also exhibit weak, short-lived changes. Additional analyses on bank and online grocery data breaches uncover even weaker effects of data breaches. Our findings imply that the perceived benefits of convenience outweigh the costs of payment security risks.
This paper examines whether perceptions of discrimination affect the economic expectations of U.S. households. We focus on two forms of expectations that play a central role in economic and financial decisions of households: labor income and inflation. Using experimental data, we demonstrate that discrimination generates greater dispersion in household forecasts. It increases subjective expectations of income uncertainty by 8% and inflation uncertainty by 5%. The impact of discrimination is concentrated among racial/ethnic minorities, inducing 12%–16% greater variation in their income uncertainty expectations and 10%–12% greater variation in their inflation uncertainty expectations. Both psychological and emotional factors appear to influence the discrimination–economic expectations relation.
For a large class of demand models that allow for multiple consumer types, we present performance guarantees for simple nonpersonalized pricing heuristics relative to optimal personalized pricing. Our results demonstrate that in a general setting, the effectiveness of pricing along a positive vector depends on how the price vector aligns with optimal personalized price vectors. We propose two positive direction vectors: the “economic” and “robust” directions. The economic direction is a convex combination of the optimal personalized price vectors and aims to do well on average. The robust direction offers the best worst-case performance guarantee. By judiciously selecting pricing directions, our results also provide performance guarantees of simple pricing strategies relative to more sophisticated pricing strategies. In particular, we provide performance guarantees for nonpersonalized, optimal linear pricing relative to optimal nonlinear, personalized pricing. Our research also examines the performance of common heuristics for bundle pricing relative to optimal, personalized, bundle-size pricing. Our experiments show that performance often improves when consumer types are clustered and each cluster is offered a price direction. We compared the performance of the k-means clustering heuristic and the farthest point first clustering heuristic. Our findings indicated that k-means clustering has significantly superior performance on average. This suggests that businesses could potentially benefit from implementing k-means clustering in their pricing strategies. In conclusion, our study offers valuable insights and performance guarantees for various pricing strategies and their relative effectiveness. These findings could inform pricing decisions and potentially lead to improved outcomes for firms.
This paper investigates the market consequences of sovereign accounting errors, opening a new area of research on sovereign accounting quality in the accounting literature. Eurostat, a division of the European Commission, provides semiannual assessments of financial reports produced by the member states of the European Union (EU) and issues reservations that detail financial reporting errors. Using a sample of Eurostat reservation issuances across 28 EU countries from 2004 to 2018, we find that sovereign bond yields abnormally increase during a reservation announcement window, especially when a reservation explicitly mentions deficit or debt, when it quantifies the extent of the errors’ financial impact, or when the errors relate to recent fiscal data. Consistent with a home bias after the release of negative news, we find that domestic holdings of sovereign debt increase after the issuance of a reservation. Overall, our evidence suggests that sovereign accounting errors have significant market consequences and raises further questions for future research in sovereign accounting quality.
We document evidence that firms systematically increase specialized, locally targeted advertising following the firm being taken to trial in that given location, precisely following initiation of the suit. In particular, we use legal actions brought against publicly traded firms over the 20-year sample period that progress to trial between 1995 and 2014. In terms of magnitude, the increase is sizable: targeted local advertising increases by 23% (t = 4.37) following the suit. They focus their advertisement spikes specifically toward jury trials, and in fact specifically toward the most likely jury pool. Last, we document that these advertising spikes are associated with verdicts, increasing the probability of a favorable outcome.
Because a hospital is an interconnected, interdependent network of care units, allocating resources—beds, nurses, and improvement initiatives—to one unit to reduce its congestion may have spillover effects on other units. If such congestion spillover is substantial, ignoring it may lead to unintended consequences and missed opportunities. We use data collected over five years from a hospital with 16 inpatient units to empirically examine whether and how much congestion propagates through the network of inpatient units. Our estimation result suggests that the magnitude of the congestion spillover is indeed substantial in our study hospital. For example, increasing one inpatient unit’s utilization by 10 percentage points today can increase its neighboring inpatient unit’s utilization by up to 4.33 percentage points tomorrow. Using counterfactual analyses, we estimate the effect of adding a bed to each unit. We find that due to congestion spillover, adding one bed to the bottleneck unit can free up 4.14 beds in the hospital, which translates to 383.53 more hospital visits per year or a 3% increase in hospital throughput. This effect is about three times bigger in magnitude compared with what one can achieve by naively choosing which unit to add a bed to. Hospitals and other manufacturing and service systems with complex interdependence across resources can use our empirical framework to examine the spillover effect of resources on performance metrics and leverage such understanding to effectively improve their operations.
We propose how to bid in first-price auctions when a bidder knows the own value but not how others will bid. To do this, we introduce a methodology to show how to make choices in strategic settings without assuming common knowledge or equilibrium behavior. Accordingly, we first eliminate environments that are believed not to occur and then find a robust rule that performs well in the remaining environments. We test our bids using data from laboratory experiments and the field and find that our bids outperform those made by real bidders.
People tasked with replacing a departing group member are disproportionately likely to choose a replacement with the same demographic identity, leading to demographic “stickiness” in group composition. We examine this effect in 2,163 U.S. federal judge appointments over 75 years, in the selection of 5,616 S&P 1500 board directors from 2014 to 2019, and in four preregistered experiments (n = 2,900). The patterns we document are generally consistent with both impact aversion (desires to minimize changes to group composition and dynamics) and diversity loss aversion (outsized concerns about losing ground on demographic diversity relative to interests in gaining ground). Ultimately, our results suggest that replacement decisions are influenced by loss-averse preferences regarding the demographic identities of departing group members. The propensity to choose new group members based on whether they demographically resemble their predecessors suggests that once progress toward diversification has occurred, it should be “sticky,” so backsliding is less likely than might otherwise be expected. An optimistic outlook is that one-time interventions to change group composition may have a lasting impact, and change agents committed to diversification may have enduring effects on equality beyond their tenure.
Matching makes or breaks peer-to-peer (P2P) platforms. As the platform-based P2P markets shift from their grassroots nature toward elite offerings, will differentiating suppliers by quality increase matching performance? Can P2P markets differentiate certain suppliers without marginalizing others? We seek answers to these questions by leveraging an empirical opportunity on Airbnb, which differentiates listings that meet high quality standards from others through its Plus program in several U.S. cities. Our findings are threefold. First, we find a sizable increase in market-level matching performance after the program launch, that is, a 15.8%–16.2% increase in the number of booked nights. Second, the rise in matching performance is attributed to reduced search frictions, evident in markets with higher discovery and evaluation costs. Third, the Plus program benefits all listing tiers through reduced search frictions, yielding the most benefits to the Plus and non-Plus listings in terms of increased bookings and prices. Regular listings also receive increased bookings because of competitive prices. Our findings hold across multiple robustness checks and offer important insights for platform design and supply management in P2P markets.
Acquisition announcements coincide with upward value revisions for the target firms’ technology peers, which are not due to economic relations based on product market, supply chain, or geographical location. Such a phenomenon is robust across subsample periods, not specific to merger or technology waves, and not related to product-market structure and the unique innovation features of certain technology-intensive industries. Firms experience more dramatic value revisions when they have deeper technology overlaps with their targets, are more dependent on technology, or when a transaction features higher premium or greater technology overlap between the acquirer and target. Our mechanism analysis provides evidence that is primarily consistent with the acquisition-probability hypothesis whereby acquisition announcements elevate expected technology synergies and merger prospects for peers and partially in line with the enhanced-investment hypothesis that peers’ revaluations correlate with increased technology investments. We do not find any evidence in line with the competition-balance hypothesis that attributes peers’ value revisions to the change in industrial competition intensity. Overall, our results demonstrate that acquisition announcements disseminate novel information about the value of technology that is of common interest among firms with close technologies.
Weather risk affects the economy, agricultural production in particular. Index insurance is a promising tool to hedge against weather risk, but current piecewise-linear index insurance contracts face large basis risk and low demand. We propose embedding a neural network-based optimization scheme into an expected utility maximization problem to design the index insurance contract. Neural networks capture a highly nonlinear relationship between the high-dimensional weather variables and production losses. We endogenously solve for the optimal insurance premium and demand. This approach reduces basis risk, lowers insurance premiums, and improves farmers’ utility.
This study demonstrates that skewness preference of investors is an important driver of various market anomalies. Using a combined measure of mispricing based on 11 prominent anomaly strategies, we show that return predictability associated with the mispricing component of market anomalies is stronger among firms with higher idiosyncratic skewness. The predictability differences are driven by the higher underperformance of high-skewness firms in short anomaly portfolios. Skewness does not affect the performance of long anomaly portfolios. Portfolio holdings data from a retail brokerage firm show that investors with stronger skewness preferences assign relatively larger weights to stocks in short anomaly portfolios.
Studying 30 countries, we find that the link between employee satisfaction and stock returns is significantly increasing in a country’s labor market flexibility. This result is consistent with employee satisfaction having greater recruitment, retention, and motivation benefits where firms face fewer hiring and firing constraints and employees have greater ability to respond to satisfaction. Labor market flexibility also increases the link between employee satisfaction and current valuation ratios, future profitability, and future earnings surprises, inconsistent with omitted risk factors and identifying channels through which employee satisfaction may affect stock returns. The findings have implications for the differential profitability of socially responsible investing strategies around the world—in particular, the importance of considering institutional factors when forming such strategies.
This study examines how equity market fragmentation affects firms’ capital investment decisions. Recent empirical research finds that market fragmentation lowers trading costs and thus improves market quality. We examine whether this increase in market quality translates into greater revelatory price efficiency, where stock prices reveal with greater precision information to managers and/or creditors about firms’ investment opportunities. Consistent with this notion, our findings reveal that the association between capital investment and investment opportunities is increasing in market fragmentation. Additional evidence suggests that (a) market fragmentation increases revelatory price efficiency at least in part by encouraging information acquisition and informed trade by equity investors and (b) the more efficient stock prices inform both managers and creditors about firms’ investment opportunities. Inferences based on difference-in-differences and instrumental variable tests are consistent with those based on our primary findings.
Using a unique setting where stand-alone banks submit filings to bank regulators instead of the U.S. Securities and Exchange Commission (SEC), we examine the consequences of fragmented securities regulation for information-processing costs and opportunistic insider trading. We find the market reaction to insider-trading filings on FDICconnect is less timely than to those on SEC’s Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system, suggesting FDICconnect generates higher information-processing costs. We also find only large investors trade more on insider-trading filings on FDICconnect than on insider-trading filings on SEC EDGAR, thus extracting benefits from the delayed market reaction to insider-trading filings on FDICconnect. Finally, we find increased insider selling in stand-alone banks prior to negative earnings news, suggesting insiders’ opportunistic use of private information. These findings collectively suggest regulatory fragmentation undermines market efficiency and distorts the level playing field.
We study the relationship between ownership concentration and firm value using hand-collected data on the stakes of owner–managers before and after initial public offerings (IPOs). We instrument for the reduction in stake using market returns shortly before IPOs. Short-run market returns are plausible instruments because owners engage in market timing by selling more when prior returns are high, but high short-run returns are unlikely to directly affect firm value years after the IPO. As predicted by agency theory, a large reduction in ownership concentration at the IPO is negatively related to valuation. Future asset growth is low when owners have low stakes.
Gifts are important instruments for forming bonds in interpersonal relationships. Our study analyzes the phenomenon of gift contagion in online groups. Gift contagion encourages social bonds by prompting further gifts; it may also promote group interaction and solidarity. Using data on 36 million online red packet gifts on a large social site in East Asia, we leverage a natural experimental design to identify the social contagion of gift giving in online groups. Our natural experiment is enabled by the randomization of the gift amount allocation algorithm on the platform, which addresses the common challenge of causal identification in observational data. Our study provides evidence of gift contagion: On average, receiving one additional dollar causes a recipient to send 18 cents back to the group within the subsequent 24 hours. Decomposing this effect, we find that it is mainly driven by the extensive margin: more recipients are triggered to send red packets. Moreover, we find that this effect is stronger for “luckiest draw” recipients, suggesting the presence of a group norm regarding the next red packet sender. Finally, we investigate the moderating effects of group- and individual-level social network characteristics on gift contagion as well as the causal impact of receiving gifts on group network structure. Our study has implications for promoting group dynamics and designing marketing strategies for product adoption.
It is well documented that people have misperceptions about energy costs. However, there is mixed empirical evidence about the effectiveness of information provision and its persistence over time. Understanding the interplay between information regarding the private and social benefits of low-carbon technologies and identifying mechanisms through which information treatments affect people’s long-term behaviors can help reconcile the mixed findings in the literature. In this paper, we measured the long-term effects of information provision through online experiments involving more than 3,000 households in China. We provided people with information on low-carbon light bulbs’ monetary and climate benefits and observed their purchasing decisions over 10 months. We find that information on private or social benefits leads to a temporary effect on most households and a persistent one on the uninformed. This evidence suggests that information provision works through two different mechanisms: increasing saliency and filling the information gap.
We study third-party loan guarantees in a model in which lenders can screen and sell loans before maturity when in need of liquidity. Loan guarantees improve market liquidity, reduce lending standards, and can have a positive overall welfare effect. Guarantees improve the average quality of nonguaranteed loans traded and thus, the market liquidity of these loans because of selection. This positive pecuniary externality provides a rationale for guarantee subsidies. Our results contribute to a debate about reforming government-sponsored mortgage guarantees by Fannie Mae and Freddie Mac, suggesting that the excessively high subsidies to these guarantees should be reduced but not completely eliminated.
We examine how time-series volatility of book-to-market (UNC) is priced in equity returns and the relative contributions of its book volatility (variations in earnings and book value) and market volatility components (shocks in required return). UNC captures valuation risk, so stocks with high valuation risk earn higher return. An investment strategy long in high-UNC firms and short in low-UNC firms generates 8.5% annual risk-adjusted return. UNC valuation risk premium is driven by outperformance of high-UNC firms facing higher information risk and is not explained by established risk factors and firm characteristics.
Using administrative universal business registration data as well as primary offline and online surveys of small businesses (including unregistered self-employments) in China, we examine (i) whether digitization helps small and medium enterprises (SMEs) better cope with the COVID-19 pandemic and (ii) whether the pandemic has spurred digital technology adoption. We document significant economic benefits of digitization in increasing SMEs’ resilience against such a large shock, as seen through mitigated demand decline, sustainable cash flow, ability to quickly reopen, and positive outlook for growth. After the January 2020 lockdown, firm entries exhibited a V-shaped pattern, with entries of e-commerce firms experiencing a less pronounced immediate drop and a quicker rebound. Moreover, the pandemic has accelerated the digital transformation of existing firms and the industry in multiple dimensions (e.g., altering operation scope to include e-commerce, allowing remote work, and adopting electronic information systems). The effect persists more than one year after reopening, and it is more pronounced for certain sectors, firms in industrial clusters, and areas with more digital inclusion but less financial efficiency, constituting initial evidence for the long-term impact of the pandemic and the supposedly transitory mitigation policies.
We study belief updating about relative performance in an ego-relevant task. Manipulating the perceived ego relevance of the task, we show that subjects substantially overweight positive information relative to negative information because they derive direct utility from holding positive beliefs. This finding provides a behavioral explanation why and how overconfidence can evolve in the presence of objective information. Moreover, we document that subjects who receive more negative information downplay the ego relevance of the task. These findings suggest that subjects use two alternative strategies to protect their ego when presented with objective information.
We study how the implementation of emissions trading systems (ETSs) impacts emissions reductions and the usage of renewable energy using a panel sample of the largest 100 countries worldwide. Exploiting cross-country variations in ETS implementations, we show that ETS adoption materially reduced greenhouse gas (carbon dioxide) emissions by 12.1% (18.1%). Moreover, ETSs reduced overall emissions by cutting the usage of fossil fuels such as coal by 23.70% while boosting the usage of renewable energy by 61.59%, on average. In contrast, introducing carbon taxes has a less effective impact on emissions reduction. It fails to boost the usage of renewable energy, though elevating tax rates and expanding tax coverage may help enhance the efficacy of carbon taxes.
Whereas corporate insiders at U.S.-listed, U.S.-domiciled companies must disclose their stock sales electronically within two business days on Form 4, the U.S. Securities and Exchange Commission (SEC) exempts insiders at U.S.-listed, foreign-domiciled companies from this requirement. Instead, these “foreign insiders” report their sales on a paper form mail-filed with the SEC. Using a unique data set compiled from digitized versions of thousands of paper forms, we examine the stock sales of foreign insiders and compare their trading to that of their U.S. counterparts. Consistent with a lack of public scrutiny facilitating opportunism, we show that foreign insiders’ stock sales are highly opportunistic and opportunistic trading is concentrated in companies that are domiciled in nonextradition countries beyond the reach of U.S. legal authorities: specifically, Russia and China. The average stock sale by foreign insiders affiliated with companies domiciled in these countries is more than four times larger than that of U.S. insiders and occurs prior to stock price declines of at least −18%. In our sample, we estimate that insiders at these companies have traded to avoid losses of more than $9 billion. Collectively, our results suggest that corporate insiders associated with Chinese and Russian companies listed on U.S. exchanges trade in a highly opportunistic and abusive manner. The SEC’s decision to exempt these insiders from Form 4 reporting requirements prevents much needed public scrutiny of their trading and, in turn, prevents market forces from disciplining their trading.
This study presents unique empirical evidence on the importance of moral support for performance. We take advantage of an unusual change in Argentinean football legislation. In August 2013, as a matter of national security, the Argentinean government forced all teams in the first division to play their games with only home team supporters. Supporters of visiting teams were not allowed to be in stadiums during league games. We estimate the effect of this exogenous variation of supporters on team performance and find that visiting teams are on average about 20% more likely to lose without the presence of their supporters. As a counterfactual experiment, we run the analysis using contemporaneous cup games, where the visiting team supporters were allowed to attend, and find no effect of the ban on those games. Moreover, the ban does not seem to bias the decisions of referees, the lineups, or the market value of the teams, suggesting that the effect on team performance is due to the loss of moral support rather than other factors. Finally, we find that moral support is more relevant when there is equal power between the two teams, suggesting that moral support compensates the power of monetary resources. This paper provides a proof of concept of moral support as an important nonmonetary resource, even in settings with high monetary incentives.
Concepts are introduced and applied for analyzing and selecting arbitrage portfolios in the face of uncertainty about initial positions and risk preferences. A stochastic arbitrage opportunity is defined as a zero-cost investment portfolio that enhances every feasible host portfolio for all admissible utility functions. The alternative to the existence of such investment opportunities is the existence of a solution to a dual system of asset pricing restrictions based on a class of stochastic discount factors. Feasible approaches to numerical optimization and statistical inference are discussed. Empirical results suggest that equity factor investing is appealing for all risk-averse stock investors with a wide range of initial position and sufficiently low transactions costs by mixing multiple factor portfolios with high after-cost appraisal ratios, low mutual correlation, and negative exposures to the relevant host portfolios. These findings weaken the case for risk-based explanations for the profitability of factor investing.
Labor market outcomes depend, in part, upon an individual’s willingness to put him- or herself forward for different opportunities. We use a series of experiments to explore gender differences in willingness to apply for higher-return, more challenging work. We find that, in male-typed domains, qualified women are significantly less likely to apply than similarly well-qualified men. We provide evidence both in a controlled setting and in the field that reducing ambiguity surrounding required qualifications increases the rate at which qualified women apply. The effects are mixed for men. Our results point to a way to increase the pool of qualified women applicants.
We study debt-contracting implications of uncertainty surrounding U.S. gubernatorial elections. In election years, loan contracts are more likely to include performance-pricing provisions, whereas loan spreads are mostly unaffected. Additionally, we find, only among those loans without performance-pricing provisions, a marginal increase in loan spreads in election years and a significant increase in loan-spread amendments postelection. The results suggest that under transitory uncertainty, performance pricing curbs an explicit rise in loan spreads and reduces ex-post renegotiations, yielding efficiency gains in contracting. Our further analysis uncovers a veiled pricing effect manifested through interest-rate contingencies: the likelihood of using rate-increasing pricing grids rises in election years for borrowers with a high exposure to uncertainty, thereby ensuring compensation to lenders for uncertainty. Overall, our findings suggest that loan contracting attempts to mitigate the problem of election uncertainty and renegotiation costs through state-contingent pricing, with which borrowers weigh initial loan spreads against the potential for loan-spread variability in the future.
We introduce a new family of mechanisms for one-sided matching markets, denoted pick-an-object (PAO) mechanisms. When implementing an allocation rule via PAO, agents are asked to pick an object from individualized menus. These choices may be rejected later on, and these agents are presented with new menus. When the procedure ends, agents are assigned the last object they picked. We characterize the allocation rules that can be sequentialized by PAO mechanisms and the ones that can be implemented in a robust truthful equilibrium. We justify the use of PAO as opposed to direct mechanisms by showing that its equilibrium behavior is closely related to the one in obviously strategy-proof (OSP) mechanisms, but implements commonly used rules, such as Gale-Shapley DA and top trading cycles, which are not OSP implementable. We run laboratory experiments comparing truthful behavior when using PAO, OSP, and direct mechanisms to implement different rules. These indicate that agents are more likely to behave in line with the theoretical prediction under PAO and OSP implementations than their direct counterparts.
We use account-level transaction data to examine trading styles and profitability in a leading derivatives market. Approximately 66% of active retail investors predominantly hold simple, one-sided positions in only one class of options, whereas institutional investors are more likely to use complex strategies. Hypothesizing that the complexity of trading styles reflects investors’ skills, we examine the effect of options trading styles on investment performance. We find that retail investors using simple strategies lose to the rest of the market. For both retail and institutional investors, selling volatility is the most successful strategy. We conclude that these style effects are persistent and cannot be fully explained by systematic risk exposure.
Existing research often assumes that firms’ financial reporting choices influence their return comovement with other firms. We examine the validity of that assumption. First, we provide initial evidence suggesting that similarity in two firms’ disclosures not only predicts but influences future return comovement between those two firms. Second, we show that this predictive ability aggregates to the market level; disclosure similarity can be used to estimate more accurate forward-looking market betas. Taken together, these two results suggest that firms’ reporting decisions can influence their firms’ betas even in the absence of changes to capital structure or operations.
Using textual analysis techniques, including seeded word embedding and bag-of-words-based content analysis, I develop a firm-level measure of supply chain risk exposure from a novel source of unstructured data—the discussion between managers and equity analysts on supply chain-related topics during firms’ quarterly earnings conference calls. I validate the measure by showing that (1) the measure exhibits intuitive variations over time and across firms, successfully capturing both routine and systematic supply chain risk events; and (2) the measure is about risk exposure, as it significantly correlates with realized and options-implied stock return volatility, even after controlling for well-known aggregate risk measures. I then demonstrate that the measure is specifically indicative of the supply chain component of risk exposure. (3) Consistent with theoretical predictions, firms facing higher supply chain risks have higher inventory buffers, particularly in raw materials and intermediate inputs, increased cash holdings in lieu of investments, and significantly lower trade credit received from suppliers. Moreover, (4) during unexpected risk episodes, such as the Tohoku earthquake, firms with higher ex ante risk exposure have worse operating and financial performance. These results indicate that the text-based measure provides a credible quantification of firm-level exposure to supply chain risks and can thus be reliably utilized as outcome or explanatory variables in empirical supply chain research.
Against the backdrop of COVID-19, we study how the interactions of mutual funds and dealers introduce fragility to the municipal bond market and induce lasting market impacts. During the crisis, trading surges, whereas dealers’ liquidity provision plunges for mutual-fund-held bonds, leading to greater price depressions in these bonds. Importantly, the crisis reshapes the market’s perceptions of mutual fund fragility risks, with the aftermath-yield spreads widening significantly more for bonds with greater mutual fund exposures. Such postcrisis pricing effects reflect dealers’ continued reluctance to provide liquidity for mutual fund–held bonds, and they are stronger for bonds whose mutual fund holders are more susceptible to investor runs.
This paper studies how regional migration to tech clusters impacts the performance of startups within the United States. Startups that move to Silicon Valley experience a significant improvement in performance. This improvement is higher than migrations to other regions in the United States, many of which report null treatment effects. The startups that benefit the most from migration are those leaving low performing entrepreneurial ecosystems and moving to high performing ecosystems, consistent with an agglomeration mechanism. Within different measures of the ecosystem, the level of local patenting predicts startup improvements more than venture capital or the quality-adjusted number of startups, suggesting the local innovation environment is more important to migrant performance than financing or the presence of other startup peers.
Neuropsychological studies propose that listeners unconsciously assess speakers’ trustworthiness via their facial expressions. Building on this theory, we investigate how investors respond to CEOs’ dynamic hemifacial asymmetry of expressions (HFAsy) shown in CNBC’s video interviews about corporate earnings. Consistent with the neuropsychological prediction that facial asymmetry induces distrust, we document that (1) the stock market reacts negatively to the CEO’s HFAsy shown in the interview video, and (2) the market reaction to favorable earnings news is negatively associated with the CEO’s HFAsy. We also find a significantly positive association between the short-window abnormal bid-ask spreads and HFAsy, suggesting a larger investor disagreement for firms with high-HFAsy CEOs. Furthermore, we show that analyst forecast revisions are negatively associated with CEOs’ HFAsy. Our results are robust to conducting a falsification test using the previous quarter as a pseudo-event, employing an alternative measure of HFAsy, and using more granular trading data at the second level. Overall, our study provides evidence that investor trust and trading behavior are affected by the dynamic hemifacial asymmetry of expressions appearing on CEOs’ faces.
This paper examines how data-driven personalized decisions can be made while preserving consumer privacy. Our setting is one in which the firm chooses a personalized price based on each new customer’s vector of individual features; the true set of individual demand-generating parameters is unknown to the firm and so must be estimated from historical data. We extend the existing personalized pricing framework by requiring also that the firm’s pricing policy preserve consumer privacy, or (formally) that it be differentially private: an industry standard for privacy preservation. We develop privacy-preserving personalized pricing algorithms and show that they achieve near-optimal revenue by deriving theoretical (upper and lower) performance bounds. Our analyses further suggest that, if the firm possesses a sufficient amount of historical data, then it can achieve a certain level of differential privacy almost “for free.” That is, the revenue loss due to privacy preservation is of smaller order than that due to estimation. We confirm our theoretical findings in a series of numerical experiments based on synthetically generated and online auto lending (CPRM-12-001) data sets. Finally, motivated by practical considerations, we also extend our algorithms and findings to a variety of alternative settings, including multiproduct pricing with substitution effect, discrete feasible price set, categorical sensitive features, and personalized assortment optimization.
An ideal that supports quality and delivery of care is to have hospital operations that are coordinated and optimized across all services in real time. As a step toward this goal, we propose a multistage adaptive robust optimization approach combined with machine learning techniques. Informed by data and predictions, our framework unifies the bed assignment process across the entire hospital and accounts for present and future inpatient flows, discharges, and bed requests from the emergency department, scheduled surgeries and admissions, and outside transfers. We evaluate our approach through simulations calibrated on historical data from a large academic medical center. For the 600-bed institution, our optimization model was solved in seconds and reduced off-service placement by 24% on average and boarding delays in the emergency department and postanesthesia units by 35% and 18%, respectively. We also illustrate the benefit of using adaptive linear decision rules instead of static assignment decisions.
If a financial asset’s price movement impacts a firm’s product demand, the firm can respond to the impact by adjusting its operational decisions. For example, in the automotive industry, automakers decrease the selling prices of fuel-inefficient cars when the oil price rises. Meanwhile, the firm can implement a risk-hedging strategy using the financial asset jointly with its operational decisions. Motivated by this, we develop and solve a general risk-management model integrating risk hedging into a price-setting newsvendor. The optimal hedging strategy is calculated analytically, which leads to an explicit objective function for optimizing price and “virtual production quantity” (VPQ). (The latter determines the service level—that is, the demand-fulfillment probability.) We find that hedging generally reduces the optimal price when the firm sets the target mean return as its production-only maximum expected profit. With the same condition on the target mean return, hedging also reduces the optimal VPQ when the asset price trend positively impacts product demand; meanwhile, it may increase the VPQ by a small margin when the impact is negative. We construct the return-risk efficient frontier that characterizes the optimal return-risk trade-off. Our numerical study using data from a prominent automotive manufacturer shows that the markdowns in price and reduction in VPQ are small under our model and that the hedging strategy substantially reduces risk without materially reducing operational profit.
Institutional investors increasingly hold firms in both the industrial and financial sectors. These cross-industry holdings link firms to “outside” banks that they have not borrowed from, creating a network between the two sectors. We show that such networks reduce firms’ loan spreads. This effect is more prominent when cross-holders are actively involved in borrowers’ routine operations and when they have stronger incentives to advocate for borrowers in the loan process. Furthermore, outside banks begin to lend more to firms once the two parties become linked. This network is “behind-the-scenes” because it does not arise from prior interactions between firms and banks, but is instead built through institutions’ cross-holdings.
We conduct a large-scale natural field experiment with a Fortune 500 company to test several light-touch approaches to attract minorities to high-profile positions. A total of 5,000 prospective applicants were randomized into treatments that vary a small portion of recruiting materials. We find that self-selection at two early-career stages exhibits a substantial race gap. We then show that this gap can be strongly influenced by several treatments, with some closing the race gap and increasing application rates of minorities by 40% and others being particularly effective for minority women. These effects are not accompanied by any declines in application rates of majority group job seekers. In addition, we do not find that endorsing the “business case” for diversity reduces the race gap or raises application rates by minorities or women. The heterogeneities we find by gender, race, and career stage shed light on the underlying drivers of self-selection barriers among minorities.
We introduce taxi ridership between the Federal Reserve (Fed) Bank of New York and large financial institutions headquartered in New York City as a novel proxy for Fed–bank face-to-face interactions. We document a negative relation between past Fed–bank interactions and future stock market returns, particularly on days around the Fed’s public announcements. We also find significantly elevated Fed–bank interactions immediately following the lifting of the Federal Open Market Committee blackout. Our findings suggest that the Fed increases its information gathering via face-to-face interactions when it possesses negative private information about the condition of the economy.
We use the setting of analyst coverage networks to shed light on the nature of peer effects in financial policies. First, we use the “friends-of-friends” approach and exploit the fact that analyst coverage networks partially overlap to identify endogenous peer effects, in which firms respond directly to the capital structure choices of their peers, separately from contextual effects, in which they respond to their peers’ characteristics. We further show evidence that analysts facilitate these peer effects through their role as informational intermediaries. Analyst network peer effects are distinct from industry peer effects and are more pronounced among peers connected by analysts that are more experienced and from more influential brokerage houses. Finally, the analyst peer effects become weaker after exogenous reductions in common coverage as a consequence of brokerage closures.
Random coefficient multinomial logit models are widely used to estimate customer preferences from sales data. However, these estimation models can only allow for products with positive sales; this selection leads to highly biased estimates in long tail markets, that is, markets where many products have zero or low sales. Such markets are increasingly common in areas such as online retail and other online marketplaces. In this paper, we propose a two-stage estimator that uses machine learning to correct for this bias. Our method first uses deep learning to predict the market shares of all products, where the neural network’s structure mirrors the random coefficient multinomial logit model’s data generating process. In the second stage, we use the predictions of the first stage to reweight the observed shares in a way that corrects for the induced bias and maintains the causal interpretation of the structural model. We show that the estimated parameters are consistent in the number of markets. Our method performs well on simulated and real long tail data, producing accurate estimates of customer behavior. These improved estimates can subsequently be used to provide prescriptive policy recommendations on important managerial decisions such as pricing, assortment, and so on.
We replicate 469 anomaly variables similar to those studied by Hou et al. (2020) using Chinese A-share data and a reliable testing procedure with mainboard breakpoints and value-weighted returns. We find that 83.37% of the anomaly variables do not generate significant high-minus-low quintile raw return spreads. Further adjusting risk increases the failure rate slightly to 84.22% based on CAPM alphas and 86.99% based on Fama–French three-factor alphas. We show that the conventional procedure using all A-share breakpoints with equal-weighted returns for the anomaly test is indeed problematic as it assigns too much weight to microcaps and has a very limited investment capacity. The CH3-factor, CH4-factor, and q-factor models show the best performance over the whole sample period. The q-factor model is the best performer in the post-2007 subsample period after significant improvements occurred in China’s financial market environment, such as the completion of the split-share structure reform and the implementation of new accounting standards conforming to the International Financial Reporting Standards. The non–state-owned enterprise subsample in the post-2007 period is a cleaner sample in which the CH4-factor and q-factor models are the best performers.
Using two proxies for investors’ political affiliation, we document sharp differences in stock returns between firms likely dominated by Democratic investors (blue stocks) and those dominated by Republican investors (red stocks) during the COVID pandemic. Red stocks have 20 basis points higher risk-adjusted returns than blue stocks on COVID news days (Partisan Return Gap). Lockdown policies, COVID cases, industry and firm fundamentals only explain at most 40% of the return gap. Polarized political beliefs about COVID, revealed through people’s social distancing behavior, contribute to about 40% of the return gap beyond the fundamental channel. Our paper provides partisanship as a novel aspect in understanding abnormal stock returns during the pandemic.
Firms adopt randomized experiments to evaluate various interventions (e.g., website design, creative content, and pricing). However, most randomized experiments are designed to identify the impact of one specific intervention. The literature on randomized experiments lacks a holistic approach to optimize a sequence of interventions along the customer journey. Specifically, locally optimal interventions unveiled by randomized experiments might be globally suboptimal when considering their interdependence as well as the long-term rewards. Fortunately, the accumulation of a large number of historical experiments creates exogenous interventions at different stages along the customer journey and provides a new opportunity. This study integrates multiple experiments within the reinforcement learning (RL) framework to tackle the questions that cannot be answered by stand-alone randomized experiments. How can we learn optimal policy with a sequence of interventions along the customer journey based on an ensemble of historical experiments? Additionally, how can we learn from multiple historical experiments to guide future intervention trials? We propose a Bayesian recurrent Q-network model that leverages the exogenous interventions from multiple experiments to learn their effectiveness at different stages of the customer journey and optimize them for long-term rewards. Beyond optimization within the existing interventions, the Bayesian model also estimates the distribution of rewards, which can guide subject allocation in the design of future experiments to optimally balance exploration and exploitation. In summary, the proposed model creates a two-way complementarity between RL and randomized experiments, and thus, it provides a holistic approach to learning and optimizing interventions along the customer journey.
During 2005–2007, the Securities and Exchange Commission conducted a randomized trial in which it removed short-sale restrictions from one third of the Russell 3000 firms. Early studies found modest market microstructure effects of removing the restrictions but no effect on short interest, stock returns, volatility, or price efficiency. More recently, many studies have attributed a wide range of indirect outcomes to this experiment, mostly without assessing the causal channels for those outcomes. We examine the three most often cited causal channels for these indirect effects: short interest, returns, and managerial fear. We find no evidence to support these channels. We then reexamine the principal findings in four recent studies using a sample that closely matches the actual experiment and a common research design and find minimal support for the reported indirect effects. Our findings highlight the importance of confirming a causal channel or an economic mechanism and show that sample selection and specification choices can produce statistical significance even without an underlying economic mechanism.
Consumer showrooming has become a common phenomenon in the retail industry, but little is known about its influence on the interplay between an upstream supplier and a downstream retailer in a distribution channel. Our study examines such an influence by considering an upstream supplier who sells a product through a downstream physical retailer based on a wholesale contract. At the same time, based on an agency contract, the supplier sells the product on an online retail platform where a nonneglectable amount of consumer returns occurs due to product misfits. We find that, on the one hand, consumer showrooming enables the supplier to take a greater share of the offline channel profit by exploiting the physical retailer’s deterrence of showrooming. On the other hand, consumer showrooming as a mixed-channel shopping method makes the supplier less capable of adjusting the direct online demand according to its marginal cost of handling returns. Such a return-loss-propagation-moderating effect protects the downstream physical retailer from the upstream supplier’s strategic pricing that intends to shift sales from offline to direct online when the supplier finds it cost efficient to handle online product returns. Our study also shows that the retailer can replace the wholesale contract with the store-within-a-store model with a rental fee contract to leverage the upstream supplier’s incentive to mitigate the adverse effect of showrooming. The findings inform the physical retailer that the more critical issue may not be showrooming itself but aligning with the upstream supplier to promote offline sales in the presence of showrooming.
Reaching for yield, which we define as an investor preference for higher-yield bonds at a given rating or for higher-rated bonds at given yields, is associated with inflated valuation and thus negatively predicts risk-adjusted returns. Controlling for ratings, alphas are lower for higher-yield bonds, whereas, controlling for yields, alphas are lower for safer-looking, higher-rated bonds. Future bond returns are particularly low when current interest rates are low and demand from yield-reaching investors increases. These bonds experience more frequent downgrades and defaults, suggesting that they are riskier despite their high valuation.
Washington policy research analysts (WAs) monitor political developments and produce research to interpret the impact of these events. We find institutional clients channel more commissions to brokerages providing policy research and commission-allocating institutional clients generate superior returns on their politically sensitive trades. We find that WA policy research reports are associated with significant price and volume reactions. Finally, we find sell-side analysts with access to WA issue superior stock recommendations on politically sensitive stocks. These effects are particularly acute during periods of high political uncertainty. Overall, we uncover a unique and an important conduit through which political information filters into asset prices.
In a wide variety of applications, including healthcare, bidding in first price auctions, digital recommendations, and online education, it can be beneficial to learn a policy that assigns treatments to individuals based on their characteristics. The growing policy-learning literature focuses on settings in which policies are learned from historical data in which the treatment assignment rule is fixed throughout the data-collection period. However, adaptive data collection is becoming more common in practice from two primary sources: (1) data collected from adaptive experiments that are designed to improve inferential efficiency and (2) data collected from production systems that progressively evolve an operational policy to improve performance over time (e.g., contextual bandits). Yet adaptivity complicates the problem of learning an optimal policy ex post for two reasons: first, samples are dependent and, second, an adaptive assignment rule may not assign each treatment to each type of individual sufficiently often. In this paper, we address these challenges. We propose an algorithm based on generalized augmented inverse propensity weighted (AIPW) estimators, which nonuniformly reweight the elements of a standard AIPW estimator to control worst case estimation variance. We establish a finite-sample regret upper bound for our algorithm and complement it with a regret lower bound that quantifies the fundamental difficulty of policy learning with adaptive data. When equipped with the best weighting scheme, our algorithm achieves minimax rate-optimal regret guarantees even with diminishing exploration. Finally, we demonstrate our algorithm’s effectiveness using both synthetic data and public benchmark data sets.
Some nonprofit organizations (NPOs) manage a complex workforce composed of a mix of volunteers, part-time workers, and full-time workers. We study the NPO’s finite-horizon staffing problem to determine the optimal initial staff planning decisions and per period optimal hiring and assignment decisions given a budget, capacity constraints, and an uncertain supply of volunteers and part-time workers. Our main goal is to solve this problem in a way that is effective and easy to implement while obtaining interesting managerial insights. To this end, we first demonstrate that the optimal staffing policies are computationally challenging to identify in general. However, we demonstrate that a prioritization assignment policy and a hire-up-to policy for part-time workers can be conveniently applied and are close to optimal. These policies are, in fact, optimal under staff scarcity and staff sufficiency. In our numerical analysis, we study the value and impact of the general optimal solution that considers flexibility and turnover of part-time workers versus the prioritization assignment policy and a constant hire-up-to policy that omit flexibility and turnover behaviors. We further suggest two easy-to-implement heuristics and theoretically analyze them and run a numerical performance study. We observe that both heuristics have low relative optimality gaps. Finally, we extend our analysis by studying how the optimal policy varies under three different practical considerations: a concave social value objective, nonzero volunteer costs, and dynamic volunteer behaviors.
In this paper, we study the design and analysis of experiments conducted on a set of units over multiple time periods in which the starting time of the treatment may vary by unit. The design problem involves selecting an initial treatment time for each unit in order to most precisely estimate both the instantaneous and cumulative effects of the treatment. We first consider nonadaptive experiments, in which all treatment assignment decisions are made prior to the start of the experiment. For this case, we show that the optimization problem is generally NP-hard, and we propose a near-optimal solution. Under this solution, the fraction entering treatment each period is initially low, then high, and finally low again. Next, we study an adaptive experimental design problem, in which both the decision to continue the experiment and treatment assignment decisions are updated after each period’s data are collected. For the adaptive case, we propose a new algorithm, the precision-guided adaptive experiment algorithm, which addresses the challenges at both the design stage and the stage of estimating treatment effects, ensuring valid post-experiment inference, accounting for the adaptive nature of the design. Using realistic settings, we demonstrate that our proposed solutions can reduce the opportunity cost of the experiments by more than 50%, compared with static design benchmarks.
The k-secretary problem deals with online selection of at most k numerically scored applicants, where selection decisions are immediate upon their arrival and irrevocable, with the goal of maximizing total score. There is, however, wide prevalence of bias in evaluations of applicants from different demographic groups (e.g., gender, age, race), and the assumption of an algorithm observing their true score is unreasonable in practice. In this work, we propose the poset secretary problem, where selection decisions must be made by observing a partial order over the applicants. This partial order is constructed to account for uncertainty and biases in applicant scores. We assume that each applicant has a fixed score, which is not visible to the algorithm and is consistent with the partial order. Using a random partitioning technique from the matroid secretary literature, we provide order-optimal competitive algorithms for the poset secretary problem and provide matching lower bounds. Further, we develop the theory of thresholding in posets to provide a tight, adaptive-thresholding algorithm under regimes where k grows quickly enough, thus matching the adaptiveness to k shown for the classical k-secretary problem. We then study a special case, in which applicants belong to g disjoint demographic groups and the bias is group specific. We provide competitive algorithms for adversarial and stochastic variants of this special case, including a framework, Gap, for parallelizing any vanilla k-secretary algorithm to the group setting. Finally, we perform a case study on real-world data to demonstrate the responsiveness of our algorithms to data and their impact on selection rates.
Low-beta stocks deliver high average returns and low risk relative to high-beta stocks, an opportunity for professional investors to “arbitrage” away. We argue that beta-arbitrage activity generates booms and busts in the strategy’s abnormal trading profits. In times of low arbitrage activity, the beta-arbitrage strategy exhibits delayed correction, taking up to three years for abnormal returns to be realized. In contrast, when arbitrage activity is high, prices overshoot and then revert in the long run. We document a novel positive-feedback channel operating through firm leverage that facilitates these boom-and-bust cycles.
The Securities and Exchange Commission’s 2016 Tick Size Pilot Program was a natural experiment that imposed increases in tick size for randomly selected small-cap firms. Using a difference-in-differences research design, we examine the effect of this increase in tick size on earnings guidance. We find that after initiation of the program, treatment firms provide significantly less earnings guidance. We provide further evidence that this decrease is driven by increases in investors’ fundamental information acquisition and in firms’ financial reporting quality, consistent with firms reducing earnings guidance when investors are already more informed. The decrease is stronger for firms with higher proprietary costs of disclosure, consistent with firms being more likely to reduce costly disclosure when investors are more informed. In contrast, the decrease is weaker for firms with greater external financing needs, consistent with these firms continuing to seek the benefits of disclosure, even when investors are more informed. Taken together, our results suggest that an increase in tick size makes investors more informed, which, in turn, reduces the need for firms to provide earnings guidance, though the extent of the reduction depends on the costs and benefits of providing earnings guidance.
Diagnostic errors are common and can result in serious patient harm. Making the right diagnosis often requires significant diagnostic effort. Yet most physician payment schemes are procedure based and do not account for diagnostic effort or accuracy because of observability issues. In this paper, we develop a parsimonious model to examine the impact of a physician payment scheme on a physician’s decisions to (1) exert diagnostic effort and (2) perform a confirmatory test. High effort provides an informative (though imperfect) signal of the patient’s true state; the test is confirmatory in that it is a prerequisite for diagnosing a severe condition. Our model uses a two-step diagnostic process to capture the interaction between the physician’s diagnostic effort and testing decisions. We show that under a fee-for-service payment scheme, the physician may view the diagnostic effort and the confirmatory test as either complementary or substitutive, depending on the additional revenue from testing. We also reveal nonmonotonic properties such that a more patient-centered physician may not exert more effort or provide a more accurate diagnosis. In addition, either a flat or differentiated payment scheme may be optimal. We also show that an alternative payment scheme, under which the revenue from the confirmatory test is contingent on its result, can induce the social optimum under certain conditions. With the advent of artificial intelligence as part of the standard of care and its increasing use as confirmatory tests, our research has implications for the design of physician payment systems in light of concerns about the potential erosion of individual attention.
Online platforms such as preprint servers have become an important way to disseminate new scientific knowledge prior to peer review. However, little is known about how attention to preprints may vary across authors from different countries of origin, particularly relative to evaluation in expert-controlled systems such as scientific journals. This study explores how readers allocated attention across preprints in the initial months of the COVID-19 pandemic, a time when there was an increase in demand for new research and a corresponding increase in the use of preprint platforms around the world. We find that, after controlling carefully for article quality and topic, as well as the prominence of the preprint’s ultimate publication outlet, preprints with authors from Chinese institutions receive less attention, and preprints with authors from U.S. institutions receive more attention, than preprints with authors from the rest of the world. In an exploration of potential mechanisms driving the observed effects, we find evidence that when evaluation is more constrained, in terms of lack of knowledge or expertise and increase in time pressure, audiences tend to make greater use of preprint authors’ country of origin as a proxy for quality or relevance. The results suggest that geographic biases may persist or even be exacerbated on platforms designed to promote unfettered access to early research findings.
We study the problem of maximizing payoff generated over a period of time in a general class of closed queueing networks with a finite, fixed number of supply units that circulate in the system. Demand arrives stochastically, and serving a demand unit (customer) causes a supply unit to relocate from the “origin” to the “destination” of the customer. The key challenge is to manage the distribution of supply in the network. We consider general controls including customer entry control, pricing, and assignment. Motivating applications include shared transportation platforms and scrip systems. Inspired by the mirror descent algorithm for optimization and the backpressure policy for network control, we introduce a rich family of mirror backpressure (MBP) control policies. The MBP policies are simple and practical and crucially do not need any statistical knowledge of the demand (customer) arrival rates (these rates are permitted to vary in time). Under mild conditions, we propose MBP policies that are provably near optimal. Specifically, our policies lose at most O(KT+1K+ηK) payoff per customer relative to the optimal policy that knows the demand arrival rates, where K is the number of supply units, T is the total number of customers over the time horizon, and η is the demand process’ average rate of change per customer arrival. An adaptation of MBP is found to perform well in numerical experiments based on data from NYC Cab.
This paper shows how convex incentives in vertical contracts between manufacturers and retailers can induce sales behavior with costs to consumers. We examine this problem in the automotive sector, where manufacturers commonly motivate new vehicle sales through dealer incentive programs with large discrete bonuses determined by monthly sales targets. Using subprime car loans from over 3,500 dealerships, we document high default rates on new car loans originated at the end of the month—the period when dealerships attempt to secure target-based bonuses by intensifying efforts to sell new cars. We provide evidence consistent with the observed higher default rates resulting from customers purchasing new vehicles at month end. New car purchases stretch borrower budgets and expose borrowers to rapid depreciation, which consigns the borrower with negative equity through much of the loan term. Our results imply that the quartile of customers with the highest payment-to-income ratio see default rates increase from 13.6% to 19.7% on the last day of the month. Although consumers bear high costs from increased defaults, we find no evidence that lenders that purchase the loans are hurt by the default increase. Our results demonstrate how the behaviors induced by convex incentive schemes for sales are borne by customers.
Due to the sheer number of available choices, online retailers frequently use tiered assortment to present products. In this case, groups of products are arranged across multiple pages or stages, and a customer clicks on “next” or “load more” to access them sequentially. Despite the prevalence of such assortments in practice, this topic has not received much attention in the existing literature. In this work, we focus on a sequential choice model that captures customers’ behavior when product recommendations are presented in tiers. We analyze multiple variants of tiered assortment optimization (TAO) problems by imposing “no-duplication” and capacity constraints. For the offline setting involving known customers’ preferences, we characterize the properties of the optimal tiered assortment and propose algorithms that improve the computational efficiency over the existing benchmarks. To the best of our knowledge, we are the first to study the online setting of the TAO problem. A unique characteristic of our online setting, absent from the one-tier MNL bandit, is partial feedback. Products in the lower priority tiers are not shown when a customer has purchased a product or has chosen to exit at an earlier tier. Such partial feedback, along with product interdependence across tiers, increases the learning complexity. For both the noncontextual and contextual problems, we propose online algorithms and quantify their respective regrets. Moreover, we are able to construct tighter uncertainty sets for model parameters in the contextual case and thus improve the performance. We demonstrate the efficacy of our proposed algorithms through numerical experiments.
Gender segregation of occupations and entire industries is widespread. The segregation could be the result of perceived job-specific productivity differences between men and women. It could also result from the belief that homogeneous teams perform better or from in-group bias of male or female recruiters. We investigate these explanations in two samples: students and personnel managers. The subjects bet on the productivity of teams that are homogeneous with respect to gender at the outset and then either remain homogeneous or become diverse. The teams work on tasks that differ with respect to gender stereotypes. We obtain similar results in both samples. Women are picked more often for the stereotypically female task, and men are picked more often for the stereotypically male task. Subjects believe that gender-diverse teams perform better, especially in the task with complementarities, and they display an own-gender bias. Elicited expectations about the bets of others reveal that subjects expect the gender stereotypes of tasks but underestimate others’ bets on diversity.
In a world of increasingly extensive information, rational investors can make better decisions. However, reinforcement-oriented investors are also more likely to observe preferred signals close to their own perception. A focus on these signals distorts the perceived aggregate signal in the direction of the prior estimate. This reduces belief adaptation. Hence, the empirically well-documented selective exposure/reinforcement theory reduces the positive impact of greater information availability on price efficiency. Additional information can sometimes even decrease perception correctness. In a market with biased investors, managers have an incentive to announce more diffuse (fewer precise) signals in case of negative (positive) information. This results in postearnings-announcement drift and dispersion anomaly. Also, the distribution shape matters for information processing. For unimodal, symmetric distributions, agents’ perceptions converge to the fundamental—even though at a reduced speed. For multimodal signal distributions, the estimate can diverge from the fundamental.
Many firms delegate pricing decisions to sales agents that directly interact with customers. A premise behind this practice is that sales agents can gather informative signals about the customer’s valuation for the good of interest. The information acquired through this interaction with the customer can then be used to make better pricing decisions. We study the underlying principal-agent problem that arises in such situations. In this setting, the agent can exert costly effort to learn a customer’s valuation and then decide on the price to quote to the customer, whereas the firm needs to offer a contract to the agent to induce its desired joint learning and pricing behavior. We analyze two versions of this problem: a base model where there is a single customer and a single good, and a generalization where there are multiple customers and limited inventory of the good. For both problems, we find a family of contracts whose payoffs can approach first-best payoffs arbitrarily closely even if the agent has limited liability, that is, garners nonnegative payments in all states of the world, and shed light on the structure and implementation of such contracts. Under reasonable assumptions, these contracts can be implemented with commissions that are convex increasing in revenues up to some cap. These contracts continue to perform well under practical adjustments such as commissions with a revenue-sharing structure.
We estimate an infinite-horizon dynamic oligopoly model of audit firm tenure and misstatements and evaluate a policy counterfactual involving mandatory audit firm rotation. Longer tenure lowers the cost of producing audits, increasing audit quality and reducing audit fees. Thus clients are less likely to misstate and more likely to keep the incumbent audit firm as tenure increases. By reducing the value of retaining audit firms, mandatory rotation leads to large increases in auditor switches, even before the term limit, implying increases in the switching costs borne by clients. Misstatement rates increase because audit firms endogenously lower audit quality and newly hired audit firms have lower quality. Overall, the model suggests caution when evaluating the costs and benefits of government oversight over the audit profession.
“Buy-now-pay-later” (BNPL) is a relatively unregulated FinTech innovation that provides consumers with easy access to credit for retail purchases. BNPL spending is projected to reach $1 trillion by 2025, but we know little about its effects. Using banking data for 10.6 million U.S. consumers, we investigate the effects of BNPL on leading indicators of users’ financial health. We find that new BNPL users experience rapid increases in bank overdraft charges and credit card interest and fees compared with nonusers, consistent with BNPL facilitating overborrowing. An instrumental variable exploiting consumers’ pre-BNPL shopping habits bolsters our inferences. Our results inform regulatory investigations into the effects of BNPL on users’ financial health and expand academia’s understanding of an important consumer credit innovation.
Using a novel data set comprising bid–ask quotes for foreign exchange swaps from individual dealers, we examine the consequences of the Swiss National Bank’s sudden termination of the Swiss franc/euro minimum exchange rate in 2015 on other pegged currencies. Our findings indicate a spillover effect as dealer banks began to reassess the risk associated with unexpected peg terminations, subsequently leading to wider bid–ask spreads for pegged currencies. This highlights that, even in strong economies, the credibility of a currency peg is influenced not only by the actions of the respective central bank but also by the stability of other pegged currencies.
We examine the uncertainty in households’ expectations regarding macroeconomic outcomes, namely inflation and the rate of nationwide home price growth. We document that people extrapolate from the instability of their personal and local environment when assessing the future volatility of these macroeconomic variables. Consequently, there are within-person spillovers in subjective uncertainty regarding different economic outcomes. This extrapolative behavior is more pronounced among lower-numeracy individuals, and it persists throughout the business cycle. People with more uncertain macroeconomic expectations are more likely to engage in precautionary behaviors regarding consumption and equity market exposure and are more concerned about credit market access.
This paper examines whether ownership by media conglomerates impacts business news reporting. To address this question, we take advantage of a unique setting when in 2007 the ownership of The Wall Street Journal (WSJ) was transferred to News Corp., a media conglomerate with broad business interests. We find that, after the acquisition, WSJ’s sentiment toward competitors of News Corp. became significantly negative in comparison with similar reporting conducted by The New York Times. Further, WSJ’s reporting influences stock prices and is associated with more profitable insider trading. This evidence suggests that ownership by media conglomerates likely influences the nature of business news coverage.
This paper documents that the ratio of residential-to-nonresidential investment is a strong in-sample and out-of-sample predictor for the dollar up to 12 quarters. The predictability is robust to a battery of additional checks and holds for other G10 currencies. We explain the predictability in an analytical model with time-varying housing preference, productivity, and volatility. In the model, the U.S. housing investment share is higher during periods with higher growth and lower uncertainty, corresponding to lower future nontradable prices, dollar index, and excess returns. We find strong empirical support for the channel. Alternative explanations, including the business and financial cycle, find less empirical support.
Exploiting a unique high-frequency, individual-level database, we (1) construct individual-level, incentive-compatible proxies of impulsivity based on video gaming behavior and (2) use these proxies to evaluate predictions concerning how impulsivity shapes individuals’ responses to a relaxation of credit constraints as captured by receiving a credit card. We discover that precard gaming intensity—as measured by the frequency and amount of game expenditures—is strongly and positively associated with (a) the probability of defaulting on credit card debt in the future, (b) postcard expenditures on luxury and addictive items, (c) surges in consumption spending immediately after receiving the credit card, and (d) rapid debt accumulation after obtaining the card. Differences in financial literacy, income, income variability, education, and demographics do not drive the results. The results are consistent with (1) neurological and psychological studies stressing that excessive gaming is associated with impulse control deficiencies and (2) behavioral theories stressing that impulsivity, i.e., time-inconsistent preferences for immediate gratification and ineffective strategies for avoiding myopic cues and temptations, substantially influence individual expenditure patterns and borrowing decisions when liquidity constraints are relaxed.
Using breadth of corporate bond fund ownership to proxy for the combination of short-sale constraints and divergence of opinions in the corporate bond market, this paper investigates whether and to what extent Miller’s overpricing theory holds for corporate bonds. Consistent with the theory, we document a significant and positive relation between changes in breadth and the cross-section of future corporate bond returns. Because of the bounded upside of debt, the ability of changes in breadth to predict returns appears to be less pronounced for corporate bonds relative to stocks. Furthermore, this predictability is stronger among bonds with higher credit risk, indicating that the effect of short-sale constraints and divergence of opinions on bond mispricing tends to be larger for riskier bonds with more upside potential and, thus, more scope for overvaluation.
When do sellers benefit from the presence of a strong buyer? In a second price auction with independent private values and entry costs for buyers, we allow for one buyer to be stochastically “strong.” We show that, when buyers make positive net profits, seller revenue is higher with a stronger buyer. In contrast, when competition is endogenous so that all buyers other than the strong buyer break even, seller revenue is always lower in the presence of a strong buyer. We also study seller actions such as providing buyer subsidies, setting reserve prices, securing multiple strong buyers, bundling assets, and optimally choosing the time of sale that might alleviate the fall in revenue.
As artificial intelligence (AI) applications become more pervasive, it is critical to understand how knowledge workers with different levels and types of experience can team with AI for productivity gains. We focus on the influence of two major types of human work experience (narrow experience based on the specific task volume and broad experience based on seniority) on the human-AI team dynamics. We developed an AI solution for medical chart coding in a publicly traded company and conducted a field study among the knowledge workers. Based on a detailed analysis performed at the medical chart level, we find evidence that AI benefits workers with greater task-based experience, but senior workers gain less from AI than their junior colleagues. Further investigation reveals that the relatively lower productivity lift from AI is not a result of seniority per se but lower trust in AI, likely triggered by the senior workers’ broader job responsibilities. This study provides new empirical insights into the differential roles of worker experience in the collaborative dynamics between AI and knowledge workers, which have important societal and business implications.
Product recommendations can benefit consumers’ online product search via multiple underlying mechanisms, such as showing products that offer them high value, facilitating navigation on the website, or exposing more product information. However, it is unclear ex ante which is the primary underlying mechanism that drives the benefits of product recommendations to consumers. We conducted a randomized field experiment to estimate the benefits of an item-based collaborative filtering (CF) recommendation system to consumers. We collect unique data on the affinity scores computed by an item-based CF algorithm to develop measures of a product’s net value and horizontal (taste) fit for consumers. Our results indicate that product recommendations help consumers search for higher-value products that are lower priced, fit their tastes better, or both. Besides that, we find that the ability to find higher-value products (rather than easy navigation or exposure to more product information) is the primary driver for consumers’ higher purchase probabilities under recommendations. We further find a higher benefit of recommendations in product categories with higher price dispersion and heterogeneity in consumers’ tastes, providing additional evidence for the lower price and better horizontal fit mechanisms. Finally, we find that when made available, consumers substitute their usage of other search tools on the website with product recommendations. Our findings have important implications for online retailers, policymakers, regulators, and item-based CF recommendation system design.
We empirically study the relationship between currency excess returns and current account uncertainty, measured as forecast dispersion. We find that investment currencies deliver low returns, whereas funding currencies offer a hedge when current account uncertainty is unexpectedly high. Moreover, an increase in current account uncertainty is associated with higher expected future excess returns on investment currencies. This mechanism is consistent with the recent advances in exchange rate theory based on capital flows in imperfect financial markets.
We run a field experiment to study the relative performance of human curation and automated personalized recommendation technology in the context of online news. We build a simple theoretical model that captures the relative efficacy of personalized algorithmic recommendations and curation based on human expertise. We highlight a critical tension between detailed, yet potentially narrow, information available to the algorithm versus broad (often private), but not scalable, information available to the human editor. Empirically, we show that, on average, algorithmic recommendations can outperform human curation with respect to clicks, but there is significant heterogeneity in this treatment effect. The human editor performs relatively better in the absence of sufficient personal data and when there is greater variation in preferences. These results suggest that reverting to human curation can mitigate the drawbacks of personalized algorithmic recommendations. Our computations show that the optimal combination of human curation and automated recommendation technology can lead to an increase of up to 13% in clicks. In absolute terms, we provide thresholds for when the estimated gains are larger than our estimate of implementation costs.
We examine whether and how the time-oriented tendency embedded in languages influences income smoothing. Separating languages into weak- versus strong-future time reference (FTR) groups, we find that firms in weak-FTR countries tend to smooth earnings more. We also find that relationships with major stakeholders (i.e., debtholders, suppliers, and employees) amplify the effect of the FTR of languages on income smoothing. Additional analyses suggest that income smoothing driven by the FTR of languages enhances earnings informativeness. These findings provide new insights on the role that language plays in financial reporting decisions and on how relationships with major stakeholders influence the relation between an important feature of language and corporate income smoothing behavior.
An applicant privately observes the value of an idea and files a patent application. After the examination, the patent examiner may not find conclusive evidence of the idea’s value. In this case, granting a patent can be the right decision or a mistake. Moreover, it can prompt obfuscation—effort to disrupt communication—by the applicant, reducing allocative efficiency. We show there is more obfuscation when examiners are more lenient, and we provide an empirical exercise supporting this conclusion. We also show that more examination resources do not prevent and may lead to more obfuscation. This result continues to hold when applicants can invest in knowledge to increase their chances of having good ideas. We also show that there is less obfuscation when good ideas are harder to find.
Short-term corporate social impact activities, wherein employees participate in firm-sponsored prosocial initiatives, are becoming increasingly common. However, it remains unclear whether short-term social impact activities affect employee behavior in a manner of relevance to the firm. Theoretically, arguments could be made in favor of or against the likelihood that such short-term activities would increase firm-benefitting employee behavior. We utilized data from a randomized field experiment implemented at a large Latin American bank to examine whether a short-term social impact activity can be beneficial for firms, focusing on one important outcome: turnover. Newly hired employees were randomly assigned to a short-term social impact activity as part of the new employee onboarding process or not. Notably, we find causal evidence that a day-long, short-term engagement reduced employee turnover almost a year later. We explore potential mechanisms behind this effect and find that employees’ perceptions of organizational justice help to explain the effects of the intervention on turnover. We also explore heterogeneous treatment effects and find more substantial effects for male, rather than female, employees. This paper advances the literature on the implications of corporate social responsibility (CSR) practices by shedding light on the causal mechanisms through which a theoretically underexplored and practically relevant type of CSR activity can benefit organizations and their employees.
We examine determinants and consequences of virtual shareholder meetings (VSMs) using a sample of voluntary (precoronavirus disease 2019) and forced (i.e., because of coronavirus disease 2019) VSM adopters. Voluntary adopters are tech firms and firms traditionally more engaged with shareholders, consistent with the stated objective to increase shareholder participation. In contrast, we do not find that firms choose the virtual format to avoid shareholders’ scrutiny. Textual analysis of transcripts suggests that in VSMs, business presentations by management are less frequent, shorter, and more generic but only among voluntary adopters, suggesting that these properties reflect a firm’s choice rather than are a by-product of the virtual format per se. VSMs are more likely to exhibit no questions during the question and answer period, but conditioned upon having one question, they exhibit the same number of questions; such questions are more negative in tone, inconsistent with managers using the virtual format to filter out hostile questions. Finally, there is some evidence of greater abnormal absolute returns around VSMs, supporting the notion that greater attendance translates into greater information content. Overall, VSMs exhibit less activity on average, consistent with critics’ concerns, but such reduced activity does not appear to cause a loss in information content nor does it appear to reflect an attempt to avoid scrutiny.
In many domains, it is necessary to combine opinions or forecasts from multiple individuals. However, the average or modal judgment is often incorrect, shared information across respondents can result in correlated errors, and weighting judgments by confidence does not guarantee accuracy. We develop a Bayesian hierarchical model of crowd wisdom that incorporates predictions about others to address these aggregation challenges. The proposed model can be applied to single questions, and it can also estimate respondent expertise given multiple questions. Unlike existing Bayesian hierarchical models for aggregation, the model does not link the correct answer to consensus or privilege majority opinion. The model extends the “surprisingly popular algorithm” to enable statistical inference and in doing so, overcomes several of its limitations. We assess performance on empirical data and compare the results with other aggregation methods, including leading Bayesian hierarchical models.
We document considerable cross-investor variation in survey expectations about aggregate stock market returns. Although most investors are extrapolators who expect higher returns after a good market performance, some are contrarians who expect lower returns after a good performance. More notably, compared with extrapolators, contrarians have less persistent expectations that are corrected more quickly. We then develop a dynamic equilibrium model accounting for these differences in expectations and find that the equilibrium stock price exhibits short-term momentum and long-term reversal as in the data. Furthermore, we test the key predictions of our model linking the observable differences in extrapolators’ and contrarians’ expectations to aggregate stock market momentum and future stock performance and find supportive evidence for our model mechanism.
A merchant dynamically sets prices in each time period when selling a product over a finite time horizon with a given initial inventory. The merchant utilizes new external information that is observed at the beginning of each time period, whereas the demand function—how the external information and the price jointly impact that single-period demand distribution—is unknown. The merchant’s decision, setting price dynamically, serves dual roles to learn the unknown demand function and to balance inventory with an ultimate objective to maximize the expected cumulative revenue. The main objective of this work is to characterize and provide a full spectrum of relations between the order of optimal expected cumulative revenue achieved in three decision-making regimes: the merchant’s online decision-making regime, a clairvoyant regime with complete knowledge about the demand function, and a deterministic regime in which all the uncertainties are relaxed to the expectations. In the analyses, we derive an unconstrained representation of the optimality gap for generic constrained online learning problems, which renders tractable lower and upper bounds for the expected revenue achieved by dynamic pricing algorithms between different regimes. This analytical framework also inspires the design of two dual-based dynamic pricing algorithms for the clairvoyant and online regimes.
We develop and implement methods for determining whether introducing new securities or relaxing investment constraints improves the investment opportunity set for prospect investors. We formulate a new testing procedure for prospect spanning for two nested portfolio sets based on subsampling and linear programming. In an application, we use the prospect spanning framework to evaluate whether well-known anomalies are spanned by standard factors. We find that of the strategies considered, a few of them expand the opportunity set of the prospect type investors and thus have real economic value for them and involve absence of loss aversion. Those are the net stock issue anomaly under the FF-5 model, the momentum and net stock issue anomalies under the M-4 model, and the momentum anomaly under the q model. In-sample and out-of-sample results prove remarkably consistent in identifying genuine anomalies for prospect investors.
This paper studies a novel social commerce practice known as “help-and-haggle,” whereby an online consumer can ask friends to help her “haggle” over the price of a product. Each time a friend agrees to help, the price is cut by a random amount, and if the consumer cuts the product price down to zero within a time limit, she will get the product for free; otherwise, the product reverts to the original price. Help-and-haggle enables the firm to promote its product and boost its social reach as consumers effectively refer their friends to the firm. We model the consumer’s dynamic referral behavior in help-and-haggle and provide prescriptive guidance on how the firm should randomize price cuts. Our results are as follows. First, contrary to conventional wisdom, the firm should not always reduce the (realized) price-cut amount if referrals are less costly for the consumer. In fact, the minimum number of successful referrals the consumer must make to have a chance to win the product can be nonmonotone in referral cost. Second, relative to the deterministic price-cut benchmark, a random price-cut scheme improves firm payoff, extracts more consumer surplus, and widens social reach. Additionally, in most instances, it also reduces the promotion expense while increasing profit from product sales at the same time. Third, help-and-haggle can be more cost effective in social reach than a reward-per-referral program that offers a cash reward for each successful referral. However, using the prospect of a free product to attract referrals cannibalizes product sales, potentially causing help-and-haggle to fall short. Yet, if consumers are heterogeneous in product valuations and referral costs or face increasing marginal referral costs, help-and-haggle can outperform the reward-per-referral program.
Many platforms host user-generated content (UGC) and content developed by professionals side by side. However, thus far, their impact on platform ecosystems has been mostly studied in isolation. In this paper, we use data from a network of 122 local news outlets hosted by an online news platform to study the spillover effects from UGC developed by citizen journalists to the content developed by professional journalists. We use the removal of a status index associated with citizen journalists as an exogenous shock to their supply of UGC to identify these spillover effects. We find that experienced citizen journalists reduce their production of content when this status index is removed. We then find that inexperienced professional journalists increase their output in response to this behavior. However, as a result of these changes, we find a reduction in the overall content hosted by the platform, especially in the case of local news and in more isolated regions. We further show that this is likely to have detrimental effects for the platform. In particular, there is a decline in overall viewership, and the platform may need to hire and pay salaries to more professional journalists to produce enough articles to close the gap left by the departing citizen journalists. Our work contributes to the literature on UGC and online platforms and to the literature on local news.
We develop a model of investor information choices and asset prices in which the availability of information about fundamentals is time-varying and responds to investor demand for information. A competitive research sector produces more information when more investors are willing to pay for that research. This feedback, from investor willingness to pay for information to more information production, generates two regimes in equilibrium, one having high prices and low volatility, the other the opposite. The low-price, high-volatility regime is associated with greater information asymmetry between informed and uninformed investors. Information dynamics move the market between regimes, creating large price drops even with no change in fundamentals. In our calibration, the model suggests an important role for information dynamics in financial crises.
This paper empirically studies the extent to which career concerns as part of a typical contract offer influence employees’ work performance in a Japanese auto dealership firm. Because career movements and base-wage adjustments rely on performance evaluation over time, we develop a dynamic structural model that allows concerns for future payoffs to impact an employee’s current work effort. A reform in personnel-management policies of the firm during the data period enables us not only to compare the performances across individuals, but also to compare within an individual the performance before and after the reform—this enhances the model identification. Our estimation results show that the added value from career movements on top of the monetary payoffs is more important than the monetary payoffs. Individuals respond to career movements and commissions differently, mainly due to varying cost of effort and different payoffs from career movements. Our counterfactual exercises suggest that, compared with the scenario when there is only monetary compensation, adding career movements in a contract package will greatly improve the firm’s gross profit. The firm can improve its net profit by making commission and promotion more performance-based.
When a benchmark model is inefficient, including test assets in addition to the benchmark portfolios can improve the performance of the optimal portfolio. In reality, the efficiency of a benchmark model relative to the test assets is ex ante unknown; moreover, the optimal portfolio is constructed based on estimated parameters. Therefore, whether and how to include the test assets becomes a critical question faced by real world investors. For such a setting, we propose a combining portfolio strategy, optimally balancing the value of including test assets and the effect of estimation errors. The proposed combining strategy can work together with some existing estimation risk reduction strategies. In both empirical data sets and simulations, we show that our proposed combining strategy performs well.
Fueled by remarkable developments in mobile and wearable technologies coupled with the power of the Internet a multibillion industry constructed on digital platforms, Connected Fitness, has emerged. Sustained engagement is a crucial prerequisite for healthy behavior, and platforms that offer connected fitness services must contend with the challenge of engaging new users. In this study, we examine whether a virtual race, an event in which runners can participate remotely in an organized activity, can serve as an effective strategy to amplify users’ long-term engagement related to healthy behavior. We study the impact of offering virtual races to runners on a digital fitness platform on their engagement with the platform and level of physical activity. Leveraging the match of the theme of the virtual races and users’ zodiac signs as the instrument to support causal inference, we find that a virtual race increases the engagement of new users by 42.8 days and total spending on related e-commerce on the platform by 48.4% during our study period. We also find that the positive impact of virtual races transcends the typical mechanism of social connections; results show that virtual races can increase the engagement with the platform even when their social connections are low. Finally, we find that existing users who participate in a virtual race outrun those in the control group by 4.41 km in a three-week period. Our study highlights the potential of new forms of digital interactions, powered by mobile and wearable technologies, on user behavior in fitness platforms.
This paper investigates whether banks’ information technology (IT) can substitute for local branch presence in the provision of small business credit. Our identification strategy relies on loan-level data and the unique institutional features of the Italian public guarantee scheme during COVID-19. Despite the availability of online applications and low screening incentives, small business lending remains local even for first-time borrowers. However, IT partly mitigates the impact of local branch presence: banks with better IT provide more, cheaper, and faster guaranteed loans and lend more in areas where they have no bank branches, especially to first-time borrowers.
U.S. K–12 school districts that traditionally utilized ongoing “formative” assessments of student progress increasingly rely on additional “interim” assessments to predict student performance on standardized tests. Moreover, some districts are experimenting with merit-based teacher bonuses tied to standardized test scores. We examine the relationship between interim assessments and teacher bonuses using a two-period principal–agent model. The school district (principal), operating under a limited budget, decides whether to implement interim assessments and how much merit pay to offer, and teachers (agents) choose how much effort to exert in each period. We use two-state (proficient versus not proficient) Markovian dynamics to describe the evolution of student test readiness, in which the transition probability in a given period depends on both teachers’ effort decisions and the starting state. Our results indicate that, despite the popularity of interim assessments, their usefulness is far from guaranteed. In particular, the accuracy promised by these assessments is a double-edged sword: positive midyear results can make it easier to incentivize second period teacher effort, but negative results can have a demotivating effect. Moreover, even when an interim assessment does result in a higher probability of the school ending the year in the proficient state, the resulting higher expected costs of merit-based bonuses for the district may exceed the available budget. Thus, even a free interim assessment might be too expensive for the school district.
Although prior research suggests strict, fair value–based securities accounting rules cause banks to sell securities into negative liquidity shocks, a value-destroying behavior called “liquidity feedback trading,” the mechanism is uncertain. We find the sooner chief executive officers (CEOs) are permitted to cash out of their stock and option grants, the more prone are their banks to feedback trading. Furthermore, the sooner CEOs can cash out, the more positive their banks’ stock price reaction to news of accounting rule relaxation. We conclude incentives for managerial short-term focus are a mechanism by which stricter accounting rules cause feedback trading. We also find evidence that regulatory compliance concerns play a role.
We study the used car market to understand how public enforcement that intends to increase information reliability affects disclosure, pricing, and liquidity. In 1986, federal rules mandated the creation of odometer records, and states enforced the law in a staggered fashion. We find that used car asking-price sensitivity to mileage increases by 32.6%, and mileage disclosure in classified ads increases by 23.4%. The findings suggest that strengthened law enforcement increases odometer credibility and enhances market participants’ use of that information. We also show significant improvement in market outcomes: a 9.7% increase in asking price and a 13.4% increase in transaction speed, as reflected in the reduction in repeated ads. The price effect arises irrespective of whether ads contain mileage disclosure, whereas the liquidity effect arises mainly through disclosure. Our study shows public enforcement can benefit the market by improving information credibility.
I examine whether financial statements are more comparable when accounting standards restrict managers’ discretion. My evidence suggests that restricting managers’ discretion is associated with reduced comparability, on average. This effect is strongest when transactions are dissimilar. To explore this relation, I develop novel measures of two distinct types of incomparability. I find that restricting managers’ discretion is associated with an increase in incomparability stemming from dissimilar transactions appearing overly similar. Together, these findings suggest that restricting managers’ discretion may be more harmful to comparability than is too much diversity in practice. However, I also find evidence that restricting managers’ discretion may enhance comparability in two scenarios. Specifically, I find that restricting managers’ discretion is associated with improved comparability when standards (1) restrict manipulation of financial reports and (2) eliminate dissimilar accounting treatments that do not reflect differences in the underlying transactions. Overall, these findings nuance our understanding of how the requirements imposed by standard setters influence financial statement comparability.
Market producers often seek to position themselves in different categories over time. Successful repositioning is difficult, however, as audiences often devalue offerings that depart from a producer’s past creations. Prior research suggests that this penalty arises as evaluators withhold opportunities for producers to reposition because of presumptions of a lack of competence in different categories. In this paper, we develop understanding of a novel evaluator-driven challenge to producers’ repositioning efforts: evaluators are prone to “categorical stickiness,” by which the categories they have come to associate with a producer through its prior offerings shape their perceptions of the producer’s subsequent offerings. The result is a systematic mismatch between what producers claim and what evaluators perceive when a producer repositions. We further propose that audience members who have the greatest prior experience with a producer are the least likely to recognize its repositioning efforts. We examine evidence for our theory using data from Goodreads.com on authors within the book publishing industry, 2007–2017. We first build a novel deep-learning framework to predict categorization of a given book based solely on an author’s description of its content. We then use data on how Goodreads users categorize and evaluate books as well as their past reading behavior to test for evidence of our proposed mechanism. Overall, our results extend understanding of the evaluative processes that generate categorical constraints and how these may differ among various types of audience members.
In U.S. mergers and acquisitions, target directors are required to consider third-party financial valuations, summarized in a “fairness opinion” before accepting a takeover offer. Critics argue (1) that these valuations are not relevant for public companies, which can assess the desirability of the deal based on the market premium, and (2) that the investment bank providers cater to management by “rationalizing” the deal price rather than providing an independent valuation check. I find that fairness valuations can provide valid information about the target’s value, incremental to its predeal stock price. They can impound expected transaction synergies, unravel rumor-driven stock price runups, and may signal ex ante fundamental mispricing. I also find evidence of the alleged catering, which I distinguish from “legitimate” valuation disagreements. This suggests that third-party fundamental valuation could play a useful role in the governance of public company takeovers, but there are flaws in its current implementation.
We examine the commonalities in international equity risk premiums by linking empirical evidence for the ability of U.S. downside and upside variance risk premiums (DVP and UVP, respectively) to predict international stock returns with implications from an empirical model featuring asymmetric economic uncertainty and risk aversion. We find that DVP and UVP predict international stock returns through U.S. bad and good macroeconomic uncertainties, respectively. Sixty percent to 80% of the dynamics of the global equity risk premium for horizons under seven months are driven by economic uncertainty, whereas risk aversion appears more relevant for longer horizons. The predictability patterns of DVP and UVP vary across countries depending on those countries’ financial and economic exposure to global shocks. In those with higher economic exposure, investors demand higher compensation for bad macroeconomic uncertainty but lower compensation for good macroeconomic uncertainty, whereas the compensation for bad macroeconomic uncertainty is lower for countries with high financial exposure.
Monetary incentives are widely used to align employee actions with employer objectives. We conducted a field experiment in a retail chain to evaluate whether an attendance bonus could reduce employee absenteeism. Apprentices in 232 stores were randomly assigned to a control group or one of two treatment groups in which a monetary or time-off attendance bonus was introduced for one year. We find that neither variant of the attendance bonus led to a systematic reduction in absenteeism. On the contrary, the monetary attendance bonus increased absenteeism substantially by around 50% on average, which corresponds to more than five additional days absent per employee and year. This effect was driven by the most recently hired apprentices. Survey results reveal that the monetary attendance bonus shifted the perception of absenteeism as acceptable behavior. The backfiring effect persisted beyond the end of the experiment, indicating a lasting erosion of social norms.
Compared with regular employees, independent contractors (ICs) offer labor flexibility and cost savings to their employers. Using a difference-in-differences design around the 2004 Massachusetts law that discourages IC usage, we find that this exogenous decrease in IC usage makes treated firms’ earnings more sensitive to changes in sales, increases labor-related expenses, and reduces profitability. Firms subsequently reduce share repurchases. The decrease is more pronounced for firms with high operating leverage and financial constraints. Our results are robust to entropy balancing. We conclude that IC usage affects firms’ operating leverage and profitability, which in turn, influence payout policy.
We study the impact of monetary policy changes on portfolio decisions of U.S. households. Contrary to the predictions of canonical portfolio choice models, interest rate increases are related to increased equity ownership and larger wealth allocations to risky assets. Inflation hedging is a likely explanation for these findings. Interest rates have a stronger impact on the equity exposure of households that experience higher inflation, especially those with greater inflation awareness and potentially stronger hedging motives. Using household portfolios at a large discount brokerage house, we provide direct evidence that investors with greater inflation sensitivity overweight stocks with high hedging potential.
This study explores the polarization of news content shared on Facebook compared with email using data from the New York Times’ Most Emailed and Most Shared lists over 2.5 years. Employing latent Dirichlet allocation and large language models (LLMs), we find that highly polarized articles are more likely to be shared on Facebook (versus email), even after accounting for factors like topics, emotion, and article age. Additionally, distinct topic preferences emerge, with social issues dominating Facebook shares and lifestyle topics prevalent in emails. Contrary to expectations, political polarization of articles shared on Facebook did not escalate post-2020 election. We introduce a novel approach to measuring polarization of text content that leverages generative artificial intelligence models, like ChatGPT, and it is both scalable and cost effective. This research contributes to the evolving intersection of LLMs, social media, and polarization studies, shedding light on descriptive patterns of content dissemination across different digital channels.
We study mergers between firms operating in data-connected markets: the data generated as a byproduct of the activity in market A can be used by firms operating in market B. The effects of such a merger depend on whether data trade among independent firms is possible and whether data use benefits consumers or leads to more surplus extraction. When data increases product B’s quality, the merger benefits consumers in both markets if data cannot be traded absent the merger and harms them otherwise. When data are used to extract consumer surplus in market B, the merger increases consumer surplus in market A and reduces it in market B.
The shift to a service-oriented economy has driven traditional product-oriented manufacturing firms to integrate various services into their businesses. This study aims to provide empirical evidence on how manufacturers’ service offerings impact demand variability and intrafirm bullwhip effects. Through “bag-of-words” text mining on 10-K filings of U.S.-listed manufacturing firms, we propose a novel measurement to identify annual services offered. We validate the measurement’s statistical and economic significance and verify its consistency with the results obtained using the large language model (i.e., GPT-4). Services are categorized as complementing product sales (e.g., maintenance and repair) or substituting product sales entirely (e.g., machine hours). Utilizing difference-in-difference techniques, we find robust evidence that manufacturers’ service offerings reduce the bullwhip effect in two steps: basic complementing services decrease demand variability, whereas advanced substituting services mitigate intrafirm bullwhip. Moreover, servitization mainly minimizes demand variability through information channels, whereas increased production efficiency decreases intrafirm bullwhip. Our findings contribute to understanding manufacturers’ business model innovations by demonstrating that servitization can smooth demand and mitigate intrafirm bullwhip.
We report a laboratory experiment examining how demand for exchange-traded fund (ETF) index products affects the prices and trading volume of assets. We compare an environment where the ETF index includes all assets against an environment where a redundant asset is excluded from the index. We find that (i) subjects place significant value on the ETF index asset beyond the value of its constituent assets; (ii) there is a substantial index premium for included assets; and (iii) the index premium persists even when short selling is permitted. The price increases of the constituent assets and of the ETF itself suggest that ETF products can distort markets to some degree.
Some mutual funds retain a fraction of securities lending income by employing in-house lending agents. In a model with heterogeneous investors and endogenous delegation to mutual funds, we show that a subset of funds optimally engages in lending fee retention and as a result, overweights high lending fee stocks that endogenously underperform. We find empirical evidence consistent with our model’s predictions; active mutual funds we identify as fee retainers invest more in high-fee stocks and underperform relative to both nonretaining and nonlending funds. We also show that fee retention helps explain the negative relation between lending fees and future fee-inclusive stock returns.
This paper demonstrates the financial and real consequences of online review aggregators. Exploiting a regression discontinuity design that overcomes the endogenous relationship between Yelp reviews and Small Business Administration loan outcomes, I show that higher coarse Yelp ratings lead to improved loan terms and performance. Specifically, a one-half-star increase in Yelp ratings corresponds to a 25-basis-point decrease in loan spread and 6% lower collateral requirements. The effects are more pronounced when banks have less borrower information. Higher Yelp ratings also contribute to increased consumer demand and the likelihood of future business openings. These findings indicate that online review aggregators influence both consumer choices and banks’ financing decisions.
Modern traceability technologies promise to improve supply chain management by simplifying recalls, increasing visibility, and verifying sustainable supplier practices. Initiatives leading the implementation of traceability technologies must choose the least-costly set of firms—or seed set—to target for early adoption. Choosing this seed set is challenging because firms are part of supply chains interlinked in complex networks, yielding an inherent supply chain effect: benefits obtained from traceability are conditional on technology adoption by a subset of firms in a product’s supply chain. We prove that the problem of selecting the least-costly seed set in a supply chain network is hard to solve and even approximate within a polylogarithmic factor. Nevertheless, we provide a novel linear programming-based algorithm to identify the least-costly seed set. The algorithm is fixed-parameter tractable in the supply chain network’s treewidth, which we show to be low in real-world supply chain networks. The algorithm also enables us to derive easily computable bounds on the cost of selecting an optimal seed set. We leverage our toolbox to conduct large-scale numerical experiments that provide insights into how the supply chain network structure influences diffusion. These insights can help managers optimize their technology diffusion strategy.
When employees work from home, organizations have traditionally placed a stronger reliance on output-based controls because of the inability to physically observe its employees. Because of recent advances in technology, however, many organizations now use input-based controls. One such input-based control is computer monitoring, which records the number of hours spent working and leaves out any time in which an employee does not perform work activities. On the one hand, computer monitoring may provide productivity-enhancing benefits by reducing the likelihood of employees experiencing self-control problems, such as procrastination. On the other hand, computer monitoring may motivate reduced effort by decreasing employees’ autonomy, which a prior study finds is an important mediator of the relation between telecommuting and productivity. We take advantage of a field-research setting in which the organization adopts computer monitoring for its telecommuting employees. We find that computer monitoring leads to a 7.1% productivity increase for telecommuting employees. We also find that computer monitoring reduces employees’ procrastination tendencies. In addition, we find that low performers’ productivity increases more following the introduction of computer monitoring. Finally, we find evidence that telecommuters’ departure rates increase significantly following computer monitoring. Our results suggest that technology can play a significant role in expanding organizations’ management control choices when achieving controls in telecommuting arrangements.
There is increasing interest in allocating treatments based on observed individual characteristics: examples include targeted marketing, individualized credit offers, and heterogeneous pricing. Treatment personalization introduces incentives for individuals to modify their behavior to obtain a better treatment. Strategic behavior shifts the joint distribution of covariates and potential outcomes. The optimal rule without strategic behavior allocates treatments only to those with a positive conditional average treatment effect. With strategic behavior, we show that the optimal rule can involve randomization, allocating treatments with less than 100% probability even to those who respond positively on average to the treatment. We propose a sequential experiment based on Bayesian optimization that converges to the optimal treatment rule without parametric assumptions on individual strategic behavior.
Would consumers be willing to pay more for goods for which there is less inequality in wages across those involved in their production? In incentive-compatible behavioral choice studies on representative samples of the English and U.S. populations, we find significantly positive willingness to pay for such inequality reductions in more than 80% of subjects. Although it varies with political leaning and the extent of the inequality reduction, willingness to pay is positive across the political spectrum and for all studied inequality differences. It is higher for more intuitive and informative inequality-reporting formats. Our findings have policy implications for both governments and firms. On the one hand, they suggest the promise of universal provision of product-level inequality information as a tool for moderating income inequality. On the other hand, they highlight the potential relevance of inequality reporting for firms’ marketing strategies.
Do competition and incentives offered to designated market makers (DMMs) improve market liquidity? We employ data from NYSE Euronext Paris to show that exogenous changes in contract design lead to significant decreases in quoted and effective spreads. In particular, market liquidity increases the most for stocks with the largest increase in competition among DMMs. Our analysis shows that competition among DMMs is an important aspect of contract design, along with elements such as rebates and requirements.
Using data from an online brokerage, we examine the role of investor logins in trading behavior. We find that a new reference point is created when an investor logs in and views the investor’s portfolio. We observe this as a disposition effect on returns since last login in addition to the traditional disposition effect on returns since purchase. Further, these reference points produce a strong interaction such that even a small loss since last login nullifies the positive effect of a gain since purchase. This interaction follows if investors select the higher, more aspirational price as a reference point.
Although internal labor market theory emphasizes promotions as the main form of mobility within organizations, many internal job moves take people sideways into jobs that are at the same hierarchical level as the one that was left. Despite the prevalence of these lateral moves, however, we have little evidence on what role they play in workers’ careers. We argue that lateral mobility can facilitate subsequent career advancement by allowing for the development and demonstration of new skills and can, therefore, help those who would struggle to be promoted from their current job to develop their careers further. We establish empirical evidence on the implications of lateral mobility using eight years of personnel data from a large U.S. healthcare company. Our analyses show that those employees who move laterally are more likely to be subsequently promoted and achieve substantially higher pay growth than a matched sample of employees. We also find that lateral moves are more likely to be undertaken by those who have spent longer in the job but have lower performance than those who are promoted. This pattern of results suggests that lateral mobility provides an important avenue for career growth as people who step sideways in organizations are more likely to subsequently step up.
Regulatory stress tests have become one of the main tools for setting capital requirements at the largest U.S. banks. The Federal Reserve uses confidential models to evaluate bank-specific outcomes for bank-specific portfolios in shared stress scenarios. As a matter of policy, the same models are used for all banks, despite considerable heterogeneity across institutions; individual banks have contended that some models are not suited to their businesses. Motivated by this debate, we ask, what is a fair aggregation of individually tailored models into a common model? We argue that simply pooling data across banks treats banks equally but is subject to two deficiencies: it may distort the impact of legitimate portfolio features, and it is vulnerable to implicit misdirection of legitimate information to infer bank identity. We compare various notions of regression fairness to address these deficiencies, considering both forecast accuracy and equal treatment. In the setting of linear models, we argue for estimating and then discarding centered bank fixed effects as preferable to simply ignoring differences across banks. We also discuss extensions to nonlinear models.
Motivated by emerging applications, such as live-streaming e-commerce, promotions, and recommendations, we introduce and solve a general class of nonstationary multi-armed bandit problems that have the following two features: (i) the decision maker can pull and collect rewards from up to K(≥1) out of N different arms in each time period and (ii) the expected reward of an arm immediately drops after it is pulled and then nonparametrically recovers as the arm’s idle time increases. With the objective of maximizing the expected cumulative reward over T time periods, we design a class of purely periodic policies that jointly set a period to pull each arm. For the proposed policies, we prove performance guarantees for both the offline and the online problems. For the offline problem when all model parameters are known, the proposed periodic policy obtains a long-run approximation ratio that is at the order of 1−O(1/K), which is asymptotically optimal when K grows to infinity. For the online problem when the model parameters are unknown and need to be dynamically learned, we integrate the offline periodic policy with the upper confidence bound procedure to construct on online policy. The proposed online policy is proved to approximately have O˜(NT) regret against the offline benchmark. Our framework and policy design may shed light on broader offline planning and online learning applications with nonstationary and recovering rewards.
We examine how information processing costs affect the extent to which depositors’ use the details in banks’ income statements. Depositors have a unique cost-benefit structure because they are nonprofessional users of financial information and have high information processing costs. At the same time, they benefit from acting quickly because failing banks make payments on a first-come, first-serve basis. This makes it likely that they will react to a prominent summary measure like the reported net income without adjusting for any risk-irrelevant information included in the line items. In our empirical analysis, we investigate depositors’ behavior as driven by the mechanical revaluations of deferred tax assets due to the 2017 Tax Cuts and Jobs Act. Using a difference-in-difference design, we find deposit withdrawals because of this risk-irrelevant information. In cross-sectional tests, we show that the withdrawals are stronger if the information acquisition costs are low, and the information integration costs are high. Overall, our results show that information processing costs are important for understanding depositors’ reactions to accounting information and can lead to deposit flows that cannot be explained by new risk-relevant information.
We conduct a field experiment in a large corporation to investigate the effects of supportive leadership behaviors on employee satisfaction, engagement, and performance. Treated leaders receive a brief training promoting leadership behaviors that encourage, assure, and value employee efforts. Our experimental design allows us to observe leaders and employees in a subsequent meeting. We find that the leadership training affects the leaders’ supportive behaviors and thereby increases employees’ self-reported satisfaction and engagement during the meeting by 0.28 and 0.18 standard deviations, respectively. The effect on team performance is 0.13 standard deviations but is not significant.
This paper studies how online grocers use service time to respond to local competition and demand conditions when prices are uniformly set at the national level. Using comprehensive data collected twice a week over three years from 172 Israeli local markets, we show that an online grocer sets identical prices in all markets. By contrast, service time is shorter in more competitive markets and on low-demand days. Next, we exploit regional and temporal variation in entry decisions to examine how the incumbent adjusts its service time when new online grocers enter the market. The incumbent’s service time falls significantly on low-demand days and in monopolistic markets. This decrease begins shortly before entry and is greater when the entrant poses a larger competitive threat. On high-demand days and in competitive markets we do not find a significant change in service time in the months surrounding entry. Our findings suggest that firms use service time to exercise their local market power when prices are unresponsive and that operational considerations affect the extent to which they respond.
Online marketplace designers frequently run randomized experiments to measure the impact of proposed product changes. However, given that marketplaces are inherently connected, total average treatment effect (TATE) estimates obtained through individual-level randomized experiments may be biased because of violations of the stable unit treatment value assumption, a phenomenon we refer to as “interference bias.” Cluster randomization (i.e., the practice of randomizing treatment assignment at the level of “clusters” of similar individuals) is an established experiment design technique for countering interference bias in social networks, but it is unclear ex ante if it will be effective in marketplace settings. In this paper, we use a meta-experiment or “experiment over experiments” conducted on Airbnb to both provide empirical evidence of interference bias in online marketplace settings and assess the viability of cluster randomization as a tool for reducing interference bias in marketplace TATE estimates. Results from our meta-experiment indicate that at least 20% of the TATE estimate produced by an individual-level randomized evaluation of the platform fee increase we study is attributable to interference bias and eliminated through the use of cluster randomization. We also find suggestive, nonstatistically significant evidence that interference bias in seller-side experiments is more severe in demand-constrained geographies and that the efficacy of cluster randomization at reducing interference bias increases with cluster quality.
Accounting standards prohibit internally created knowledge and organizational capital from being disclosed on firm balance sheets. As a result, balance sheets exhibit downward biases that have become exacerbated by increasing levels of intangible investments. To offset these biases, researchers must estimate the value of these off-balance sheet intangibles by capitalizing prior flows of research and development (R&D) and selling, general, and administrative (SG&A). In doing so, a set of capitalization parameters must be assumed (i.e., the R&D depreciation rate and the fraction of SG&A that represents a long-lived asset). We estimate these parameters using market prices from firm exits and use them to capitalize intangibles for a comprehensive panel of firms from 1978 to 2017. We then use a series of validation tests to examine the performance of our intangible capital stocks versus those developed from commonly used parameters. On average, our estimates of intangible capital are 15% smaller than estimates from status quo parameters while exhibiting larger variation across industry. Intangible capital stocks derived from exit price parameters outperform existing measures when explaining market enterprise values and identifying human capital risk. Adjusting book values with exit-based intangible capital stocks markedly attenuates well-documented biases in market-to-book and return on equity ratios while increasing the precision of the high-minus-low asset pricing factor. We conclude that our capitalization parameters create intangible stocks that perform equal to or better than status quo measures in various applications.
Important decisions are often complex, and existing evidence suggests that complexity can affect economic behavior. It is an open question, however, exactly when and how complexity matters. We hypothesize that salient cues—standing out in the choice context—mitigate the effect of complexity on choices. We theoretically develop and experimentally test this hypothesis in the context of portfolio selection. We find that, in both simple and complex problems, subjects seek highly right-skewed portfolios, which have an extreme and salient upside, and avoid highly left-skewed portfolios with an extreme and salient downside. Complexity does affect, however, choices among symmetric portfolios, which have neither a salient upside nor downside. Absent a salient cue, subjects diversify naively. Evidence on response times and memory supports our salience-based explanation.
This paper uses novel, firm-level communication measures derived from communications metadata several months before and after a CEO transition for 102 firms to study whether and how this organizational event is reflected in employees’ communication flows. We find that CEO turnover is associated with an initial decrease in intrafirm communication (−20% relative to the pre-CEO transition period), followed by a significant increase approximately five months after the CEO turnover (+20% relative to the pre-CEO transition period). The increase in communication is driven primarily by interdepartmental (i.e., communication involving employees of different functional departments) and vertical (i.e., communication among managers and employees) communication flows. This study suggests that communications metadata are a useful tool to examine how organizational change and related uncertainty impact information flows within firms.
We document a novel platform effect caused by the emergence of fintech platforms in the intermediation of financial products. In China, platform distributions of mutual funds emerged in 2012 and grew quickly into a formidable presence. Utilizing the staggered entrance of funds onto platforms, we find a marked increase of performance chasing driven by the centralized information flow unique to fintech platforms. This pattern is further confirmed using proprietary data from a top platform. Examining the platform impact on fund managers, we find that incentivized by the amplified performance chasing, fund managers increase risk-taking to enhance their probability of getting onto the top ranking.
The currency market is the world’s largest financial market by trading volume. We show that even in this highly liquid market, exposure to liquidity risk commands an economically significant risk premium of up to 3.6% per year. Liquidity risk is not subsumed by existing currency risk factors and successfully prices the cross section of currency excess returns. Moreover, we find that liquidity risk and carry trade premia are correlated, although this correlation is limited to static rather than dynamic carry trades. Building on this result, we propose a liquidity-based explanation for the carry trade, which adds significant explanatory power to existing theories.
We study the impact of two changes in the minimum tick size, a reduction and a subsequent increase, on the trading behavior of fast and slow traders in the spot foreign exchange market. We find that the most notable impact of the tick size reduction is a substantial increase in the liquidity demand of high-frequency traders (HFTs) and not the decrease in their liquidity provision discussed by prior literature. We show that this change in behavior is linked to the higher frequency of price signals that arises with the smaller tick size and to the ability of fast traders to profit from it, often at the detriment of slower traders. Following the tick size decrease and the increase in liquidity demand by HFTs in the spot market, the role of the spot market in price discovery drops relative to that of the futures market. We discuss these findings in the context of the impact of HFTs on the information content of financial markets.
When pursuing goals, we commonly choose between going it alone versus teaming up together. In a field experiment (n = 774), we tested the benefits of rewarding individual versus tandem goal pursuit. In a standard-reward condition, we experimentally offered gym members an individual cash reward each day they visited the gym for four weeks. Participants in a tandem-reward condition could earn the same reward but only if they surmounted an extra logistical hurdle: they had to visit the gym with a friend. Although this additional requirement made it more difficult for participants in the tandem-reward condition to earn equivalent incentives, participants with this extra hurdle visited the gym about 35% more frequently than those earning a standard reward. A follow-up survey suggests that tandem rewards provide nonmonetary incentives that change behavior, including increased accountability and enjoyment. Our findings illustrate the advantages of making desired behaviors social to promote follow-through.
A significant element of managerial post-COVID job design regards remote work. In an era of renewed recognition of diversity, employers may wonder how diverse (gender and race) and experienced job applicants respond to remote job listings, especially for high-skilled technical and managerial positions. Prior work has shown that while remote work allows employee flexibility, it may limit career promotion prospects, so the net effect of designating a job as remote-eligible is not clear from an applicant interest standpoint, particularly when recruiting females and underrepresented minorities (URM). We analyze job applicant data from a leading startup job platform that spans long windows before and after the COVID-19 pandemic-induced shutdowns of March 2020. To address the empirical challenge that remote job designation may be codetermined with unobserved job and employer characteristics, we leverage a matching approach (and an alternative method which leverages the sudden shutdowns) to estimate how applicant characteristics differ for otherwise similar remote and onsite job postings. We find that offering remote work attracts more experienced and diverse (especially URM) job applicants, with larger effects in less diverse geographic areas. A discrete change in job posting to remote status (holding all else constant) is associated with an approximately 15% increase in applicants who are female, 33% increase in applicants with URM status, and 17% increase in applicant experience. Using the application data, we estimate an average estimated compensating wage differential for remote work that is about 7% of posted salaries in this labor market.
We examine how corporate bond fund managers manipulate portfolio risk in response to incentives. We find that liquidity risk concerns drive the allocation decisions of underperforming funds, whereas tournament incentives are of secondary importance. This leads laggard fund managers to trade off yield for liquidity while holding the exposure to other sources of risk constant. The documented derisking is stronger for managers with shorter tenure and is reinforced by a more concave flow to performance sensitivity and by periods of market stress. Derisking meaningfully supports ex post laggard fund returns. Flexible net asset values (swing pricing) may, however, reduce derisking incentives and create moral hazard.
Site visits allow visitors to physically inspect productive resources and interact with on-site employees and executives face to face. We posit that, by allowing visitors to acquire investment-related information and monitor the management team, site visits offer disciplinary benefits for corporate investments. Using mandatory disclosures of site visits in China, we find that corporate investments become more responsive to growth opportunities as the intensity of site visits increases, consistent with the notion that site visits yield disciplinary benefits. We also find that the positive association between site visits and investment efficiency is more pronounced when visitors can glean more investment-related information and when they have stronger incentives and greater power to monitor managers. This positive association is also stronger among firms with more severe agency problems and higher asset tangibility. The overall evidence supports the notion that site visits serve as a unique venue for institutional investors and financial analysts to acquire valuable information and serve a monitoring function, which generates disciplinary benefits for corporate investments.
Using a double list experiment designed to elicit views free from social desirability bias, we find that support in the United States for transgender people in the labor market is significantly overreported by 8%–10%. After correcting for this overreporting, we still find that over two-thirds of respondents would be comfortable with a transgender manager and support employment nondiscrimination protection for transgender people. However, respondents severely underestimate this level of support. We also show that stated labor market support for transgender people is lower than support for gay, lesbian, and bisexual people. Our results advance our understanding of employment discrimination against transgender people.
Credence goods, such as repair and healthcare services, are characterized by profound information asymmetries between less-informed customers and better-informed expert sellers. These information asymmetries open the door for fraudulent behavior on the seller side. In a preregistered natural field experiment, we vary in one dimension the seller’s perception of whether the service is an ordinary or a credence good service and in the second dimension whether the customer is a member of a minority or a member of the majority. This allows us to measure the size of the induced credence goods markup and to address the question whether it interacts systematically with discrimination. We document the existence of a large credence goods markup, on average. Moreover, we find that members of the minority pay a sizeable discriminatory markup if the good is perceived as a credence good but not if it is perceived as an ordinary good. Our results show that sellers engage in sophisticated discrimination where informational asymmetries are used to hide discriminatory (fraudulent) behavior. With the help of an ex post survey, we derive a possible explanation for our results.
The prevalence of racial bias in policing has long concerned social scientists and policymakers. This article studies a predecessor mechanism that constitutes an important source of policing bias in American society: calls by individuals to the police to investigate “suspicious” behaviors, often involving neighbors. We construct a novel data set of more than 39 million 911 calls across 14 U.S. cities from 2011 to 2020. These data, obtained through the digitization initiatives of local governments, provide us with a unique opportunity to study neighborhood-level trust and social cohesion and demonstrate how changes to a neighborhood’s composition lead to systematic increases in the prevalence of “unfounded” suspicion calls to the police. Across a range of specifications, the proportion of unfounded suspicion calls increases as more non-Black residents move into neighborhoods with historically high levels of Black residents. This relationship is exacerbated in gentrifying neighborhoods and those with public spaces that enable more contact between community members. However, we also find some evidence that Black leadership and public support of Black citizens in communities mitigate the association between non-Black residents and the proportion of unfounded 911 calls. We discuss our results and implications for future research and policy.
Internally developed intangibles are not included in reported assets under U.S. generally accepted accounting principles. The omission of this increasingly important class of assets reduces the usefulness and relevance of financial statement analysis, conducted with reported values of equity and assets. Recent studies overcome this deficiency by capitalizing research and development (R&D) expenses to estimate the value of knowledge capital and by capitalizing selling, general, and administrative (SG&A) expenses to estimate the value of organization capital. Those two estimates are then added to reported values for financial statement analysis. For convenience, many studies rely on two rules of thumb and assume them to be equally applicable in all instances: (1) 30% of SG&A and 100% of R&D expenses are investments, and (2) the useful life of SG&A and R&D investments is three and five years, respectively. We propose a more flexible approach by estimating the capitalization and amortization parameters on an industry–year–specific basis. Our modified values of total assets and equity, inclusive of the value of capitalized intangibles, exhibit greater association with future returns and investments compared with as-reported values and values estimated with uniform rules of thumb. We provide a better estimate of intangible capital for the consumers of financial statements.
We study the impact of reviews on product choices when consumers can negotiate price. Although prior research has examined the impact of reviews in various contexts, none has considered a setting in which the purchase price is negotiated. We postulate that online reviews affect demand through consumer baseline product preference and consumer bargaining power. Leveraging a unique data set of consumer reviews of new car purchases, we developed and estimated a structural model to decompose and quantify the two mechanisms of how reviews affect demand: baseline preference mechanism versus bargaining power mechanism. Our counterfactual analysis suggests that the bargaining power mechanism is more important than the baseline preference mechanism in explaining how historical price and discount frequency information in reviews affect seller profits. Ignoring the impact of reviews on the negotiated price leads to a biased estimation of the effectiveness of reviews. We call into caution a popular selling practice of using discounts in exchange for positive reviews, as it can backfire when future consumers leverage this discount information to negotiate a lower price.
We develop a measure of merger and acquisition (M&A) quality using accounting theory. This measure, implied return-on-equity improvement (IRI), quantifies the minimum improvement in the target’s post-acquisition return on equity (ROE) the acquirer must attain to break even on the acquisition price. Employing a large sample of M&As from 1980 to 2018, we find that a high IRI is, on average, less attainable ex post and predicts worse acquirer financial performance. The acquirer’s ROE growth over the first three years after the M&A is 11 percentage points lower for high-IRI M&As compared with low-IRI M&As. Worse high-IRI acquirer performance is observable through higher operating costs, tighter financial constraints, lower investments, and larger and more frequent goodwill impairments. We also find that IRI increases with acquiring chief executive officers’ overconfidence, incentive misalignment, and difficulty in estimating synergies; IRI decreases with acquirers’ financial discipline and due diligence effort. As such, overestimating synergies and managerial incentives that drive overpayment are potential mechanisms underlying IRI’s negative association with acquirers’ post-M&A performance.
While patents have been shown to play a role as barriers to entry, they also reveal information about incumbent strategies and risk being invalidated. We examine the trade-off incumbents face between using their patents ex ante as entry deterrents or ex post once competitors have revealed their moves. Leveraging the unique characteristics of the pharmaceutical sector, in which it is possible to observe exactly when a competitor entry threat materializes, and exploiting exogenous variation in that timing, we show that incumbents intentionally fragment and delay the full disclosure of their intellectual property rights through continuation patents. They disproportionately reveal continuation patents after a competitor entry threat becomes concrete, tailoring their response to the threat they have received and successfully delaying competitor entry through litigation. The detected incumbents’ reaction is stronger when their attacked drugs are valuable and when the patents listed at the Food and Drug Administration approval of a drug are relatively narrow in scope.
Advertising is a crucial revenue source for e-commerce platforms and a vital online marketing tool for their sellers. In this paper, we explore dynamic ad allocation with limited slots upon each customer’s arrival for an e-commerce platform, where customers follow a choice model when clicking the ads. Motivated by the recent advocacy for the algorithmic fairness of online ad delivery, we adjust the value from advertising by a general fairness metric evaluated with the click-throughs of different ads and customer types. The original online ad-allocation problem is intractable, so we propose a novel stochastic program framework (called two-stage target-debt) that first decides the click-through targets and then devises an ad-allocation policy to satisfy these targets in the second stage. We show the asymptotic equivalence between the original problem, the relaxed click-through target optimization, and the fluid-approximation (Fluid) convex program. We also design a debt-weighted offer-set algorithm and demonstrate that, as long as the problem size scales to infinity, this algorithm is (asymptotically) optimal under the optimal first-stage click-through target. Compared with the Fluid heuristic and its resolving variants, our approach has better scalability and can deplete the ad budgets more smoothly throughout the horizon, which is highly desirable for the online advertising business in practice. Finally, our proposed model and algorithm help substantially improve the fairness of ad allocation for an online e-commerce platform without significantly compromising efficiency.
Queues where people misreport their private information to access service faster are everywhere. Motivated by the prevalence of such behavior in practice, we construct a queueing-game-theoretic model where customers make strategic claims to reduce their waiting time and where the manager decides on the static scheduling policy based on those claims to minimize the expected delay cost in the system. We develop a lying-aversion model where customers incur both delay and lying costs. We run controlled experiments to validate our modeling assumptions regarding customer misreporting behavior. In particular, we find that people do incur lying costs, and we find that their misreporting behavior does not depend on changes in waiting times but rather, on the scheduling parameters. Based on the validated lying-aversion model, we study the equilibrium that arises in our game. We find that under certain conditions, the optimal policy is to use an honor policy where service priority is given according to customer claims. We also find that it may be optimal to incentivize more honesty by means of an upgrading policy where some customers who claim to not deserve priority are upgraded to the priority queue. We find that the upgrading policy deviates from the celebrated cµ rule.
We consider a distribution system in which retailers replenish perishable goods from a warehouse, which, in turn, replenishes from an outside source. Demand at each retailer depends on exogenous features and a random shock, and unfulfilled demand is lost. The objective is to obtain a data-driven replenishment and allocation policy that minimizes the average inventory cost per time period. The extant data-driven methods either cannot guarantee a feasible solution for out-of-sample feature observations or generate one with excessive computational time. We propose a policy that resolves these issues in two steps. In the first step, we assume that the distributions of features and random shocks are known. We develop an effective heuristic policy by using Taylor expansion to approximate the retailer’s inventory cost. The resulting solution is closed-form, referred to as Taylor Approximation (TA) policy. We show that the TA policy is asymptotically optimal in the number of retailers. In the second step, we apply the linear quantile regression and kernel density estimation to the TA solution to obtain the data-driven policy called Data-Driven Taylor Approximation (DDTA) policy. We prove that the DDTA policy is consistent with the TA policy. A numerical study shows that the DDTA policy is very effective. Using a real data set provided by Fresh Hema, we show that the DDTA policy reduces the average cost by 11.0% compared with Hema’s policy. Finally, we show that the main results still hold in the cases of correlated demand features, positive lead times, and censored demand.
Using proprietary data on nonperforming loan (NPL) transactions in China, we document that banks conceal NPLs in disposal transactions that are supposed to resolve NPLs. In these transactions, banks provide financing and retain the credit risks and debt collection obligations. Transaction prices contradict underlying credit risks: Despite being delinquent for 4.5 years, the median NPL package is sold at face value with zero haircut. The majority of NPL packages are resold at a premium to banks’ non–government-owned borrowers, further supporting the concealment view. Banks under stricter regulation conceal NPLs more and engage in more illicit concealment. NPL-concealing banks increase zombie lending, suggesting that an ineffective resolution exacerbates credit misallocation. Recognizing the hidden NPLs implies the total NPLs in China is two to three times the reported amount.
We investigate an unexplored mechanism of earnings management: income shifting from not-wholly-owned subsidiaries to help the parent company avoid losses at the expense of subsidiaries. Consolidated net income attributable to the parent company (i.e., net income) increases through this mechanism, as the parent company enjoys the full amount of the shifted earnings rather than sharing them with minority investors. We design an empirical model to directly estimate the amount of income shifted from subsidiaries to parent firms. Employing this measure, we find that firms opportunistically decrease earnings of their not-wholly-owned subsidiaries to manage net income upward to avoid losses. The results are stronger for firms with high noncontrolling ownership, firms with large subsidiaries, firms with strong influence over not-wholly-owned subsidiaries, and firms with a high level of related-party transactions. Our results are robust to alternative research designs, including controls for within-firm variations, alternative earnings thresholds, propensity score matching, and entropy balancing techniques. Our mechanism of earnings management is generalizable to other earnings management scenarios, such as share pledging.
A critical element of word of mouth (WOM) or buzz marketing is to identify seeds, often central actors with high degree in the social network. Seed identification typically requires data on the relevant network structure, which is often unavailable. We examine the impact of WOM seeding strategies motivated by the friendship paradox, which can obtain more central nodes without knowing network structure. Higher degree nodes may be less effective as seeds if these nodes communicate less with neighbors or are less persuasive when they communicate; therefore, whether friendship paradox–motivated seeding strategies increase or reduce WOM and adoption remains an empirical question. We develop and estimate a model of WOM and adoption using data on microfinance adoption across village social networks in India. Counterfactuals show that the proposed strategies with limited seeds are about 13%–30% more effective in increasing adoption relative to random seeding. These strategies are also on average 5%–11% more effective than the firm’s leader seeding strategy. We also find these strategies are relatively more effective when we have fewer seeds.
Using a randomized experiment in auto lending, we find that algorithmic underwriting outperforms the human underwriting process, resulting in 10.2% higher loan profits and 6.8% lower default rates. The human and machine underwriters show similar performance for low-risk, less complex loans. However, the performance of human underwritten loans largely declines for riskier and more complex loans, whereas the machine performance stays relatively stable across various risk dimensions and loan characteristics. The performance difference is more pronounced at underwriting thresholds with a high potential for agency conflict. These results are consistent with algorithmic underwriting mitigating agency conflicts and humans’ limited capacity for analyzing complex problems.
This paper investigates whether limited liability and moral hazard affect risk taking through motivated beliefs. On the one hand, limited liability encourages investors to take excessive risks. On the other, excessive risk taking makes it hard for investors to maintain a positive self-image when moral hazard is present. Using a novel experimental design, we show that subjects form motivated beliefs to self-justify their excessive risk taking. For the same investment opportunity, subjects invest more and are significantly more optimistic about the success of the investment if its failure can harm others. We show that more than one third of the investment increases under limited liability, and moral hazard can be explained through motivated beliefs. Moreover, through a treatment with limited liability but no moral hazard, we show that motivated beliefs are formed subconsciously and can lead to the paradoxical result of investors taking larger risks when their investment can harm a third party compared with when it cannot. These results underscore the importance of motivated beliefs in regulatory policy, emphasizing that policymakers must not only address bad incentives, but also address the role of bad beliefs.
In community first responder (CFR) systems, traditional emergency service response is augmented by a network of trained volunteers who are dispatched via an app. A central application of such systems is out-of-hospital cardiac arrest (OHCA), where a very fast response is crucial. For a target performance level, how many volunteers are needed, and from which locations should they be recruited? We model the presence of volunteers throughout a region as a Poisson point process, which permits the computation of the response-time distribution of the first-arriving volunteer. Combining this with known survival-rate functions, we deduce survival probabilities in the cardiac arrest setting. We then use convex optimization to compute a location distribution of volunteers across the region that optimizes either the fraction of incidents with a fast response (a common measure in the industry) or patient survival in the case of OHCA. The optimal location distribution provides a bound on the best possible performance with a given number of volunteers. This can be used to determine whether introducing a CFR system in a new region is worthwhile or can serve as a guide for additional recruitment in existing systems. Effective target areas for recruitment are not always obvious because volunteers recruited from one area may be found in various areas across the city depending on the time of day; we explicitly capture this issue. We demonstrate these methods through an extended case study of Auckland, New Zealand.
In the classic Adwords problem introduced by [Mehta A, Saberi A, Vazirani U, Vazirani V (2007) Adwords and generalized online matching. J. ACM 54(5):22-es.], we have a bipartite graph between advertisers and queries. Each advertiser has a maximum budget that is known a priori. Queries are unknown a priori and arrive sequentially. When a query arrives, advertisers make bids, and we (immediately and irrevocably) decide which (if any) Ad to display based on the bids and advertiser budgets. The winning advertiser for each query pays their bid up to their remaining budget. Our goal is to maximize total budget used without any foreknowledge of the arrival sequence (which could be adversarial). We consider the setting where the online algorithm does not know the advertisers’ budgets a priori and the budget of an advertiser is revealed to the algorithm only when it is exceeded. A naïve greedy algorithm is 0.5 competitive for this setting, and finding an algorithm with better performance remained an open problem. We show that no deterministic algorithm has competitive ratio better than 0.5 and give the first (randomized) algorithm with strictly better performance guarantee. We show that the competitive ratio of our algorithm is at least 0.522 but also strictly less than (1−1/e). We present novel applications of budget oblivious algorithms in search ads and beyond. In particular, we show that our algorithm achieves the best possible performance guarantee for deterministic online matching in the presence of multichannel traffic [Manshadi V, Rodilitz S, Saban D, Suresh A (2022) Online algorithms for matching platforms with multi-channel traffic. Proc. 23rd ACMConf. Econom. Comput. (ACM, NewYork), 986–987.].
Advances in information technology (IT) may affect the organizational design of production. Exploiting the rapid diffusion of the internet in the United States, we assess the sensitivity of production chain organization to this innovation in IT access and use. Extending theories of the firm that recognize the importance of downstream transfers (selling as opposed to sourcing) and plural governance in organizational design, we predict IT-driven shifts in downstream vertical integration. In a detailed panel of Census Bureau data for over 5,600 manufacturing plants, we observe the extent of a production unit’s downstream transactions within the firm alongside concurrent sales to external customers—a mix we refer to as plural selling. Our main finding is that the use of the internet for external coordination precipitated a significant decline in downstream vertical integration across the manufacturing sector. Instrumental variables estimation points to a causal relationship but also heterogeneous treatment effects. Key drivers of plural organization, such as complementarities and constraints across differently governed transactions, help explain such heterogeneity, as does concurrent use of internal production management IT. Our study is the first study to leverage a plural governance framework and large-scale microdata to understand how U.S. production chain organization shifted in response to this rapid and far-reaching technological change.
Standard search models assume that consumers actively decide on the order, identity, and number of products they search. We document that online, a large fraction of searches happen in a more passive manner, with consumers merely reacting to online advertisements that do not allow them to choose the timing or the identity of products to which they will be exposed. Using a clickstream panel data set capturing full URL addresses of websites that consumers visit, we show how to detect whether a click is ad initiated. We then report that in the apparel category, ad-initiated clicks account for more than half of all website arrivals, are more concentrated early on in the consumer search process, and lead to less in-depth searches and fewer transactions, consistent with the passive nature of these searches. To account for these systematic differences between active and passive searches, we propose and estimate a simple model that accommodates both types of searches. Our results show that incorrectly treating all searches as active inflates the estimated value of brands that advertise frequently. Our model can more accurately recover data patterns, especially for advertising brands. We finish with model extensions and a discussion of the managerial implications.
Government ownership of financial intermediaries is pervasive around the world. In this study, we examine the impact of common government ownership between the brokerage and listed firms on the information production role of brokerage firms. We show that affiliated analysts tend to issue more optimistic recommendations for stocks of firms controlled by the same government entity that controls their brokerage firms. This optimistic bias is particularly pronounced during periods of economic shocks. Our study demonstrates this by utilizing additional tariff impositions and tariff exemptions during the U.S.–China trade war as exogenous negative and positive shocks, respectively. Additionally, our study indicates that stocks recommended by politically affiliated analysts tend to underperform those recommended by independent analysts, implying that the optimism stems from conflicts of interest rather than superior information. Furthermore, our research highlights that sophisticated investors perceive the potential bias and incorporate it into their trading. Consistent with an exchange of favors story, politically affiliated brokerage firms receive a larger allocation during the issuance of local government debt, whereas governments subscribe for more shares during seasoned equity offerings by these affiliated brokerage firms.
We investigate a bargaining setting between an “informed” player, who has private information, and an “uninformed” player. The informed player has the option to truthfully disclose private information in two unique environments. In the first environment, the informed player is randomly matched with an uninformed player and, after matching, can voluntarily disclose private information prior to a negotiation taking place. In the second environment, the informed player can voluntarily disclose private information before any endogenous matching between players (and subsequent negotiation) takes place. We begin by examining these environments theoretically. When disclosure occurs after matching, we show that low informed types should disclose more often than high informed types to avoid disagreement. However, when disclosure occurs before matching, for a range of parameter values, we show that high informed types should disclose more than low informed types to secure a better match. We test these predictions in a controlled human-subjects experiment and verify many of the theoretical predictions. Among other results, we find that low informed types do indeed disclose their private information to avoid disagreement and that, when it is in their interest to do so, high informed types disclose their private information to secure a better match. Another key insight is that high uninformed types benefit from disclosure regardless of whether it occurs before or after matching.
This paper considers sensitivity analysis of the cost coefficients in multiobjective integer linear programming problems. We define the sensitivity region as the set of simultaneous changes to the coefficients for which the efficient set and its structure remain the same. In particular, we require that the component-wise relation between efficient solutions is preserved and that inefficient solutions remain inefficient, and we show that this is sufficient for the efficient set to be the same upon changes. For a single coefficient, we show that a subset of the inefficient solutions can be excluded from consideration. More specifically, we prove that it suffices to inspect the inefficient solutions of a q-objective problem that are efficient in one of two related q + 1-objective problems. Finally, we show that the sensitivity region is a convex set (an interval). Our approach generalizes to simultaneous changes in multiple coefficients. For illustration, we consider mean-variance capital budgeting and determine the region for which the set of efficient portfolios remains the same, despite a misspecification or a change in the net present values of the projects. Further computational experience with multiobjective binary and integer knapsack problems demonstrates the general applicability of our technique. For instance, we obtain all sensitivity intervals for changes to single coefficients of biobjective problems with 500 binary variables in less than half an hour of CPU time by excluding a large number of inefficient solutions. In fact, the number of excluded solutions is above 100 orders of magnitude larger than the number of remaining solutions.
We investigate the relation between the usefulness of earnings and firm cost structure. We document that the usefulness of earnings to external users decreases in the degree of operating leverage, which measures the proportion of fixed to variable costs, and that this decline extends at least partially from the relation between the degree of operating leverage and two earnings properties: aggressive revenue recognition to meet an earnings target and earnings volatility. Our results illustrate how firm fundamentals influence a firm’s information environment and thereby the usefulness of earnings to external users.
The topic of learning to solve optimization problems has received interest from both the operations research and machine learning communities. In this paper, we combine ideas from both fields to address the problem of learning to generate decisions to instances of optimization problems with potentially nonlinear or nonconvex constraints where the feasible set varies with contextual features. We propose a novel framework for training a generative model to produce provably optimal decisions by combining interior point methods and adversarial learning, which we further embed within an iterative data generation algorithm. To this end, we first train a classifier to learn feasibility and then train the generative model to produce optimal decisions to an optimization problem using the classifier as a regularizer. We prove that decisions generated by our model satisfy in-sample and out-of-sample optimality guarantees. Furthermore, the learning models are embedded in an active learning loop in which synthetic instances are iteratively added to the training data; this allows us to progressively generate provably tighter optimal decisions. We investigate case studies in portfolio optimization and personalized treatment design, demonstrating that our approach yields advantages over predict-then-optimize and supervised deep learning techniques, respectively. In particular, our framework is more robust to parameter estimation error compared with the predict-then-optimize paradigm and can better adapt to domain shift as compared with supervised learning models.
Firms with only one bank relationship make up the majority of firms in many economies. This paper explores whether policy-driven lending is differentially transmitted to single-bank firms in comparison with the multibank firms that are the focus of the literature. Using unique variation in the ECB’s very long-term refinancing operations (VLTROs), which affected lending to firms discontinuously across credit ratings but within banks, we find selective transmission of VLTRO liquidity to single-bank firms. Banks apply higher lending standards to single-bank firms, with banking relationships determining both new lending and lending maturity. By contrast, banks appear to transmit policy lending near-uniformly across multibank firms.
We examine the effect of trade secret protection laws on internal information integration (i.e., the extent to which economic agents are provided with access to decision-relevant information from other economic agents within a firm). We argue that stronger trade secret protection laws increase firms’ internal information integration because they reduce the proprietary costs of information leakage. To test our prediction, we measure a firm’s internal information integration by the share of its sites integrated into its enterprise management system. Exploiting the staggered adoption of trade secret protection laws via the Uniform Trade Secrets Act (UTSA), we find that these laws increase firms’ internal information integration. This effect is stronger (weaker) for firms with higher proprietary costs (coordination benefits). Further, we provide evidence that the UTSA-induced increase in internal information integration translates into improvements of firms’ internal information quality and decision-making quality. Taken together, our results enhance the understanding of the economic trade-offs shaping firms’ internal information environment.
Ranking and selection (R&S) aims to select the best alternative with the largest mean performance from a finite set of alternatives. Recently, considerable attention has turned toward the large-scale R&S problem which involves a large number of alternatives. Ideal large-scale R&S procedures should be sample optimal; that is, the total sample size required to deliver an asymptotically nonzero probability of correct selection (PCS) grows at the minimal order (linear order) in the number of alternatives, k. Surprisingly, we discover that the naïve greedy procedure, which keeps sampling the alternative with the largest running average, performs strikingly well and appears sample optimal. To understand this discovery, we develop a new boundary-crossing perspective and prove that the greedy procedure is sample optimal for the scenarios where the best mean maintains at least a positive constant away from all other means as k increases. We further show that the derived PCS lower bound is asymptotically tight for the slippage configuration of means with a common variance. For other scenarios, we consider the probability of good selection and find that the result depends on the growth behavior of the number of good alternatives: if it remains bounded as k increases, the sample optimality still holds; otherwise, the result may change. Moreover, we propose the explore-first greedy procedures by adding an exploration phase to the greedy procedure. The procedures are proven to be sample optimal and consistent under the same assumptions. Last, we numerically investigate the performance of our greedy procedures in solving large-scale R&S problems.
We study whether banks internalize congestion externalities when lending to zombie firms. We conjecture that banks should be better informed about the presence of zombie firms and the congestion externalities that such firms exert on healthy borrowers in industries where banks are specialized and show that banks’ credit supply to zombie firms relates negatively to their industry specialization. This relation is stronger when congestion externalities are likely to have stronger adverse effects, namely when zombie firms take a higher fraction of resources in the industry or when the industry is geographically more concentrated. Additionally, this relation is weaker in industries with higher asset specificity as zombie firms’ default (and potential asset fire sales) could reduce healthy borrowers’ collateral value.
Using an occupational probability of computerization, we measure a firm’s ability to replace labor with automated capital. Our evidence suggests that the potential to automate a workforce enhances operating flexibility, allowing firms to hold less precautionary cash. To provide evidence for this mechanism, we exploit the 2011–2012 Thailand hard drive crisis as an exogenous shock to the cost of automation. In addition, the negative relation between prospective automation and cash holdings is greater for firms with a lower expected cost of worker displacement and greater labor-induced operating leverage.
We study the blocking effect of patents on follow-on innovation by others. We posit that follow-on innovation requires freedom to operate (FTO), which firms typically obtain through a license from the patentee holding the original innovation. Where licensing fails, follow-on innovation is blocked unless firms gain FTO through patent invalidation. Using large-scale data from post-grant oppositions at the European Patent Office, we find that patent invalidation increases follow-on innovation, measured in citations, by 16% on average. This effect exhibits a U-shape in the value of the original innovation. For patents on low-value original innovations, invalidation predominantly increases low-value follow-on innovation outside the patentee’s product market. Here, transaction costs likely exceed the joint surplus of licensing, causing licensing failure. In contrast, for patents on high-value original innovations, invalidation mainly increases high-value follow-on innovation in the patentee’s product market. We attribute this latter result to rent dissipation, which renders patentees unwilling to license out valuable technologies to (potential) competitors.
Through a large-scale field experiment with 393,036 customers considering opening a credit card account with a nationwide retail bank, we investigate how providing transparency into an offering’s tradeoffs affects subsequent rates of customer acquisition and long-run engagement. Although we find tradeoff transparency to have an insignificant effect on acquisition rates, customers who were shown each offering’s tradeoffs selected different products than those who were not. Moreover, prospective customers who experienced transparency and subsequently chose to open an account went on to exhibit higher-quality service relationships over time. Monthly spending was 9.9% higher and cancellation rates were 20.5% lower among those who experienced transparency into each offering’s tradeoffs. Increased product use and retention accrued disproportionately to customers with prior category experience: more-experienced customers who were provided transparency spent 19.2% more on a monthly basis and were 33.7% less likely to defect after nine months. Importantly, we find that these gains in engagement and retention do not come at the expense of customers’ financial well-being: the probability of making late payments was reduced among customers who experienced transparency. We further find that the positive effects of tradeoff transparency on engagement and retention were attenuated in the presence of a promotion that provided financial incentives to choose particular offerings. Taken together, these results suggest that providing transparency into an offering’s tradeoffs may be an effective strategy for informing customer choices, leading to better outcomes for customers and firms alike.
We find that, overall, blockholder diversity, i.e., the firm shareholder base including several different types of blocks, is detrimental to firm performance. We show that lagged disclosure, on exogenous predetermined dates, that reveals an increase in block diversity is followed by a negative market reaction. Firms held by heterogeneous blockholders consistently perform worse than firms held by homogeneous blockholders. Block diversity is particularly detrimental when uncertainty is high. Disagreement among shareholders (e.g., as reflected in the frequency of lawsuits being filed) increases when the blockholder base is diverse. We make our blockholder data set public for the benefit of other researchers.
Largely constant average acquirer returns over the past four decades mask fundamental changes in the takeover market. Controlling for bidder composition, the common component of acquirer returns has increased by five percentage points relative to the 1980s. Offsetting this increase, the average bidder-specific component has declined. We propose a theory of bidder-specific synergies to help interpret these opposing trends. In our theory and in the data, acquirer returns increase with the extent to which synergies are unique to that bidder. The composition effect reflects bidder uniqueness. Overall, the evidence is consistent with rising merger synergies that have become less bidder specific.
This paper examines the incentive to register for deceased organ donation under alternative organ allocation priority rules, which may prioritize registered donors and/or patients with higher valuations for organ transplantation. Specifically, the donor priority rule grants higher priority on the organ waiting list to those who have previously registered as donors. The dual-incentive priority rules allocate organs based on donor status, followed by individual valuations within the same donor status, or vice versa. Both theoretical and experimental results suggest that the efficacy of the donor priority rule and the dual-incentive priority rules critically depends on the information environment. When organ transplantation valuations are unobservable prior to making donation decisions, the hybrid dual-incentive rules generate higher donation rates. In contrast, if valuations are observable, the dual-incentive priority rules create unbalanced incentives between high- and low-value agents, potentially undermining the efficacy of the hybrid dual-incentive rules in increasing overall donation rates.
Consistent with hypotheses underlying firm advertising, we find that targets with pretakeover advertising obtain higher premiums, whereas their acquirers earn lower announcement returns. These economically significant effects suggest that through advertising, targets increase their profile and negotiating power. Further, targets that advertise are more likely to initiate their takeovers, attract multiple bidders, receive enhanced bids, capture more merger rents, and even in failed acquisitions, experience a 1% permanent revaluation. The latter result differentiates between information asymmetry and behavioral explanations for the target advertising. Overall, the results support the hypothesis that management advertises to transmit information to investors and potential acquirers.
Two events, with major implications for U.S. voters, occur after each decennial census. First, congressional “apportionment” takes place, followed by congressional “districting.” Apportionment determines how to allocate the 435 seats in the House of Representatives across the 50 states, whereas districting determines the geographic boundaries assigned to representatives within each state. Although districting and the practice of gerrymandering often receive great attention in the media and courts, the best way to apportion representatives across states has been debated for nearly 250 years. Historical methods (including the current method) each satisfy some desirable optimality criteria that the others are not guaranteed to satisfy. Moreover, none are guaranteed to optimize certain reasonable fairness measures (e.g., minimum range, minimum bias). To our knowledge, we are the first to formulate and analyze a multiobjective optimization approach to apportionment, allowing policymakers to identify Pareto-optimal allocations and quantify their trade-offs between several competing criteria. Some of these models can be formulated and solved as mixed-integer linear programs, whereas others require the solution of mixed-integer, nonconvex, quadratically constrained quadratic programs. We take advantage of recent software advances that allow one to solve these problems with optimality guarantees. Policy implications of our work include Pareto curves from historical censuses and simulations, which suggest opportunities for improvement in some objectives at little sacrifice to others.
Crowdsourcing platforms typically take a passive approach, and they let the competing firms freely design their own contests and allow every solver to self-select and join any of the concurrently running contests. In a model of competing noise-driven contests, we show that the duopoly prize allocation has fewer (but larger) prizes compared with a monopolist contest designer. We also find that contests with firm-chosen budgets and solvers’ endogenous participation create coordination inefficiencies. Thus, platform policies that constrain the competing firms from freely choosing their budgets and offer solvers non-enforceable recommendations toward specific noise-driven contests strictly enhance total welfare. Extending our framework to include arbitrarily correlated ability-driven contests, we highlight the critical role of inter-contest dependence on the efficacy of a platform’s interventions. Specifically, platform nudges to improve solver-contest (mis)matches are welfare enhancing only when the contests are sufficiently related, and allowing solvers to self-sort is appropriate otherwise.
This study proposes an approach to measure conservatism using machine learning techniques that are not constrained by functional form restrictions. We extend the differential timeliness model to allow for observable characteristics related to conservatism to follow nonlinear relationships. By developing machine learning measures of conservatism, we draw attention to potential benefits and drawbacks and show how its insights complement conventional measures. Our broader goal is to investigate the effectiveness of machine learning algorithms for filtering noise in traditional archival studies and uncovering more complex empirical patterns.
Wrongful discharge laws (WDLs) provide limits to the employment-at-will doctrine, and thus impair operating flexibility, increasing expected financial distress costs by making it costly to fire employees. This impairment is detrimental to start-ups, leading to a decline in venture capital (VC) investment. Using a difference-in-differences framework enabled by the staggered adoption of WDLs across the U.S. states, we show VC investment declines after a state adopts the good faith exception (the strongest form of WDL). This decline is most pronounced in sectors with high labor dependency.
The assignment of individuals with different observable characteristics to different treatments is a central question in designing optimal policies. We study this question in the context of increasing workers’ performance via targeted incentives using machine learning algorithms with worker demographics, personality traits, and preferences as input. Running two large-scale experiments, we show that (i) performance can be predicted by accurately measured worker characteristics, (ii) a machine learning algorithm can detect heterogeneity in responses to different schemes, (iii) a targeted assignment of schemes to individuals increases performance significantly above the level of the single best scheme, and (iv) algorithmic assignment is more effective for workers who have a high likelihood to repeatedly interact with the employer or who provide more consistent survey answers.
We consider model-free reinforcement learning (RL) in nonstationary Markov decision processes. Both the reward functions and the state transition functions are allowed to vary arbitrarily over time as long as their cumulative variations do not exceed certain variation budgets. We propose Restarted Q-Learning with Upper Confidence Bounds (RestartQ-UCB), the first model-free algorithm for nonstationary RL, and show that it outperforms existing solutions in terms of dynamic regret. Specifically, RestartQ-UCB with Freedman-type bonus terms achieves a dynamic regret bound of O˜(S13A13Δ13HT23), where S and A are the numbers of states and actions, respectively, Δ>0 is the variation budget, H is the number of time steps per episode, and T is the total number of time steps. We further present a parameter-free algorithm named Double-Restart Q-UCB that does not require prior knowledge of the variation budget. We show that our algorithms are nearly optimal by establishing an information-theoretical lower bound of Ω(S13A13Δ13H23T23), the first lower bound in nonstationary RL. Numerical experiments validate the advantages of RestartQ-UCB in terms of both cumulative rewards and computational efficiency. We demonstrate the power of our results in examples of multiagent RL and inventory control across related products.
We examine a beauty contest game with an option to analyze an additional disclosure. We analytically prove that in some scenarios, coordination incentives cause sophisticated players who can comprehend disclosures to choose not to analyze them to match unsophisticated players’ actions, a phenomenon we call “coordinated inattention.” Laboratory experiments provide support for the coordinated inattention mechanism: Coordination incentives reduce sophisticated subjects’ propensity to analyze disclosures, especially when they believe others are unlikely to comprehend them. We further find that psychological biases help reduce coordinated inattention. Subjects are overconfident, sophisticated subjects overestimate others’ ability to comprehend disclosures, and both biases are associated with a higher tendency to analyze disclosures. Our analysis suggests that unsophisticated decision makers’ inability to comprehend complex disclosures has a negative spillover effect by reducing sophisticated decision makers’ attention to disclosures. Our results highlight the importance of the recent efforts of the Securities and Exchange Commission (SEC) and the Financial Accounting Standards Board (FASB) to make disclosures easier to comprehend.
This paper studies a key element of discrimination, namely, when stereotypes translate into discriminatory actions. Using a hiring experiment, we rule out taste-based discrimination by design and test for the presence of two types of belief-based gender discrimination. We document evidence of explicit discriminators—individuals who are willing to discriminate even when their hiring choices are highly revealing of their gender-biased beliefs. Crucially, we also identify implicit discriminators—individuals who do not discriminate against women when taking a discriminatory action is highly revealing of their biased beliefs, but do discriminate against women when their biased motive is obscured. Our analysis highlights the central role played by features of the choice environment in determining whether and how discrimination will manifest. We conclude by discussing the implications for policy design.
Hedge funds with larger macroeconomic-risk betas do not earn higher returns, in contrast to the theoretically predicted risk-return trade-off. Meanwhile, high macro-beta funds deliver higher returns than low macro-beta funds following a low-sentiment period, whereas the risk-return relation is flat following a high-sentiment period. We show that the sophisticated management of hedge funds explains this pattern. The relation between funds’ macro-risk betas and the timing abilities/investor flows is sentiment dependent, and such variation likely drives the contrasting beta-return trade-offs after high- and low-sentiment periods. A similar pattern is also observed in mutual funds.
Advancements in technology have reduced information acquisition costs, creating an improved information environment for retail investors. Specifically, new technologies, such as application programming interface (API), deliver high-volume, institutional-like raw data directly to Main Street investors. Although greater availability of information can be beneficial, it may also exacerbate retail investors’ existing trading deficiencies. Exploiting the sudden shutdown of Yahoo! Finance API, the largest free API for retail investors, this study examines how access to tech-enabled raw financial data affects retail investment. We find that retail trading volumes in stocks favored by active retail investors dropped by 8.6%–10.5% within one month of the API shutdown. The remaining retail trades collectively became more predictive of future returns, suggesting less gambling-like behavior after the API shutdown. Moreover, our randomized controlled experiment affirms the underlying mechanism: tech-enabled access to high-volume historical price data increases individuals’ overconfidence, which further leads them to engage in excessive trading. The study reveals an unintended consequence of technology-led, wider data access for retail investors.
This study examines the effect of firms’ participation in platform-endorsed review solicitation programs on consumers’ online review generation. We leverage a natural experiment on TripAdvisor, which launched a review solicitation program that allows hotels to collect reviews directly from guests after their stays with the aid of certified connectivity partners. Applying a two-stage difference-in-differences approach to a panel data set of online reviews for a matched set of hotels across TripAdvisor and Expedia, we find that hotels’ participation in the review solicitation program results in a 34.3% increase in review volume, a 0.151 increase in review rating, but a 16.9% decrease in review length. Review solicitation, however, generates a notable negative spillover effect on the volume of organic reviews. Specifically, the volume of organic reviews is reduced by 15.5% after hotels start soliciting reviews. We provide evidence that the motivational crowding-out effect plays an important role in driving this negative spillover. Further analyses reveal that the effects of review solicitation are heterogeneous with respect to hotels of different types and consumers with different demographic and behavioral characteristics. Finally, using a novel structural topic model, we detect a significant shift in review content from specific and concrete topics to general and abstract topics. Our findings suggest that review platforms and firms should be cautious about the unintended negative consequences of review solicitation on consumers’ review generation.
Valuation plays a central role in determining Chapter 11 reorganization outcomes. However, obtaining accurate valuation estimates of reorganized firms is challenging because of limited firm-specific market-based information and the oft-conflicting incentives of claimholders. We examine the role of industry peer information in reducing misvaluations and its implications for unintended interclaimant wealth transfers and postreorganization performance. First, we find that the availability of relevant industry peer information is negatively associated with equity valuation errors for firms emerging from Chapter 11. Cross-sectional results suggest that the relation between industry peer information and valuation errors varies substantially with debtors’ information environment and case characteristics. Second, we find that industry peer information quality is associated with better ex post financial performance of emerged firms because of lower overvaluation. Finally, we document the role of industry peer information in substantially reducing the frequency and magnitude of unintended wealth transfers between claimants arising from equity valuation errors.
Detours are considered key for the efficient operation of a shared rides service, but they are also a major pain point for consumers of such services. This paper studies the relationship between the value generated by shared rides and the detours they create for riders. We establish a limit on the sum of value and detour, and we prove that this leads to a tight bound on the Pareto frontier of values and detours in a general setting with an arbitrary number of requests. We explicitly compute the Pareto frontier for one family of city topologies and construct it via simulation for several more networks, including one based on ride-sharing data from commute hours in Manhattan. We find that average detours are usually small, even in low-demand-density settings. We also find that by carefully choosing the match objective, detours can be reduced with a relatively small impact on values and that the density of ride requests is far more important than detours for the effective operations of a shared rides service. In response, we propose that platforms implement a two-product version of shared rides and limit the worst-case detours of its users.
A group of individuals is interested in predicting the outcome of a current problem. Each individual has access to private data that are used to form a prior probability over possible outcomes. While individuals may be reluctant or unable to disclose their private data, they are willing to publish their priors. We characterize a procedure for revising the individuals’ prior probabilities based on the published priors of others and discuss its implications regarding the formation of a common prior in the group, both in the short and long run.
The transition from a traditional manufacturing-based economy to a knowledge- and service-based economy over recent decades resulted in a considerable rise in intangible capital, most of which is not reported on companies’ balance sheets. As a result, balance sheet-based valuation ratios, investment measures, and other firm characteristics that do not incorporate off-balance sheet (OBS) intangible capital suffer from significant measurement error problems. We incorporate a new measure of OBS intangible capital into firm characteristics, such as book to market, investment, and profitability, to address these measurement errors. These OBS intangible adjustments improve the performance of the Fama–French three- and five-factor models and the q-factor model, especially during recent decades. We further find that the value factor is no longer redundant in these empirical factor models.
High-dimensional weakly coupled Markov decision processes (WDPs) arise in dynamic decision making and reinforcement learning, decomposing into smaller Markov decision processes (MDPs) when linking constraints are relaxed. The Lagrangian relaxation of WDPs (LAG) exploits this property to compute policies and (optimistic) bounds efficiently; however, dualizing linking constraints averages away combinatorial information. We introduce feasibility network relaxations (FNRs), a new class of linear programming relaxations that exactly represents the linking constraints. We develop a procedure to obtain the unique minimally sized relaxation, which we refer to as self-adapting FNR, as its size automatically adjusts to the structure of the linking constraints. Our analysis informs model selection: (i) the self-adapting FNR provides (weakly) stronger bounds than LAG, is polynomially sized when linking constraints admit a tractable network representation, and can even be smaller than LAG, and (ii) self-adapting FNR provides bounds and policies that match the approximate linear programming (ALP) approach but is substantially smaller in size than the ALP formulation and a recent alternative Lagrangian that is equivalent to ALP. We perform numerical experiments on constrained dynamic assortment and preemptive maintenance applications. Our results show that self-adapting FNR significantly improves upon LAG in terms of policy performance and/or bounds, while being an order of magnitude faster than an alternative Lagrangian and ALP, which are unsolvable in several instances.
We build a risk management model that incorporates variable capital utilization and time-varying collateral capacity. The former lets firms optimally choose capital utilization, and hence production, which affects capital depreciation and risk exposure. The latter means firms’ ability to borrow and hedge increases with expected earnings and thus utilization. Calibrated solutions show both ingredients matter for firm value. We test the novel model predictions using a new data set of oil and gas producers. Consistent with model predictions, we find utilization is negatively correlated with firm liquidity, while hedging is positively correlated with liquidity and expected profitability.
We study the asset pricing implications of being able to optimally early exercise plain vanilla puts, contrasting expected raw and delta-hedged returns across equivalent American and European puts. Our theory suggests that American puts yield less negative raw but more negative delta-hedged expected returns than equivalent European puts. The raw (delta-hedged) spread widens with a higher early exercise probability as induced through, for example, moneyness, time to maturity, and underlying asset volatility (variance and jump risk premiums). An empirical comparison of single-stock American puts with equivalent synthetic European puts formed from put–call parity supports our theory if and only if we allow for optimal early exercises in our return calculations. More strikingly, allowing for optimal early exercises significantly alters the profitability of 14 out of 15 well-known option anomalies with the average absolute change equal to 33% and five anomalies becoming insignificant.
I use an important ruling, Marblegate Asset Management v. Education Management Corporation (EMC), to study the economic role of two publicly traded debt restructuring methods: coercive bond exchange offers and Chapter 11. This ruling restricted firms from employing coercive bond exchange offers to facilitate out-of-court restructurings, thereby increasing the likelihood of restructuring publicly traded debt under Chapter 11. Following the ruling, investment in affected distressed firms decreased substantially, but investment efficiency improved. The changes in covenant, maturity, and offering yield of newly issued bonds suggested that existing bondholders with covenants gained more bargaining power than shareholders and new bondholders. The paper provides causal evidence from a large sample analysis, demonstrating the divergent effects of these two publicly traded debt restructuring methods on investment policies.
We investigate how key features associated with the Proof-of-Work consensus mechanism of Bitcoin (commonly referred to as mining) affect pricing. In a controlled laboratory experiment, we observe that price bubble formation can be attributed to mining. Moreover, overpricing is more pronounced if the mining capacity is centralized to a small group of individuals. The order book data reveal that miners seem to play a crucial role in bubble formation. Further probing the mechanism in a second study, we find that both mining costs and decisions jointly with the sluggish rate of supply of the asset contribute to the bubble formation. Our results demonstrate that erratic pricing is an inherent feature of cryptocurrencies based on a mining protocol, thus seriously limiting any prospects for such assets becoming a medium of exchange.
Practitioners and academics have long appreciated the benefits of covariate balancing when they conduct randomized experiments. For web-facing firms running online A/B tests, however, it still remains challenging in balancing covariate information when experimental subjects arrive sequentially. In this paper, we study an online experimental design problem, which we refer to as the online blocking problem. In this problem, experimental subjects with heterogeneous covariate information arrive sequentially and must be immediately assigned into either the control or the treated group. The objective is to minimize the total discrepancy, which is defined as the minimum weight perfect matching between the two groups. To solve this problem, we propose a randomized design of experiment, which we refer to as the pigeonhole design. The pigeonhole design first partitions the covariate space into smaller spaces, which we refer to as pigeonholes, and then, when the experimental subjects arrive at each pigeonhole, balances the number of control and treated subjects for each pigeonhole. We analyze the theoretical performance of the pigeonhole design and show its effectiveness by comparing against two well-known benchmark designs: the matched-pair design and the completely randomized design. We identify scenarios when the pigeonhole design demonstrates more benefits over the benchmark design. To conclude, we conduct extensive simulations using Yahoo! data to show a 10.2% reduction in variance if we use the pigeonhole design to estimate the average treatment effect.
Many professional and educational settings require individuals to be willing and able to perform under time pressure. We use a laboratory experiment and survey data to study preferences for working under time pressure. We make three main contributions. First, we develop an incentivized method to measure preferences for working under time pressure and document that participants in our laboratory experiment are averse to working under time pressure on average. Second, we show that there is substantial heterogeneity in the degree of time pressure aversion across individuals and that these individual preferences can be partially captured by simple survey questions. Third, we include these questions in a survey of bachelor’s degree students and a nationally representative survey panel and show that time pressure preferences predict career choices and income. Our results indicate that individual differences in time pressure aversion could be an influential factor in determining labor market outcomes.
Industry 4.0 integrates digital and physical technologies to transform work management, where two core enablers are the internet of things (IoT) and three-dimensional printing (3DP). IoT monitors complex systems in real time, whereas 3DP enables agile manufacturing that can respond to real-time information. However, the details of how these two can be integrated are not yet clear. To gain insights, we consider a scenario where a three-dimensional (3D) printer supplies a critical part to multiple machines that are embedded with sensors and connected through IoT. Although the public perception indicates that this integration would enable on-demand printing, our research suggests that this is not necessarily the case. Instead, the true benefit is the ability to print predictively. In particular, it is typically more effective for the 3D printer to predictively print to stock based on a threshold that depends on the system’s status. We also identify a printing mode called predictive print on demand that allows for minimal inventory, and we find the speed of 3DP to be the primary factor that influences its optimality. Furthermore, we assess the value of IoT in cost reductions by separately analyzing the impact of advance information from embedded sensors and the real-time information fusion through IoT. We find that IoT provides significant value in general. However, the conventional wisdom that IoT’s value scales up for larger systems is suitable only when the expansion is paired with appropriate 3DP capacity. Our framework can help inform investment decisions regarding IoT/embedded sensors and support the development of scheduling tools for predictive 3DP.
We build a benchmark for AAA-rated tranches of collateralized loan obligations (CLOs) using business development companies (BDCs), which hold a diversified portfolio of loans as CLOs do. BDCs are publicly listed, and their share price, equity volatility, and borrowing cost can be easily obtained. Applying a structural model to BDCs, we extract market-implied correlation in their loan portfolio, compare spreads on CLO tranches and BDC-implied benchmark, and find that observed large credit spreads on CLO senior tranches after the financial crisis are a fair reflection of the systematic risk of correlated loan defaults.
Research offers conflicting predictions about the impact of credit conditions on mental health. We first assess how bank regulatory reforms that improved credit conditions, for example, by enhancing the efficiency of credit allocation and lowering lending rates, impacted mental health. We discover that among low-income individuals, these regulatory reforms reduced mental depression, boosted labor market outcomes, eased access to mortgage debt, and reduced the ranks of the “unbanked.” We also find that mergers of large regional banks that led to branch closures and tighter credit constraints in affected counties harmed the mental health of lower-income individuals in treated counties.
We consider a finite-horizon multiarmed bandit (MAB) problem in a Bayesian setting, for which we propose an information relaxation sampling framework. With this framework, we define an intuitive family of control policies that include Thompson sampling (TS) and the Bayesian optimal policy as endpoints. Analogous to TS, which at each decision epoch pulls an arm that is best with respect to the randomly sampled parameters, our algorithms sample entire future reward realizations and take the corresponding best action. However, this is done in the presence of “penalties” that seek to compensate for the availability of future information. We develop several novel policies and performance bounds for MAB problems that vary in terms of improving performance and increasing computational complexity between the two endpoints. Our policies can be viewed as natural generalizations of TS that simultaneously incorporate knowledge of the time horizon and explicitly consider the exploration-exploitation trade-off. We prove associated structural results on performance bounds and suboptimality gaps. Numerical experiments suggest that this new class of policies perform well, in particular, in settings where the finite time horizon introduces significant exploration-exploitation tension into the problem. Finally, inspired by the finite-horizon Gittins index, we propose an index policy that builds on our framework that particularly outperforms the state-of-the-art algorithms in our numerical experiments.
Do firms fully capitalize on the financial strength of their customer-based brand equity (CBBE)? If not, how large is the gap to a benchmark condition where CBBE is financially fully leveraged? Which market strategies, if any, should the firm emphasize to close the gap? This research addresses these questions. The authors introduce a framework and methodology that enables firms to identify and measure their “brand capabilities gap,” which captures the firm’s potential for leveraging CBBE into firm value when the most effective and efficient brand leverage strategy is in place, and, if necessary, take corrective action to realize their brand leverage potential. The authors further examine three market strategy moderators that firms can steer via their marketing programs and tactics to close the brand capabilities gap. The application of the framework and methodology to a wide-ranging sample of firms from 2005 to 2013 reveals that, on average, firms only realize 35% of their financial brand leverage potential. This translates into an average brand capabilities gap worth $2.1 bn per brand, which represents 4.3% of firm value. These findings have important implications for strategic brand analysis and planning and can help firms design a more effective financial brand leverage strategy.
This paper examines how different advertising categories are associated with attributes of brand asset recognition arising in the context of acquisitions. Prior research documents that aggregate advertising is positively associated with firm sales and brand values, but it fails to consider major recent shifts in advertising from traditional channels (such as TV) to digital advertising (such as paid search and online display). Using proprietary data, I decompose advertising expenditures into the categories of traditional, online display, and paid search. Consistent with expectations, results confirm that target firms’ traditional and online display advertising exhibit a higher likelihood of brand asset recognition and higher recognized brand values as compared with paid search advertising. Confirming the economic substance of the recognized brands, additional results reveal positive investor equity market reactions to brand value, which are driven by firms investing more in traditional and online display advertising. Overall, these results confirm that certain advertising channels exhibit stronger associations with the recognition and characteristics of the underlying brand asset, consistent with their heterogeneous properties.
Scientific conferences are an underexplored channel by which firms can learn from science. We provide empirical evidence that firms learn from scientific conferences in which they participate but also that this is conditional on intense participation. Using data from conference papers in computer science since the 1990s, we show that corporate investments in participation are both frequent and highly skewed, with some firms contributing to a given conference scientifically, some as sponsors, and some doing both. We use direct flights as an instrumental variable for the probability that other scientists participate in the same conference as a firm, altering the knowledge set to which the firm is exposed. We find that a firm’s use of scientists’ knowledge increases when they participate in the same conferences. Greater participation efforts, where the firm seeks the spotlight by both sponsoring the conference and contributing to its scientific discourse, foretell research collaborations and a stronger learning effect. Such learning is disproportionately concentrated among the most prominent firms and scientists rather than benefitting those without alternative interaction channels. Therefore, on average, firms learn from scientists that they encounter at conferences, but the substantial heterogeneity of the effect reflects the influence of reputation mechanisms in social interactions.
Determining emergency department (ED) nurse staffing decisions to balance quality of service and staffing costs can be extremely challenging, especially when there is a high level of uncertainty in patient demand. Increasing data availability and continuing advancements in predictive analytics provide an opportunity to mitigate demand uncertainty by using demand forecasts. In this work, we study a two-stage prediction-driven staffing framework where the prediction models are integrated with the base (made weeks in advance) and surge (made nearly real-time) nurse staffing decisions in the ED. We quantify the benefit of having the ability to use the more expensive surge staffing and identify the importance of balancing demand uncertainty versus system stochasticity. We also propose a near-optimal two-stage staffing policy that is straightforward to interpret and implement. Last, we develop a unified framework that combines parameter estimation, real-time demand forecasts, and nurse staffing in the ED. High-fidelity simulation experiments for the ED demonstrate that the proposed framework has the potential to reduce annual staffing costs by 10%–16% ($2 M–$3 M) while guaranteeing timely access to care.
Using a representative survey of U.S. lawyers, we document a sizeable gender gap in early partnership aspirations, which explains half of the later gender promotion gap. We further document that the correlation between aspirations and effort provides a “mechanical link” between aspirations and promotion. Early workplace experiences, such as harassment and demeaning comments, are linked to promotion aspirations. Moreover, early aspirations provide insight into eventual promotion outcomes that goes beyond what can be drawn only from expectations. Our study highlights that measuring aspirations and adapting the corporate culture that shapes them are key components for firms to improve workplace environments.
Unlike issuer-paid credit rating agencies (CRAs), investor-paid CRAs are compensated by investors for providing rating services. Exploiting the staggered timing of rating initiation by an investor-paid rating agency (the Egan Jones Ratings (EJR)), I document that the coverage by EJR increases rated firm managers’ voluntary disclosure of negative news. Consistent with EJR’s rating coverage deterring managerial bad news hoarding by informing investors of downside risks, I find that the effect of EJR coverage is more pronounced when issuer-paid CRAs tend to assign inflated ratings and when rated firms’ managers have a stronger incentive to conceal bad news. I also document that firms unwind upward earnings management after being covered by EJR. In contrast, coverage by an issuer-paid CRA (Standard & Poor’s) is not associated with changes in managerial information disclosure. I conclude that investor-paid CRAs function as a type of effective information intermediary to discipline firm managers and improve corporate transparency.
Although much attention is accorded to star performers, this paper considers the extent to which stars, themselves, benefit from the contribution of their collaborators (the constellation). By considering stars, constellations, and the synergies between them, we address a key question: To what extent is collaboration performance driven by the great individual or by great constellations? We introduce a novel approach that uses a matching model to uncover the complementarities driving collaboration formation. We use formal value-capture theory to estimate the relative contribution of stars and constellations to joint value creation. Analyzing a sample of academic research collaborations, we document that stars’ relative contribution exceeds that of their constellations in less than 15% of collaborations, although constellations provide a greater relative contribution in 9%. In most collaborations, neither party dominates: Innovation is a collective endeavor driven equally by the star and the constellation. Joint value creation and relative contribution are explained by the subtle interplay between complementarities in joint work and the substitutability of collaborative parties in the market. Joint value creation increases with the strength of complementarities between parties in a match. Relative value creation, and hence dominance, increases with the substitutability of one’s collaborative partner. Interestingly, joint value creation is greatest in collaborations where both stars and constellations offer bundles of rare attributes and where neither the star nor the constellation dominates.
We investigate the association between technological changes and corporate tax policies in 34 OECD countries between 1996 and 2016. We use a shift-share design to capture the differential exposure of countries to U.S. technological advancements. Our study shows that countries’ antitax avoidance rules are tightened as their exposure to U.S. technological advancements increases. The tightening is particularly concentrated in countries that are larger, more exposed to intangibles, and have higher profit shifting incentives. Our findings have important implications for corporate executives as a country’s anti–tax avoidance rules are associated with foreign technological advancements.
We document strong global commonality in country idiosyncratic return variances across 23 developed markets, which is stronger than international return commonality. The global common factor of idiosyncratic return variances is highly correlated with that of idiosyncratic cash flow variances and is also significantly related to variables capturing aggregate discount rate variation and the conditional market variance. Furthermore, aggregate idiosyncratic return and cash flow variances are mostly but not always countercyclical.
Descriptive norms, the behavior of other individuals in one’s reference group, play a key role in shaping individual decisions in managerial contexts and beyond. Organizations are increasingly using information about descriptive norms to nudge positive behavior change. When characterizing peer decisions, a standard approach in the literature is to focus on average behavior. In this paper, we argue both theoretically and empirically that not only averages but also the shape of the whole distribution of behavior can play a crucial role in how people react to descriptive norms. Using a representative sample of the U.S. population, we experimentally investigate how individuals react to strategic environments that are characterized by different distributions of behavior, focusing on the distinction between tight (i.e., characterized by low behavioral variance), loose (i.e., characterized by high behavioral variance), and polarized (i.e., characterized by u-shaped behavior) environments. We find that individuals indeed strongly respond to differences in the variance and shape of the descriptive norm they are facing: Loose norms generate greater behavioral variance and polarization generates polarized responses. In polarized environments, most individuals prefer extreme actions, which expose them to considerable strategic risk, to intermediate actions that minimize such risk. Furthermore, in polarized and loose environments, personal traits and values play a larger role in determining actual behavior. These nuances of how individuals react to different types of descriptive norms have important implications for company culture, productivity, and organizational effectiveness alike.
Consensus analyst target prices are widely available online at no cost to investors. In this paper, we examine how the amount of dispersion in the individual target prices comprising the consensus affects the predictive association between the consensus target price and future returns. We find that returns implied by consensus target prices and realized future returns are positively correlated when dispersion is low, but they become highly negatively correlated when dispersion is high. Further analyses suggest that the differing effect of dispersion stems from incentive-driven staleness in price targets by some analysts after bad news. As a stock performs poorly and some analysts are slow to update their target prices, dispersion increases, and the consensus target price becomes too high. This has important implications for how consensus analyst target prices should inform investment decisions. We show that a hedge strategy taking a long (short) position in stocks with the highest predicted returns among stocks with the lowest (highest) dispersion earns more than 11% annually. Finally, we show that the negative correlation between consensus-based predicted returns and future realized returns for high-dispersion stocks exists mainly for stocks with high retail interest, suggesting that unsophisticated investors are misled by inflated target prices that are available freely online.
Existing studies assume that all lenders have similar incentives to take on risks during different phases of the lending cycle. We show that foreign lenders and low market share lenders extend more credit in comparison with other lenders during lending booms leading to banking crises but not during other credit expansions. These less established lenders also shorten loan maturity and increase the amount of credit they extend to riskier borrowers without asking for collateral or imposing covenants and higher interest rates. Our results suggest that foreign lenders and low market share lenders contribute disproportionately to credit misallocation and risk accumulation in precrisis periods.
We study the market consequences of advances in consumption tracking technologies—such as mobile banking apps that help consumers monitor their spending and avoid overdrawn accounts—using a two-period consumption model. In the model, consumers pay a penalty fee if they consume in both periods. In the second period, consumers may be forgetful of their first-period consumption, although the use of consumption tracking can remind them. According to our analysis, the availability of consumption tracking often helps consumers at the expense of the firm; such benefits may be direct, where consumers make use of the technology to avoid penalty fees, or indirect, where the mere availability of consumption tracking forces the firm to lower its penalty fee. If consumers are partially sophisticated regarding their forgetfulness, however, the availability of consumption tracking may instill a false sense of security in that consumers expect to use consumption tracking to avoid penalty fees but ultimately, decide not to bother, making them especially susceptible to penalty fees. In some cases, the availability of consumption tracking may actually compel a firm to impose a penalty fee that would not otherwise be viable, leading to higher profits and lower consumer surplus. As we show, this scenario is attained within an intermediate range of forgetfulness and at a level of (partial) sophistication for which consumers overestimate their demand for the technology.
Online retail settings often present shoppers with large, complex choice sets where they need to quickly and dynamically weigh the benefits and costs of search within each web page. We build a model of information search within a web page using eye-tracking data collected during two incentive-compatible online shopping experiments, in which participants browsed the websites of two different clothing retailers (Experiments 1 and 2), as well as previously reported data from a laboratory experiment involving choices among snack food assortments (Experiment 3). Our model incorporates features that build upon recent advances in descriptive and normative models of information sampling and search in psychology and economics. First, our model captures how people decide where to look by treating eye fixations on clickable options as a series of “split-second” decisions that depend on estimates of option attractiveness and navigation effort. Second, our model assumes that the value of each option is learned via Bayesian updating. Third, the choice to end search on the web page depends on a dynamic decision threshold. Our model outperforms benchmarks that assume random search, instant learning, fixed thresholds, nonheterogeneous thresholds, and stochastic accumulator stopping rules. Explicitly modeling the sequence of eye fixation decisions results in accurate counterfactual simulations of the effects of hypothetical product orderings on search duration and quality as verified using experimental manipulation, and it can be applied flexibly to a wide range of web-page layouts. Systematic differences across experiments highlight the importance of accounting for product familiarity, choice-set size, and the role of category outside options.
We examine the role of AI analytics in facilitating innovation in firms that have gone through IPO. Using patent data on over 1,000 publicly traded firms, we find that firms acquiring AI analytics capability post-IPO experience less of a decline in innovation quality compared with similar firms that have not acquired that capability. This effect is greater when only machine learning capabilities are considered. Moreover, we find this sustained rate of innovation is driven principally by the continued development of innovations that combine existing technologies into new ones—a form of innovation that is especially well supported by analytics. By examining three main mechanisms that hampered post-IPO innovation, we find that AI analytics can ameliorate the pressure to meet short-term financial goals and disclosure requirements. However, it has limited effect in addressing managerial incentives. For firms with long product cycles, the disclosure effect is reduced to a greater extent than it is for those with short cycles. Overall, our results show the importance of examining technology as a critical input factor in innovation. We show that the increased deployment of AI analytics may reduce some of the innovative penalties suffered by IPOs and that investors and managers can potentially mitigate post-IPO reductions in innovative output by directing capital acquired in the IPO process to the acquisition of AI analytics capabilities.
Using a uniquely constructed data set of trades by corporate insiders in all stocks, we find that, after insider trading regulations become stricter, insiders are 20% more likely to trade in peer stocks and that such trades become more profitable. The increase in both the probability and profitability of peer-stock trades is driven by the insider’s information that is fungible to industry peers. Stricter insider trading laws are designed to improve liquidity and price informativeness in capital markets. We show that peer trading dampens these intended benefits of the insider trading regulation.
Neuroscience shows that human brains are neurologically constrained to process small numbers linearly and large numbers logarithmically, leading to underreactions to larger numbers as their perceived difference becomes smaller. We test this hypothesis in the context of earnings announcements and find that investors respond less in the short term to earnings news for stocks with high earnings per share magnitudes, exacerbating postearnings announcement drift for these stocks. These findings are distinct from and incremental to several risk-based and behavioral explanations, attenuated by robot presence and present in a quasi-experimental design using stock splits. Our evidence suggests that number processing constraints have implications for stock price efficiency.
Many IT professionals seek to improve their job prospects by engaging as leaders of online communities, for example, by serving as a moderator or admin. We investigate whether such community leadership leads to (causal) improvements in individuals’ careers. We assemble a data set, including job histories of IT professionals who have sought election as moderators (mods) in Stack Exchange question-and-answer communities, between 2010 and 2020. We estimate the career benefits of moderatorship under two complementary identification strategies: difference-in-differences (DID) and regression discontinuity (RD). We observe qualitatively consistent results under each design, finding that election to a moderator role has a significant, causal, positive effect on job mobility. We estimate that moderatorship increases the probability of a job change by between 4.7 and 12.3 percentage points over the two years following the election. We also report a series of secondary analyses that speak to associated salary increases and show evidence consistent with the notion that social capital and signaling play a role. Our findings help us understand the benefits of online community leadership, and they extend our understanding of the motivations for online community engagement.
We develop the operational data analytics (ODA) framework for the classical service design problem of G/G/c/k systems. The customer arrival rate is unknown. Instead, some historical data of interarrival times are collected. The data-integration model, specifying the mapping from the arrival data to the service rate, is formulated based on the time-scaling property of the stochastic service process. Validating the data-integration model against the long-run average service reward leads to a uniformly optimal service rate for any given sample size. We further derive the ODA-predicted reward function based on the data-integration model, which gives a consistent estimate of the underlying reward function. Our numerical experiments show that the ODA framework can lead to an efficient design of service rate and service capacity, which is insensitive to model specification. The ODA solution exhibits superior performance compared with the conventional estimation-and-then-optimization solutions in the small sample regime.
The topic of related diversification is of fundamental interest to corporate strategy research. However, the understanding of how the potential for resource redeployment might encourage related diversification is limited. This paper explores the impact of the 2006 German real estate transfer tax (RETT) reform, which led to higher RETT rates but retained the exemption from RETT for corporate restructurings. We propose that firms might have reacted strategically to the threat of higher transfer taxes by combining related businesses into a portfolio, providing an efficient internal market for reallocating property assets in the event of business-specific shocks. Consistent with a dynamic perspective on relatedness, difference-in-differences analyses of the European retail market show that the probability of related diversification increased significantly for firms that were affected by the reform, relative to control firms. This finding contributes to the literature that links firm boundary choices to intertemporal economies of scope and offers new insights into the real effects of transfer tax policies.
A principal often needs to match agents to perform coordinated tasks, but agents can quit or slack off if they dislike their match. We study two prevalent approaches for matching within organizations: centralized assignment by firm leaders and self-organization through market-like mechanisms. We provide a formal model of the strengths and weaknesses of both methods under different settings, incentives, and production technologies. The model highlights trade-offs between match-specific productivity and job satisfaction. We then measure these trade-offs with data from a large organization’s internal talent market. Firm-dictated matches are 33% more valuable than randomly assigned matches within job categories (using the firm’s preferred metric of quality). By contrast, preference-based matches (using deferred acceptance) are only 5% better than random but are ranked (on average) about 38 percentiles higher by the workforce. The self-organized match is positively assortative and helps workers grow new skills; the firm’s preferred match is negatively assortative and harvests existing expertise.
In the mobile video games industry, a common in-app purchase is for additional “moves” or “time” in single-player puzzle games. We call these in-app purchases bonus actions. In some games, bonus actions can only be purchased in advance of attempting a level of the game (pure advance sales (PAS)), yet in other games, bonus actions can only be purchased in a “spot” market that appears when an initial attempt to pass the level fails (pure spot sales). Some games offer both advance and spot purchases (hybrid advance sales). This paper studies these selling strategies for bonus actions in video games. Such a question is novel to in-app tools selling in video games, and it cannot be answered by previous advance selling studies focusing on end goods. We model the selling of bonus actions as a stochastic extensive form game. We show how the distribution of skill among players (i.e., their inherent ability to pass the level) and the inherent randomness of the game influence selling strategies. For casual games, where low-skill players have a sufficiently high probability of success in each attempt, if the proportion of high-skill players is either sufficiently large or sufficiently small, firms should adopt PAS and shut down the “spot” market. Furthermore, the player welfare-maximizing selling strategy is to sell only in the spot market. Hence, no “win-win” strategy exists for casual games. However, PAS can be a win-win for hardcore games, where low-skill players have a sufficiently low success probability for each attempt.
The idiosyncratic volatility (IVol) effect is robust to restricting the sample to New York Stock Exchange (NYSE) firms (once the proper listing indicator is used) and to excluding from the sample small, illiquid, and low-price stocks. The idiosyncratic volatility effect is also unlikely to stem from the short-run reversal, as the IVol effect stays significant for about six months and seems stronger for high turnover firms, which do not exhibit short-term reversal. The IVol effect also does not seem to weaken postpublication.
We build a model with inalienable human capital, in which investors finance individuals who can potentially become skilled. Although investment in skill is always optimal, it does not take place in some states of the world, due to moral hazard. In intermediate states of the world, individuals acquire skill; however outside investors and individuals inefficiently share risk. We show that this simple moral hazard problem, combined with risk aversion of individuals and outside investors, amplifies the equity premium, lowers the risk-free rate, and leads to disaster states that fall especially heavily on some agents but not on others. We show that the possibility of disaster states distorts risk prices and affects wealth inequality, even under calibrations in which they never occur in equilibrium.
Consumption decisions are partly influenced by values and ideologies. Consumers care about global warming, child labor, fair trade, etc. We develop an axiomatic model of intrinsic values—those that are carriers of meaning in and of themselves—and argue that they often introduce discontinuities near zero. For example, a vegetarian’s preferences would be discontinuous near zero amount of animal meat. We distinguish intrinsic values from instrumental ones, which are means rather than ends and serve as proxies for intrinsic values. We illustrate the relevance of our value-based model in different contexts, including equity concerns and prosocial behavior.
In customer support contact centers, every service interaction involves a messaging dialogue between a customer and an agent; together, they exchange information, solve problems, and collectively co-produce the service. Because the service progression is shaped by the history of conversation thus far, we propose a bivariate marked Hawkes process cluster model of the customer-agent interaction. To evaluate our stochastic model of service, we apply it to an industry contact center data set containing nearly 5 million messages. Through both a novel residual analysis comparison and several Monte Carlo goodness-of-fit tests, we show that the Hawkes cluster model indeed captures dynamics at the heart of the service and surpasses classic models that do not incorporate the service history. Furthermore, in an entirely data-driven simulation, we demonstrate how this history-dependent model can be leveraged operationally to inform a prediction-based routing policy. We show that widely used and well-studied customer routing policies can be outperformed with simple modifications according to the Hawkes model. Through analysis of a stylized model proposed in the contact center literature, we prove that service heterogeneity can cause this underperformance and, moreover, that such heterogeneity will occur if service closures are not carefully managed.
We investigate the extent to which trade-offs involving the sharing of personal information exhibit consistency with an underlying rational preference for privacy. In an experiment, people engage in trade-offs across two domains of personal information sharing, allowing us to classify whether their choices satisfy the generalized axiom of revealed preference. They also subsequently price the sharing of their personal information. Sixty-three percent of subjects act consistently with a rational preference ordering when allocating privacy levels, a level comparable to that observed in other domains. Individuals who are inconsistent when engaging in simple privacy trade-offs exhibit substantially more costly preference reversals when pricing personal information sharing. Only one-third of subjects exhibit consistency across both the selection of personal information to share and the pricing of such information sharing. Our results imply that monetizing personal information can have distinct welfare consequences for people with different degrees of rationality in their underlying ability to make sensible trade-offs involving personal information. We also provide evidence that preferences elicited over choices in our experiment correlate with real-world privacy behaviors.
Legal scholars highlight the tensions that exist between different classes of shareholders in startups. We model a startup owned by undiversified investors with heterogeneous capital contributions and risk preferences. A social planner runs the firm on behalf of all investors. We compare investors’ expected utility with a hypothetical first-best decentralized benchmark. The startup’s optimal investment policy is procyclical and a time-varying weighted average of shareholders’ optimal investment policies. The optimal contracts issued to investors are tailor-made, interdependent, and include equity claims resembling preferred stock with heterogeneous payout caps, leading to a complex capitalization table as more investors join the startup.
In this paper, we pursue a correlation-robust study of the influence maximization problem. Departing from the classic independent cascade model, we study a diffusion process adversarially adapted to the choice of seed set. More precisely, rather than the independent coupling of known individual edge probabilities, we now evaluate a seed set’s expected influence under all possible correlations, specifically, the one that presents the worst case. We find that the worst case expected influence can be efficiently computed, its NP-hard optimization done approximately (1−1/e) with greedy construction, and we provide complete, efficient characterizations of the adversarial coupling, the random graph, and the random number of influenced nodes. But, most importantly, upon mixing the independent cascade with the worst case, we attain a tunable and more comprehensive model better suited for real-world diffusion phenomena than the independent cascade alone and without increased computational complexity. Extensions to the correlation-robust study of risk follow along with numerical experiments on network data sets with demonstration of how our models can be tuned.
One measure of the ex ante cost of disasters is the welfare gain from shortening their expected duration. We introduce a stochastic clock into a standard disaster model that summarizes information about progress (positive or negative) toward disaster resolution. We show that the stock market response to duration news is essentially a sufficient statistic to identify the welfare gain to interventions that alter the state. Using information on clinical trial progress during 2020, we build contemporaneous forecasts of the time to vaccine deployment, which provide a measure of the anticipated length of the COVID-19 pandemic. The model can thus be calibrated from market reactions to vaccine news, which we estimate. The estimates imply that ending the pandemic would have been worth from 5% to 15% of total wealth as the expected duration varied in this period.
Data analytics enable firms to offer personalized prices to targeted consumers but at a cost. We study a competitive personalized pricing game where the entrant is uncertain about the incumbent’s targeting cost. We demonstrate that implementing personalized pricing through a “list price-discount” scheme allows the incumbent to signal its targeting cost via the list price. This signaling mechanism is effective because the list price serves as a price ceiling, which limits the incumbent’s ability to extract consumer surplus through personalized discounts. The high-cost incumbent can strategically set its list price below the full-information level to separate itself from the low-cost incumbent. Interestingly, the high-cost incumbent prefers separating over pooling only when there is a moderate variation in the incumbents’ targeting costs. Personalized pricing can affect firms differently, benefiting the incumbent but hurting the entrant. Asymmetric information about targeting costs weakens the high-cost incumbent’s incentive to offer personalized discounts, resulting in lower total targeting costs and potentially increasing social surplus. These findings shed light on government regulations and transparency policies regarding personalized pricing.
Many consumers across the world struggle to gain access to credit because of their lack of credit scores. This paper explores the potential of a new alternative data source, grocery transaction data, for evaluating consumers’ creditworthiness. Our analysis takes advantage of a unique, individual-level match of credit card data and supermarket loyalty card data. By developing credit scoring algorithms that either exclude or include grocery data, we illustrate both the incremental value of grocery data for credit decisions and its boundary conditions. We demonstrate that signals from grocery data can improve credit approval decisions, particularly for individuals who lack traditional credit scores. Furthermore, as a consumer establishes a relationship with lenders and builds a credit history, the marginal value of incorporating grocery data diminishes. These findings highlight the potential of grocery data in informing credit decisions and, consequently, in enabling financial institutions to extend credit to consumers who lack traditional credit scores.
We estimate the size of the likely unofficial income of the government official household by assessing the difference between the income necessary to explain their observed home purchase behavior and the official income. Using unique and comprehensive House Provident Fund and home purchases data between 2006 and 2013 in a large Chinese city, we reach three conclusions. First, an average official’s unofficial income is 83% of her official income; this percentage increases sharply with the official’s rank. Second, about 13% of the officials have an unofficial income, and the proportion also increases with rank. Third, government officials are not underpaid, and their unofficial incomes are not compensation for low government salaries. Additionally, evidence suggests that unofficial incomes have declined since the recent anticorruption campaign. An analysis of a separate crosscity data set corroborates the key conclusion.
Unit donations are an alternative fundraising scheme in which potential donors choose how many units of a charitable good to fund rather than just giving money. Based on evidence from an online experiment with 8,673 participants, we demonstrate that well-designed unit donation schemes can significantly boost giving above and beyond the standard money donation scheme. A decomposition of the underlying mechanisms shows patterns consistent with the conjecture that unit donations increase impact salience and leverage donors’ cognitive biases by changing the metric of the donation space. Managers need to weigh the potential fundraising benefits of a unit scheme against some important challenges, such as expert handling of the choice of unit sizes.
We use disaggregated U.S. data and a border discontinuity design to show that more generous unemployment insurance (UI) policies lower bank deposits. We test several channels that could explain this decline and find evidence consistent with households lowering their deposit holdings due to reduced precautionary savings. Because deposits are the largest and most stable source of funding for banks, the decrease in deposits affects bank lending. Banks that raise deposits in states with generous UI policies reduce their loan supply to small businesses. Furthermore, counties that are served by these banks experience a higher unemployment rate and lower wage growth.
This paper proposes a conditional asset pricing model that integrates environmental, social, and governance (ESG) demand and supply dynamics. Shocks in the demand for sustainable investing represent a novel risk source, characterized by diminishing marginal utility and positive premium. Green assets exhibit positive exposure to ESG demand shocks, hence commanding higher premia. Conversely, time-varying convenience yield leads to lower expected returns for green assets. Moreover, ESG demand shocks have positive contemporaneous effects on unexpected returns, contributing to large positive payoffs in the green-minus-brown portfolio over extended horizons. The model predictions align closely with evidence on return spreads between green and brown assets, further reinforcing the apparent gap between realized and expected spreads.
Using data on 10,776 firms across 22 emerging markets, we show that both credit constraints and weak green management hold back corporate investment in green technologies embodied in new machinery, equipment, and vehicles. In contrast, investment in measures to explicitly reduce emissions and other pollution is mainly determined by the quality of a firm’s green management and less so by binding credit constraints. Data from the European Pollutant Release and Transfer Register reveal the environmental impact of these organizational constraints. In areas where more firms are credit constrained and weakly managed, industrial facilities systematically emit more CO2 and pollutants. A counterfactual analysis shows that credit constraints and weak management have respectively kept CO2 emissions 4.5% and 2.3% above the levels that would have prevailed without such constraints. This is further corroborated by our finding that in localities where banks had to deleverage more due to the global financial crisis, carbon emissions by industrial facilities remained 5.6% higher a decade later.
Transition probability density functions (TPDFs) are fundamental to computational finance, including option pricing and hedging. Advancing recent work in deep learning, we develop novel neural TPDF generators through solving backward Kolmogorov equations in parametric space for cumulative probability functions. The generators are ultra-fast, very accurate and can be trained for any asset model described by stochastic differential equations. These are “single solve,” so they do not require retraining when parameters of the stochastic model are changed (e.g., recalibration of volatility). Once trained, the neural TDPF generators can be transferred to less powerful computers where they can be used for e.g. option pricing at speeds as fast as if the TPDF were known in a closed form. We illustrate the computational efficiency of the proposed neural approximations of TPDFs by inserting them into numerical option pricing methods. We demonstrate a wide range of applications including the Black-Scholes-Merton model, the standard Heston model, the SABR model, and jump-diffusion models. These numerical experiments confirm the ultra-fast speed and high accuracy of the developed neural TPDF generators.
We investigate the optimal clearing policy for a financial payment system composed of a number of member banks and a central bank in a dynamic setting, when new payment obligations or debts between member banks are generated over time. The central bank clears the debts among members in the system in order to minimize the costs, including the setup cost of each clearing, the variable cost of clearing the net debts, and the liquidity cost of uncleared debts. We formulate the problem using dynamic programming via state space reduction that provides a tractable framework to analyze and compute the optimal policy. We characterize the structure of the optimal policy and show that it is optimal for the central bank either to clear all the debts in the system or not to clear at all in each period under mild conditions. This structure leads to efficient computation of the optimal clearing policy. We further characterize the optimal clearing frequency based upon the deterministic approximation for the debt process. We conduct a comprehensive case study based on the data acquired from our industry partner, Payments Canada, to demonstrate the performance of the policy and its feasibility in industrial-size problems.
The value of a firm’s service lies both in its workers and its relationship with clients. In this paper, we study the interaction between the client-specific experience accumulated by workers, poaching behaviour from clients, and strategic rotation of workers by firms. Using detailed personnel data from a security service firm, we show that an increase in client-specific experience increases both the productivity of workers and their probability of being poached. The firm reacts to this risk by rotating workers across multiple clients, and more frequently so for those workers more likely to be poached. Furthermore, we find that after a policy change that prohibited poaching, the firm sharply decreased the frequency of rotation, which in turn increased workers’ productivity. We propose a theoretical model that guides the empirical patterns and allows us to argue their external validity beyond our specific empirical setting. Finally, we provide survey evidence from the security service sector, demonstrating the consistency between our findings and industry observations.
In many settings, a person’s outcome depends not only on her own behavior, but also on her counterpart’s. Such strategic decisions have traditionally been studied using normative game theory, which assumes that people adopt equilibrium strategies and will reach the same decision, regardless of how the problem is described (framed). We examine a potentially important type of framing effect—focusing on how the relationship between players’ actions generates joint outcomes. Any strategic interaction can be described by either spelling out the outcomes of all possible action combinations (which we call “outcome framing,” or simply “O-framing”) or describing what will happen if different players choose the same action or choose different actions (which we call “relation framing,” or simply “R-framing”). O-framing has been the typical way to describe a strategic problem in prior work, whereas R-framing is commonly employed in real-life communications. We propose that these functionally equivalent frames induce different psychological processes and lead to different decisions: Relative to O-framing, R-framing increases players’ beliefs about their counterparts’ likelihood of coordinating on a cooperative option. We demonstrate this effect in the context of classic games such as the Prisoner’s Dilemma and the Stag Hunt. We find that, compared with O-framing, R-framing significantly increases people’s likelihood to choose the action that maximizes collective benefits rather than individual interests, and it does so by increasing beliefs that one’s partner will choose the same action as well. We derive conditions when this effect is likely to emerge and discuss the managerial implications of this research.
We examine team diversity and performance in the asset management industry through the lens of identity. Focusing on political ideology as the relevant dimension of identity, we find that diverse teams outperform homogeneous teams. The mechanism involves both improved decision making due to more diverse perspectives and increased monitoring by heterogeneous team members. The benefits of ideological diversity are undone when political polarization is higher, consistent with increased intrateam conflict. In examining why less diverse teams are prevalent in asset management, we find entrenched managers prefer homogeneous teams, and the local labor market supply of ideologically diverse managers is constrained.
Efficiently managing inventories requires an accurate estimation of stockout costs. This estimation is complicated by challenges in determining how to compensate consumers monetarily to ensure they will maintain the same level of utility they would have obtained had stockouts not occurred. This paper presents an analysis of these compensation costs as applied to the design of optimal stockout rates by an online retailer marketing to consumers aesthetically substandard fruits and vegetables rejected by mainstream grocery chains. Because growers face high uncertainty in their harvesting conditions and in the aesthetic quality of their crops and there are little data on hand to predict the value consumers attach to the availability of subpar produce, it is difficult to optimally match the supply of these products with consumers’ demand. Our analysis draws from a multiple discrete-continuous extreme value (MDCEV) choice model to calculate consumer compensations that the retailer can use to estimate the opportunity costs of stockouts to manage its inventory. We show that not taking into account these compensation costs could unduly inflate the optimal stockout rates for these products. Armed with these compensation cost estimates, we show how these costs can serve as incentives for retailers to source greater inventory amounts of imperfect produce from growers and how this will ultimately translate into less waste in the supply chain.
Relationships with peers help entrepreneurs learn and improve firm performance. Recent scholarship confirms that events—social gatherings such as mixers, conferences, or training programs—can help entrepreneurs build valuable social connections. Yet, for entrepreneurs in developing economies, networking frictions may make connecting with peers challenging and undermine the benefits of events. We argue that when networking frictions are high, the value of events will lie more in connecting neighbors rather than bringing together distant peers. In the presence of networking frictions, neighbors are both less likely to be someone the entrepreneur has already learned from and easier to sustain a relationship with. To test this argument, we use data from a series of networking events in Togo during which entrepreneurs were randomly assigned to meet with peers from across the city of Lomé. We find that entrepreneurs who were assigned to neighboring peers were much more likely to sustain a relationship, learn from their peer’s management knowledge, and in turn benefit more: Profits increase by 10% when entrepreneurs get to know peers who are located on average 1 km closer to them. Our results highlight the central role that networking frictions play in shaping who entrepreneurs in developing economies can successfully learn from.
Despite evidence of the importance of sleep for cognitive performance, prior research finds limited effects of sleep disruptions on financial markets. This is puzzling because financial decisions rely on higher-order cognitive processes that are typically affected by sleep. We reconcile this dissonance by examining forecasts of corporate earnings by nonprofessional and professional forecasters. We find that nonprofessional forecasters exhibit a significant decline in their forecast accuracy following spring daylight saving time changes relative to professional forecasters. Using a separate sample of professional equity analysts’ forecasts, we continue to find that the information processing of analysts with less expertise is disproportionately negatively affected following sleep disruptions. Placebo tests provide support for sleep disruptions as the main driver of these effects. Overall, our evidence that information processing by less experienced and skilled market participants is particularly vulnerable to sleep disruptions provides one compelling reason why aggregate financial markets seem to be immune to sleep effects even though individual participants are not.
Insider trading conveys insiders’ private information to outsiders. This private information potentially benefits rival firms, which may reduce the competitive advantage of the insiders’ firms. Using a composite proprietary cost measure, we find proprietary costs are negatively associated with insiders’ purchases, especially when their trades are more likely to be informative to rivals. Consistent with proprietary costs increasing the costs of insider purchases and, hence, the expected benefits required to trade, insiders earn significantly higher abnormal profits when proprietary costs are higher. Exploiting settings with exogenous and event-driven variation in proprietary costs, we find insiders significantly reduce their purchases when noncompete agreement enforceability is high and before new product launches. Moreover, firms with higher proprietary costs are more likely to impose window-based insider trading restrictions and insiders with greater equity holdings reduce their purchases more strongly in the presence of proprietary costs. Finally, we provide evidence of real effects of insider trading on rivals’ investment decisions. We find that investments are associated with insiders’ purchases at rival firms, and these associations are stronger when proprietary costs at rivals are higher. Our findings indicate insiders and firms are aware of potential proprietary costs when insiders trade on private information and respond accordingly.
Does online fundraising increase charitable giving? Using the Facebook advertising tool, we implemented a natural field experiment across Germany, randomly assigning almost 8,000 postal codes to Save the Children fundraising videos or to a pure control. We studied changes in the donation revenue and frequency for Save the Children and other charities by postal code. Our geo-randomized design circumvented many difficulties inherent in studies based on click-through data, especially substitution and measurement issues. We found that (i) video fundraising increased donation revenue and frequency to Save the Children during the campaign and in the subsequent five weeks, that (ii) the campaign was profitable for the fundraiser, and that (iii) the effects were similar independent of video content and impression assignment strategy. However, we also found some crowding out of donations to other similar charities or projects. Finally, we demonstrated that click data may be an inappropriate proxy for donations and recommend that managers use careful experimental designs that can plausibly evaluate the effects of advertising on relevant outcomes.
We explore how information ambiguity and traders’ attitudes toward such ambiguity affect expectations and asset prices under three different market institutions. Specifically, we test a theoretical prediction that information ambiguity will lead market prices to overreact to bad news and underreact to good news. We find that such an asymmetric reaction exists and is strongest in individual prediction markets. It occurs to a lesser extent in single price call markets. It is weakest of all in double auction markets, in which buyers’ asymmetric reaction to good/bad news is cancelled out by the opposite asymmetric reaction of sellers.
With soaring labor and logistics costs in developing countries, supply chain disruptions during the COVID-19 pandemic triggered Western firms to “reshore” some of their offshore operations (performed in-house or outsourced) for certain strategically important products or production processes from foreign countries to their home countries. Although reshoring can create more domestic jobs and reduce supply chain risks, the impact of various external and internal risks associated with reshoring on market reaction remains unclear. This observation motivates us first to conduct a text mining analysis, revealing four important types of reshoring risks inherent to (1) foreign currency fluctuation, (2) intellectual property (IP) protection, (3) reshoring types (in-house, insourced, or outsourcing-to-outsourcing (OTO)), and (4) reshoring location choice (Republican- versus Democrat-led states). We then examine how these risk factors help explain the variations in reshoring’s market valuation based on 281 reshoring initiatives of 132 publicly traded firms in the United States announced between 2009 and 2022. Our empirical analysis reveals that the market reacts more positively to a firm’s reshoring announcement when the firm reshores under a high-currency-fluctuation environment or from countries with weak IP protection. However, the market’s reaction is more negative when the firm’s reshoring announcement entails insourced reshoring operations or when the reshored location is a Democrat- rather than Republican-led state. We do not find a significant market reaction to OTO reshoring.
We propose a new optimization-based method for learning causal structures from observational data, a process known as causal discovery. Our method takes as input observational data over a set of variables and returns a graph in which causal relations are specified by directed edges. We consider a highly general search space that accommodates latent confounders and feedback cycles, which few extant methods do. We formulate the discovery problem as an integer program, and propose a solution technique that exploits the conditional independence structure in the data to identify promising edges for inclusion in the output graph. In the large-sample limit, our method recovers a graph that is (Markov) equivalent to the true data-generating graph. Computationally, our method is competitive with the state-of-the-art, and can solve in minutes instances that are intractable for alternative causal discovery methods. We leverage our method to develop a procedure for investigating the validity of an instrumental variable and demonstrate it on the influential quarter-of-birth and proximity-to-college instruments for estimating the returns to education. In particular, our procedure complements existing instrument tests by revealing the precise causal pathways that undermine instrument validity, highlighting the unique merits of the graphical perspective on causality.
We study matching queues with abandonment. The simplest of these is the two-sided queue with servers on one side and customers on the other, both arriving dynamically over time and abandoning if not matched by the time their patience elapses. We identify nonasymptotic and universal scaling laws for the matching loss due to abandonment, which we refer to as the “cost of impatience.” The scaling laws characterize the way in which this cost depends on the arrival rates and the (possibly different) mean patience of servers and customers. Our characterization reveals four operating regimes identified by an operational measure of patience that brings together mean patience and utilization. The four regimes subsume the regimes that arise in asymptotic (heavy-traffic) approximations. The scaling laws, specialized to each regime, reveal the fundamental structure of the cost of impatience and show that its order of magnitude is fully determined by (i) a “winner-take-all” competition between customer impatience and utilization, and (ii) the ability to accumulate inventory on the server side. Practically important is that when servers are impatient, the cost of impatience is, up to an order of magnitude, given by an insightful expression where only the minimum of the two patience rates appears. Considering the trade-off between abandonment and capacity costs, we characterize the scaling of the optimal safety capacity as a function of costs, arrival rates, and patience parameters. We prove that the ability to hold inventory of servers means that the optimal safety capacity grows logarithmically in abandonment cost and, in turn, slower than the square-root growth in the single-sided queue.
Online retailers are facing an increasing variety of product choices and diversified consumer decision journeys. To improve many operations decisions for online retailers, such as demand forecasting and inventory management and pricing, an important first step is to obtain an accurate estimate of the substitution patterns among a large number of products offered in the complex online environment. Classic choice models either do not account for these substitution patterns beyond what is reflected through observed product features or do so in a simplified way by making a priori assumptions. These shortcomings become particularly restrictive when the underlying substitution patterns get complex as the number of options increases. We provide a solution by developing a high-dimensional choice model that allows for flexible substitution patterns and easily scales up. We leverage consumer clickstream data and combine econometric and machine learning (graphical lasso, in particular) methods to learn the substitution patterns among a large number of products. We show our method offers more accurate demand forecasts in a wide range of synthetic scenarios when compared with classical models (e.g., the independent and identically distributed Probit model), reducing out-of-sample mean absolute percentage error by 10%–30%. Such performance improvement is further supported by observations from a real-world empirical setting. More importantly, our method excels in precisely recovering substitution patterns across products. Compared with benchmark models, it reduces the percentage deviation from the underlying elasticity matrix by approximately half. This precision serves as a critical input for enhancing business decisions such as assortment planning, inventory management, and pricing strategies.
Hedge fund managers contribute substantial personal capital, or “skin in the game,” into their funds. Although these allocations may better align incentives, managers may also strategically allocate their private capital in ways that negatively affect outside investors. We find that funds with more inside investment outperform other funds within the same family. This relationship is driven by managerial decisions to invest their own capital in their least-scalable strategies and restrict the entry of new outsider capital into these funds. Our results suggest that insider capital may work as a rent-extraction mechanism at the expense of fund participation of outside investors.
Approximate linear programs (ALPs) are well-known models based on value function approximations (VFAs) to obtain policies and lower bounds on the optimal policy cost of discounted-cost Markov decision processes (MDPs). Formulating an ALP requires (i) basis functions, the linear combination of which defines the VFA, and (ii) a state-relevance distribution, which determines the relative importance of different states in the ALP objective for the purpose of minimizing VFA error. Both of these choices are typically heuristic; basis function selection relies on domain knowledge, whereas the state-relevance distribution is specified using the frequency of states visited by a baseline policy. We propose a self-guided sequence of ALPs that embeds random basis functions obtained via inexpensive sampling and uses the known VFA from the previous iteration to guide VFA computation in the current iteration. In other words, this sequence takes multiple shots at randomly approximating the MDP value function with VFA-based guidance between consecutive approximation attempts. Self-guided ALPs mitigate domain knowledge during basis function selection and the impact of the state-relevance-distribution choice, thus reducing the ALP implementation burden. We establish high-probability error bounds on the VFAs from this sequence and show that a worst-case measure of policy performance is improved. We find that these favorable implementation and theoretical properties translate to encouraging numerical results on perishable inventory control and options pricing applications, where self-guided ALP policies improve upon policies from problem-specific methods. More broadly, our research takes a meaningful step toward application-agnostic policies and bounds for MDPs.
We examine whether managers’ activities in striving to reach earnings targets through real earnings manipulation affect their firms’ product recalls. Our evidence implies that firms suspected of manipulating real activities in trying to meet earnings benchmarks exhibit a higher likelihood and frequency of product recalls. In cross-sectional results consistent with expectations, we find that the impact of exploiting real activities to attain earnings benchmarks on product recalls intensifies for firms whose managers have stronger incentives to manage earnings and subsides for firms subject to greater customer power and firms with more growth opportunities. Additional analysis shows that lowering product quality to meet or beat earnings expectations undermines firms’ future performance.
Sequential decision making often requires dynamic policies, which are computationally not tractable in general. Decision rules provide approximate solutions by restricting decisions to simple functions of uncertainties. In this paper, we consider a nonparametric lifting framework where the uncertainty space is lifted to higher dimensions to obtain nonlinear decision rules. Current lifting-based approaches require predetermined functions and are parametric. We propose two nonparametric liftings, which derive the nonlinear functions by leveraging the uncertainty set structure and problem coefficients. Both methods integrate the benefits from lifting and nonparametric approaches, and hence provide scalable decision rules with performance bounds. More specifically, the set-driven lifting is constructed by finding polyhedrons within uncertainty sets, inducing piecewise-linear decision rules with performance bounds. The dynamics-driven lifting, on the other hand, is constructed by extracting geometric information and accounting for problem coefficients. This is achieved by using linear decision rules of the original problem, also enabling one to quantify lower bounds of objective improvements over linear decision rules. Numerical comparisons with competing methods demonstrate superior computational scalability and comparable performance in objectives. These observations are magnified in multistage problems with extended time horizons, suggesting practical applicability of the proposed nonparametric liftings in large-scale dynamic robust optimization.
This paper studies two competing firms’ choices between the contingent-price contract (CPC) and fixed-price contract (FPC) in global commodity procurement. The FPC price is determined when signing the contract, whereas the CPC price is pegged to an underlying index and remains open until the delivery date. Under both contracts, each firm determines its order quantity based on the updated belief about the market demand. The unrealized CPC price correlates with the market demand, allowing a firm to update its belief about the CPC price using demand information, thereby generating a price-learning effect. We find that, contrary to conventional wisdom, a larger price volatility could benefit the firms, and, under differentiated contracts, a firm might benefit from the improvement of forecast accuracy at its rival. We further show that the price-learning effect plays a critical role in the firms’ contract choices. First, significant price volatility forces the firms to pursue the responsiveness of the CPC. Second, the firms may adopt differentiated contracts to enhance their responses to market changes and dampen competition, and a higher competition intensity more likely leads to contract differentiation. Third, the firms in a small market seek responsiveness and contract differentiation rather than cost efficiency. This study reveals the bright side of price volatility and takes a step toward understanding the effect of two-dimensional information updating.
Across multiple studies, we investigate whether there are gender differences in preferences for receiving performance feedback. We vary many features of the feedback context: whether the performance task is a cognitive test or a mock interview, the feedback is objective or subjective, and it is possible for the provider of the feedback to discriminate on the basis of gender. Consistent with past work, we find that women are less optimistic about their performance than men and that, on average, more optimistic individuals have greater demand for feedback. Results like these have been hypothesized in the literature to imply that women will shy away from performance feedback more so than men. And, when we survey participants from a similar population, they also anticipate that women will demand feedback at lower rates than men. Yet, across our two incentivized studies, we find that women are no less eager to receive performance feedback than men. Understanding whether and how these results might generalize to broader contexts, particularly those with more social factors, is an important question for future work.
We estimate and test long-run risk models using international macroeconomic and financial data. The benchmark model features a representative agent who has recursive preferences with a time preference shock, a persistent component in expected consumption growth, and stochastic volatility in fundamentals characterized by an autoregressive gamma process. We construct a comprehensive data set with quarterly frequency for 10 developed countries and employ an efficient likelihood-based Bayesian method that exploits up-to-date sequential Monte Carlo methods to make full econometric inference. Our empirical findings provide international evidence in support of long-run risks, time-varying preference shocks, and countercyclicality of the stochastic discount factor. We show the existence of a global long-run consumption factor driving equity returns across individual countries.
I analyze runs on credit lines in an infinite-horizon banking model, focusing on the strategic complementarity between bankers and credit line borrowers. Panic drawdowns by borrowers coupled with bank liquidity rationing can mutually reinforce each other, creating a vicious cycle. Using data from U.S. banks, I estimate the model and quantify the amplification effect arising from strategic complementarity. This amplification effect accounted for two-thirds of the contraction of bank credit during the 2008–2009 crisis. Moreover, I show that policies targeting borrowers have a crowding-in effect and can effectively contain credit contraction.
New digital technologies allow companies to provide employees with performance feedback in real time. We investigate the causal effects of such real-time feedback on individual effort and the quality of initial (prefeedback) and final (postfeedback) decisions by exploiting a natural quasi-experiment in the setting of professional soccer refereeing. Our results suggest that real-time feedback has differential effects for experienced compared with inexperienced agents. Specifically, we find some evidence that experienced agents decrease their effort under real-time feedback without harming the quality of their initial decisions. This results in better final decisions because real-time feedback allows agents to correct wrong initial decisions upon feedback. In contrast, inexperienced agents increase their effort, but make worse initial decisions. Upon feedback, they manage to compensate for their increased number of wrong initial decisions, but see no improvement in the quality of final decisions.
We propose a theory for rating financial securities in the presence of structural maximization by the issuer in a market with investors who rely on credit rating. Two types of investors, simple investors who price tranches solely based on the ratings and model-based investors who use the rating information to calibrate models, are considered. Concepts of self-consistency and information gap are proposed to study different rating criteria. In particular, the expected loss criterion used by Moody’s satisfies self-consistency, but the probability of default criterion used by Standard & Poor’s does not. Moreover, the probability of default criterion typically has a higher information gap than the expected loss criterion. Empirical evidence in the post–Dodd–Frank period is consistent with our theoretical implications. We show that a set of axioms based on self-consistency leads to a tractable representation for all self-consistent rating criteria, which can also be extended to incorporate economic scenarios. New examples of self-consistent and scenario-based rating criteria are suggested.
To what extent do firms know available information on key competitor decisions, and how does competitor information change their own strategic choices? These questions are fundamental to understanding how firms compete and make strategic decisions, yet systematic evidence on them remains limited. I designed a field experiment across 3,218 differentiated firms in the personal care industry, where firms randomly assigned to treatment received easily accessible information on competitor prices. At baseline, nearly half of treatment firms appeared to lack knowledge of competitor prices. Once treatment firms received competitor information, they were more likely to change their own decisions, aligning them with competitors rather than differentiating. These changes were driven by firms that were more misaligned in their price and quality decisions, and appear to have been performance enhancing. If competitor information was both easily accessible and decision relevant, why did firms not use this information on their own? Results from a follow-up experiment suggest that this lack of knowledge may have been driven by managerial inattention. These findings highlight that limited information processing is a key problem for firms and a central issue in strategy, and raise the possibility that growing availability of competitor data may lead firms to align their decisions more with their competitors.
The advent of cryptocurrencies and digital assets holds the promise of improving financial systems by offering cheap, quick, and secure transfer of value. However, it also opens up new payment channels for cybercrimes. Assembling a diverse set of public on- and off-chain, proprietary, and hand-collected data, including attacker–victim negotiations and dark web conversations in Russian, we present an initial anatomy of crypto-enabled cybercrimes, highlighting relevant economic issues and proposing areas for future research. Among others, we find ransomware, as the most dominant organized crypto-enabled cybercrime, entails criminal gangs that operate like firms who adopt modern revenue models and carefully manage their reputations. We suggest that blanket restrictions on cryptocurrency usage may prove counterproductive. Instead, blockchain transparency enables effective forensics for tracking, monitoring, and shutting down dominant cybercriminal organizations, which potentially facilitates a more secure and reliable crypto ecosystem in the longer term.
Abstract
					Concerns that algorithms may discriminate against certain groups have led to numerous efforts to 'blind' the algorithm to race. We argue that this intuitive perspective is misleading and may do harm. Our primary result is exceedingly simple, yet often overlooked. A preference for fairness should not change the choice of estimator. Equity preferences can change how the estimated prediction function is used (e.g., different threshold for different groups) but the function itself should not change. We show in an empirical example for college admissions that the inclusion of variables such as race can increase both equity and efficiency.
Abstract
					Economists use a welfarist approach to policy evaluation, but doing so can lead to recommendations that violate seemingly wise non-welfarist principles. I explore one example: the proper tax policy response to envy. Using a novel survey, I find that a majority of US respondents are skeptical of envy-based redistribution, even if they are told that its direct effect is to increase welfare. This skepticism is consistent with a long history of judgments by economists, including leading welfarists. Such conflicts between direct welfarist analysis and prevailing normative principles challenge economists to consider modifying our dominant method of policy evaluation.
Abstract
					Advances in machine learning (ML) are poised to transform numerous occupations and industries. This raises the question of which tasks will be most affected by ML. We apply the rubric evaluating task potential for ML in Brynjolfsson and Mitchell (2017) to build measures of "Suitability for Machine Learning" (SML) and apply it to 18,156 tasks in O*NET. We find that (i) ML affects different occupations than earlier automation waves; (ii) most occupations include at least some SML tasks; (iii) few occupations are fully automatable using ML; and (iv) realizing the potential of ML usually requires redesign of job task content.
Abstract
					Prior episodes of automation have led to economic growth and also to many changes in the workplace. We expect the same from artificial intelligence (AI). The link between AI and labor is complex, however. To assist researchers and policymakers, we provide a method that links advances in AI to occupational abilities, and then aggregates from these abilities to the occupation level. We demonstrate the method by estimating which occupational descriptions have changed the most due to advances in AI between 2010 and 2015, and check our estimates using the Bureau of Labor Statistics scheduled update to occupational descriptions in 2016.
Abstract
					We estimate a model of consumer choices over restaurants using data from several thousand anonymous mobile phone users. Restaurants have latent characteristics (whose distribution may depend on restaurant observables) that affect consumers' mean utility as well as willingness to travel to the restaurant, while each user has distinct preferences for these latent characteristics. We analyze how consumers reallocate their demand after a restaurant closes to nearby restaurants versus more distant restaurants, comparing our predictions to actual outcomes. We also address counterfactual questions such as what type of restaurant would attract the most consumers in a given location.
Abstract
					Many households in developing countries lack access to credit: physically providing small loans to poor and remote populations is costly. However, the digitization of developing countries enables a new model: digital credit delivered directly via mobile phones. Mobile money enables inexpensive financial transfers, and mobile phones capture behavior that can predict repayment when mined with machine learning. This paper evaluates the potential for digital credit to reach those excluded from current financial systems.
Abstract
					For some entities, the utility-maximizing evasion rate depends substantially on tax audit features, such as audit probabilities and penalty rates. Bergolo et al. (2017) document large misperceptions about these features. In this paper, we expand the analysis of survey data to explore potential sources of these misperceptions. Of all the channels that we explore, recent contact with audits best explains differences in misperceptions.
Abstract
					Despite concern about the viability of public retirement programs and potential undersaving for retirement, we still know little about the impact of government provided information on individual behavior. We exploit plausibly exogenous variation in exposure to the world's largest personalized retirement benefits statement from the US Social Security Administration to evaluate the effects of information and encouragement on individual retirement savings decisions. Using three natural experiments between 2011 and 2014 and administrative data, we find no impact of the statements on individual retirement savings in their employer provided retirement accounts.
Abstract
					We present new evidence on global inequality and growth since 1980 using the World and Wealth Income Database. We plot the curve of cumulated growth from 1980 to 2016 by percentile of the global distribution of income per adult. This curve has an elephant shape due to high growth rates at the median (fast growth in China and India), modest growth rates above the median, and explosive growth rates at the top. We project the evolution of global inequality between now and 2050 combining projected macro growth rates and within country inequality evolution based on past trends.
Abstract
					This paper combines national accounts, survey, wealth, and fiscal data (including recently released tax data on high-income taxpayers) in order to provide consistent series on the accumulation and distribution of income and wealth in China and Russia over the 1978–2015 period. We contrast the different privatization strategies implemented in the two countries and observe their impacts on the evolution of inequality.
Abstract
					A generalized Pareto curve is defined as the curve of inverted Pareto coefficients b(p), where b(p) is the ratio between average income or wealth above rank p and the p-th quantile. We present this concept and show how it can be used to better estimate distributions, especially from tax tabulations. By providing a simple decomposition of top shares, we discuss how studying inverted Pareto coefficients can improve the understanding of inequality dynamics. We also show how it helps to better analyze wealth and income concentrations along the distribution, using data for France, Spain, the United States, and China.
Abstract
					Concerns about emissions leakage, i.e., the increase in emissions in foreign jurisdictions induced by a domestic emissions regulation, loom large in debates about unilateral climate change policy. Correctly identifying the kinds of economic activities most at risk of carbon leakage is a critical first step in the design of effective leakage mitigation. We briefly summarize current approaches to assessing leakage risk and highlight a sizable gap between academic research and real-world policy implementation. An emerging research agenda that aims to close this gap is discussed.
Abstract
					This paper provides the first estimates of within-industry heterogeneity in energy and CO2 productivity for the entire US manufacturing sector. We measure energy and CO2 productivity as output per dollar energy input or per ton CO2 emitted. Three findings emerge. First, within narrowly defined industries, heterogeneity in energy and CO2 productivity across plants is enormous. Second, heterogeneity in energy and CO2 productivity exceeds heterogeneity in most other productivity measures, like labor or total factor productivity. Third, heterogeneity in energy and CO2 productivity has important implications for environmental policies targeting industries rather than plants, including technology standards and carbon border adjustments.
Abstract
					Prior work has shown that people appear insensitive to the scope of their altruistic acts and prosocial behavior. While they respond positively when their choices lead to increasing rewards for themselves, people do not change their behavior when the outcomes for others increase. We demonstrate that the scope sensitivity of altruism depends critically on its tangibility, and suggest that this relationship operates through mental accounting. We show that by increasing the level of tangibility, people can become just as sensitive to changes in the size of rewards for others as if they were earning the rewards themselves.
Abstract
					In many situations, mental budgeting and relative thinking predict results that are in opposing directions. We study a situation of this type and provide evidence for when the predictions of one model will dominate the other.
Abstract
					To inform economists and policymakers about whether the effects of nudges are persistent in one specific context, we study the choice architecture of the Swedish Premium Pension Plan. The data we study consist of all initial choices and subsequent rebalancing activities by the entire population of 7.3 million retirement savers in Sweden during the period 2000 to 2016. Based on our analysis of these data, we conclude that the effects of nudging in this case were surprisingly persistent and seem to last nearly two decades, if not forever.
Abstract
					What explains substantial variation across countries in household stock market participation? Building on Ke (2018), I hypothesize that the prevailing gender norm in a country is an important cultural factor. In a cross-country analysis, I find that households in countries with strong gender norms are less likely to participate in the stock market. In addition, I present suggestive evidence that gender role attitudes help explain cross-country differences in participation among the wealthy. My findings underscore the role of social norms in shaping household balance sheets across countries.
Abstract
					Constraints that prevent women from working longer hours are argued to be important drivers of the gender wage gap in the United States. We provide evidence that in couples where the wife's working hours exceed the husband's, the wife reports lower life satisfaction. By contrast, there is no effect on the husband's satisfaction. The results still hold when controlling for relative income. We argue that these patterns are best explained by perceived fairness of the division of household labor, which induces an aversion to a situation where the wife works more at home and on the labor market.
Abstract
					We use experimental and observational data to examine whether people respond differently to questions posed by females versus males. We document significant differences in the language of responses, both in terms of the distribution of language utilized, and the sentiment of this language (positive or negative). In the observational data, we also document differences in the language and sentiment of questions posed by gender. This highlights the importance of using experimental data to identify the causal role that gender plays in influencing the language choice of individuals responding to questions from males versus females.
Abstract
					This paper examines the existence of an unwelcoming or stereotypical culture using evidence on how women and men are portrayed in anonymous discussions on the Economics Job Market Rumors forum (EJMR). I use a Lasso-Logistic model to measure gendered language in EJMR postings, identifying the words that are most strongly associated with discussions about one gender or the other. I find that the words most predictive of a post about a woman are typically about physical appearance or personal information, whereas those most predictive of a post about a man tend to focus on academic or professional characteristics.
Abstract
					In this imaginary dialogue, a populist and an economist discuss the role of economic shocks to explain populism. A simple correlation between economic shocks and populism is weak. However, economic shocks can explain well the phenomenon of populism in countries with low pre-existent level of trust. This is confirmed both at the macro cross-country level and also by micro evidence obtained from surveys. Finally, this finding is consistent with the "ideational approach" in political science, which emphasizes how the populist narrative opposes the "corrupt elite" to the "virtuous people."
Abstract
					In this paper we merge individual income data, firm-level data, patenting data, and IQ data in Finland over the period 1988–2012 to analyze the returns to invention for inventors and their coworkers or stakeholders within the same firm. We find that: (i) inventors collect only 8 percent of the total private return from invention; (ii) entrepreneurs get over 44 percent of the total gains; (iii) bluecollar workers get about 26 percent of the gains and the rest goes to white-collar workers. Moreover, entrepreneurs start with significant negative returns prior to the patent application, but their returns subsequently become highly positive.
Abstract
					Standardized test scores and value-added measures largely determine "grades" assigned to teachers and schools. Poor evaluations have severe implications: in some cases, entire schools may be closed or replaced by a charter. Although evaluation depends on within-school factors, random shocks external to the school environment may affect measured test performance and evaluations. In this article, we study a salient shock: violent crime. Our results suggest exposure to an additional violent event is associated with decreased test performance. These performance declines are consequential as the schools impacted by within-testing period violent crime are also less likely to meet accountability standards.
Abstract
					There is little empirical evidence as to whether protests against policy brutality impact the behavior of police forces. We seek to close this gap by considering the impact of the racial civil disturbances in the 1960s and 1970s on deaths by legal intervention using an event study approach. In the first three years after a protest in a county, police killings of white Americans increase by 0.4–1.0 annually in impacted counties and killings of non-whites increase by 0.7 annually. In subsequent years, the impact on killings of white Americans disappears while the impact on killings of non-whites persists.
Abstract
					Applying big data methods to biomedical science articles, we show that women and underrepresented racial and ethnic groups are less likely to be last authors, an indicator of career independence. We leverage the massive size of our data to highlight the importance of intersectionality, the idea that ethnicity, gender, and race are not necessarily additive, but interact to determine experiences and outcomes. In particular, gender gaps are smaller among blacks and Hispanics than among non-Hispanic whites. Our analysis is timely given serious concerns with under-representation of women and minorities in biomedicine and other STEM fields.
Abstract
					Police use of force, particularly lethal force, is one of the most divisive issues of the twenty-first century. To understand the nexus of race, criminal justice, and police brutality, academics and journalists have begun to amass impressive datasets on officer-involved-shootings (OIS). I compare the data and methods of three investigative journalism articles and two publications in the social sciences on a set of five rubrics and conclude that the stark differences between their findings are due to differences in what qualifies for a valid research design and not underlying differences in the datasets.
Abstract
					An often overlooked population in discussions of prison reform is the children of inmates. How a child is affected depends both on what incarceration does to their parent and what they learn from their parent's experience. To overcome endogeneity concerns, we exploit the random assignment of judges who differ in their propensity to send defendants to prison. Using longitudinal data for Norway, we find that imprisonment has no effect on fathers' recidivism but reduces their employment by 20 percentage points. We find no evidence that paternal incarceration affects a child's criminal activity or school performance.
Abstract
					A large body of multidisciplinary research has documented how sentencing outcomes vary tremendously across racial and ethnic groups. The research challenge lies in establishing whether these sentencing differentials are driven by unobserved heterogeneity correlated to defendant race/ethnicity, or whether they reflect discrimination. We add to the debate by examining the robustness of racial/ethnic sentencing gaps, by gender, when allowing for selection on unobservables. We do so in the context of federal criminal cases, considering 250,000 cases, and using a dataset containing a rich set of covariates relating to defendant and legal characteristics of cases.
Abstract
					Who are the female entrepreneurs who end up starting and completing entrepreneurial training programs? In this paper, relying on a large set of baseline characteristics collected before the entrepreneurs are selected into an entrepreneurial training program in Mexico, we analyze how the women entrepreneurs who complete the training program differ from those who do not take it up, as well as how those who take it up but drop off before completing differ from those who do not even start. We uncover large differences in performance and non-cognitive skills but no differences in inputs used.
Abstract
					We explore potential causes for the well-documented profit gap between male- and female-owned microenterprises in low-income countries. We use rich data from an ongoing field project in Ghana's garment making sector, and our study sample consists of all garment making firms in a midsize district capital. Even within the same industry, male-owned firms earn nearly twice as much profit as female-owned firms. Furthermore, we find the large and persistent gender difference in profits cannot be explained by our extensive firm- and owner-level characteristics. We conclude that factors outside of individual firm or firm-owner characteristics are likely to be at play.
Abstract
					Personal initiative training—a psychology-based mindset training program—delivers lasting improvements for female business owners in Togo. Which types of women benefit most? Theories of dynamic complementarity would suggest training should work better for those with higher pre-existing human capital, but there are also reasons why existing human capital might inhibit training participation or substitute for its effects. We examine the heterogeneity in treatment impact according to different types of human capital. We find little evidence of either complementarities or substitutability, suggesting this new business training approach can work for a wide range of human capital levels.
Abstract
					Theory suggests that disability discrimination protections may adversely affect the hiring of individuals with disabilities by making them more expensive. Using SIPP data, we explore how the ADA Amendments Act (ADAAA), which expanded disability discrimination protections, affected the relative hiring rate of individuals with disabilities. We employ new categorizations of disability type: salient physical conditions, non-salient physical conditions, mental retardation and developmental disability, and other mental conditions. We find that the ADAAA had no effect other than increasing hiring for those with non-salient physical conditions. These results suggest that condition saliency may mediate the effects of discrimination protections on hiring.
Abstract
					Social Security Disability Insurance (SSDI) affects the labor supply of applicants through its work discouragement and through human capital deterioration regardless of the ultimate acceptance or denial of the claim. In this paper, I provide an estimate of the causal effect of SSDI application on denied applicants using non-applicants as a comparison group. Exploiting instrumental variable approach, I find that the SSDI causes a 36 percentage point reduction in employment of the denied applicants of ages 50 to 58 in the short run. The loss of potential employment of the denied SSDI applicants is a welfare loss to the society.
Abstract
					Return-to-work policies in disability insurance (DI) programs allow beneficiaries to collect a portion of their benefits while working. I investigate whether a large increase in incentives to work in a return-to-work policy could induce benefit recipients to increase their labor supply. I quantify the effects on earnings and labor force participation using a sharp discontinuity in the induced incentives to work at the month of the policy change in a DI program in Canada. Using administrative data, I document that large incentives to work could induce beneficiaries to increase their labor supply both in intensive and extensive margins.
Abstract
					The PSID has major advantages for studying income volatility and, because of this, research using it has been responsible for major improvements in the methodology of studying income volatility. Its research on calendar trends finds a reasonably consistent pattern for phased but rising male earnings volatility since 1970. Female earnings volatility has declined and household income volatility has risen. Some other datasets find similar patterns but others do not, suggesting the need for more research. A new earnings volatility model is estimated on PSID men through 2014, showing similar patterns but with a large jump during the Great Recession.
Abstract
					In this paper we highlight the importance of the PSID in understanding income dynamics and consumption insurance. We explore the nonlinear nature of income shocks and describe a new quantile-based panel data framework for income dynamics. In this approach the persistence of past income shocks is allowed to vary according to the size and sign of the current shock. The model provides a good match with data and we confirm the results on population register data from Norway. Using the enhanced consumption and asset data in the PSID, nonlinear persistence is shown to have key implications for consumption insurance.
Abstract
					Despite the rise in cross-sectional inequality since the late 1990s, there is little consensus on trends in earnings volatility during this period. Using consistent samples and methods in administrative earnings data matched to the Survey of Income and Program Participation (SIPP GSF) and survey data from the Panel Study of Income Dynamics (PSID), we examine earnings volatility for men from 1978 through 2011. In contrast to the apparent inconsistency in trends across administrative and survey data in the existing literature, we find recent increases in volatility in the SIPP GSF and the PSID, though increases are larger in the PSID.
Abstract
					A large body of knowledge on income dynamics in the United States is based on data from the Panel Study of Income Dynamics (PSID). We find two sets of households in the PSID that differ dramatically in the dynamics of their income. Households headed by the sons of original PSID members have a highly persistent income process, whereas households headed by males who marry daughters of the original PSID members have a much less persistent income process. Using a variety of methods for identification of the persistence of income shocks, we show that these differences, albeit surprising, are highly robust.
Abstract
					Cognitive scientists have identified fundamental principles influencing learning: deliberate practice, interleaving, retrieval practice, spacing, metacognition, desirable difficulties, limited working memory, curse of knowledge, schema generation, and constructivism. STEM education researchers have repeatedly shown improved learning when instruction employs these principles. Particularly, teaching methods like flipping and clickers work best when implemented using them. These "research-based teaching methods" are becoming the norm in STEM teaching. A macro principles course was redesigned using these principles. Five hundred and eight students in this course achieved 0.77 standard deviations more learning than principles students normally do on the macroeconomic Test of Understanding of College Economics (TUCE).
Abstract
					The cognitive science literature provides several research based instructional strategies (RBIS) proven to be effective in student learning. Given the prevalence of lecture-based classes and instructor-led discussions, understanding the effects of switching from the "traditional" method to employing RBIS would provide a better understanding of the value of these strategies. In this paper, we conduct a controlled experiment comparing a "traditional," lecture-based class to a class in which these RBIS are carefully employed. Students exposed to the refined RBIS approach show significantly higher gains in learning of approximately 15 percentage points when compared to students in a traditional class.
Abstract
					We re-examine the Lake Wobegon effect for reported GPA using data from students enrolled in principles of economics courses. Students are well known to over-report their GPA on surveys. In addition to standard measures of the mean difference, we extend the analysis to understand the distribution of response error using quantile regression analysis. We also examine how survey conditions impact response error. Our findings suggest that response error is more complicated than simple over-confidence. Students respond differently throughout the distribution of GPA and respond differently when seated near friends. Modeling response error should take these findings into account.
Abstract
					We provide stylized facts on the existence and dynamics over time of the large firm wage premium for four countries. We examine matched employer-employee micro-data from Brazil, Germany, Sweden, and the UK, and find that the large firm premium exists in all these countries. However, we uncover substantial differences among them in the evolution of the wage premium over the past several decades. Moreover, we find no clear evidence of common cross-country industry trends. We conclude by discussing potential explanations for this heterogeneity, and proposing some questions for future work in the area.
Abstract
					The literature has established two robust stylized facts: (i) the existence of a firm size-wage premium and (ii) a positive relationship between firm size and productivity. However, the existing evidence is mainly based on manufacturing, which nowadays accounts for a small share of the economy. Using a unique micro-aggregated dataset covering 17 countries over 1994–2012, this paper compares these relationships across sectors. While the size-wage and size-productivity premia are significantly weaker in market services compared to manufacturing, the link between wages and productivity is stronger. In a service economy the stylized fact is a "productivity-wage premium" rather than a "size-wage premium."
Abstract
					Kidney exchange platforms serve patients who need a kidney transplant and who have a willing, but incompatible, donor. These platforms match patients and donors to produce transplants. This paper documents operational details of the three largest platforms in the United States. It then uses the framework developed in Agarwal et al. (2017) to examine how practical details influence platform productivity. The results show that reducing frictions in accepting proposed matches, frequent matching, and encouraging altruistic donors are important ways in which a platform can increase its productivity.
Abstract
					Many scarce public resources are allocated through wait lists that use priorities for individual agents. A new priority system for allocating deceased donor kidneys was adopted in 2014. This redesign was guided by simulations that held decision-rules fixed. We synthesize recent theoretical results to show that the welfare effects of a mechanism depend on the interaction between dynamic incentives and heterogeneity in preferences. We show evidence suggesting that patient decisions on the deceased donor kidney wait list respond to dynamic incentives. Therefore, an empirical approach to dynamic mechanism design is an essential complement to mechanism design theory in dynamic environments.
Abstract
					Half a century of economic research asks how economic inequality evolves during aggregate economic progress. I extend this literature to quantify inequality in the incidence of child death across mothers and study its evolution during aggregate mortality decline. Data from 238 household surveys in 79 developing countries show that as child mortality falls in aggregate, it becomes more unequally distributed across mothers.
Abstract
					We document evidence on preferences for childbearing in developing countries. Across countries, men usually desire larger families than women do. Within countries, we find wide dispersion in spouses' desired fertility: there are many couples whose ideal family size differs by five children or more. This disagreement between spouses suggests that the extent to which women are empowered should matter for fertility choices. We point to evidence at both the macro and micro levels that this is indeed the case. We conclude that taking account of household bargaining and women's empowerment in analyses of fertility is an important challenge for research.
Abstract
					Female labor force participation varies significantly even among countries with similar levels of economic development. Recent studies have shown that gender norms can help explain these differences in women's work, but the channels through which norms impact women's employment decisions are not well understood. We present novel data on spouses' preferences and perceptions of community attitudes about female labor in rural India and document associations with female work. We find that the perceived social cost of women's work falls on men and that husbands' opposition to female labor is associated with their wives' lower take-up of employment.
Abstract
					Tougher immigration enforcement has been responsible for approximately 1.8 million deportations between 2009 and 2013 alone. Children enter the foster care system when their parents are apprehended, deported, and unable to care for them. We find that the average increase in interior immigration enforcement over the 2001 through 2015 period contributed to raising the share of Hispanic children entering foster care between 15 and 21 percent. Given the revival of police-based immigration enforcement by President Donald Trump, further analyses of its consequences on families are well warranted.
Abstract
					This research is the first to uncover evidence of an intergenerational effect of education on substance use in the United States. Using data from the NLSY79 and variation in education induced by changes in compulsory schooling laws, we study the effect of parental education on children's use of drugs and alcohol. We find that an increase in parental education decreases alcohol consumption—binge drinking, in particular—and has no effect on marijuana consumption among youth. Given the high social costs associated with alcohol abuse, we conclude that previous research on education may appreciably underestimate the benefits of investments in education.
Abstract
					We document the differences in food insecurity incidence and severity by race/ethnicity and immigrant status over the Great Recession. We show that the disadvantaged groups with a higher incidence of food insecurity do not necessarily have a higher severity of food insecurity, which underscores the importance of examining both the extensive and intensive margins of food insecurity. Our decomposition analysis indicates that the contribution of compositional and structural factors to the observed differences in exposure to food insecurity is heterogeneous across these groups and over the Great Recession. Finally, SNAP does not seem to fundamentally change the patterns documented.
Abstract
					In 2016, total Medicaid spending, $574.2 billion, represented one-third of state budgets. Descriptive studies indicate that state policymakers adjust social welfare programs during times of financial distress, particularly Medicaid. The challenge of formally estimating this effect is that macroeconomic shocks increase Medicaid enrollment and state-level financial stress. We use an exogenous measure of Medicaid generosity to estimate the elasticity of Medicaid generosity with respect to financial conditions. We find Medicaid generosity is not adjusted during periods of fiscal distress, whether anticipated or not. Instead, we find a counter-cyclical Medicaid effect with generosity increasing with increases in the unemployment rate.
Abstract
					We use novel administrative data to study trends and disparities in usage of California's first-in-the-nation paid family leave (PFL) program. We show that take-up for both bonding with a new child and caring for an ill family member increased over 2005–2014. Most women combine PFL with maternity leave from the State Disability Insurance system, resulting in leaves longer than 6 weeks. Most men take less than the full 6 weeks of PFL. Individuals in the lowest earnings quartile and in small firms are the least likely to take leave. There are important differences in take-up across industries, especially for men.
Abstract
					There is considerable interest in the success of women in overwhelmingly male environments. One hypothesized determinant of success is the increased presence of other women. However, the theoretical direction of this effect is uncertain. Previous studies of heavily male contexts have had mixed results. We take advantage of random peer group assignment at West Point military academy to identify gender peer effects in the first years in which women were admitted. We find that women do significantly better when placed in companies with more women peers. The addition of one woman peer reduces the gender progression gap by half.
Abstract
					I examine the relationship between Social Security benefits, a major component of income in older age, and intergenerational transfers of financial help and caregiving. I find that the net pass-through rate of Social Security benefits from parents to children is about 15 percent, including only monetary inter vivos transfers. Parents with higher Social Security benefits provide more hours of help to children in the form of grandchild care, even though children significantly withdraw caregiving to parents along this dimension. Taken together, these results are consistent with parental altruism and have strong implications for the distributional consequences of Social Security reform.
Abstract
					Over the past couple of decades, older Americans have become considerably more leveraged. This paper considers whether household debt affects the timing of retirement and Social Security benefit claiming. Using data from the Health and Retirement Study, we find that older adults with debt are more likely to work and less likely to receive Social Security benefits than those who are debt-free. Indebted adults are also more likely to delay fully retiring from the labor force and claiming their benefits. Among the sources of debt, mortgages have a stronger impact on older adults' behavior than do other sources of debt.
Abstract
					We investigate changes in older individuals' financial fragility as they stand on the verge of retirement. Using data from the Health and Retirement Study (HRS), we compare how debt has changed for successive cohorts of people age 56–61. Our analysis shows that recent older Americans close to retirement hold more debt, and hence face greater financial insecurity, than earlier generations. This is primarily due to having bought more expensive homes with smaller down payments. We discuss possible policy implications.
Abstract
					Since 2001, several sub-Saharan African countries have eliminated user fees for childbirth. Although intended to improve maternal and infant health outcomes, service quality also fell as facilities became overburdened. In this paper I combine DHS surveys on births from countries eliminating user fees. I identify the effect of user fee elimination using (i) a maternal fixed effect and (ii) an event study within a small geographic area. I find that user fees increase the likelihood of delivery at public sector facilities by 2–7 percentage points. I also find that user fee elimination reduces maternal mortality but may increase neonatal mortality.
Abstract
					I propose a new approach to estimate the impact of changes in indoor air quality on infant mortality. I use a quasi-experimental design, leveraging the largest kerosene to liquid petroleum gas (LPG) conversion program implemented in a developing country. The Indonesian government redirected kerosene subsidy budgets to LPG, a more efficient and cleaner fuel compared to kerosene. LPG produces significantly less indoor air pollution compared to kerosene. I find 4 fewer infants died per 10,000 live births due to the program. In the developing world, one billion kerosene users switching to LPG can save about 7,000 infants per year.
Abstract
					This study evaluates the short-term impacts of a livestock transfer and training program in Nepal using an RCT with three treatments to capture differential effects of program components. We also evaluate a unique "pay it forward" program rule where recipients are encouraged to share newly acquired knowledge and accumulated wealth to other households in need. After 1.5 years, financial inclusion increases by 0.3 standard deviations and empowerment increases by 0.2 standard deviations among direct beneficiaries. "Pay it forward" impacts are of approximately the same magnitude as direct beneficiaries in the treatments that include encouragement to "pay it forward."
Abstract
					Recent work on macroeconomic trends has emphasized slowing capital investment, but strong business profits and valuations. The retail sector is a microcosm of these trends, and accounts for a large share of the increase in aggregate business concentration also observed in recent years. We show that, in that sector, weak investment and rising concentration are associated with rising productivity. Additionally, stronger productivity is correlated with intangible investment, both over time and across subindustries. Intangible investment may thus provide a joint explanation for rising productivity, weak capital investment, and increasing industry concentration.
Abstract
					The US business sector has underinvested relative to profits, funding costs, and Tobin's Q since the early 2000s. Building on prior work, we argue that decreasing competition, rising intangibles, and tightening governance explain, respectively, about one-half, one-third, and one-sixth of the investment gap. In particular, quasi-indexer ownership appears to lower investment, and this effect is stronger in noncompetitive industries.
Abstract
					We provide evidence of strategic behavior in financial decisions that occur in the workplace and are observable to one's employer. We offer factory workers the opportunity to sign up for a commitment savings account that credibly signals the intention to remain employed with the firm. Workers are more likely to sign up when the account is endorsed by their employer and their decision is observed by the firm. An experiment with the firm's managers shows that the firm is more likely to invest in the human capital of workers with accounts, suggesting that financial commitment is rewarded by the employer.
Abstract
					Debit cards reduce the travel distance to access bank accounts and can increase financial inclusion. We show that in Mexico, cash transfer beneficiaries who already received their transfers in bank accounts and subsequently received debit cards reduce their median distance to access the account from 4.8 to 1.3 kilometers and report being less likely to forgo important activities (childcare, work) to withdraw their transfer. Using account level data, we find a strong negative correlation between the reduction in travel distance and financial activity: beneficiaries facing the largest reductions in distance increase both their number of withdrawals and their savings balances.
Abstract
					Checking overdrafts are an expensive yet common way for bank account holders to obtain short-term credit when faced with unexpected shocks. In developing countries, one common shock that many salaried workers face is late or erratic payment from their employer. We use administrative data from a rural bank in Ghana to show that receiving a late salary payment increases the likelihood of taking an overdraft by 10 percent. Past overdrafts are also associated with future overdrafts, suggesting that late payment exacerbates the risk of trapping workers in a costly debt cycle.
Abstract
					China has launched seven regional pilots of emission trading scheme (ETS) to limit its carbon emissions. Taking advantage of the variations in the regional ETS pilots across regions and sectors and over time, we employ a difference-in-difference-in-differences (DDD) approach to evaluate the effect of ETS on low-carbon innovation at the firm level. Using patent application data of publicly-listed firms in China between 2003 and 2015, we find that the ETS pilots induced innovation in low-carbon technologies. The more active pilots—measured by carbon price and turnover rate of allowance trading—are associated with more intense low-carbon innovation.
Abstract
					GDP is a measure of production, and yet it is widely used as a proxy for well-being. It is particularly ill-suited for assessing the contributions of digital goods which are free to consumers and thus excluded from GDP measures. This underscores the need to develop new measures of well-being which can assess not only the contributions of digital goods but also welfare more generally. In Brynjolfsson, Eggers, and Gannamaneni (2017), we propose a new way of measuring consumer welfare using massive online choice experiments. This brief paper motivates the need for such an approach and introduces the method.
Abstract
					We show that online prices can be used to construct quarterly purchasing power parities (PPPs) with a closely matched set of goods and identical methodologies in a variety of developed and developing countries. Our results are close to those reported by the International Comparisons Program (ICP) in 2011 and the OECD in 2014, and can be used to obtain more up-to-date estimates of real consumption across countries without the need for consumer price index extrapolations. We discuss advantages and limitations associated with the use of online prices for PPs, including issues of representativeness and limited coverage of product categories and countries.
Abstract
					The rise of ecommerce and the contrasts between online and traditional retail pricing behavior have raised questions about potential bias in CPI inflation. Using new data on online transactions, this paper shows that aggregate matched-model inflation online from 2014–2017 was more than a full percentage point lower than in the corresponding CPI. In addition, new products were tremendously important. Quantifying the net increase in number of new goods minus the exit of old goods suggests that actual inflation online may have been an additional 1.5 to 2.5 percentage points lower than indicated in matched model price indices like the CPI.
Abstract
					In Caballero and Simsek (2017), we develop a model of fickle capital flows and show that, when countries are similar, international flows create global liquidity and mitigate crises despite their fickleness. In this paper, we focus on the asymmetric situation of Emerging Markets (EM) exchanging flows with Developed Markets (DM) that feature lower returns but less frequent crises. Relatively high DM returns help to mitigate EM crises by reducing fickle inflows and by providing greater liquidity. The situation dramatically changes as the DM returns fall, as this increases the fickle inflows driven by reach for yield and exacerbates EM crises.
Abstract
					This paper examines the claim that exchange rate regimes are of little relevance in the transmission of global financial conditions to domestic financial and macroeconomic conditions. Our findings suggest that exchange rate regimes do matter, at least for emerging market economies. The transmission of global financial shocks to domestic variables is magnified under fixed exchange rate regimes relative to more flexible regimes. For advanced economies, however, the jury is still out, as the recent paucity of truly fixed regimes among these economies poses a challenge for estimating the effect of exchange rate flexibility.
Abstract
					In Coimbra and Rey (2017) we develop a dynamic macroeconomic model with heterogeneous financial intermediaries and endogenous entry. It features time-varying endogenous macroeconomic risk that arises from the risk-shifting behavior of financial intermediaries. We test empirically in a broad panel of countries the implication that credit creation is more elastic to funding costs when the distribution of leverage in the banking system is more positively skewed.
Abstract
					We use the narrative approach to identify tax changes unrelated to current economic conditions and estimate the effects of these changes on macroeconomic variables during and outside of the zero lower bound period in Japan. We find little difference in the output responses across the two periods. However, the responses of aggregate consumption, investment, and imports are significantly different in the two periods within the first few quarters.
Abstract
					Unconventional fiscal policy uses announcements of future increases in consumption taxes to generate inflation expectations and accelerate consumption expenditure. It is budget neutral and time consistent. We provide preliminary evidence for the effectiveness of such policies using changes in value-added tax (VAT) and household survey data for Poland. We find households increased their inflation expectations and willingness to purchase durables before the increase in VAT. Future research has to ensure income, wealth effects, or intratemporal substitution channels cannot explain these results and ideally exploit exogenous variation in VAT in a fixed nominal interest rate environment.
Abstract
					The literature on fiscal multipliers is far from having reached an agreed upon conclusion. One result, however, seems very robust: in OECD economies, fiscal consolidations based upon expenditure cuts are much less costly than those performed on the tax side. The purpose of this paper is twofold. First, we review recent evidence which considers multi-year fiscal plans. Analyzing multi-year plans is a better way of studying the effects of fiscal policy because governments typically adopt multi-year budget laws. Second, we illustrate alternative theoretical explanations for our findings and we discuss which ones seem more appropriate in different cases.
Abstract
					We present theory showing that the spot dollar exchange rate reflects the value of all future convenience yields that foreign investors assign to US Treasuries. The convenience yield also creates wedge, the Treasury-based dollar basis, between the yield on foreign bonds and the currency-hedged yield on US Treasury bonds. We use the Treasury basis to measure the foreign convenience yield and show that an increase in the basis coincides with an appreciation of the dollar, consistent with the theory. The variation in the Treasury basis accounts for 25 percent of the quarterly variation in the dollar between 1988 and 2017.
Abstract
					We develop a model that shows how the currency denomination of a country's imports influences the funding structure of its banking system, and in turn, the currency composition of its central bank's reserve holdings. The link between the dollar's role in bank funding and its role as a central bank reserve currency is stronger when the country's fiscal capacity is limited, and when exchange rates are volatile. In the data, there is a pronounced cross-country relationship between the fraction of imports that are dollar invoiced, and the fraction of central-bank foreign-exchange reserves that are held in dollars.
Abstract
					Bems and di Giovanni (2016) establish that income-induced expenditure switching (IIES) from foreign goods to cheaper domestic substitutes played a significant role in external rebalancing during the 2008–2009 financial crisis in Latvia. In this paper, we examine the welfare consequences of IIES under different external sector rebalancing scenarios. We find that IIES reduced the negative welfare consequences that accompany external rebalancing by between 12–17 percent. We also show, using a historical decomposition, that IIES accounted for 18 percent of the 2008–2009 collapse in imports, which is greater than the 14 percent contribution due to the conventional price-induced expenditure switching channel.
Abstract
					We document that the likelihood of having assets in foreign currency is increasing in households' income within many emerging economies. This pattern leads to heterogeneous exposures of wealth to exchange rate movements. We analyze the redistribution of liquid wealth across households after nominal exchange rate devaluations due to the revaluation of nominal net wealth denominated in different currencies. We find that there is an important degree of redistribution from households with low income to those with high income.
Abstract
					Comparing US states that implemented policies generating state-specific variation in the take-up rate for food stamps, I find that food stamp eligible households experienced lower inflation and a faster increase in product variety in states with a larger increase in take-up (i.e., with increasing demand from the eligible population). Consistent with a causal interpretation, the effects are driven by food products with strong local brands and there is no comparable pattern for ineligible households across the income distribution. Thus, the long-run supply response to changes in demand from food stamp recipients has a first-order impact on the program's cost-benefit analysis.
Abstract
					Cravino and Levchenko (2017) establish that the 1994 Mexican peso devaluation raised the prices of consumption baskets of low-income households substantially more than the prices of the consumption baskets of high-income households. In this paper, we explore this result further by focusing on the regional variation in how much prices of consumption baskets changed following the devaluation. Our main finding is that the devaluation was anti-poor in all regions, but there is substantial regional dispersion in the relative inflation faced by the poor.
Abstract
					Despite the recent revolution in central bank communication, it is unclear the general public have benefitted from these enhanced central bank communications. We first analyze the growth of communication and some reasons that the public may have missed the revolution. We then discuss the desirability of greater communication with the public. The case for feasibility of such communication examines observational and experimental evidence following the recently adopted use of a layered communication at the Bank of England. We find that, while more research is needed, there are compelling reasons for central banks to continue trying to target communication at the public.
Abstract
					Massive dollars shuttled back and forth among firms on the twisted path to and passage of the 2017 tax reform. Prices of individual stocks responded to the difference between initial and revised expectations. From the bill's initiation in the House to final passage, high-tax firms gained significantly, given the dramatic cut from 35 percent to 21 percent in the corporate tax rate. Internationally-oriented firms suffered notably, since investors assessed that the surprisingly high repatriation tax outweighed the benefits from territorial taxation. Daily price movements show that the aggregate market responded positively to lower expected taxes.
Abstract
					Survey respondents who associate themselves with the "winning team" in an election, substantially increase their expectations for their stock market, but only modestly increase their expected household economic well-being. The impact of the election outcome on the "losing team" is comparatively muted. Yet, the dramatic shifts in survey responses from "winning team" respondents do not manifest themselves in their actual behavior as revealed by their online search behavior, in contrast to members of the "losing team," whose decline in durable goods purchases correlate with their stated economic expectations. Combining novel survey data and search, this study provides a uniquely meaningful comparison of stated attitudes with actual behaviors.
Abstract
					US cities today are vastly more educated and skill-intensive than they were five decades ago. Yet, urban non-college workers perform substantially less skilled jobs than 
decades earlier. This deskilling reflects the joint effects of automation and, secondarily, rising international trade, which have eliminated the bulk of non-college 
production, administrative support, and clerical jobs, yielding a disproportionate polarization of urban labor markets. The unwinding of the urban non-college 
occupational skill gradient has, I argue, abetted a secular fall in real non-college wages by: (1) shunting non-college workers out of specialized middle-skill occupations 
into low-wage occupations that require only generic skills; (2) diminishing the set of non-college workers that hold middle-skill jobs in high-wage cities; and (3) 
attenuating, to a startling degree, the steep urban wage premium for non-college workers that prevailed in earlier decades. Changes in the nature of work—many of 
which are technological in origin—have been more disruptive and less beneficial for non-college than college workers.
Abstract
					We examine the impact of "big data" on firm performance in the context of forecast accuracy using proprietary retail sales data obtained from Amazon. We measure the accuracy of forecasts in two relevant dimensions: the number of products (N), and the number of time periods for which a product is available for sale (T). Theory suggests diminishing returns to larger N and T, with relative forecast errors diminishing at rate 1/sqrt(N)+1/sqrt(T). Empirical results indicate gains in forecast improvement in the T dimension but essentially flat N effects.
Abstract
					We study a model where firms accumulate data as a valuable intangible asset. Data accumulation affects firms' dynamics. It increases the skewness of the firm size distribution as large firms generate more data and invest more in active experimentation. On the other hand, small data-savvy firms can overtake more traditional incumbents, provided they can finance their initial money-losing growth. Our model can be used to estimate the market and social value of data.