顶刊速览|Management Science 2025年第10期

• Who Is AI Replacing? The Impact of Generative AI on Online Freelancing Platforms

• Innovation Diffusion Among Coworkers: Evidence from Senior Doctors

• Revealed Wisdom of the Crowd: Bids Predict Loan Quality

• Cultural Homophily and Collaboration in Superstar Teams

• Designing Information to Engage Customers

• Can Socially Minded Governance Control the Artificial General Intelligence Beast?

• Sampling-Based Approximation for Series Inventory Systems

• A Preregistered Falsification Test of the Decision by Sampling Model and Rank-Order Effect

• Machine Data: Market and Analytics

• Sharing the Fame but Taking the Blame: When Declaring a Single Person Responsible Solves a Free Rider Problem

• To Ask or Not to Ask: The Effects of Broadly and Narrowly Adopted Peer-Recognition Systems on Help Seeking

• The Value of Specific Knowledge: Evidence from Disruptions to the Patient–Physician Relationship

• Play it Again, Sam? Reference-Point Formation and Product Differentiation in the Music Industry

• Revenue Sharing at Music Streaming Platforms

• Pricing Durable Add-Ons: Selling vs. Leasing

• Short of Capital: Stock Market Implications of Short Sellers’ Losses

• Consumer Privacy in Online Retail Supply Chains

• Stressed Banks? Evidence from the Largest-Ever Supervisory Review

• Gender Differences in High-Stakes Performance and College Admission Policies

• Market Ambiguity Attitude Restores the Risk-Return Trade-Off

• Limits of Disclosure Regulation in the Municipal Bond Market

• Secondary Market Monetization and Willingness to Share Personal Data

• Managing Channel Profits with Positive Demand Externalities

• Retention or Acquisition? Behavior-Based Quality Disclosure

• Choose Your Battles Wisely: The Consequences of Protesting Government Procurement Contracts

• Auditing with a Chance of Whistleblowing

• The Effects of MiFID II on Voluntary Disclosure

• Combining Ad Targeting Techniques: Evidence from a Field Experiment in the Auto Industry

• Trading Volume Manipulation and Competition Among Centralized Crypto Exchanges

• Do Institutional Investors Stabilize Equity Markets in Crisis Periods? Evidence from COVID-19

• Speed Matters: Limited Attention and Supply Chain Information Diffusion

• Consumption Commitments and Housing Dynamics

• Antisocial Responses to the “Coal to Gas” Regulation: An Unintended Consequence of a Residential Energy Policy

• A Structural Model of a Firm’s Operating Cash Flow with Applications

• Long Lags and Large Returns: Experimental Evidence from Advertising to Businesses

• Source Theory: A Tractable and Positive Ambiguity Theory

• CEO Hometown Preference in Corporate Environmental Policies

• The Effect of On-the-Job Experience on Base-Rate Neglect: Evidence from Medical Professionals

• Selling Data to Marketers

• Platform Competition and Interoperability: The Net Fee Model

• Economics of Social Media Fake Accounts

• How Do Domestic and Foreign Firms Respond to a Reduction in Competition from the Public Sector? Evidence from Vaccine Markets in India

• On Greedy-Like Policies in Online Matching with Reusable Network Resources and Decaying Rewards

• Evaluating Mortgage Renegotiation Strategies: A Data-Driven Framework for Investors

• Exchanges for Government Bonds? Evidence During COVID-19

• Adaptive Pricing in Combinatorial Auctions

Ozge Demirci,Jonas Hannane,Xinrong Zhu 

Abstract:This paper studies the impact of generative artificial intelligence (AI) technologies on the demand for online freelancers using a large data set from a leading global freelancing platform. We identify the types of jobs that are more affected by generative AI and quantify the magnitude of the heterogeneous impact. Our findings indicate a 21% decrease in the number of job posts for automation-prone jobs related to writing and coding compared with jobs requiring manual-intensive skills within eight months after the introduction of ChatGPT. We show that the reduction in the number of job posts increases competition among freelancers, whereas the remaining automation-prone jobs are of greater complexity and offer higher pay. We also find that the introduction of image-generating AI technologies led to a 17% decrease in the number of job posts related to image creation. We use Google Trends to show that the more pronounced decline in the demand for freelancers within automation-prone jobs correlates with their higher public awareness of ChatGPT’s substitutability.

Eliana Barrenho,Eric Gautier,Marisa Miraldo,Carol Propper,

Christiern Rose

Abstract:Using a novel 15-year data set on surgeon adoption of a complex surgical innovation in the English National Health Service and an identification strategy based on surgeon mobility, this paper disentangles three channels of coworker influence on innovation diffusion: (1) peer network size, (2) influential “key players,” and (3) cumulative peer adoption. We find that a one standard deviation in peer connections boost innovation by 16%. Key players can either amplify or dampen diffusion, and peer adoption has a greater impact on less experienced individuals. These results highlight the value of targeting training to high impact network members to speed up diffusion. This work advances our understanding of how professional networks shape innovation diffusion, with implications for technology implementation.

Jiayu Yao,Mingfeng Lin,D. J. Wu 

Abstract:Despite the popularity of the phrase “wisdom of the crowd,” not all crowds are wise because not everyone in them acts in an informed, rational manner. Identifying informative actions, therefore, can help to isolate the truly wise part of a crowd. Motivated by this idea, we evaluate the informational value of investors’ bids using data from online, debt-based crowdfunding, in which we were able to track both investment decisions and ultimate repayment statuses for individual loans. We propose several easily scalable variables derived from the heterogeneity of investors’ bids in terms of size and timing. We first show that loans funded with larger bids relative to the typical bid amount in the market or to the bidder’s historical baseline, particularly early in the bidding period, are less likely to default. More importantly, we perform theory-driven feature engineering and find that these variables improve the predictive performance of state-of-the-art models that have been proposed in this context. Even during the fundraising process, these variables improve predictions of both funding likelihood and loan quality. We discuss the implications of these variables, including loan pricing in secondary markets, crowd wisdom in different market mechanisms, and financial inclusion. Crowdfunding platforms can easily implement these variables to improve market efficiency without compromising investor privacy.

Gábor Békés,Gianmarco I. P. Ottaviano

Abstract:One may reasonably think that cultural homophily, defined as the tendency to associate with others of similar culture, affects collaboration in multinational teams in general but not in superstar teams of professionals at the top of their industry. The analysis of an exhaustive data set on the passes made by professional European football players in the top five men’s leagues reveals that on the contrary, cultural homophily is persistent, pervasive, and consequential, even in superstar multinational teams of very-high-skill individuals with clear common objectives and aligned incentives who are involved in interactive tasks that are well defined and not particularly culture intensive.

Liang Guo

Abstract:Collaborative customization is common in many markets. Sellers and customers can collectively discover the value of the product’s basic design. Customers’ willingness to pay can also be increased by being engaged to improve the product design. We study how a seller can design the information structure of collective learning about the product’s basic value to induce costly customer engagement. We consider a setting in which the parties’ expected payoffs are determined endogenously through the strategic interaction between seller pricing and customer engagement and purchase. The customer tends to be engaged in equilibrium, as the uncertainty on the product’s basic value becomes higher to dilute the responsiveness of the optimal price to customer engagement. Therefore, the seller seeks to maximize the probability of generating an intermediate posterior belief. Thus, the optimal information design involves either exaggerating or downplaying the product’s basic value when the prior is low or high, respectively. We show that considering restrictive information structures can generate qualitatively different implications. We also examine how the unobservability of engagement may influence the parties’ strategic interaction, their expected payoffs, and the seller-optimal information design. Interestingly, unobservability may lead to lower equilibrium engagement, hurt the seller, render the customer’s equilibrium expected payoff to increase as customer engagement becomes less important or more costly, and yield more or less information provision.

Joshua S. Gans 

Abstract:This paper robustly concludes that it cannot. A model is constructed under idealized conditions that presume that the risks associated with artificial general intelligence (AGI) are real, that safe AGI products are possible, and that there exist socially minded funders who are interested in funding safe AGI, even if this does not maximize profits. It is demonstrated that a socially minded entity formed by such funders would not be able to minimize harm from AGI that unrestricted products released by for-profit firms might create. The reason is that a socially minded entity can only minimize the use of unrestricted AGI products in ex post competition with for-profit firms at a prohibitive financial cost and so, does not preempt the AGI developed by for-profit firms ex ante.

Kairen Zhang,Xiangyu Gao,Zhanyue Wang,Sean X. Zhou

Abstract:We study inventory management of an infinite-horizon, series system with multiple stages. Each stage orders from its immediate upstream stage, and the most upstream stage orders from an external supplier. Random demand with unknown distribution occurs at the most downstream stage. Each stage incurs inventory holding cost while the most downstream stage also incurs demand backlogging cost when it experiences inventory shortage. The objective is to minimize the expected total discounted cost over the planning horizon. We apply the sample average approximation (SAA) method to obtain a heuristic policy (SAA policy) using the empirical distribution function constructed from a demand sample (of the underlying demand distribution). We derive an upper bound of sample size (viz., distribution-free bound) that guarantees that the performance of the SAA policy be close (i.e., with arbitrarily small relative error) to the optimal policy under known demand distribution with high probability. This result is obtained by first deriving a separable and tight cost upper bound of the whole system that depends on (given) echelon base-stock levels and then showing that the cost difference between the SAA and optimal policies can be measured by the distance between the empirical and the underlying demand distribution functions. We also provide a lower bound of sample size that matches the upper bound (in the order of relative error). Furthermore, when the demand distribution is continuous and has an increasing failure rate (IFR), we derive a tighter sample size upper bound (viz., distribution-dependent bound). Both distribution-free and distribution-dependent bounds for the newsvendor problem, a special case of our series system, improve the previous results. In addition, we show that both bounds increase polynomially as the number of stages increases. The performance of SAA policy and the sample size bounds are illustrated numerically. Finally, we extend the results to finite-horizon series systems.

Mattias Forsgren,Lars Frimanson,Peter Juslin

Abstract:Many social scientists have assumed that people’s preferences can be described by stable and coherent “utility” functions. This notion of stable utility functions has been challenged by cognitive psychologists who suggest that preferences are malleable and constructed in the moment, but neither camp has explained how the subjective valuations underpinning preferences arise. One influential attempt to do so is the Decision by Sampling (DbS) model, which suggests that a quantitative attribute’s (e.g., money sum’s) subjective value is its rank order in a momentarily activated memory sample. DbS thus implies that manipulating the recently experienced attribute distribution should change people’s subsequent valuations of that attribute: for example, from the typically assumed concave shape of the utility function to a convex shape. However, recent studies have pointed out methodological concerns in the evidence previously thought to support this prediction (and thus, DbS). In this preregistered study, we replicate the previous paradigm but address the methodological concerns to test if such a “rank-order” manipulation does change valuations. We derive qualitative predictions from DbS to verify that our conditions yield distinct predictions. We find strong evidence against the DbS’s prediction that a “rank-order” manipulation changes what options the participants select and how strongly they prefer the options. We also find extreme evidence in favor of a contextualization effect, implying that people value formally identical gambles differently depending on whether they cue a real-life setting or not. Although we encourage replication by independent laboratories, these results suggest that the DbS is falsified for this binary choice task.

Giacomo Calzolari,Anatole Cheysson,Riccardo Rovatti

Abstract:Machine data (MD), that is, data generated by machines, are increasingly gaining importance, potentially surpassing the value of the extensively discussed personal data. We present a theoretical analysis of the MD market, addressing challenges such as data fragmentation, ambiguous property rights, and the public-good nature of MD. We consider machine users producing data and data aggregators providing MD analytics services (e.g., with digital twins for real-time simulation and optimization). By analyzing machine learning algorithms, we identify critical properties for the value of MD analytics, Scale, Scope, and Synergy. We leverage these properties to explore market scenarios, including anonymous and secret contracting, competition among MD producers, and multiple competing aggregators. We identify significant inefficiencies and market failures, highlighting the need for nuanced policy interventions.

Xinyu Li,Wendelin Schnedler 

Abstract:Teams are formed because input from different people is needed. Providing incentives to team members, however, can be difficult. According to received wisdom, declaring all members responsible fails because real responsibility for team output “diffuses.” But why? Also, why and when does formally declaring one member “responsible” mean that this member can be attributed real responsibility? We offer a model that answers these questions. We identify when jointly declaring a team responsible results in reputation free riding. We show that declaring one person responsible can overcome this problem but only if all other team members are protected from being sanctioned.

Joseph Burke,Ryan D. Sommerfeldt,Laura W. Wang 

Abstract:Many companies now use peer-recognition systems that allow employees to publicly recognize their peers for positive behaviors. Practitioners have touted the potential for these systems to increase helping among employees. However, the extent to which employees actually use these systems to recognize their peers varies across organizations; some are used broadly by many employees across all functional, specialty, geographic, and hierarchical subgroups of the organization, whereas others are only narrowly used by some, but not all, subgroups. Across three experiments, we examine how peer-recognition systems impact employees’ propensity to ask others for help (i.e., help seeking) based on whether the system is broadly used by all subgroups or only narrowly used by specific subgroups. We predict and find that a peer-recognition system broadly used by all subgroups strengthens employees’ perception of a help-seeking norm. This perception increases employees’ propensity to seek help directly through norm conformity and indirectly by reducing the perceived psychological costs of help seeking. We also predict and find that the effect of peer-recognition systems that are narrowly used by specific subgroups is moderated by whether employees belong to the subgroups using the system; whereas it increases help-seeking propensity for members of the subgroups using the system, it decreases help-seeking propensity for nonmembers relative to when there is no peer-recognition system. Our theory and results suggest that peer-recognition systems can increase help seeking, but these same systems could decrease help seeking for employees belonging to subgroups that do not use the system.

Stephen D. Schwab 

Abstract:When a member of a work team leaves, some knowledge is lost to the organization. Exploiting quasi-random turnover among military physicians because of deployments, I estimate the effects of turnover on patients and other providers in the same care team. I find that a discontinuity in primary care leads to a 3%–5% increase in costs driven primarily by an increase in the use and intensity of specialty care with no observable benefit to the patient as measured by potential reductions in hospitalization rates and emergency department usage. This indicates that the full cost of turnover includes a reduction in access to knowledge among remaining members of the team.

Abhishek Deshmane,Victor Martínez-de-Albéniz 

Abstract:Newly released music is never assessed in isolation by audiences, who tend to compare objective (sonic attributes) and subjective (genre affiliations) elements of its identity with the previous musical catalog of the corresponding artist, his or her musically proximal peers, and the most successful releases in the market. In this paper, we provide a general framework that disentangles the objective and subjective identities of new musical releases and evaluates how differentiation affects audience reactions. We posit that radio stations, who seek commercial success, have different preferences toward differentiation in comparison with critics, who grant cultural legitimacy in the industry. Combining play numbers, reviews, and music description data from different sources, we find that radio stations prefer consistency in the musical style of successive releases by the focal artist and especially favor albums that sound similar to chart toppers. Critics, however, exhibit opposite preferences, preferring novelty compared with past works of the artist, while remaining uninfluenced by contemporaneous music dynamics. Our moderation checks reveal that decisions around genre affiliations and scheduling of new releases aid music producers to effectively manage audience expectations. This suggests that data-driven decision support systems can help artists to strategically release new products that cater to heterogeneous tastes.

Gustavo Bergantiños,Juan D. Moreno-Ternero 

Abstract:We study the problem of sharing the revenues raised from subscriptions to music streaming platforms among content providers. We provide direct, axiomatic, and game-theoretical foundations for two focal (and somewhat polar) methods widely used in practice: pro rata and user centric. The former rewards artists proportionally to their number of total streams. With the latter, each user’s subscription fee is proportionally divided among the artists streamed by that user. We also provide foundations for two families of methods addressing the rising concern in the music industry to explore new streaming models that better align the interests of artists, fans, and streaming services. One of the families offers a natural compromise between the pro rata and user-centric methods. The other family generalizes the user-centric method while capturing various formalizations of incentives for artists and users.

Peiwen Yu,Haiyang He,Lei Lei 

Abstract:Many firms offer products that consist of a durable base good (e.g., a vehicle) and a durable add-on (e.g., autopilot software). Some lease the add-on, whereas others sell it through intertemporal price discrimination or bundled pricing. Motivated by these practices, we examine whether a monopolistic firm should lease or sell the add-on when offering both durables. The literature suggests that leasing is preferable for a single durable good as it avoids the time inconsistency problem associated with selling. However, because leasing lacks an intertemporal link, it is less efficient than selling in balancing surplus extraction across consumers, leading to what we call the intraperiod imbalance problem. When the firm sells the base good, this can resolve the time inconsistency of add-on selling but perpetuate the intraperiod imbalance of add-on leasing. Thus, selling the add-on can be more profitable than leasing it. When the firm can choose between selling or leasing the base good, selling both the base good and the add-on can be more profitable than leasing both.

Antonio Gargano,Juan Sotes-Paladino,Patrick Verwijmeren 

Abstract:We provide evidence that losses constrain short sellers but not the transmission of information to prices. Using unique data on U.S. equity lending, we document a negative impact of the mark-to-market losses of a stock’s short sellers, but no impact of their gains, on the future shorting of the stock. Consistent with funding and institutional constraints limiting short selling, we further show that the effect is highly asymmetric across different loss levels and stronger among stocks facing higher margin requirements. However, loss-making short selling has no impact on price efficiency or predictive power for returns, suggesting that these constraints affect mostly uninformed shorting activity.

Fasheng Xu,Xiaoyu Wang,Fuqiang Zhang 

Abstract:Exploitation of consumer data allows online retailers to enhance services provided to consumers but at the risk of causing unintended privacy issues. There has been debate about whether to devise regulation policies to restrict data collection and usage by online retailers. This paper studies the implications of newly adopted privacy policies, such as the General Data Protection Regulation (GDPR), for an online retail supply chain comprising a retailer and a supplier. We find that despite the GDPR’s intention to safeguard consumer privacy, it may inadvertently hurt consumer surplus while benefiting the retailer. In fact, the GDPR may lead to an outcome detrimental to all parties involved, including the retailer, supplier, and consumers, thereby resulting in a triple-lose situation. Our results hold significant implications for consumers, supply chain firms, and policymakers alike, contributing to the existing literature on evaluating the impact of privacy regulations on technology innovation and adoption.

Puriya Abbassi,Rajkamal Iyer,José-Luis Peydró,Paul E. Soto

Abstract:We study short-term and medium-term changes in bank risk-taking as a result of supervision and the associated real effects. For identification, we exploit the European Central Bank’s asset-quality-review (AQR) in conjunction with security and credit registers. After the AQR announcement, reviewed banks reduce riskier securities and credit supply, with the greatest effect on riskiest securities. We find negative spillovers on asset prices and firm-level credit availability. Moreover, nonbanks with higher exposure to reviewed banks acquire the shed risk. After the AQR compliance, reviewed banks reload riskier securities but not riskier credit, resulting in negative medium-term firm-level real effects. These effects are especially strong for firms with high ex ante credit risk. Among these nonsafe firms, even those with high ex ante productivity, experience negative real effects. Our findings suggest that banks’ liquid assets help them to mask risk from supervisors and risk adjustments banks make in response to supervision have persistent corporate real effects.

Andreu Arenas,Caterina Calsamiglia 

Abstract:The Gale-Shapley algorithm is one of the most popular college allocation mechanisms around the world. A crucial policy question in its setting is designing admission priorities for students, understanding how they disadvantage certain demographic groups, and whether these differences relate to differences in college performance potential. Studying a policy change in Spain, we find a negative effect of increasing the weight of standardized high-stakes exams on female college admission grades, driven by students expected to be at the top. The impact on admission grades does not affect enrollment, but the percentage of female students in the most selective degrees declines, along with their career prospects. Using data on the college performance of prereform cohorts, we find that female students most likely to lose from the reform tend to do better in college than male students expected to benefit from the reform. The results show that rewarding high-stakes performance in selection processes may come along with gender differences unrelated to the determinants of subsequent performance.

Soroush Ghazi,Mark Schneider,Jack Strauss

Abstract:A positive relation between the conditional mean and conditional volatility of aggregate stock returns, although viewed as a fundamental law of finance, has been challenging to find empirically. We consider a representative agent asset pricing model with Knightian uncertainty and demonstrate that this risk-return trade-off depends on the agent’s ambiguity attitude (reflecting the agent’s degree of optimism or pessimism). The model predicts that the conditional equity premium is increasing in market volatility, but its slope flattens as market optimism rises. We develop a methodology to extract the representative agent’s ambiguity attitude from our asset pricing model. Results validate our model predictions. We document the significant in-sample and out-of-sample explanatory power of ambiguity attitude in explaining the risk-return trade-off. In our sample, market volatility is not significant in forecasting returns. However, including the market ambiguity attitude leads to a significant positive relationship between volatility and future returns. Hence, our model and results identify market ambiguity attitude as a missing state variable that can explain why the literature has found it difficult to empirically validate the risk-return trade-off.

Ivan T. Ivanov,Tom Zimmermann,Nathan W. Heinrich 

Abstract:We examine the effectiveness of recent federal disclosure regulation aiming to improve transparency in the $4 trillion municipal bond market. Governments fail to disclose material private placements 50%–60% of the time and, conditional on disclosure, filings often omit contract details essential for bond pricing. Noncompliant issuers are significantly riskier than compliers, with disclosure decreasing in the potential of privately placed debt to adversely affect bondholders. We show that disclosure reveals positive news and is especially informative to investors in low-rated bonds or during market crises. Overall, privately placed debt continues to pose significant risks to municipal bond investors.

Joy Wu

Abstract:People are often unaware that their personal data can serve as valuable inputs for economic activities in secondary data markets. However, whether secondary monetization of personal data determines privacy preferences remains unclear. I examine whether privacy decisions are motivated by the data recipient’s ability to benefit from trading individuals’ data with a third party. A large online laboratory experiment involving personally identifiable psychometric data is implemented with real data-sharing consequences and monetary benefits. I find that individuals decrease their willingness to share data—both in terms of their likelihood of participating in the data market and the prices demanded for such participation—when the recipient’s ability to monetize the data through secondary trade is salient. Strategic responses to updated beliefs about the recipient’s gain from the trade are ruled out via the chosen price elicitation. I find that increased data exposure (to more recipients) does not explain the significant revealed disutility from secondary monetization. These findings are also robust to controlling for the risk exposure differences between data recipients and third parties.

Long Gao,Dawei Jian,Mehmet Gumus,Birendra K. Mishra

Abstract:Demand externalities arise when past sales stimulate future demand. They pervade many consumer markets. To penetrate such markets, how should manufacturers contract with retailers? We formulate the problem as a dynamic game, wherein the retailer can privately observe and control evolving market conditions, and consumers can act either myopically or strategically. Our contribution is threefold. (i) We characterize the optimal contract: It resolves a dynamic tradeoff between exploiting demand externalities, screening new information, and optimizing channel efficiency; moreover, it has a simple implementation of quantity discount. (ii) We characterize the dual role of demand externalities. Although demand externalities can improve channel surplus by expanding market size, they can also exacerbate information friction by enhancing the retailer’s ability to manipulate the market. Ignoring the dark side of the agency cost, previous studies may have overestimated the benefit of demand externalities. (iii) We provide new practical guidance. We show private information per se need not hurt channel efficiency: The manufacturer can use recursive advance selling to extract new information for free. Our results also shed light on when and why manufacturers should moderate demand externalities and prefer long-term contracts. By highlighting the dual role of demand externalities in long-run channel performance, this study sharpens our understanding of channel theory and practice.

Jianqiang Zhang,Krista J. Li 

Abstract:Firms frequently extend their brand by introducing products in new categories and offering free samples or demonstrations for customers to learn about the quality of the new products before purchasing. When introducing new products based on customers’ purchase behavior in the former category, firms face two customer segments: their own customers and the competitor’s customers. Firms can practice behavior-based pricing (BBP) by charging different prices in the two segments. Moreover, firms can practice behavior-based quality disclosure (BBD) by disclosing quality information to the two customer segments differently. In this paper, we examine how firms practice BBD in addition to BBP. We find that in contrast to the acquisition-focused BBP, firms perform retention-focused BBD by increasing quality disclosure to their own customers and decreasing it to the competitor’s customers. Unlike BBP, which decreases second-period profit and increases first-period profit, BBD increases second-period profit but decreases first-period profit when the disclosure cost is low. We also show that when firms endogenously choose BBD, the equilibrium is a prisoner’s dilemma when the disclosure cost is low and a win-win outcome when the disclosure cost is high.

Mehmet I. Canayaz,Jess Cornaggia,Kimberly Cornaggia 

Abstract:We examine the relationship between a firm’s successful protest of a government agency’s conduct or terms of a procurement contract and the amount of business the firm conducts with the government going forward. We find firms receive fewer and less valuable government contracts, face more contract cancellations, and experience significant reductions in sales growth and employee growth. Despite widespread belief, successful bid protesters do not delay government procurement because of lengthy dispute resolutions. Overall, we provide the first analysis of corporate interactions with the United States government bid protest system.

Lin Nan,Chao Tang,Minlei Ye 

Abstract:This paper investigates the impact of promoting whistleblowing on audit quality and the efficiency of detecting misstatements. On the one hand, whistleblowing alerts the enforcer to the possibility of a misstatement and intensifies the regulatory effort, thereby incentivizing the auditor to improve audit quality. On the other hand, promoting whistleblowing reduces the enforcer’s investigative effort when there is no whistleblowing allegation, which in turn dampens the auditor’s incentive to enhance audit quality. We demonstrate that, under certain conditions, encouraging more whistleblowing can impair audit quality and reduce detection efficiency. We also examine the socially optimal whistleblowing program, and our analysis implies that the optimal whistleblowing intensity is increasing in investment cost and the quality of the whistleblower’s information.

Chongho Kim,Jihwon Park,Edward Sul 

Abstract:We examine the effect of the Markets in Financial Instruments Directive (MiFID) II’s controversial unbundling provision on corporate voluntary disclosure. Although prior research has largely focused on changes in sell-side research post-MiFID II, changes in voluntary disclosure and their effects on the information environment are less known. We find that European Union (EU) firms significantly increased the propensity and frequency of management earnings guidance issuance after MiFID II enactment. This effect is more pronounced among firms experiencing a decline in the quantity of sell-side research or a reduction in the consumption and dissemination of analyst reports and is more muted among firms witnessing improvements in research quality. Furthermore, we find that post-MiFID earnings guidance by EU firms becomes more thorough and elicits stronger market reactions. Moreover, we demonstrate that the increased guidance effectively alleviates the negative liquidity effects of MiFID II. Collectively, we contribute to the ongoing debate on the efficacy of MiFID II’s unbundling provision by providing evidence that voluntary disclosure plays a key role in mitigating the unintended net negative consequences on the information environment following the regulation.

Albert Valenti,Chadwick J. Miller,Catherine E. Tucker 

Abstract:Retargeted advertising that tries to entice potential customers back to a website is widely used by advertisers and has often replaced more traditional forms of targeting, such as contextual targeting that tries to match ads to website content. However, existing research has not investigated the extent to which these different targeting techniques compete with or complement each other. To investigate this, we conduct a large-scale field experiment with an automobile manufacturer to investigate how retargeting meshes with more traditional techniques of contextual targeting online and in turn how that should affect ad content. We investigate this using three different measures of online advertising effectiveness: website visits, engagement, and soft conversions. We find that combining contextual targeting and retargeting is more effective for all three measures. However, to unleash this effectiveness, marketers have to pay attention to the ad content in their retargeted ads. We find that when combining retargeted advertising with contextual targeting, ads that prompt users to customize an offering are the most effective. Last, we provide empirical evidence for understanding the underlying mechanism associated with our findings and replicate those findings with a laboratory experiment.

Dan Amiram,Evgeny Lyandres,Daniel Rabetti 

Abstract:How competition affects manipulation by firms of information about important attributes of their products and how such information manipulation impacts firms’ short-term and long-term performance are open empirical questions. We use a setting that is especially suitable for answering these questions—centralized crypto exchanges, on which information manipulation takes the form of inflated trading volume. We find that static and dynamic competition measures are positively associated with volume inflation, indicating that competition may lead to increased information manipulation. Exchanges that manipulate volume obtain short-run benefits but are punished in the long run, consistent with the trade-off between short-lived increases in rents and future losses because of damaged reputation.

Simon Glossner,Pedro Matos,Stefano Ramelli,

Alexander F. Wagner 

Abstract:During the COVID-19 stock market crash, U.S. stocks with higher institutional ownership (IO) performed worse than those with lower IO. By studying firm-level changes, we identify two mechanisms behind this effect: a sudden downscaling of institutional capital in the equity market and a collective attempt by institutions to reposition their equity portfolios toward more COVID-resilient stocks. The stock price effects of their “portfolio downscaling” trades quickly reversed in the market’s recovery phase, whereas those of their “portfolio repositioning” trades lingered. The institutional rush for firm resilience also caused price pressures, with retail investors providing liquidity to stocks sold by institutional investors, both during the crisis and afterward. Overall, our results indicate that when a tail risk is realized, institutional investors amplify price crashes.

Ling Cen,Michael Hertzel,Christoph Schiller 

Abstract:We develop a measure of the speed of firm-level information diffusion, study how it is affected by limited attention, and examine its effect on real corporate decisions. Using local flu epidemics as exogenous attention shocks, we show that inattention from dual-covering analysts and cross-holding institutions reduces the speed of information diffusion from customer to supplier stock prices. We find that the speed of information diffusion along the supply chain affects the price feedback effect for corporate investment decisions and facilitates coordination between customers and suppliers. Our findings demonstrate that coattention from key market participants affects information efficiency and generates real economic outcomes.

Preetesh Kantak

Abstract:Using a measure of local long-run growth prospects, I uncover a novel link between economic fundamentals and house prices. Whereas excess housing returns are positively associated with economic growth prospects, housing valuations are negatively associated with shocks to growth prospects. I document an explanation in metro-area consumption: housing consumption is asymmetrically exposed to economic prospects in that it expands more quickly when prospects are strong than it contracts when prospects are poor. I explain these findings through the lens of an asset pricing model that focuses on a trade-off between nonseparable committed housing and nonhousing consumption.

Jing Cao,Tracy Xiao Liu,Rong Ma,Ang Sun 

Abstract:Policies geared toward environmental and economic improvement could unexpectedly lead to negative consequences in other dimensions. Such cases raise a red flag to economists and policymakers who aim to deliver comprehensive and sensible policy evaluations. This article investigates antisocial behaviors in response to the Clean Winter Heating Policy (CWHP), which seeks to improve outdoor air quality. Our results show that participating villagers are more likely to violate laws to burn agricultural waste and exhibit lower prosociality in incentivized dictator games and public goods games. We further explore treatment heterogeneities and find that two channels are likely to play a part. First, the CWHP was perceived as a negative income shock. Therefore, the villagers would want to reduce their expenditure on straw disposal and behave less generously in the incentivized games. Second, the CWHP could trigger discontent and directly affect social preference. Additional evidence suggests that the antisocial (less prosocial) responses could have been avoided by granting larger upfront subsidies.

Kashish Arora,Vishal Gaur 

Abstract:Effective management of a firm’s operating cash flow is essential for supporting growth, servicing debt, and maintaining overall financial health. Mismanagement of cash flows can result in severe liquidity challenges and even business failure. However, managing operating cash flow is complex because of its intricate, endogenous relationships with operational variables, like sales, operating costs, inventory, payables, and the impact of exogenous macroeconomic factors on a firm. In this paper, we present a structural model of operating cash flow that untangles this endogeneity, allows us to estimate causal relationships among these variables, and provides a valuable tool for evaluating cash flow management policies. Applying our model to quarterly financial data from S&P’s Compustat database spanning from 1990 to 2020 along with macroeconomic indicators, we provide empirical evidence of the endogenous nature of cash flow with other operational variables. We then showcase the practical value of our model by (i) identifying the characteristics of structural shocks and the new equilibria they induce within the system; (ii) offering a tool for evaluating alternative managerial actions or policy decisions to counteract these shocks; (iii) predicting the impacts of macroeconomic events, such as global recessions and fluctuations in economic sentiment, on firm performance; and (iv) demonstrating superior forecasting performance compared with traditional univariate models. In summary, our structural model of operating cash flow enhances our understanding of its dynamics, enabling better-informed decision making and more effective cash flow management in firms.

Michael Thomas,Marcel Goic,Kirthi Kalyanam 

Abstract:Using a multiyear experiment, we show that advertising to businesses can generate very different responses than has been observed for consumers. First, we estimate larger advertising returns than typically found for consumers: a return on ad spend of 12.0 (95% CI: 4.8–24.5). Second, we estimate longer lags between ad delivery and purchase: 1–5 months for first-time purchases and 5–12 months or longer for repeat purchases. Third, we find that existing business customers are responsible for most of the revenue lift from advertising, though this appears to be driven by existing business customers purchasing new parts. Additionally, our results demonstrate that geography-based switchback experiments that randomize the time between treatments can provide an effective means of estimating lagged effects. For this study we randomized the delivery of digital display ads for electronic components offered by a semiconductor manufacturer; nearly all electronic products require such components.

Aurélien Baillon,Han Bleichrodt,Chen Li,Peter P. Wakker

Abstract:This paper introduces source theory, a new theory for decision under ambiguity (unknown probabilities). It shows how Savage’s subjective probabilities, with source-dependent nonlinear weighting functions, can model Ellsberg’s ambiguity. It can do so in Savage’s framework of state-contingent assets, permits nonexpected utility for risk, and avoids multistage complications. It is tractable, shows ambiguity attitudes through simple graphs, is empirically realistic, and can be used prescriptively. We provide a new tool to analyze weighting functions: pmatchers. They give Arrow–Pratt-like transformations but operate “within” rather than “outside” functions. We further show that ambiguity perception and inverse S probability weighting, seemingly unrelated concepts, are two sides of the same “insensitivity” coin.

Wei Li,Qiping Xu,Qifei Zhu 

Abstract:We exploit within-firm variations in plant-level toxic releases to examine the effect of managerial hometown preference on corporate environmental policies. We find that pollution levels are about 30% lower for plants located near chief executive officers’ (CEOs’) hometowns. This reduction is achieved through resource-intensive pollution control efforts, including source reduction and waste management activities. Analyses using CEO turnover provide causal inferences. Local residents benefit from CEO hometown pollution reduction as localities hosting more hometown plants experience improved environmental conditions and better residential health outcomes. On the other hand, some evidence suggests that CEOs’ hometown preference is related to agency frictions. Overall, our findings reveal the impact of CEOs’ personal motivations on corporate pollution dynamics and their consequential effects on the well-being of local communities.

Felipe A. Araujo,Christopher F. Chabris,Michelle N. Meyer 

Abstract:We study the effect of on-the-job experience on base-rate neglect, which is a common bias in assessing conditional probabilities. We do so by carrying out experiments with medical professionals, who are routinely exposed to conditional-probability problems in the form of diagnostic tests, and nonmedical professionals, who are not. As such, medical workers with more years of experience will have had more exposure to base-rate type problems than nonmedical workers with similar years of experience. We estimate the effect of on-the-job experience by comparing the answers of more or less experienced professionals in both the medical and nonmedical domains. Although the incidence of the bias is high for both groups and all levels of experience, we find that more experienced medical workers (a) have lower rates of perfect base-rate neglect (i.e., completely ignoring the base rates), (b) provide more accurate posterior estimates, and (c) adjust their estimates more in response to changes in the base rates. We observe no such difference for nonmedical workers. We conduct a number of robustness checks and consider possible mechanisms, such as education, job or survey attrition, selectivity into medical professions, and experience with false positives. Our results suggests that on-the-job experience mitigates, but does not eliminate, base-rate neglect.

Liang Guo 

Abstract:The supply and the demand for data analytics are growing rapidly. Recent years have seen a surge in the availability of massive consumer data and in the emergence of data sellers (e.g., brokers and intermediaries). Unlike standard products, the value of data analytics for marketers depends on how their business decisions can be enabled and improved. In this paper, we investigate how a monopoly seller can offer a menu of data-based service plans to screen heterogenous marketers that can decide whether to take an action (e.g., direct selling, targeting, lending) with uncertain value and privately known cost. We characterize how, and for which marketers, the provision of information may be distorted in the optimal design. We present conditions under which optimally supplied information can be socially excessive or insufficient. It is shown that selling data appends may yield double reversals in the optimal service plans, in comparison with those when the seller’s offerings serve as marketing lists (i.e., the action is infeasible under the marketers’ outside option). We articulate how these results are coherently driven by the same underlying mechanism regarding how the marginal value of information is endogenously derived from the improvement in the marketers’ decision making over their default action. We also examine how our results can be enriched in alternative settings on the production and the costs of information.

Mehmet Ekmekci,Alexander White,Lingxuan Wu 

Abstract:Is more competition the key to mitigating dominance by large tech platforms? Could regulation of such markets be a better alternative? We study the effects of competition and interoperability regulation in platform markets. To do so, we propose an approach of competition in net fees, which is well-suited to situations in which users pay additional charges, after joining, for on-platform interactions. Compared with existing approaches, the net fee model expands the tractable scope to allow variable total demand, platform asymmetry, and merger analysis. Regarding competition, we find that adding more platforms to the market may lead to the emergence of a dominant firm. In contrast, we find that interoperability can play a key role in reducing market dominance and lowering prices. Broadly speaking, our results favor policy interventions that assure the formidability of the competition that dominant platforms face.

Zihong Huang,De Liu 

Abstract:Amid the rise of the influencer economy, fake social media accounts have become prevalent on many social media platforms. Yet the problem of fake accounts is still poorly understood, and so is the effectiveness of coping strategies. This research models the ecosystem of fake accounts in an influencer economy and obtains insights on fake account purchasing behaviors, the impact of antifake efforts, and the roles of various contextual factors. We show that as the antifake effort increases, the equilibrium may transition from a “pooling” equilibrium, where a low-quality influencer buys fake accounts to mimic a high-quality one, to a “costly separating” equilibrium, where a high-quality influencer may buy fake accounts to prevent mimicry from a low-quality influencer, and to a “naturally separating” equilibrium where low- and high-quality influencers are separated without buying fake accounts. We find that increasing antifake efforts and increasing social media literacy may sometimes result in more fake accounts. A purely profit-driven platform always prefers a pooling equilibrium with zero antifake effort. As a platform puts more weight on consumer welfare, it may exert a positive effort to induce a separating equilibrium, but the platform’s preferred antifake effort tends to be lower than that of consumers. We also find that the platform sometimes prefers a lower social media literacy and a lower fake account base price, whereas consumers prefer the opposite. In contrast, improving the antifake technology level can benefit both the platform and consumers. Our main insights are applicable to scenarios with more influencer types and repeated interactions.

Arzi Adbi,Anant Mishra,Chirantan Chatterjee

Abstract:This study examines how domestic and foreign firms in the private sector respond to a reduction in competition from the public sector. Our analysis exploits the sudden suspension of production licenses of vaccine-manufacturing public sector units (PSUs) in January 2008 in India, home to more than 1.2 billion people at that time. Although the suspension presented a window of opportunity for private sector firms, the opportunity was laden with high uncertainty. Despite this uncertainty, we find that the suspension was associated with revenue gains driven by new product introductions and that these gains persist even after the supply suspension revocation in February 2010. Importantly, unlike domestic firms, foreign firms gain little. We explore potential mechanisms and find evidence consistent with one of them being the major reason for the differing impact: Domestic firms better navigate the challenges arising from political pluralism, an institutional feature characterized by a lack of political alignment between national and subnational governments. In the presence of political alignment, domestic firms do not have a comparative advantage over foreign firms. Our evidence thus highlights that even an uncertain and transitory window of opportunity arising from a controversial reduction in competition from the public sector can lead to a fundamental market restructuring in ways that can diverge from the intended policy objective. In particular, domestic firms’ ability to manage political pluralism is a core mechanism explaining their comparative advantage over foreign firms in the wake of a national crisis.

David Simchi-Levi,Zeyu Zheng,Feng Zhu

Abstract:We build a unified modeling framework for classical online matching problems and emerging online matching problems with three additional practical features: reusable resources, network resources, and decaying rewards. For online matching problems in the unified framework, we provide a unified performance analysis tool for the greedy policy and its simple variants, which we refer to as greedy-like policies. We prove that greedy-like policies can achieve near-optimal performances for online matching problems in the unified framework, where the policy performance is measured by competitive ratios under adversarial environments. We then analyze several representative special classes of online matching problems, which incorporate additional realistic structural assumptions on top of the unified framework. Specifically, we consider online matching problems with each of the following three additional structures: (i) singleton resources with time-decaying rewards; (ii) network resources with accept/reject decisions; and (iii) network resources with interval-type bundles. We show that for these special classes of online matching problems, slight modifications to greedy-like policies can successfully utilize additional structural information to further enhance policy performances. This work may suggest that the greedy policy and its variants, despite its simplicity, can achieve reliable performances for a number of emerging online matching problems.

Sanket Korgaonkar 

Abstract:This paper offers a novel framework to quantify the expected gains from renegotiating delinquent loans. The framework accounts for important trade-offs between concessions to borrowers, postdelinquency loan performance, and expected collateral values. The framework’s parameters are calibrated using data on renegotiated 30-year residential fixed-rate mortgages that went delinquent during the Great Recession. Our model-implied expected gains increase during the 2007–2009 period coinciding with an increase in the rate of loan renegotiation. Counterfactual analyses show that larger expected gains can be generated from employing principal forbearance and extensions of the term to maturity compared with principal write-downs and interest-rate reductions. On the other hand, principal write-downs can be a powerful tool when borrowers are deeply underwater. Our analyses illustrate how lenders or policymakers might deploy this framework when faced with another delinquency crisis.

Ari Kutai,Daniel Nathan,Milena Wittwer 

Abstract:We leverage the unique institutional feature that the Israeli government bond market operates on an exchange rather than over-the-counter to analyze whether and why having an exchange affects market liquidity during a crisis. We document how the liquidity crisis in March 2020 affected the Israeli government bond market and conduct difference-in-differences analyses, comparing bid-ask spreads in exchange markets, such as the Israeli government bond market, with markets lacking an exchange. Our findings support the idea that having an exchange enhances market liquidity. A counterfactual analysis using trade data from the Israeli exchange suggests that this is due to the ability of investors to readily provide liquidity to one another and the efficient netting of trade flows on an exchange.

Sébastien Lahaie,Benjamin Lubin

Abstract:We introduce the first adaptively priced iterative combinatorial auction design, which gradually extends price expressiveness as the rounds progress. This mechanism achieves both high efficiency and fast convergence across a wide range of valuation domains. We implement our auction design using polynomial prices, show how to detect when the current price structure is insufficient to clear the market, and show how to correctly expand the polynomial structure to guarantee progress. An experimental evaluation confirms that our auction is competitive with bundle-price auctions in domains where these excel, namely multiminded valuations, but also performs well in domains favorable to linear prices, such as valuations with pairwise synergy.