Publications

To provide a fuller picture of compliance with ASC 350-20, we hand-collect data to track 893 large acquisitions across time. Our model, which links impairments to post-acquisition accounting and market performance declines as well as acquisition-year attributes, identifies 349 acquisitions as likely to impair. We provide evidence that 65 percent of these at-risk acquisitions impair in the next two years. Our study should be useful to future research as it clarifies the role of hand-collection, market to book ratios, segment-level data, and volatility. We also offer descriptive evidence on impairment patterns. Overall, we find high levels of compliance and little opportunism.

When do people make optimistic forecasts about goal adherence? Nine preregistered studies find that a recent streak of goal-consistent behavior increases the predicted likelihood that the individual will persist, compared to various other patterns holding the rate of goal adherence constant. This effect is due to perceiving a higher level of commitment following a streak. Accordingly, the effect is larger when the behavior requires commitment to stick with it, compared to when the same behavior is enjoyable in its own right. Furthermore, the effect is weaker in the presence of another diagnostic cue of commitment: when the individual has a high historic rate of goal adherence. People also behave strategically in ways consistent with these inferences (e.g., are less likely to adopt costly goal support tools following a streak, choose partners with recent streaks for joint goal pursuit). Together, these results demonstrate the significance of streaky behavior for forecasting goal adherence.

The conduct described in the US complaints against Amazon harms competition between online stores and among the merchants who sell via them.

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.

Several papers argue that financial economics faces a replication crisis because the majority of studies cannot be replicated or are the result of multiple testing of too many factors. We develop and estimate a Bayesian model of factor replication that leads to different conclusions. The majority of asset pricing factors (i) can be replicated; (ii) can be clustered into 13 themes, the majority of which are significant parts of the tangency portfolio; (iii) work out-of-sample in a new large data set covering 93 countries; and (iv) have evidence that is strengthened (not weakened) by the large number of observed factors.

European countries experienced a large increase in labor supply due to the influx of Ukrainian refugees after the 2022 Russia invasion. We study its dynamic effects in a spatial model with forward-looking households of different skills, trade, and endogenous capital accumulation. We find that real GDP increases in Europe in the long term, with large distributional effects across countries and skill groups. In the short run, an increase in the supply of labor strains the use of capital structures that takes time to build. Over time, countries that build capital structures increase output, resulting in potential long run benefits.

Leveraging an established exercise in negotiation education, we build a novel dataset for studying how the use of language shapes bilateral bargaining. Our dataset extends existing work in two ways: 1) we recruit participants via behavioral labs instead of crowdsourcing platforms and allow participants to negotiate through audio, enabling more naturalistic interactions; 2) we add a control setting where participants negotiate only through alternating, written numeric offers. Despite the two contrasting forms of communication, we find that the average agreed prices of the two treatments are identical. But when subjects can talk, fewer offers are exchanged, negotiations finish faster, the likelihood of reaching agreement rises, and the variance of prices at which subjects agree drops substantially. We further propose a taxonomy of speech acts in negotiation and enrich the dataset with annotated speech acts. We set up prediction tasks to predict negotiation success and find that being reactive to the arguments of the other party is advantageous over driving the negotiation.

In many online platforms, customers’ decisions are substantially influenced by product rankings as most customers only examine a few top-ranked products. This induces a race for visibility among sellers, who may be incentivized to artificially inflate their position by employing fake users as exemplified by the emergence of click farms. Motivated by such fraudulent behavior, we study the problem of learning product rankings when a platform faces a mixture of real and fake users who are indistinguishable from one another. We first show that existing learning algorithms—that are optimal in the absence of fake users—may converge to highly suboptimal rankings under manipulation. To overcome this deficiency, we develop efficient learning algorithms under two informational settings: when the platform is aware of the number of fake users and when it is agnostic to this number. For both these settings, we prove that our algorithms converge to the optimal ranking, yet being robust to the aforementioned fraudulent behavior; we also present worst case performance guarantees for our methods and show that they outperform existing algorithms. At a high level, our work employs several novel approaches to guarantee robustness, such as (i) encoding product relationships using graphs and (ii) implementing multiple levels of learning as well as judicious cross-learning. Overall, our results indicate that online platforms can effectively combat fraudulent users even when they are completely oblivious to the number and identity of the fake users.

We review theoretical and empirical work on the economic effects of the United States and China trade relations during the past 20 years. We first discuss the origins of the China shock and its measurement and present methods used to study its economic effects on different outcomes. We then focus on the recent US–China trade war. We review methods used to evaluate its effects, describe its economic effects, and analyze whether this increase in trade protectionism reverted the effects of the China shock. The main lessons learned in this review are that (a) the aggregate gains from US–China trade created winners and losers; (b) China's trade expansion seems not to be the main cause of the decline in US manufacturing employment during the same period; and (c) the recent trade war generated welfare losses, had small employment effects, and was ineffective in reversing the distributional effects due to the China shock.

With $50+ billion in annual sales and operations around the world, Mars Inc. was one of the world’s largest family-held companies. Since its founding, Mars believed in the importance of “mutuality” with groups with which the company interacted. In 1947, then-chairman Forrest Mars, Sr. described “the total purpose for which the company exists” as promoting “a mutuality of service and benefits” among a wide range of stakeholders: consumers, distributors, competitors, direct suppliers, governmental bodies, employees, and shareholders. As a family-owned, family-managed company, this philosophy was understood and passed from one generation to the next.

By the 2010s, few members of the Mars family worked in the company. The family saw a need to codify the ethos of Mars as well as their expectations as shareholders, to articulate what the company was doing and why Mars was doing it.

In 2016, family members and Mars senior management began to develop a framework linking corporate purpose to shareholder objectives. Called “Mars Compass,” the new tool included financial measures as well as a commitment to make a meaningful difference in the lives of people in the company’s extended supply chain.

By spring 2023, Mars had fully embedded Compass into enterprise and segment planning and its regular operating cadence. For the company’s top 400 leaders, Mars Compass was used to determine incentive compensation, including substantial, long-term pay for meeting select sustainability goals. In 2024 this approach was extended to an additional 1,000 leaders. Would Mars Compass work as desired?

This study explores the relationship between migration and household resilience during a global crisis that eliminated the option to migrate. We link prior data from four populations in Bangladesh and Nepal to new phone surveys conducted during the early months of the COVID-19 pandemic. While earnings fell universally, pandemic-induced declines were 14%–25% greater among previously migration-dependent households and urban migrant workers, with household remittance losses far exceeding official statistics. Heightened economic exposure during the pandemic erased prior gains achieved by transnational migrants and caused fourfold greater prevalence of food insecurity among domestic subsistence migrants. Economic distress spilled over onto non-migrants in high-migration villages and labor markets. We show that migration contributed to economic contagion independent of its role in disease transmission. Losing the option to migrate differentially increased the vulnerability of migration-dependent households during a crisis.

We review a program of research examining three questions. First, why is the morality of people's behavior inconsistent across time and situations? We point to people's ability to convince themselves they have a license to sin, and we demonstrate various ways people use their behavioral history and others—individuals, groups, and society—to feel licensed. Second, why are people's moral judgments of others' behavior inconsistent? We highlight three factors: motivation, imagination, and repetition. Third, when do people tolerate others who fail to practice what they preach? We argue that people only condemn others' inconsistency as hypocrisy if they think the others are enjoying an “undeserved moral benefit.” Altogether, this program of research suggests that people are surprisingly willing to enact and excuse inconsistency in their moral lives. We discuss how to reconcile this observation with the foundational social psychological principle that people hate inconsistency.

Open banking facilitates data sharing consented to by customers who generate the data, with the regulatory goal of promoting competition between traditional banks and challenger fintech entrants. We study lending market competition when sharing banks’ customer transaction data enables better borrower screening for fintechs. Open banking promotes competition if it helps level the playing field for all lenders in screening borrowers; however, if it over-empowers fintechs, it can also hinder competition and leave all borrowers worse off. Due to the credit quality inference from borrowers’ sign- up decisions, this remains true even if borrowers have the control of whether to share their banking data. We also study extensions with fintech affinities and data sharing on borrower preferences.

We investigate whether a market served by a multiproduct monopolistic seller can be segmented in a way that benefits all consumers and the seller. The seller can offer a different product menu in each market segment, combining second- and third-degree price discrimination. We show that markets for which profit maximization leads to inefficiency can, generically, be segmented into two market segments in a way that increases the surplus of all consumers weakly and of some consumers and the seller strictly. Our constructive proof is based on deriving implications of binding incentive compatibility constraints when profit maximization implies inefficiency.

The role of demand curvature in determining firm behavior in symmetric oligopolistic product markets is well-understood. We consider the empirically relevant discrete choice differentiated product demand and point to two forces that drive curvature in logit demand: the impact of outside-good spending on the consumer’s indirect utility and the heterogeneity in this response across consumers. We use the canonical example of the ready-to-eat cereal market (Nevo, 2000) to contrast elasticity and curvature estimates across several workhorse models. We illustrate that the log-concave Multinomial Logit and Nested Logit demands yield significantly biased curvature estimates. In contrast, a Mixed Logit specification generates a wider range of curvatures, including curvatures larger than one. These results are of immediate relevance to the robust assessment of tax incidence and the pass-through of cost savings, such as from a horizontal merger, in differentiated product markets.