Publications

Objectives Previous studies have reported inconsistent findings regarding the relationship between COVID-19 restrictions and suicide rates, particularly concerning business and social restriction policies. This study aimed to address this gap by analyzing detailed US county-level restriction and suicide death data. Study Design Data from the US Centers for Disease Control and Prevention (CDC) were obtained for county-level suicide rates by race, sex, and age from 2016 to 2023. Yale School of Management-Tobin Center State and Local COVID Restriction Database provided data on COVID-19 social and business restrictions. These datasets were combined with other relevant data on county-level demographics, gross domestic product (GDP), unemployment, and population density. Methods Poisson interrupted time-series regression was employed to assess whether these restrictions were associated with changes in suicide rates during pandemic (2020 and 2021) and post-pandemic (2022 and 2023) periods. Results During the pandemic restriction era of 2020–2021, stricter business capacity limits were linked to lower suicide rates overall (Poisson coefficient: -0.90 [95% CI -1.54, -0.25, p = 0.006]), and in particular among males (Poisson coefficient: -1.13 [-1.94, -0.32, p = 0.006]). The estimated coefficient was not statistically significant for females. Among age groups, individuals aged 25–34 and 35–44 experienced lower suicide rates in counties with tighter restrictions, while other age and sex groups did not show similar trends. Additionally, no statistically significant correlations were found across racial groups. In contrast, social gathering restrictions had a less consistent relationship with suicide rates; while those aged 15–24 experienced an increase in rates under tighter restrictions, those aged 25–34 had a decrease. No other demographic groups yielded statistically significant coefficient estimates. Conclusions Results underscore the importance of considering differential effects of business and social restrictions on suicide rates, and to tailor interventions to address the unique needs of specific populations during public health crises.

This paper studies how mandatory greenhouse gas disclosure affects new business formation. We find a significant increase in business entry following the implementation of the Greenhouse Gas Reporting Program in affected industries, relative to unaffected controls. We propose two channels. First, through a production channel, disclosure pressures incumbent firms to reduce emissions by scaling back production or reallocating resources toward cleaner technologies, weakening incumbents’ competitive positions and creating space for new entrants. Second, through an information channel, public disclosure of previously proprietary emissions data helps potential entrepreneurs identify viable entry opportunities. We present evidence consistent with both channels. Incumbent firms reduce economic activity and experience declines in profitability, and entry is concentrated in industries facing greater emissions reductions and public scrutiny. Additionally, regulatory and industry commentary highlights concerns over the proprietary nature of disclosed emissions data. Overall, our findings reveal an unintended yet economically meaningful consequence of environmental disclosure mandates.

Nature provides essential inputs to the economy through ecosystem services. In Asia and the Pacific, accelerating biodiversity loss is eroding these services, increasing vulnerability to shocks, and creating new risks for investors and governments.

The Securities and Exchange Commission (SEC) reviews firms’ financial reports and issues comment letters to ensure compliance with applicable disclosure and accounting requirements. We explore the nature, determinants, and consequences of SEC comment letters that refer to information disclosed in voluntary earnings conference calls. Using hand-collected data, we document that the SEC primarily references these voluntary disclosures to illustrate insufficiencies and, less commonly, inconsistencies in mandatory filings across a wide range of topics. These letters are more likely to be issued when filing reviews are more complex, SEC staff are less resource constrained, and for firms with more institutional investors and analysts. Conference call-related comments tend to occur during higher-quality review processes and require greater remediation costs than other comments. The SEC’s use of call disclosures also leads to more pronounced changes in firms’ subsequent mandatory filings, particularly when the firm indicates agreement with SEC comments. However, we observe a mixed effect on the overall information environment, consistent with possible unintended consequences for the quality of firms’ voluntary disclosures.

In most evaluation systems—such as those governing the allocation of prestigious awards—the evaluator’s primary task is to reward the highest quality candidates. However, these systems are imperfect; top performers may not be acknowledged and thus be underrecognized, and low performers may receive unwarranted recognition and thus be overrecognized. An important feature of many evaluation systems is that people alternate between being candidates and being evaluators. How does experiencing misrecognition as a candidate affect how people subsequently evaluate others? We develop novel theory that underrecognition and overrecognition lead people to reproduce those experiences when they are evaluators. Across three studies—a quasi-natural experiment and two preregistered, multistage experiments, we find that underrecognized evaluators are less likely to grant recognition to others—even to the highest-performing candidates. Conversely, overrecognized evaluators are more likely to grant rewards to others—even to the lowest-performing candidates. Whereas underrecognized evaluator behavior is driven by individuals’ perceptions that their experience was unfair, overrecognized evaluator behavior is driven by the informational cues people glean on how to evaluate others. Thus, in evaluation processes where people oscillate between being the evaluated and being the evaluator, we show how and why seemingly innocuous initial inefficiencies are reproduced in subsequent evaluations.

Should central banks’ inflation targets remain set in stone? We study a dynamic mechanism
design problem between a government (principal) and a central bank (agent). The central
bank has persistent private information about structural shocks. Firms learn the state from the
central bank’s reports and form inflation expectations. A dynamic inflation target implements the
full-information commitment allocation. The central bank is delegated the authority to adjust
the level and flexibility of its target as long as it does so one period in advance. All history
dependence of the mechanism is summarized by the current period’s target. We show that
a declining natural interest rate and a flattening Phillips curve imply opposite optimal target
adjustments. We leverage our framework to study longer-horizon time consistency problems
and speak to practical policy questions of inflation target design.

This paper develops a theory-based, explainable deep learning convolutional neural network (CNN) classifier to predict the time-varying emotional response to music. We design novel CNN filters that leverage the frequency harmonics structure from acoustic physics known to impact the perception of musical features. Our theory-based model is more parsimonious, but it provides comparable predictive performance with atheoretical deep learning models while performing better than models using handcrafted features. Our model can be complemented with handcrafted features, but the performance improvement is marginal. Importantly, the harmonics-based structure placed on the CNN filters provides better explainability for how the model predicts emotional response (valence and arousal) because emotion is closely related to consonance—a perceptual feature defined by the alignment of harmonics. Finally, we illustrate the utility of our model with an application involving digital advertising. Motivated by YouTube’s midroll ads, we conduct a laboratory experiment in which we exogenously insert ads at different times within videos. We find that ads placed in emotionally similar contexts increase ad engagement (lower skip rates and higher brand recall rates). Ad insertion based on emotional similarity metrics predicted by our theory-based, explainable model produces comparable or better engagement relative to atheoretical models.

This article concerns itself with fees that Apple and Google might charge to business users in their respective mobile ecosystems. We lay out the economic analysis behind the goals of the DMA—contestability and fairness—as they apply to third-party app store access fees. We focus on the access fees for alternatives to the Apple App Store, as this has become contentious in the early enforcement of the DMA. Much of our analysis, however, also applies also to Google and/or any other designated gatekeeper.

We study a retirement savings plan with a default contribution rate of 12% of income, which is much higher than previously studied defaults. Twenty-five percent of employees had not opted out of this default 12 months after hire; a literature review finds that the corresponding fraction in plans with lower defaults is approximately one-half. Because only contributions above 12% were matched by the employer, 12% was likely to be a suboptimal contribution rate for employees. Employees who remained at the 12% default contribution rate had average income that was approximately one-third lower than would be predicted from the relationship between salaries and contribution rates among employees who were not at 12%. Defaults may influence low-income employees more strongly in part because these employees face higher psychological barriers to active decision making.

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.

We hypothesize that “low-contrast categories” (those lacking sharp differentiation from adjacent categories) catalyze the creation of groundbreaking inventions by influencing two key stages in the life of an invention: (1) idea-creation and (2) idea-positioning. During “idea-creation,” low-contrast categories increase the likelihood that descendant inventions will combine the focal invention with more (a) boundary-spanning, (b) novel, (c) original, and (d) atypical knowledge inputs. During “idea-positioning,” they allow greater leeway in articulating how descendant inventions depart from the focal invention’s lineage and chart new technological directions. We find robust support for our hypothesis using data from the United States Patent and Trademark Office’s classification system spanning nearly four decades. Further analyses demonstrate that the catalyzing effect of low-contrast categories has important material consequences: inventions classified in low-contrast categories spur descendant inventions that generate substantially higher economic value and exert more enduring technological impact than those in high-contrast categories. By introducing the concept of catalyzing categories, this study offers a novel theoretical perspective on the genesis of groundbreaking inventions and the role of categorical structures in the inventive process.

The credit-implied volatility (CIV) surface is introduced as an organizing framework for analysis of credit spreads, providing a description of CDS spreads for firms across the credit spectrum, of varying maturities, and at all points throughout the credit cycle.

We model the optimal resolution of insolvent firms in general equilibrium. Collateral- constrained banks lend to (i) solvent firms to finance investments and (ii) distressed firms to avoid liquidation. Liquidations create negative fire-sale externalities. Liquidations also re- lieve bank balance-sheet congestion, enabling new firm loans that generate positive collateral externalities by lowering bank borrowing rates. Socially optimal interventions encourage liqui- dation when firms have high operating losses, high leverage, or low productivity. Surprisingly, larger fire sales promote interventions encouraging more liquidations. We study synergies be- tween insolvency interventions and macroprudential regulation, bailouts, deferred loss recog- nition, and debt subordination. Our model elucidates historical crisis interventions.

The origin of the modern joint-stock company is typically traced to the concomitant appearance of large-scale maritime trading companies in England and the Netherlands in the early seventeenth century. Highlighting medieval cases in southern Europe, we claim that the joint-stock company emerged earlier in history. These prior appearances support the theory of convergent evolution towards the joint-stock company. We document alternative and largely independent developmental paths that suggest the joint-stock company can emerge in a variety of legal, political and socioeconomic contexts. This evidence has implications for identifying the necessary background underlying the emergence of the joint-stock company, and for the debate regarding the link between business institutions and economic growth.

Growing socio-economic inequality poses one of the greatest challenges to society, thereby raising new questions about the responsibility of corporations to address its effects. Inequality also poses material risks to business performance. Like climate risk, inequality can impact business across a broad set of sectors and economies on a global scale. To mitigate risks and leverage opportunities to generate positive outcomes from corporate sustainability investments, managers and investors need better data on the business risks posed by inequality and the impact of corporate conduct on it. However, the current transparency infrastructure is inadequate to meet this need. This article reviews the current state of corporate disclosure on inequality and assesses its utility to companies as well as investors and other stakeholders. Drawing on innovations in climate disclosure, we suggest a path forward for companies and investors to drive improved disclosure from companies on the risks presented by socio-economic inequality.

We analyze survey data on ESG beliefs and preferences in a large panel of retail investors linked to administrative data on their investment portfolios. The survey elicits investors’ expectations of long-term ESG equity returns and asks about their motivations, if any, to invest in ESG assets. We document four facts. First, investors generally expected ESG investments to underperform the market. Between mid-2021 and late-2022, the average expected 10-year annualized return of ESG investments relative to the overall stock market was –1.4%. Second, there is substantial heterogeneity across investors in their ESG return expectations and their motives for ESG investing: 45% of survey respondents do not see any reason to invest in ESG, 25% are primarily motivated by ethical considerations, 22% are driven by climate hedging motives, and 7% are motivated by return expectations. Third, there is a link between individuals’ reported ESG investment motives and their actual investment behaviors, with the highest ESG portfolio holdings among individuals who report ethics-driven investment motives. Fourth, financial considerations matter independently of other investment motives: we find meaningful ESG holdings only for investors who expect these investments to outperform the market, even among those investors who reported that their most important ESG investment motives were ethical or hedging reasons.

While traditional change management emphasizes gradual tactics like pursuing small wins and building coalitions, in turbulent times these gradual tactics aren’t necessary—and they can hold leaders back from taking advantage of bigger opportunities. Research from healthcare settings during Covid show that both senior leaders and frontline managers are more successful at prompting change during turbulent times when they do three things: 1. Selecting a shovel-ready idea and reframing it as a solution to a problem at hand as well as long-term success, 2. Moving quickly to take advantage of a window in time when people are more open to change, and 3. Thinking more expansively about what’s possible.

We empirically characterize how China is internationalizing its bond market by staggering the entry of different types of foreign investors into its domestic market and propose a dynamic reputation model to explain this strategy. Our framework rationalizes China's strategy as trying to build credibility as a safe issuer while reducing the cost of capital flight. We use our framework to shed light on China's response to episodes of capital outflows.