Publications

The Occupational Safety and Health Administration (OSHA) is decentralized, wherein field offices coordinated at the state level undertake inspections. We study whether this structure can lead to interstate frictions in sharing information and how this impacts firms’ compliance with workplace safety laws. We find that firms caught violating in one state subsequently violate less in that state but violate more in other states. Despite this pattern, and in keeping with information frictions, violations in one state do not trigger proactive OSHA inspections in other states. Moreover, firms face lower monetary penalties when subsequent violations occur across state lines, likely due to the lack of documentation necessary to assess severe penalties. Finally, firms are more likely to shift violating behavior into states with greater information frictions. Our findings suggest that internal information within regulators impacts the likelihood and location of corporate misconduct.

The usefulness of financial disclosures is a source of considerable debate in the municipal setting, given their lack of timeliness. This paper empirically examines the extent to which municipal financial disclosures have information content. Using the entire universe of annual financial disclosures from 2009 to 2020, we show that trading activity in the secondary market for municipal bonds increases after the disclosures are filed. Both institutional and retail trades increase around disclosure filings, but the effect is pronounced for retail traders, for whom the reports are more likely to provide new information. Moreover, the heightened trading is pronounced for timelier disclosures, consistent with regulators' views that untimely disclosures are less likely to provide new information. We also find a pronounced response when investors' risk is high and when the disclosures contain risk-related discussions. Our results contrast with earlier research and provide the first large-scale evidence that participants in the U.S. market for municipal bonds perceive financial disclosures to have informational value.

Research in psychology and medicine has linked mental health disorders, and particularly bipolar disorder (BD), to employment in creative professions. Little is known, however, about the mechanisms for this link, which could be due to biology (primarily through a person’s genes) or environmental (through socioeconomic status). Using administrative data on mental health diagnoses and occupations for the population of Denmark, we find that people with BD are more likely to be musicians than the population, but less likely to hold other creative jobs. Yet, we also show that healthy siblings of people with BD are significantly more likely to work in creative professions. Notably, people from wealthy families are consistently more likely to work in creative professions, and access to family wealth amplifies the likelihood that siblings of people with BD pursue creative occupations. Nevertheless, family wealth explains only a small share of the correlation between BD and creative employment.

We propose a model of advertising content that focuses on messenger selection, where advertising can generate product-match signals for consumers. We consider advertis- ing as a problem of Bayesian persuasion with costly information processing, where the type of communication messenger is costless to observe and determines the information structure consumers will face, thereby affecting their attention decisions. Messengers are classified as high type or low type based on their likelihood of generating positive signals about product match. Our findings highlight that the optimal choice of messengers depends on their signal elasticities and the firm’s decision on whether to induce consumer attention. In particular, we find that when it is crucial to raise prices and high-type messengers overshadow the product match value by providing generally positive signals, a low-type messenger can effectively capture consumer attention and persuade them to pay a higher premium. This holds true even if high-type messengers can better grab consumers’ attention by providing additional entertainment value or when some consumers are naive in belief updating.

Over the last two decades, the sociology of entrepreneurship has exploded as an area of academic inquiry. Most of this research has been focused on understanding the environmental conditions that promote entrepreneurship and processes related to the initial formation of an organization. Despite this surge in activity, many important questions remain open. Only more recently have scholars begun to turn their attention to what happens to organizations, and the people connected to them, as they mature and move through the life cycle of entrepreneurship. These open questions, moreover, connect to many classic themes in the literature on careers, organizational sociology, stratification, and work and occupations. Using a framework that focuses on three phases of the entrepreneurial life cycle—pre-entry, entry, and post-entry—we summarize sociological research on entrepreneurship and highlight opportunities for future research.

Online reviews serve as a guide for consumer choice. With advancements in large language models (LLMs) and generative AI, the fast and inexpensive creation of human-like text may threaten the feedback function of online reviews if neither readers nor platforms can differentiate between human-written and AI-generated content. In two experiments, we found that humans cannot recognize AI-written reviews. Even with monetary incentives for accuracy, both Type I and Type II errors were common: human reviews were often mistaken for AI-generated reviews, and even more frequently, AI-generated reviews were mistaken for human reviews. This held true across various ratings, emotional tones, review lengths, and participants’ genders, education levels, and AI expertise. Younger participants were somewhat better at distinguishing between human and AI reviews. An additional study revealed that current AI detectors were also fooled by AI-generated reviews. We discuss the implications of our findings on trust erosion, manipulation, regulation, consumer behavior, AI detection, market structure, innovation, and review platforms.

As a board-certified neurosurgeon, Dr. Ryan Grant believed that the medical system treating chronic musculoskeletal (MSK) pain was inherently broken. He believed that he could challenge the way traditional medical institutions treated back pain and build a profitable and sustainable company, even in a tough funding environment. His goal was audacious, but as a successful healthcare entrepreneur with multiple start-ups, Grant was used to winning.

MSK pain was one of the most widespread health conditions, with major impacts on the lives of many individuals as well as their employers and society as a whole. According to the World Health Organization, approximately 1.7 billion people had musculoskeletal conditions. Low back pain was the single leading cause of disability, estimated to affect up to half of all Americans at some point in their lives.

However, according to recent research, the traditional treatment protocol for MSK pain was deficient. The surgical option was overused; multiple studies had demonstrated that less invasive and expensive treatments could often be just as effective as surgery. Treatment was fragmented, requiring patients to visit multiple health practitioners, who often had limited interactions with each other.

In 2020, Grant founded Vori Health along with Dr. Mary O’Connor, a new clinical service company that he believed would help patients with MSK achieve their quality-of-life goals while avoiding costly and unnecessary surgeries. Vori Health devised a system through which clinicians would develop personalized evaluation and treatment plans based on patient-defined needs and then deliver non-surgical therapies to reduce pain levels. A doctor-led team that integrated experts in physical therapy, nutrition, and other lifestyle support options would support each patient’s treatment plan. Vori Health services were primarily delivered virtually, although in-person services were also available.

In 2021, Vori raised over $68 million in Series A financing. Preliminary results for patients had been encouraging and the company benefited from the trend away from fee-for-service payment for medical services. In 2023, Vori's management was contemplating a Series B financing round. They recognized that the investment environment had changed since the company their Series A round. Vori saw that investors were using profitability as a metric over revenue and were less eager to fund organizations holding large customer contracts that required a significant level of resources, preferring companies that could grow organically from their own profits. Vori had to meet these investor expectations while facing a stiff competitive market, including traditional providers that continued to favor the surgery-first model, other virtual services, and new deep-pocketed entrants that had entered the healthcare field.

In light of these developments, Vori needed to develop a go-to-market plan that allowed it to reach its potential customers and scale. This would mean identifying potential healthcare partners as well as fashioning pricing models and operational enhancements to facilitate working within an expanded network. 

US state governments frequently provide firms with targeted subsidies. In exchange, recipients promise to create or retain a certain number of jobs in the subsidizing state. Using novel hand-collected data, we address three questions: (i) the extent to which firms meet job creation targets promised in their applications, (ii) the factors that determine which firms meet the targets, and (iii) the benefits to firms from meeting those targets. We find that 63% of subsidies awarded to publicly traded U.S. firms between 2004 and 2015 meet their promised job creation targets. Firms with poorer labor practices are less likely to meet their targets, as are politically connected firms that receive subsidies in election years. Conversely, promised job targets are also more likely to be met for subsidies accompanied by government press releases but less likely to be met for subsidies accompanied by firm press releases; the latter likely reflects the fact that firms put out press releases for larger subsidies with more ambitious job targets. In terms of consequences, firms that meet job targets are more successful at obtaining subsequent subsidies both in and out of subsidizing states. However, while firms’ success in meeting job targets is associated with an uptick in positive media coverage, this does not flow through to ESG ratings, even on scores specific to community impact. Our results should be of interest to both academics and policymakers interested in the design of state-level economic incentives.

When employees are living a calling at work, they tend to experience greater well-being and the organization also benefits. Despite the integral role of the organization, research has not sufficiently explored what organizational factors might help employees live a calling. Drawing on a tripartite theoretical framework of living a calling— characterized by destiny, personal significance, and social significance— and Work as a Calling Theory, we hypothesize that needs-supplies fit, empowerment, and servant leadership are positively related to living a calling. Further, we hypothesize that the benefits of living a calling extend to the organization via a negative association with deviant behaviors, a positive association with LMX relationships, and that consistency of interests (a facet of grit) is a boundary condition of the proposed relationships. Through testing our hypotheses in a multi-wave, multi-source field study of employees and supervisors in a park district, we find that needs-supplies fit and empowerment facilitate living a calling in an organization. Further, consistency of interests moderates the relationship between living a calling and deviant behaviors and LMX. Our findings indicate how employers might help employees live their callings, and, in turn, mitigate negative and attain positive outcomes.

Volunteer crowdsourcing platforms match volunteers with tasks which are often recurring. To ensure completion of such tasks, platforms frequently use a lever known as ``adoption,'' which amounts to a commitment by the volunteer to repeatedly perform the task. Despite reducing match uncertainty, high levels of adoption can decrease the probability of forming new matches, which in turn can suppress growth. We study how platforms should manage this trade-off. Our research is motivated by a collaboration with Food Rescue U.S. (FRUS), a volunteer-based food recovery organization active in over 30 locations. For platforms such as FRUS, effectively utilizing non-monetary levers, such as adoption, is critical. Motivated by the volunteer management literature and our analysis of FRUS data, we develop a model for two-sided markets which repeatedly match volunteers with tasks. We study the platform's optimal policy for setting the adoption level to maximize the total discounted number of matches. When market participants are homogeneous, we fully characterize the optimal myopic policy and show that it takes a simple extreme form: depending on volunteer characteristics and market thickness, either allow for full adoption or disallow adoption. In the long run, we show that such a policy is either optimal or achieves a constant-factor approximation. We further extend our analysis to settings with heterogeneity and find that the structure of the optimal myopic policy remains the same if volunteers are heterogeneous. However, if tasks are heterogeneous, it can be optimal to only allow adoption for the harder-to-match tasks. Our work sheds light on how two-sided platforms need to carefully control the double-edged impacts that commitment levers have on growth and engagement. Setting a misguided adoption level may result in marketplace decay. At the same time, a one-size-fits-all solution may not be effective, as the optimal design crucially depends on the characteristics of the volunteer population.

Markets and firms offer contrasting methods to arrange production. In markets, contracts govern the purchase of parts and services. In firms, the shared values, customs and norms coming from a corporate culture govern employees' joint development of parts and services. We argue for this distinction as a theory of the firm. Firms exist because corporate culture at times is more efficient to carry out production than are detailed contracts. The firm's boundary encircles the areas of production for which a manager optimally chooses corporate culture as the organizing device. Consistent with empirical evidence, the model explains why some mergers and acquisitions fail, and why corporate cultures are hard to change.

The emergence of the AI-driven digital economy has raised concerns among economists and policymakers regarding the market power of firms that sell data. The unique characteristics of data, such as its large fixed cost and ability to be replicated at zero marginal cost, suggest the potential for natural monopolies in data markets. However, little is known about how data markets function and how data is priced. This paper documents characteristics of data markets, explores the potential market power of data monopolists through theoretical analysis and uses modeling and data together to explore how consumers fare under different data pricing models. The authors develop a dynamic model of a monopolist data seller with two crucial features: Data that many other buyers also have loses value, and data sellers cannot commit not to sell the same data to more buyers in the future. In such circumstances, even data monopolists have limited power to extract profits. Customers who anticipate more future sales of the data they buy will discount the value of the data. Customers’ willingness to pay for something that many others will know tomorrow is low. Thus, the concern shifts from excessive profits to potential under-provision of data.

We present evidence that is consistent with large disparities across firms in their on-the-job learning opportunities, using administrative datasets from Brazil and Italy. We categorize firms into discrete “classes”—which our conceptual framework interprets as skill-learning classes—using a clustering methodology that groups together firms with similar distributions of unexplained wage growth. Mincerian returns to experience vary widely across experiences acquired in different firm classes. Four tests leveraging firm stayers and movers, occupation and industry switchers, hiring wages, and displaced workers point towards a portable and general human capital interpretation. Heterogeneous employment experiences explain an important share of wage variance by age 35, thus contributing to shape wage inequality. Firms’ observable attributes only mildly predict on-the-job learning opportunities.

We examine whether investors value workforce gender diversity. Consistent with the view that investors believe that workforce gender diversity can be valuable in major firms, we use event studies to demonstrate that U.S. technology firms and U.S. financial firms experience more positive stock price reactions when it is revealed that they have relatively higher (versus lower) workforce gender diversity numbers. For instance, we find that Google’s revelation of relatively low workforce gender diversity numbers triggered a negative stock price reaction, whereas eBay’s revelation of relatively high workforce gender diversity numbers triggered a positive stock price reaction. These stock price reactions are both economically and statistically significant; e.g., we estimate that if a technology firm had revealed gender diversity numbers that were one standard deviation higher, its market valuation would have increased by $1.11 billion. Corroborating this plausibly causal field evidence, we also find positive investor reactions to workforce gender diversity in randomized experiments using Prolific participants with investing experience; these reactions seem to be underpinned by investors’ beliefs about potential upsides of diversity for the firm (e.g., reduced legal risks; increased creativity) but not by investors’ beliefs about potential downsides of diversity for the firm (e.g., increased conflict). Our findings highlight the importance of understanding investors’ intuitions or beliefs about major organizational phenomena such as workforce gender diversity. Our results also point towards a new type of business case for diversity, driven by investors: if major firms had more workforce gender diversity, investors may “reward” them with substantially higher valuations.

We study mislearning from equilibrium prices, and contrast this with mislearning from exogenous fundamentals. We micro-found mislearning from prices with a psy- chologically founded theory of “Partial Equilibrium Thinking” (PET), where traders learn fundamental information from prices, but fail to realize others do so too. PET leads to over-reaction, and upward sloping demand curves, thus contributing to more inelastic markets. The degree of individual-level over-reaction, and the extent of in- elasticity varies with the composition of traders, and with the informativeness of new information. More generally, unlike mislearning from fundamentals, mislearning from prices i) generates a two-way feedback between prices and beliefs that can provide an arbitrarily large amount of amplification, and ii) can rationalize both over-reaction and more inelastic markets. The two classes of biases are not mutually exclusive. Instead, they interact in very natural ways, and mislearning from prices can vastly amplify mislearning from fundamentals.

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.

We view innovation investments as real options and explore the implications of risk (volatility) as well as a newly defined outcome independent measure of ambiguity—Knightian uncertainty— for innovation decisions. The empirical analysis uses stock returns to compute an implementable measure of ambiguity. We also control for risk and other determinants of innovation. We find a consistently significant negative effect of ambiguity on R&D, patents, and citations, as predicted. The effect of risk on R&D is positive and significant, but the corresponding effect on patents and citations is negative and significant. Ambiguity matters more for high-tech firms, consistent with intuition.

We propose that investment strategies should be evaluated based on their net-oftrading- cost return for each level of risk, which we term the “implementable efficient frontier.” While numerous studies use machine learning return forecasts to generate portfolios, their agnosticism toward trading costs leads to excessive reliance on fleeting small-scale characteristics, resulting in poor net returns. We develop a framework that produces a superior frontier by integrating trading-cost-aware portfolio optimization with machine learning. The superior net-of-cost performance is achieved by learning directly about portfolio weights using an economic objective. Further, our model gives rise to a new measure of “economic feature importance.”