Publications

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.

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.

Can information from a credible messenger shift behavior in an information-saturated environment? In a randomized controlled trial involving twenty-eight million individuals in West Bengal, we find that SMS-delivered video messages containing information about COVID-19 symptoms and health-preserving behaviors recorded by a credible messenger increased adherence to targeted and non- targeted preventive behaviors, measured by two objective measures (symptoms reported to a health worker, and phone usage at home), as well as self-reported behaviors. We find large spillovers onto non- targeted recipients. Credible light-touch messaging can play an important role in crisis response, even when similar information is widely available.

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.

We study regressions with multiple treatments and a set of controls that is flexible enough to purge omitted variable bias. We show that these regressions generally fail to estimate convex averages of heterogeneous treatment effects—instead, estimates of each treatment’s effect are contaminated by non-convex averages of the effects of other treatments. We discuss three estimation approaches that avoid such contamination bias, including the targeting of easiest-to-estimate weighted average effects. A re-analysis of nine empirical applications finds economically and statistically meaningful contamina- tion bias in observational studies; contamination bias in experimental studies is more limited due to idiosyncratic effect heterogeneity

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.

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 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.

The real estate market is highly intermediated, with 90 percent of buyers and sellers hiring an agent to help them transact a house. However, low barriers to entry and fixed commission rates result in a market where inexperienced intermediaries have a large market share, especially following house price booms. Using rich micro-level data on 8.5 million listings and a novel instrumental variables research design, we first show that houses listed for sale by inexperienced real estate agents have a lower probability of selling, and this effect is strongest during the housing bust. We then study the aggregate implications of the distribution of agents’ experience on housing market liquidity by building a dynamic entry and exit model of real estate agents with aggregate shocks. We find that 3.7% more listings would have been sold in a flexible commission equilibrium. Eighty percent of this improvement comes from competition driving down overall seller commissions, while the remaining share can be attributed to commission variation across experience levels.

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.”

Persuasive communication functions not only through content but also delivery, e.g., facial expression, tone of voice, and diction. This paper examines the persuasiveness of delivery in start-up pitches. Using machine learning (ML) algorithms to process full pitch videos, we quantify persuasion in visual, vocal, and verbal dimensions. Positive (i.e., passionate, warm) pitches increase funding probability. Yet conditional on funding, high-positivity startups underperform. Women are more heavily judged on delivery when evaluating single-gender teams, but they are neglected when co-pitching with men in mixed-gender teams. Using an experiment, we show persuasion delivery works mainly through leading investors to form inaccurate beliefs.

We investigate the role of private equity (PE) in the resolution of failed banks after the 2008
financial crisis. Using proprietary failed bank acquisition data from the FDIC combined with data
on PE investors, we find that PE investors made substantial investments in underperforming and
riskier failed banks. Further, these acquisitions tended to be in geographies where the other local
banks were also distressed. Our results suggest that PE investors helped channel capital to
underperforming failed banks when the “natural” potential bank acquirers were themselves
constrained, filling the gap created by a weak, undercapitalized banking sector. Next, we use a
quasi-random empirical design based on proprietary bidding data to examine ex post performance
and real effects. We find that PE-acquired banks performed better ex post, with positive real effects
for the local economy. Our results suggest that private equity investors had a positive role in
stabilizing the financial system in the crisis through their involvement in failed bank resolution.