Published Papers

An entrepreneur makes offers to multiple investors to fund a project that requires a minimum investment. Concerned about other investors' decisions, each investor strategically communicates information about the project to others. When investors have conflicts of interest, those with contractually stronger incentives to invest attempt to persuade others to invest. Depending on the project's ex ante quality, the entrepreneur may promise investors different returns to create conflicts of interest and induce persuasion, or may promise investors an identical return to align their interests and induce truthful communication. The paper illustrates a new motivation for syndication and hierarchy withinsyndicates.

We study information sharing between strategic investors who are informed about asset fundamentals. We demonstrate that a coarsely informed investor optimally chooses to share information if his counterparty investor is well informed. By doing so, the coarsely informed investor invites the other investor to trade against his information, thereby reducing his price impact. Paradoxically, the well informed investor loses from receiving information because of the resulting worsened market liquidity and the more aggressive trading by the coarsely informed investor. Our analysis sheds light on phenomena such as private communications among investors and public information sharing on social media.

We explore the extent to which aspirations - such as those forged in the course of social interactions - explain 'puzzling' behavioral patterns in investment decisions. We motivate an aspirational utility, reminiscent of Friedman and Savage (1948), where social considerations (e.g., status concerns) provide an economic foundation for aspirations. We show this utility can explain a range of observed investor behaviors, such as the demand for both right- and left-skewed assets; aspects of the disposition effect; and patterns in stock-market participation consistent with empirical observations. We corroborate our theoretical findings with two novel laboratory experimental studies, where we observed participants' preference for skewness in risky lotteries shift as lab-induced aspirations shifted.

A nationwide panic forced President Roosevelt to declare a banking holiday in March 1933. The government reopened banks sequentially using a process that sent noisy signals about banks' health. New microdata reveals that the public responded to these signals. Deposits at rapidly reopened banks rebounded quicker than at comparable or stronger banks that reopened even a few days later. The stigma of late reopening shifted funds from stigmatized to lauded banks and among communities that they served. Despite persisting over a decade, the shift had no measurable impact on the rate at which localities recovered from the Great Depression.

In the dynamic landscape of the digital economy, social trading platforms are experiencing rapid growth. Our study delves into the impact of changes in social audience size-measured by the number of followers-on traders' performance and behaviors. Utilizing data from a company-led randomized field experiment conducted on a prominent cryptocurrency social trading platform, we unearth intriguing findings. Traders garnering increased social audience size exhibit tendencies to trade more frequently, utilize higher leverage, and, surprisingly, attain poorer performance. Notably, these adverse effects intensify among traders who previously excelled, suggesting a link to overconfidence. Interestingly, our research uncovers a reference point effect associated with social audience size. Removing accumulated social audience size does not alleviate the negative consequences, instead, they persist. Additionally, we observe an extra adverse effect when traders experience a reduction in the digit magnitude of follower counts, supporting our hypothesis about social audience size serving as a reference point. Our study carries significant implications for the design of social trading platforms. It serves as a crucial reminder for both traders and platform managers to carefully navigate the interplay between social audience size dynamics and trading decisions.

We examine how indirect connections (i.e., friends of friends), an important yet understudied feature of social networks, may affect bank loan contracts. Using a sample of bank loans issued by U.S. public firms, we find that indirect networks built on board interlocks significantly reduce loan spreads. However, bank monitoring and loan quality are negatively affected, suggesting that indirect networks may give rise to favoritism treatment by banks. A novel set of difference-in-difference tests exploiting changes in higher-order network structures provides the network foundation and lends support to a causal interpretation of our findings. Overall, our results suggest that indirect connections within social networks, specifically through board interlocks, can have significant economic impacts on bank loan contracts.

We show that the social capital embedded in employees' networks contributes to firm performance. Using novel, individual-level network data, we measure a firm's social capital derived from employees' connections with external stakeholders. Our directed network data allow for differentiating those connections that know the employee and those that the employee knows. Results show that firms with more employee social capital perform better; the positive effect stems primarily from employees being known by others. We provide causal evidence exploiting the enactment of a government regulation that imparted a negative shock to networking with specific sectors and provide evidence on the mechanisms.

How do venture capital investors react to social movements, including those that relate to historical underrepresentation in funding? We use image and name algorithms combined with clerical review to classify race for 150,000 founders and 30,000 investors. These data allow us to assess the impact of George Floyd's murder on VC funding of Black entrepreneurs and identify which VCs were most responsive. Although VCs responded swiftly, investment in Black-founded startups reverted to prior levels within two years. This temporary reaction was concentrated among those who had never previously invested in any Black entrepreneur. Moreover, the investors who responded were less likely to invest in more than one Black-founded startup and were less inclined to engage deeply by taking a board seat. Finally, it appears that the best Black entrepreneurs may have anticipated this "token" response because they did not match with investors who had no experience funding Black startups.

Tipping is a complex phenomenon cutting across various stakeholders-firms, customers, and workers. Analyzing the long-run impact of policies related to tipping is therefore challenging. To facilitate such analysis, we develop a modeling framework in which a tipping norm forms endogenously in a market consisting of a firm that offers service to potential customers. Customers choose whether to consume the service or not and, if yes, how much to tip the server afterward. With tipping, customers show appreciation to the server by sharing a fraction of their surplus but also undergo social pressure to comply with the prevailing norm. This tipping norm is shown to evolve endogenously through a dynamic process of sequential market adjustments over time: the average tip in each period determines the tipping norm for the following period, causing the firm to adapt the price and customers to adapt their tips accordingly. Characterization of this equilibrium outcome allows us to derive qualitative results on the long-run impact of different exogenous factors on tipping: we find that the equilibrium tip-to-price ratio increases when customers are more sensitive to social pressure, their range of service valuations spreads out, or they consider the service more valuable. Building on this framework, we further investigate several economic implications of tipping pertaining to social welfare, labor cost, and service quality, thus uncovering incentives and trade-offs to which the tipping mechanism gives rise from the firm, the worker, and the customer's perspectives.

We examine the consequences of an intrusive debt-collection tactic that targets delinquent borrowers' social circles. Our identification strategy relies on the fact that some of the delinquent loans are not worked on because of collection agents' excessive workload. Using two approaches to estimate the local treatment effect, we show that this social-shaming tactic backfires and substantially increases the borrowers' default rate. Borrowers with better outside options for credit access and male borrowers respond more strongly after they are shamed socially. These findings are in general consistent with the negative reciprocity interpretation; angered borrowers retaliate by defaulting on their loans.

Performance ranking can trigger multiple social incentives for workers. On one hand, it offers status rewards to induce them to increase effort. On the other, better-ranked workers may reduce effort to conform to coworkers' productivity in fear of social retribution. This paper uses a field experiment in a sweater factory to disentangle the incentives underlying performance ranks. Treated workers receive ranks either privately or publicly. I find that private ranks do not have any effect on average but that public ranks reduce worker productivity. Additional evidence confirms that productivity declines because of workers' social concerns and their desire to conform to the productivity of their friends. Cooperation between workers decreases too but with limited effect on productivity. The paper illustrates how inducing worker competition may be counterproductive for firms.

Honest and dishonest behaviors may both diffuse among the members of an organization. Knowing which of the two spreads faster is important because it impacts the extent to which managers will need to resort to other, potentially more costly solutions to curb dishonest behavior. Assessing empirically which of honest behavior or dishonest behavior spreads faster is challenging because this requires field measurements of social relationships and dishonest behavior of individual members, which poses both measurement and inference problems. We examine an original fine-grained data set from a large company that allows for identifying agents likely to be dishonest and interactions among employees while offering a natural experiment that circumvents the inference problems associated with identifying peer-to-peer diffusion. We find (1) that dishonest behavior diffuses, whereas honest behavior does not; (2) that diffusion likely operates through spreading information about opportunities for collusion; and (3) that policies that screen on dishonesty at hiring may be efficient to curb dishonest behavior in environments with high turnover.