Published Paper

"The Optimal Assortativity of Teams Inside the Firm" (with Ashwin Kambhampati), RAND Journal of Economics, 2022

Abstract

How does a profit-maximizing manager form teams and compensate workers when workers have private information about their productivity and exert hidden effort once in a team? We study a team-production model in which positive assortative matching is both efficient and profit-maximizing under pure adverse selection and pure moral hazard. We show that the interaction of adverse selection and moral hazard can lead to nonassortative matching if complementarities are sufficiently weak. When this is the case, the manager may prefer to delegate matching, allowing workers to sort themselves into teams.  

Working Papers

"Selling Data"

Abstract

A profit-maximizing monopolist (seller) sells multi-attribute consumer data to a firm (buyer). The seller is uncertain about which consumer characteristic the buyer is interested in forecasting and how much the buyer values information. In order to screen among potential buyers along both margins, the seller chooses a menu of statistics of the data to offer and the price of each statistic. Assuming that the data and unknown characteristics follow an elliptical distribution, I obtain two results. First, I show that the seller optimally offers statistics that are linear combinations of the data. Second, I show that the seller might need to offer a continuum of statistics, and that they are less correlated than they would be if the seller could perfectly discriminate. Every optimal statistic contains information about every variable in the data, and does not include uncorrelated noise.

"Inverse Selection" (with Markus Brunnermeier and Rohit Lamba)

Abstract

Big data and AI inverts adverse selection problems. It allows insurers to infer statistical information and thereby reverses information advantage from the insuree to the insurer. In a setting with two dimensional type space whose correlation can be inferred with big data we derive three results: First, a novel trade-off between obfuscation and price discrimination—the insurer tries to exploit but also protect its statistical information by offering few screening con- tracts. Second, insuree’s ability to do perfect Bayesian inference limits the returns to inverse selection for the insurer. Third, competition and forced transparency reduces total surplus and insurer’s payoff while increasing insuree’s payoff. 

"Matching to Produce Information: A Model of Self-Organized Research Teams" (with Ashwin Kambhampati  and Peng Shao)

Abstract

In recent decades, research organizations have brought the “market inside the firm” by allowing workers to sort themselves into teams. How do research teams form absent a central authority? We introduce a model of team formation in which workers first match and then non-cooperatively produce correlated signals about an unknown state. Our analysis identifies matching inefficiencies arising from two channels. First, productive teams composed of workers producing complementary information may form at the expense of excluded workers who must form relatively unproductive teams consisting of workers producing substitutable information. Second, even when productive teams are efficient, they need not form; a worker in such a team may prefer to join a less productive team if she can exert less effort in this deviating team. We discuss the implications of these results for organizational design.

"Higher Order Information Complementarities and Polarization"

Abstract

I study endogenous network formation in an environment in which individuals want to forecast a stochastic state and it is costly for them to communicate with others to exchange some exogenously observed information. Due to the existence of information complementarities, individuals’ preferences for networks in which they have multiple neighbors cannot be characterized by a linear ranking of the pairwise correlations between their signals. Instead, these complementarities generate a counterintuitive result: for a fixed number of individuals, information structures exist in which all signals are conditionally positively correlated, and these are preferred to a structure in which all signals are conditionally independent. Therefore, it may be that the only strongly stable network consists of two cliques with signals that are highly positively correlated within each clique that generate different beliefs across cliques, even when there are opportunities to exchange information with individuals sharing less correlated signals. Thus, this model exemplifies how homophily and belief polarization can coexist in a rational environment.