Ashwin Kambhampati

Ashwin Kambhampati
Job Market Paper

Robust Performance Evaluation

I consider a moral hazard in teams model in which a principal knows that the agents she compensates are identical and independent, but does not know all of the actions they can take. I show that any worst-case optimal contract exhibits joint performance evaluation and is nonlinear in team output. Hence, when robustness is a concern, nonlinear team-based incentive schemes — such as team bonuses and employee stock options — are justified, even if tasks are completed individually and individual performances are uncorrelated. This result contrasts with the classical theory of incentives, which finds independent performance evaluation to be Bayesian optimal, and with the recent literature on robust contracting with unbounded uncertainty, which finds linear incentive schemes to be worst-case optimal. Moreover, it reveals a new channel leading to the optimality of joint performance evaluation and formalizes a longstanding idea that interdependent incentive schemes are advantageous due to their flexibility.

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Other Research

The Optimal Assortativity of Teams Inside the Firm, with Carlos Segura-Rodriguez

How does a profit-maximizing manager form teams and compensate workers in the presence of both adverse selection and moral hazard? Under complete information, it is well known that any complementarity in characteristics implies that positive assortative matching is productively efficient. But, under asymmetric information, we uncover the problem of disassortative incentives: incentive costs may increase in assortativity. Profit maximization thus prescribes either random or negative assortative matching, both productively inefficient, when complementarities are weak and effort costs are high enough. When this is the case, the manager may instead prefer to delegate matching, allowing workers to sort themselves into teams. Our results shed light on recent empirical work documenting patterns of non-assortative matching inside of firms.

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Matching to Produce Information: A Model of Self-Organized Research Teams, with Carlos Segura-Rodriguez and Peng Shao

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. We uncover a novel form of moral hazard: an efficient team of workers producing complementary signals may be disrupted if one of its members can form an inefficient team in which she exerts less effort. This inefficiency rationalizes targeted management interventions which designate specific workers as “project leaders” with more assumed responsibilities.

 

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Payoff Continuity in Games of Incomplete Information: An Equivalence Result

Monderer and Samet (1996) and Kajii and Morris (1998) define notions of proximity for countable, common prior information structures that preserve equilibrium payoff continuity. Monderer and Samet (1996) fix a common prior and perturb lists of partitions, while Kajii and Morris (1998) fix a type space and perturb common priors. Due to these differences, the precise relationship between the two papers has remained an open question. We establish an equivalence between them by mapping pairs of partition lists to pairs of common priors, and vice-versa. The key condition of the mapping ensures that belief types are changed independently of payoff types in the Kajii and Morris (1998) perturbation.

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Interests

Microeconomic Theory, Industrial Organization, Matching and Market Design

Email

akambh@sas.upenn.edu

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Advisors

George J. Mailath

References

J. Aislinn Bohren 

Steven A. Matthews

Juuso Toikka

Job Market Candidate Status
I am a PhD candidate in the Department of Economics at the University of Pennsylvania. I am a microeconomic theorist, with a particular interest in the form and function of modern organizations. I will be available for interviews on the 2020/2021 job market.