Team Formation And Incentives

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Degree type
Doctor of Philosophy (PhD)
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Economics
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Economics
Economic Theory
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2022-09-09T20:21:00-07:00
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Kambhampati, Ashwin
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Abstract

This dissertation analyzes the incentives of workers in organizations that utilize teams. In Chapter 1, I study 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. In the face of this uncertainty, the principal chooses a symmetric contract that yields her the highest worst-case expected profit. I show that, counterintuitively, any such contract exhibits joint performance evaluation — each agent's pay is increasing in the performance of the other — and is nonlinear in team output. In Chapter 2, Carlos Segura-Rodriguez and I study profit-maximizing matching in the presence of adverse selection and moral hazard. We show that when productive complementarities between workers are weak and effort costs are high, expected wage payments increase in the assortativity of the matching the manager implements. Hence, either random or negative assortative matching can be profit-maximizing, even when positive assortative matching is efficient. Finally, in Chapter 3, Carlos Segura-Rodriguez, Peng Shao, and I study the efficiency of decentralized team formation inside research organizations through the lens of a one-sided matching model with non-cooperative after-match information production. Our equilibrium analysis identifies two inefficiencies observed inside of non-hierarchical organizations. 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.

Advisor
George Mailath
Date of degree
2021-01-01
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