Incentive Design with Spillovers

TL;DR

This paper develops a multi-agent model for incentive design in team projects, revealing how optimal payments depend on individual productivity, centrality, and responsiveness, with implications for rewarding ability versus collaborativeness.

Contribution

It extends classic contract theory to multi-agent settings using network game methods, providing new insights into optimal incentive allocation in teams.

Findings

Optimal incentives balance individual productivity, organizational centrality, and responsiveness.

Rewarding ability versus collaborativeness depends on the strength of complementarities.

Pay dispersion is shaped by the nature of team complementarities.

Abstract

A principal uses payments conditioned on stochastic outcomes of a team project to elicit costly effort from the team members. We develop a multi-agent generalization of a classic first-order approach to contract optimization by leveraging methods from network games. The main results characterize the optimal allocation of incentive pay across agents and outcomes. Incentive optimality requires equalizing, across agents, a product of (i) individual productivity (ii) organizational centrality and (iii) responsiveness to monetary incentives. We specialize the model to explore several applied questions, including whether compensation should reward individual ability or collaborativeness and how the strength of complementarities shapes pay dispersion.