Task assignment as dynamic incentives

TL;DR

This paper explores how dynamic task assignment can serve as an effective incentive mechanism in repeated work settings, emphasizing the importance of evolving priority rankings to optimize effort and manage inequality.

Contribution

It introduces a model where task assignment acts as a dynamic incentive, deriving optimal priority rankings and analyzing their effects on effort and inequality among workers.

Findings

Optimal incentives require strict, evolving priority rankings.

Workforce size and monitoring influence incentive scope.

Assignment policies affect workload distribution and inequality.

Abstract

We study repeated task assignment as an instrument for providing effort incentives. Unlike traditional incentive instruments, assignment of a task both determines who produces and provides incentives, and incentives for one worker spill over to others because assignment is exclusive. We show that optimal incentives require a strict and evolving priority ranking through which workers are assigned the task. This ranking implies that workers' average workloads differ even when they are symmetric in all payoff-relevant respects. We characterize how workforce size, monitoring, and working conditions shape the scope of optimal incentive provision and the resulting inequality among workers.