Data and Incentives

TL;DR

This paper examines how the introduction of new data influences agents' effort incentives in markets, revealing that the effect depends on whether the data predicts long-term quality or short-term shocks, with implications for social welfare.

Contribution

It provides a theoretical analysis of how new data affects effort incentives and social welfare, highlighting the role of covariate informativeness and distributional effects.

Findings

Measurement of covariates influences effort levels systematically.

Strong homoskedasticity leads to uniform effects across agents.

Distributional impacts of new data significantly affect social welfare.

Abstract

"Big data" gives markets access to previously unmeasured characteristics of individual agents. Policymakers must decide whether and how to regulate the use of this data. We study how new data affects incentives for agents to exert effort in settings such as the labor market, where an agent's quality is initially unknown but is forecast from an observable outcome. We show that measurement of a new covariate has a systematic effect on the average effort exerted by agents, with the direction of the effect determined by whether the covariate is informative about long-run quality or about a shock to short-run outcomes. For a class of covariates satisfying a statistical property we call strong homoskedasticity, this effect is uniform across agents. More generally, new measurements can impact agents unequally, and we show that these distributional effects have a first-order impact on social welfare.