Data and incentives

• 成果类型: Article
• 署名作者: Liang, Annie; Madsen, Erik
• 署名单位: Northwestern University; New York University
• 刊物名称: THEORETICAL ECONOMICS
• ISSN/ISSBN: 1933-6837
• DOI: 10.3982/TE5289
• 发表日期: 2024-01-01
• 页码: 407-448
• 关键词: big data; forecasting; effort incentives; career concerns; C72; D83; L51

摘要

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 versus a shock to short-run outcomes. For a class of covariates satisfying a statistical property that 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.