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Data and Incentives

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  • Annie Liang
  • Erik Madsen

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.

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  • Annie Liang & Erik Madsen, 2020. "Data and Incentives," Papers 2006.06543, arXiv.org, revised Sep 2022.
    • Handle: RePEc:arx:papers:2006.06543
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    References listed on IDEAS

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    Cited by:

    1. Dirk Bergemann & Marco Ottaviani, 2021. "Information Markets and Nonmarkets," Cowles Foundation Discussion Papers 2296, Cowles Foundation for Research in Economics, Yale University.
    2. Dirk Bergemann & Alessandro Bonatti & Tan Gan, 2022. "The economics of social data," RAND Journal of Economics, RAND Corporation, vol. 53(2), pages 263-296, June.
    3. Raluca Elena Narita, 2023. "Consumption of Healthcare Services in the United States: The Impact of Health Insurance," JRFM, MDPI, vol. 16(5), pages 1-16, May.
    4. Mohammad Rasouli & Michael I. Jordan, 2021. "Data Sharing Markets," Papers 2107.08630, arXiv.org, revised Jul 2021.

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