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Data and incentives

Author

Listed:
  • Liang, Annie

    (Department of Economics, Northwestern University)

  • Madsen, Erik

    (Department of Economics, New York University)

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.

Suggested Citation

  • Liang, Annie & Madsen, Erik, 2024. "Data and incentives," Theoretical Economics, Econometric Society, vol. 19(1), January.
    • Handle: RePEc:the:publsh:5289
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    References listed on IDEAS

    as
    1. Sinem Hidir & Nikhil Vellodi, 2021. "Privacy, Personalization, and Price Discrimination," Journal of the European Economic Association, European Economic Association, vol. 19(2), pages 1342-1363.
    2. Mathias Dewatripont & Ian Jewitt & Jean Tirole, 1999. "The Economics of Career Concerns, Part I: Comparing Information Structures," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 66(1), pages 183-198.
    3. Kai Hao Yang, 2022. "Selling Consumer Data for Profit: Optimal Market-Segmentation Design and Its Consequences," American Economic Review, American Economic Association, vol. 112(4), pages 1364-1393, April.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Dirk Bergemann & Marek Bojko & Paul Dutting & Renato Paes Leme & Haifeng Xu & Song Zuo, 2024. "Data-Driven Mechanism Design: Jointly Eliciting Preferences and Information," Papers 2412.16132, arXiv.org, revised Dec 2025.

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    More about this item

    Keywords

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    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L51 - Industrial Organization - - Regulation and Industrial Policy - - - Economics of Regulation

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