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Valuing Intrinsic and Instrumental Preferences for Privacy

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  • Tesary Lin

    (Boston University Questrom School of Business, Boston, Massachusetts 02215)

Abstract

I empirically separate two components in a consumer’s privacy preference. The intrinsic component is a “taste” for privacy, a utility primitive. The instrumental component comes from the consumer’s anticipated economic loss from revealing his private information to the firm and arises endogenously from a firm’s usage of consumer data. Combining an experiment and a structural model, I measure the revealed preferences separately for each component. Intrinsic preferences have seemingly small mean values, ranging from $0.14 to $2.37 per demographic variable. Meanwhile, they are highly heterogeneous across consumers and categories of data: The valuations of consumers at the right tail often exceed the firm’s valuation of consumer data. Consumers’ self-selection into data sharing depends on the respective magnitudes and correlation between the two preference components and often deviates from the “low types are more willing to hide” argument. Through counterfactual analysis, I show how this more nuanced selection pattern changes a firm’s inference from consumers’ privacy decisions and its data-buying strategy.

Suggested Citation

  • Tesary Lin, 2022. "Valuing Intrinsic and Instrumental Preferences for Privacy," Marketing Science, INFORMS, vol. 41(4), pages 663-681, July.
  • Handle: RePEc:inm:ormksc:v:41:y:2022:i:4:p:663-681
    DOI: 10.1287/mksc.2022.1368
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    References listed on IDEAS

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    Cited by:

    1. Natvik, Gisle J. & Tangerås, Thomas, 2023. "Paying with Personal Data," Working Paper Series 1481, Research Institute of Industrial Economics.
    2. Groh, Carl-Christian, 2023. "Search, Data, and Market Power," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277701, Verein für Socialpolitik / German Economic Association.
    3. Kinshuk Jerath & Klaus M. Miller, 2024. "Consumers' Perceived Privacy Violations in Online Advertising," Papers 2403.03612, arXiv.org, revised May 2024.
    4. Dana Turjeman & Fred M. Feinberg, 2024. "When the Data Are Out: Measuring Behavioral Changes Following a Data Breach," Marketing Science, INFORMS, vol. 43(2), pages 440-461, March.
    5. Olivier Armantier & Sebastian Doerr & Jon Frost & Andreas Fuster & Kelly Shue, 2024. "Nothing to hide? Gender and age differences in the willingness to share data," BIS Working Papers 1187, Bank for International Settlements.
    6. Amalia R. Miller & Kamalini Ramdas & Alp Sungu, 2023. "Browsers Don’t Lie? Gender Differences in the Effects of the Indian COVID-19 Lockdown on Digital Activity and Time Use," NBER Working Papers 31919, National Bureau of Economic Research, Inc.

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