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Data Privacy and Temptation

Author

Listed:
  • Zhuang Liu

    (University of California, Berkeley)

  • Michael Sockin

    (University of Texas at Austin)

  • Wei Xiong

    (Princeton University)

Abstract

This paper microfounds a consumer's preference for data privacy as a mechanism for concealing behavioral vulnerabilities. This approach facilitates a welfare analysis of different data privacy regulations, such as the GDPR and CCPA. Sharing data with a digital platform benefits a consumer through improved matching efficiency with normal consumption goods at the expense of exposing those with self-control issues to temptation goods. Although the GDPR and CCPA empower consumers to opt in or out of data sharing, our analysis also highlights the limitations of these regulations because of nuanced data sharing externalities induced by consumers' active and default choices.

Suggested Citation

  • Zhuang Liu & Michael Sockin & Wei Xiong, 2021. "Data Privacy and Temptation," Working Papers 2021-77, Princeton University. Economics Department..
    • Handle: RePEc:pri:econom:2021-77
    as

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    File URL: http://wxiong.mycpanel.princeton.edu/papers/Privacy.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    data; privacy; data privacy regulations; GDPR; CCPA;
    All these keywords.

    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • K33 - Law and Economics - - Other Substantive Areas of Law - - - International Law
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy

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