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Data Privacy and Algorithmic Inequality

Author

Listed:
  • Zhuang Liu
  • Michael Sockin
  • Wei Xiong

Abstract

This paper develops a foundation for consumer privacy preferences by linking them to the desire to conceal behavioral vulnerabilities. Although data sharing with digital platforms improves matching efficiency for products and services, it also exposes individuals with self-control issues to predatory lending practices, creating a new form of inequality in the digital era—algorithmic inequality. Privacy regulations empower consumers to opt out of data sharing, but cannot fully protect vulnerable individuals because of data-sharing externalities. Moreover, coordination frictions among consumers may generate multiple equilibria with drastically different levels of data sharing, amplifying both efficiency gains and inequality risks.

Suggested Citation

  • Zhuang Liu & Michael Sockin & Wei Xiong, 2023. "Data Privacy and Algorithmic Inequality," NBER Working Papers 31250, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:31250
    Note: AP CF EFG
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    Cited by:

    1. Youichiro Higashi & Kazuya Hyogo & Gil Riella, 2024. "Dynamically consistent menu preferences," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 78(4), pages 1047-1074, December.
    2. 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.

    More about this item

    JEL classification:

    • D0 - Microeconomics - - General
    • E0 - Macroeconomics and Monetary Economics - - General

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