Data Privacy and Algorithmic Inequality

Zhuang Liu, Michael Sockin and Wei Xiong

No 31250, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: D0 E0 (search for similar items in EconPapers)
Date: 2023-05
New Economics Papers: this item is included in nep-ain, nep-pay and nep-reg
Note: AP CF EFG
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.nber.org/papers/w31250.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberwo:31250

Ordering information: This working paper can be ordered from
http://www.nber.org/papers/w31250

Access Statistics for this paper

More papers in NBER Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by ().