Title

Data Privacy and Temptation

Author(s)

Zhuang Liu Zhuang Liu (University of California, Berkeley)

Michael Sockin Michael Sockin (University of Texas at Austin)

Wei Xiong 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.

Creation Date

2021-12

Section URL ID

Paper Number

2021-77

URL

http://wxiong.mycpanel.princeton.edu/papers/Privacy.pdf

File Function

Jel

C80, K33, O38

Keyword(s)

data, privacy, data privacy regulations, GDPR, CCPA

Suppress

false

Series

13