Data Privacy and Algorithmic Inequality

作者：Zhuang Liu, Michael Sockin, Wei Xiong · 发表于：National Bureau of Economic Research · 年份：2023 · DOI：10.3386/w31250 · 被引用次数：16 · 研究领域：Privacy-Preserving Technologies in Data、Cryptography and Data Security、Blockchain Technology Applications and Security

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.