Suboptimal Provision of Privacy and Statistical Accuracy When They are Public Goods
This addresses the issue of suboptimal privacy and statistical accuracy in public goods for policymakers and data users.
The paper tackles the problem of private tech companies providing population statistics with differential privacy guarantees, and finds that private provision leads to inefficiently low data quality.
With vast databases at their disposal, private tech companies can compete with public statistical agencies to provide population statistics. However, private companies face different incentives to provide high-quality statistics and to protect the privacy of the people whose data are used. When both privacy protection and statistical accuracy are public goods, private providers tend to produce at least one suboptimally, but it is not clear which. We model a firm that publishes statistics under a guarantee of differential privacy. We prove that provision by the private firm results in inefficiently low data quality in this framework.