# Suboptimal Provision of Privacy and Statistical Accuracy When They are Public Goods

## TL;DR

This paper models how private firms providing population statistics under differential privacy tend to produce suboptimal levels of data quality and privacy, highlighting inefficiencies in public goods provision.

## Contribution

It introduces a formal model analyzing the incentives and outcomes when private firms supply privacy-protected statistical data as public goods.

## Key findings

- Private firms often underprovide data quality due to conflicting incentives.
- Differential privacy guarantees lead to inefficiently low data accuracy.
- The model demonstrates suboptimal provision of both privacy and accuracy.

## Abstract

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.