The purpose of this document is to provide scholars with a comprehensive list of readings relevant to the economic analysis of formal privacy, and particularly its application to public statistics. Statistical agencies and tech giants are rapidly adopting formal privacy models which make the tradeoff between privacy and data quality precise. The question then becomes, how much privacy loss should they allow? Abowd and Schmutte (2019) argue that this choice ultimately depends on how decision makers weigh the costs of privacy loss against the benefits of higher-quality data. Making progress on these questions requires familiarity with new tools from computer science and statistics, the objectives and policy environment within which statistical agencies operate, along with the economic analysis of information. We have organized these references into a reading course focused on 10-15 primary references in each of six different topics: Formal Privacy Policy and Official Statistics Statistical Disclosure Limitation Economics of Privacy Value of Privacy and Data Accuracy

Supported by Alfred P. Sloan Foundation Grant G-2015-13903 and NSF Grant SES-1131848