1 Overview

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? J. M. Abowd & 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:

In the remainder of this document, for each topic, we provide a very brief description of the papers in the reading course and why we selected them. In each case, we orient the reader to the key issues, concepts, and tools in each topic. All references are repeated in the global References section. In addition to this reading course, we have also curated a much more extensive list of references, available here.

1.1 Contributing

We encourage interested readers and researchers to use these readings for their classes and training, modifying it as needed. You can fork the source code at https://github.com/labordynamicsinstitute/privacy-bibliography. This document is licensed under the Creative Commons Attribution-NonCommercial 4.0 International Public License (CC BY-NC 4.0). Should that license not meet your needs, contact us to discuss possible modifications or exemption. Please send us any improvements, corrections, or other contributions, either by e-mail, or via the “Issues” feature on Github, or by clicking on the edit button in the toolbar above.

1.2 Acknowledgements

We gratefully acknowledge the support of Alfred P. Sloan Foundation Grant G-2015-13903 and NSF Grant SES-1131848.

1.3 Disclaimer

The views expressed in this paper are those of the authors and not those of the U.S. Census Bureau or other sponsors.


Abowd, J. M., & Schmutte, I. M. (2019). An economic analysis of privacy protection and statistical accuracy as social choices. American Economic Review, 109(1), 171–202. https://doi.org/10.1257/aer.20170627