Economic Analysis and Statistical Disclosure Limitation
Forthcoming, Brookings Papers on Economic Activity
Abowd, John M.; Schmutte, Ian M.
This paper explores the consequences for economic research of methods used by data publishers to protect the privacy of their respondents. We review the concept of statistical disclosure limitation for an audience of economists who may be unfamiliar with these methods. We characterize what it means for statistical disclosure limitation to be ignorable. When it is not ignorable, we consider the effects of statistical disclosure limitation for a variety of research designs common in applied economic research. Because statistical agencies do not always report the methods they use to protect confidentiality, we also characterize settings in which statistical disclosure limitation methods are discoverable; that is, they can be learned from the released data. We conclude with advice for researchers, journal editors, and statistical agencies.
The authors acknowledge direct support from the Alfred P. Sloan Foundation (Grant G-2015-13903) and, of course, the Brookings Institution. Abowd acknowledges direct support from the National Science Foundation (NSF Grants BCS-0941226, TC-1012593, and SES-1131848). This paper was written while Abowd was visiting the Center for Labor Economics at the University of California, Berkeley.
SDL, statistical disclosure limitation, confidentiality, economic analysis, data, privacy
Abowd, John M. and Ian M. Schmutte (2015) “Economic Analysis and Statistical Disclosure Limitation,” Brookings Papers on Economic Activity (Spring): 271-293 (including discussion), URL: https://www.brookings.edu/wp-content/uploads/2015/03/2015a_abowd.pdf.
Replication code can be found at https://doi.org/10.5281/zenodo.377008 and our Github repository (https://github.com/labordynamicsinstitute/Economic-Analysis-and-SDL-replication). Replication data created by and used by the code can be found at openICPSR under https://doi.org/10.3886/E100505V1.