Full Citation

Title: How differential privacy will affect our understanding of population growth in the United States

Citation Type: Working Paper

Publication Year: 2020

ISBN:

ISSN:

DOI: http://dx.doi.org/10.31235/osf.io/pmux7

NSFID:

PMCID:

PMID:

Abstract: The implementation of a proposed differential privacy algorithm to 2020 US Census data releases, and other census products has brought about discussions about the consistency and reliability of the data produced under the proposed disclosure avoidance system. We test the potential impact of this change in disclosure avoidance systems to the tracking of population growth and distribution using county-level population counts. We ask how population counts produced under the differential privacy algorithm might lead to different conclusions regarding population growth for the total population and three major racial/ethnic groups in comparison to counts produced using the traditional methods. Our results suggest that the implementation of differential privacy, as proposed, will impact our understanding of population changes in the US. We find potential for overstating and understating growth and decline, with these effects being more pronounced for non-Hispanic blacks and Hispanics, as well as for non-metropolitan counties. These findings draw attention to the potential local consequences of the implementation of differential privacy for tracking demographic changes of the US population, which is bound to have implications for our understanding of the transformations the nation is going through.

Url: http://dx.doi.org/10.31235/osf.io/pmux7

User Submitted?: No

Authors: Santos-Lozada, Alexis R.; Perez-Rivera, Danilo T.; Bhat, Aarti C.

Series Title:

Publication Number:

Institution:

Pages:

Publisher Location:

Data Collections: IPUMS NHGIS

Topics: Other, Population Data Science, Race and Ethnicity

Countries: