Abstract
The recently finalized changes to the disclosure avoidance policies of the US Census Bureau for the 2020 census, grounded in differential privacy, have faced increasing criticism from demographers and other social scientists. Scholars have found that estimates generated via census-released test data are accurate for aggregate total population statistics of larger spatial units (e.g., counties), but introduce considerable discrepancies for estimates of subgroups. At present, the ramifications of this new approach remain unclear for rural populations. In this brief, we focus on rural populations and evaluate the ability of the finalized differential privacy algorithm to provide accurate population counts and growth rates from 2000 to 2010 across the rural–urban continuum for the total, non-Hispanic white, non-Hispanic Black, Hispanic or Latino/a, and non-Hispanic American Indian population. We find the method introduces significant discrepancies relative to the prior approach into counts and growth rate estimates at the county level for all groups except the total and non-Hispanic white population. Further, discrepancies increase dramatically as we move from urban to rural. Thus, the differential privacy method likely introduced significant discrepancies for rural and non-white populations into 2020 census tabulations.
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Data Availability
The data utilized in our study is available through the National Historical Geographic Information System (NHGIS, https://www.nhgis.org/).
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Our code is available upon request by interested parties. Requests for our code should be sent to the corresponding author.
Notes
Due to population counts of zero, the calculation of ratios of relative discrepancies, growth rates, and subsequent growth rate ratios resulted in a number of inestimable rates and rate ratios due to the presence of zeroes in denominators. These observations have been treated as missing for this analysis and include 1 county for Hispanic or Latina/o, 33 counties for non-Hispanic Black, and 7 counties for non-Hispanic American Indian for relative differences in 2010 statistics. For growth rate ratios, this includes 1 county for Hispanic or Latina/o, 74 counties for non-Hispanic Black, and 13 counties for non-Hispanic American Indian.
A metropolitan county is defined by the Office of Management and Budget as a county with either a core population of at least 50,000, or that is connected to a core metropolitan county by greater than 25% of commuting (Office of Management and Budget, 2010). Please see https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/documentation/ for a map of RUCC distribution across the United States.
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The authors are thankful to the IPUMS and NHGIS for making the materials required for this publication accessible through their platforms.
Funding
Alexis R. Santos-Lozada is funded by the Social Science Research Institute and PRI at the Pennsylvania State University. PRI is supported by a grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (P2CHD041025). Santos-Lozada is also funded by a Diversity Supplement from the National Institute on Aging through the Interdisciplinary Network on Rural Population Health and Aging (R24-AG065159 and R24-AG065159-03S1).
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Mueller and Santos-Lozada had full access to all data and take full responsibility for the integrity of the data and the accuracy of the data analysis. Mueller and Santos conceptualized and designed the study, acquired, and analyzed the data. Both authors contributed to the data interpretation. Mueller drafted the original manuscript and the revised versions. Santos contributed to all versions of the manuscript.
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Because these data are country-level aggregate counts, this study is not considered to be research involving human subjects as defined by US regulation (45 CFR 46.102(d)).
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Mueller, J.T., Santos-Lozada, A.R. The 2020 US Census Differential Privacy Method Introduces Disproportionate Discrepancies for Rural and Non-White Populations. Popul Res Policy Rev 41, 1417–1430 (2022). https://doi.org/10.1007/s11113-022-09698-3
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DOI: https://doi.org/10.1007/s11113-022-09698-3