Differential Privacy in the 2020 Census Will Distort COVID-19 Rates

Abstract

Scholars rely on accurate population and mortality data to inform efforts regarding the coronavirus disease 2019 (COVID-19) pandemic, with age-specific mortality rates of high importance because of the concentration of COVID-19 deaths at older ages. Population counts, the principal denominators for calculating age-specific mortality rates, will be subject to noise infusion in the United States with the 2020 census through a disclosure avoidance system based on differential privacy. Using empirical COVID-19 mortality curves, the authors show that differential privacy will introduce substantial distortion in COVID-19 mortality rates, sometimes causing mortality rates to exceed 100 percent, hindering our ability to understand the pandemic. This distortion is particularly large for population groupings with fewer than 1,000 persons: 40 percent of all county-level age-sex groupings and 60 percent of race groupings. The U.S. Census Bureau should consider a larger privacy budget, and data users should consider pooling data to minimize differential privacy’s distortion.

The Role of Chance in the Census Bureau Database Reconstruction Experiment

“…The results of the database reconstruction experiment do not justify the substantial degradation of the nation's statistical infrastructure resulting from the implementation of differential privacy. The intentional errors introduced by Census Bureau's new disclosure control methods will compromise the utility of the data for demographic analysis, policy research, and planning (e.g., Hauer & Santos-Lozada, 2021;Santos-Lozada et al, 2020;Winkler et al, 2021). Weighing the high cost of the new disclosure protocol against negligible benefit for respondent confidentiality, it is apparent that differential privacy for census data is an unfortunate mistake.…”

“…Rigorous evaluation of the Census Bureau's database reconstruction experiment is important because differential privacy will add error to every statistic the agency produces for geographic units below the state level, and this error will significantly reduce the usability of census data for social, economic, and health research, and will compromise basic demographic measures (Hauer & Santos-Lozada, 2021;Ruggles et al, 2018a, 2018bSantos-Lozada et al, 2020Winkler et al, 2021).…”

Rural-urban differences in the labor-force impacts of COVID-19 in the United States

“…Beyond these compositional differences, access to healthcare is also lower in rural areas, likely amplifying differences in COVID-19-related employment disruptions between rural and urban residents as many rural residents may not be able to be tested for COVID-19 or seek medical care upon disease contraction (Burton et al 2013;Peters 2020;Souch and Cossman 2021). While COVID-19 rates in rural counties have generally been lower than rates in urban counties (Karim and Chen 2021;Paul et al 2020); the exact differences in rates is difficult to estimate due to both differences in testing (Souch and Cossman 2021) and underlying data constraints affecting data quality for rural populations (Hauer and Santos-Lozada 2021).…”

“…The majority of empirical work on the pandemic to date has focused on either the nation as a whole or the urban population, with rural areas-approximately 46 million people-remaining largely ignored (Mueller et al 2021). 1 This evidence gap reflects an absence of high-quality secondary data on rural areas (Hauer and Santos-Lozada 2021;Mueller et al 2021;Pender 2020). Further, the limited evidence that is available suggests the presence of considerable geographic heterogeneity between rural and urban areas in the pandemic's impacts.…”

The 2020 US Census Differential Privacy Method Introduces Disproportionate Discrepancies for Rural and Non-White Populations

“…The Census Bureau has provided researchers with test data for evaluation which applies versions of the new 2020 system to 2010 data, and multiple papers have found significant issues with each successive vintage of experimental data. In general, demographers find there is little issue with overall total population counts for larger geographic units such as counties and blockgroups, but serious issues for population subgroups and small geographic areas (Hauer & Santos-Lozada, 2021;Kenny et al, 2021;Santos-Lozada, Howard, & Verdery, 2020). For example, researchers found that migration estimates derived from prior vintages of demonstration data were only usable for about half of US counties (Winkler et al, 2021), and that the algorithm produced inaccurate infant mortality rates for rural counties (Santos, 2021).…”