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The United States Census Bureau faces a
difficult trade-off between the accuracy of Census statistics and
the protection of individual information. We conduct the first
independent evaluation of bias and noise induced by the Bureau's two
main disclosure avoidance systems: the TopDown algorithm employed
for the 2020 Census and the swapping algorithm implemented for the
1990, 2000, and 2010 Censuses. Our evaluation leverages the recent
release of the Noisy Measure File (NMF) as well as the availability
of two independent runs of the TopDown algorithm applied to the 2010
decennial Census. We find that the NMF contains too much noise to be
directly useful alone, especially for Hispanic and multiracial
populations. TopDown's post-processing dramatically reduces the NMF
noise and produces similarly accurate data to swapping in terms of
bias and noise. These patterns hold across census geographies with
varying population sizes and racial diversity. While the estimated
errors for both TopDown and swapping are generally no larger than
other sources of Census error, they can be relatively substantial
for geographies with small total populations. |
Kenny, Christopher T., Shiro Kuriwaki, Cory
McCartan, Evan T.R. Rosenman, Tyler Simko, and Kosuke Imai. (2021)
``The Use of Differential Privacy for
Census Data and its Impact on Redistricting: The Case of the 2020
U.S. Census.'' Science Advances, Vol. 7, No. 7
(October), pp. 1-17. |
Kenny, Christopher T., Shiro Kuriwaki, Cory
McCartan, Evan Rosenman, Tyler Simko, and Kosuke
Imai. (2023). ``Comment: The
Essential Role of Policy Evaluation for the 2020 Census Disclosure
Avoidance System..'' Harvard Data Science
Review, Special Issue 2: Dierential Privacy for the 2020
U.S. Census (January). |
McCartan, Cory, Tyler Simko, and Kosuke
Imai. (2023). ``Researchers
need better access to US Census data.''
Science, Vol. 380, No. 6648 pp. 902-903 |
McCartan, Cory, Tyler Simko, and Kosuke
Imai. (2023). ``Making Differential
Privacy Work for Census Data Users.'' Harvard Data Science
Review, Vol. 5, No. 4 (Fall).
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