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.Comment: 21 pages, 6 figure