  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 three
previous Censuses. Our evaluation leverages the Noisy Measure File (NMF) as
well as 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, especially for Hispanic and multiracial populations. TopDown's
post-processing dramatically reduces the NMF noise and produces data whose
accuracy is similar to that of swapping. While the estimated errors for both
TopDown and swapping algorithms are generally no greater than other sources of
Census error, they can be relatively substantial for geographies with small
total populations.
