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Replication data for "Evaluating Bias and Noise Induced by the U.S. Census Bureau's Privacy Protection Methods"

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication data for "Evaluating Bias and Noise Induced by the U.S. Census Bureau's Privacy Protection Methods"
 
Identifier https://doi.org/10.7910/DVN/TMIN3H
 
Creator Kenny, Christopher
Kuriwaki, Shiro
McCartan, Cory
Simko, Tyler
Imai, Kosuke
 
Publisher Harvard Dataverse
 
Description 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.
 
Subject Law
Social Sciences
 
Date 2024-02-01
 
Contributor Kenny, Christopher