A Comparison of Training Modules for Administrative Records Use in Nonresponse Followup Operations: The 2010 Census and the American Community Survey

Melissa Chow (), Hubert Janicki, Mark Kutzbach, Lawrence Warren and Moises Yi

Working Papers from U.S. Census Bureau, Center for Economic Studies

Abstract: While modeling work in preparation for the 2020 Census has shown that administrative records can be predictive of Nonresponse Followup (NRFU) enumeration outcomes, there is scope to examine the robustness of the models by using more recent training data. The models deployed for workload removal from the 2015 and 2016 Census Tests were based on associations of the 2010 Census with administrative records. Training the same models with more recent data from the American Community Survey (ACS) can identify any changes in parameter associations over time that might reduce the accuracy of model predictions. Furthermore, more recent training data would allow for the incorporation of new administrative record sources not available in 2010. However, differences in ACS methodology and the smaller sample size may limit its applicability. This paper replicates earlier results and examines model predictions based on the ACS in comparison with NRFU outcomes. The evaluation consists of a comparison of predicted counts and household compositions with actual 2015 NRFU outcomes. The main findings are an overall validation of the methodology using independent data.

Pages: 64 pages
Date: 2017-01
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Citations: View citations in EconPapers (2)

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https://www2.census.gov/ces/wp/2017/CES-WP-17-47.pdf First version, 2017 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:cen:wpaper:17-47

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