Affiliations: [a] U.S. Census Bureau, Washington, DC 20233, USA | [b] Federal Deposit Insurance Corporation, USA
Correspondence:
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Corresponding author: Hubert P. Janicki, U.S. Census Bureau, 4600 Silver Hill Road, Washington, DC 20233, USA. Tel.: +1 301 763 8705; E-mail: hubert.p.janicki@census.gov.
Note: [1] Any opinions and conclusions expressed in this paper are those of the author(s) and do not necessarily represent the views of the U.S. Census Bureau or the FDIC. All results have been reviewed to ensure that no confidential information is released. This research was largely conducted while Mark Kutzbach was affiliated with the Census Bureau.
Abstract: Previous studies have shown that modeling based on administrative records can be predictive of Nonresponse Followup (NRFU) enumeration outcomes in U.S. Census Bureau Decennial data collection operations. We compare model predictive power when varying training data sources and evaluate the extent to which survey data can be used to reduce enumerator workload when combined with available administrative data. We perform the evaluation using the 2010 Census and the 2014 American Community Survey. Our main finding is that a large survey-based training dataset, such as the American Community Survey, can provide results comparable to Census data. Robustness checks then illustrate that even small sample survey-based training datasets can also yield comparable predictions. We also discuss a broader role for use of existing survey data in NRFU operations of statistical agencies outside of the United States when national Census or administrative data sources have only incomplete coverage of the population.
Keywords: Count imputation, administrative records, nonresponse, American Community Survey
