Assessing the 2020 Census Final Report (2023) / Chapter Skim
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Appendix D: Extensions of Census Coverage Evaluations
Appendix D: Extensions of Census Coverage Evaluations
Pages 459-468
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From page 459... ...
A further analysis of census errors by component (e.g., duplications, omissions) would partition the population in the PES sample into two groups -- those addresses, households, or individuals for which attempts at enumeration resulted in a census component enumeration error and those that did not.
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From page 460... ...
" Specific questions follow, including types of housing units that were missed or duplicated more often than others and types of people who were missed, erroneously enumerated, duplicated, or counted in the wrong locations more often than others. Statistical modeling could help answer such questions, with the expectation that the findings would identify pathways for census improvement in 2030.
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From page 461... ...
Candidates for predictor variables are identified by the National Research Council (2009:123) : To understand which subset of individuals and housing units are more frequently subject to coverage errors of the four indicated types, and to understand what census processes contributed to those errors, it is necessary to focus on predictors that distinguish between individuals and housing units that are likely to have different interactions with census enumeration processes, as well as predictors that indicate the census processes 2 Some of these analyses might be improved through use of a non-dichotomous loss structure reflecting errors of differing gravity.
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From page 462... ...
• Housing unit variables -- candidate variables include whether the house hold itself is newly constructed or is part of a small multiunit building and whether the housing unit is part of a GQ and, if so, what type. • People's demographic characteristics -- candidate variables include indi cators for demographic groups that are historically subject to varying degrees of net census undercoverage (e.g., ages 18–22 are associated with increased chances of duplication and ages 0–4 with increased chances of omission)
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From page 463... ...
Indicators of contextual factors could include: • Variables associated with neighborhood characteristics, such as the percentage of people in an area that own their own residences, the local mail return rate, the local crime rate, and the health of the local economy. D.1.2 Implementation Issues As noted in National Research Council (2009:126)
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From page 464... ...
In addition, the Census Bureau should also examine the possibility of using separate regression models for separate geographic domains. D.1.3 PES Redesign While the usual structure of the PES could be continued in 2030 for both the traditional net and gross coverage error estimation and the proposed discriminant analysis, other designs could provide greater utility in this regard.
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From page 465... ...
The goal would be to tease out patterns and characteristics that suggest strategies that could improve the census count for some groups in some areas.3 The study would need to be carried out by Census Bureau staff or, perhaps better, by a consortium of researchers working with census staff and people with local and group knowledge at a Federal Statistical Research Data Center. It would be ideal if the study could be completed within 18–24 months and presented to the Census Bureau and stakeholder leadership with recommendations for actionable ideas to pursue (such as obtaining authorization for a particular type of administrative record or illustrating the extent of positive effects on selfresponse of universal affordable broadband)
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From page 466... ...
For analysis purposes, a possible plan could be to identify the lowest one-quarter of census tracts in Self-Response -- about 21,000 tracts -- in states for which the Census Bureau has Supplemental Nutrition Assistance Program records (setting aside tracts in remote areas, on American Indian or Alaska Native reservations, and the like for separate attention)
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From page 467... ...
There would be error in matching, as there is in the PES, but matching at a microlevel on the scale proposed (about 2.5 million addresses) and with as many record sources as possible could hopefully identify a number of avenues for testing in the lead-up to the 2030 Census.
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