Intrastate applicability of National Census Response Rate Models
The case of the 2020 Census in the state of Illinois, United States
Bibliographic Data
| ID | 12788138 |
|---|---|
| Authors | Jaeyong Shin (0000-0002-2955-6382, University of Illinois Chicago, corresponding author), Ahoura Zandiatashbar (0000-0001-8198-4619, San Jose State University), Dean Massey (0000-0003-4043-7226, Northeastern University), Anton Rozhkov (0000-0002-7814-1538, University of Illinois Chicago), Janet L Smith (0000-0002-0664-9228, University of Illinois Chicago), Moira Zellner (0000-0002-4111-3357, Northeastern University) |
| Year | 2022 |
| Volume | 28 |
| Issue | 8 |
| Publication date | 2022-08-21 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Population Space and Place (JOURNAL) |
| Journal identifiers | ISSN: 1544-8444 • E-ISSN: 1544-8452 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/psp.2608 |
| OpenAlex | W4292560102 |
| Language | EN |
| Citations received | 2 |
| References cited | 8 |
For the 2020 Census, the U.S. Census Bureau developed the Low Response Score (LRS) to predict census self‐response participation. Based on a previous hard‐to‐count metric, which accounts for people who are difficult to survey or count, the LRS was undoubtedly an improvement over previous census effort. However, census response behaviour can be sensitive to inter‐ and intrastate locational and socioeconomic differences; in other words, geographic differences should be accounted for prediction. In this study, we strive to assess the assumption that geographic difference influences census participation and is one of the key factors in predictive models. The framework presented in this study is composed of three steps: (i) detecting the geographic differences in census participation; (ii) building a statistical model to estimate impact of geographic difference on response rates; and (iii) comparing the model results with actual final self‐response rates of the 2020 Census to suggest policy implications. Our findings confirmed that geographic differences in census participation matter, independent of socioeconomic and demographic characteristics. The results show that the combined impacts of sociodemographic and geographic factors are important in estimating census participation. Our findings also show high accuracy in predicting census responses with a parsimonious model that includes variables for geographic differences. The results suggest a localized approach with consideration of the geographic differences, as well as different populations and neighbourhoods within a state to tailor outreach campaigns for greater effectiveness and impact
American Community Survey · Cartography · Census · Economics · Geographic information system · Geography · Metric (unit · Operations management · Population · Small area estimation · Socioeconomic status · Sociology · Statistics · Census and Population Estimation · Data-Driven Disease Surveillance · Demography · Mathematics · Survey Methodology and Nonresponse
| Citations per year | 0.67 |
|---|---|
| Citation span | 2023 - 2023 (1) |
| Highly cited | No |