Robert Bozick, Lane F Burgette, Ethan Sharygin, Regina A Shih, Beverly Weidmer, Michael Tzen, Aaron Kofner, Jennie E Brand, Hiram Beltrán-Sánchez
{"title":"Evaluating the Accuracy of 2020 Census Block-Level Estimates in California.","authors":"Robert Bozick, Lane F Burgette, Ethan Sharygin, Regina A Shih, Beverly Weidmer, Michael Tzen, Aaron Kofner, Jennie E Brand, Hiram Beltrán-Sánchez","doi":"10.1215/00703370-11075209","DOIUrl":null,"url":null,"abstract":"<p><p>In this study, we provide an assessment of data accuracy from the 2020 Census. We compare block-level population totals from a sample of 173 census blocks in California across three sources: (1) the 2020 Census, which has been infused with error to protect respondent confidentiality; (2) the California Neighborhoods Count, the first independent enumeration survey of census blocks; and (3) projections based on the 2010 Census and subsequent American Community Surveys. We find that, on average, total population counts provided by the U.S. Census Bureau at the block level for the 2020 Census are not biased in any consistent direction. However, subpopulation totals defined by age, race, and ethnicity are highly variable. Additionally, we find that inconsistencies across the three sources are amplified in large blocks defined in terms of land area or by total housing units, blocks in suburban areas, and blocks that lack broadband access.</p>","PeriodicalId":48394,"journal":{"name":"Demography","volume":" ","pages":"1903-1921"},"PeriodicalIF":3.6000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Demography","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.1215/00703370-11075209","RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"DEMOGRAPHY","Score":null,"Total":0}
引用次数: 0
Abstract
In this study, we provide an assessment of data accuracy from the 2020 Census. We compare block-level population totals from a sample of 173 census blocks in California across three sources: (1) the 2020 Census, which has been infused with error to protect respondent confidentiality; (2) the California Neighborhoods Count, the first independent enumeration survey of census blocks; and (3) projections based on the 2010 Census and subsequent American Community Surveys. We find that, on average, total population counts provided by the U.S. Census Bureau at the block level for the 2020 Census are not biased in any consistent direction. However, subpopulation totals defined by age, race, and ethnicity are highly variable. Additionally, we find that inconsistencies across the three sources are amplified in large blocks defined in terms of land area or by total housing units, blocks in suburban areas, and blocks that lack broadband access.
期刊介绍:
Since its founding in 1964, the journal Demography has mirrored the vitality, diversity, high intellectual standard and wide impact of the field on which it reports. Demography presents the highest quality original research of scholars in a broad range of disciplines, including anthropology, biology, economics, geography, history, psychology, public health, sociology, and statistics. The journal encompasses a wide variety of methodological approaches to population research. Its geographic focus is global, with articles addressing demographic matters from around the planet. Its temporal scope is broad, as represented by research that explores demographic phenomena spanning the ages from the past to the present, and reaching toward the future. Authors whose work is published in Demography benefit from the wide audience of population scientists their research will reach. Also in 2011 Demography remains the most cited journal among population studies and demographic periodicals. Published bimonthly, Demography is the flagship journal of the Population Association of America, reaching the membership of one of the largest professional demographic associations in the world.