Bozick Robert, Burgette Lane F, Sharygin Ethan, Shih Regina A, Weidmer Beverly, Tzen Michael, Kofner Aaron, Brand Jennie E, Beltrán-Sánchez Hiram
Department of Economics, Sociology, and Statistics, RAND Corporation, Santa Monica, CA, USA.
Population Research Center, Portland State University, Portland, OR, USA.
Demography. 2023 Dec 1;60(6):1903-1921. doi: 10.1215/00703370-11075209.
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.
在本研究中，我们对2020年人口普查的数据准确性进行了评估。我们比较了加利福尼亚州173个普查街区样本的街区层面人口总数，数据来源于三个渠道：（1）2020年人口普查，为保护受访者隐私已注入误差；（2）加利福尼亚社区计数，这是对普查街区的首次独立人口清查调查；（3）基于2010年人口普查及后续美国社区调查的预测。我们发现，平均而言，美国人口普查局在2020年人口普查中提供的街区层面总人口数在任何一致方向上都没有偏差。然而，按年龄、种族和族裔定义的亚人口总数差异很大。此外，我们发现，在按土地面积或总住房单元定义的大型街区、郊区街区以及缺乏宽带接入的街区中，这三个来源之间的不一致性会被放大。
