Adjusting the 1980 Census of Population and Housing
调整美国1980年人口与住房普查结果AI 解读 ↓
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调整美国1980年人口与住房普查结果
- 研究问题
- 论文研究如何纠正人口普查中不同群体和地区的差异性漏计，尤其关注大城市黑人与西班牙裔人口漏计对人口份额的影响。
- 方法与思路
- 作者比较合成估计与大子类样本估计，并提出回归复合调整方法，以矩阵权重结合地理区域的样本漏计率和回归估计。
- 材料陈述的贡献
- 摘要称多种调整方法及假设变化均指向大城市人口份额增加、少数族裔较少地区份额减少，并报告估计对所考察变化较稳定。
- 阅读关注点
- 应核对普查后调查的覆盖与匹配误差、复合估计权重及预测变量选择，并检查敏感性分析是否涵盖关键数据和模型不确定性。
- 阅读边界
- 所给 OpenAlex 索引摘要未注明稿件版本；不能据有限敏感性分析断言漏计完全纠正，所述调整也不构成人口份额的因果效应估计。
原文摘要
In 1980, several cities and states sued the U.S. Census Bureau to correct census results. This correction would adjust for the differential undercounting of Blacks and Hispanics, especially in cities. In this article, the authors, each of whom testified for New York City and State in their joint lawsuit against the Census Bureau, describe the likely pattern of the undercount and present a method to adjust for it. We first explain why the undercount is concentrated among minority populations living in large cities. We describe the demographic and survey data available for adjustment from the Census Bureau's Post Enumeration Program. We present adjustment results obtained by two simple methods—synthetic estimation, and sample estimation for a few large subclasses. The Census Bureau used the latter method, known as the National Vacancy Check, to adjust the results of the 1970 census. We also describe our regression-based, composite method for adjustment. This method takes sample estimates of the undercount rate for a set of mutually exclusive geographic areas, and regresses these estimates upon available predictor variables. The composite estimates of the undercount rate are matrix-weighted averages of the original sample and regression estimates. We compute estimates for 66 areas: 16 large cities, the remainders of the 12 states in which those cities are located, and 38 whole states. As expected, we find that the highest undercount rates are in large cities, and the lowest are in states and state remainders with small percentages of Blacks and Hispanics. Next, we analyze how sensitive our estimates are to changes in data and modeling assumptions. We find that these changes do not affect the estimates very much. Our conclusion is that regardless of whether we use one of the simple methods or the composite method and regardless of how we vary the assumptions of the composite method, an adjustment reliably reduces population shares in states with few minorities and increases the shares of large cities.