Does contact across social groups influence sociopolitical behavior? This question is among the most studied in the social sciences with deep implications for the harmony of diverse societies. Yet, despite a voluminous body of scholarship, evidence around this question is limited to cross-sectional surveys that only measure short-term consequences of contact or to panel surveys with small samples covering short time periods. Using advances in machine learning that enable large-scale linkages across datasets, we examine the long-term determinants of sociopolitical behavior through an unprecedented individual-level analysis linking contemporary political records to the 1940 U.S. Census. These linked data allow us to measure the exact residential context of nearly every person in the United States in 1940 and, for men, connect this with the political behavior of those still alive over 70 years later. We find that, among white Americans, early-life exposure to black neighbors predicts Democratic partisanship over 70 years later.

跨社会群体的接触会影响社会政治行为吗？这个问题是社会科学中研究最多的问题之一，对不同社会的和谐有着深远的影响。然而，尽管有大量的学术研究，围绕这个问题的证据仅限于仅衡量接触的短期后果的横断面调查或涵盖短期的小样本面板调查。利用机器学习的进步，实现跨数据集的大规模联系，我们通过前所未有的个人层面分析，将当代政治记录与 1940 年美国人口普查联系起来，研究社会政治行为的长期决定因素。这些关联数据使我们能够衡量 1940 年美国几乎每个人的确切居住环境，对于男性而言，将此与 70 多年后仍然活着的人的政治行为联系起来。我们发现，在美国白人中，早年与黑人邻居的接触预示着 70 多年后民主党的党派偏见。