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Childhood cross-ethnic exposure predicts political behavior seven decades later: Evidence from linked administrative data.

Jacob R BrownRyan D EnosJames FeigenbaumSoumyajit Mazumder
Published in: Science advances (2021)
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.
Keyphrases
  • early life
  • cross sectional
  • machine learning
  • big data
  • healthcare
  • electronic health record
  • artificial intelligence
  • deep learning
  • air pollution
  • middle aged
  • molecular dynamics