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Medicaid Expansions and Private Insurance 'Crowd-Out' (1999-2019).

Jason Semprini
Published in: Social science quarterly (2023)
Recent Medicaid expansions have rekindled the debate around private insurance "crowd-out". Prior research is limited by short-time horizons and state-specific analyses. Our study overcomes these limitations by evaluating twenty years of Medicaid expansions across the entire United States. We obtain data from the U.S. Census Bureau for all U.S. states and D.C. for private insurance coverage rates of adults 18-64, for years 1999-2019. After estimating a naïve, staggered Two-Way Fixed Effects Difference-in-Differences regression model, we implement four novel econometric methods to diagnose and overcome threats of bias from staggered designs. We also test for pre-treatment differential trends and heterogenous effects over time. Our findings suggest that Medicaid expansion was associated with a 1.5%-point decline in private insurance rates (p < 0.001). We also observe significant heterogeneity over time, with estimates peaking four years after expansion. The importance of a 1-2%-point crowd-out, we leave for future research and debate.
Keyphrases
  • affordable care act
  • health insurance
  • healthcare
  • machine learning
  • current status
  • deep learning
  • artificial intelligence