This study examines the economic returns to education in the U.S. using 2024 CPS data and compares Ordinary Least Squares (OLS) regression with a Double Machine Learning (DML) framework incorporating models such as random forests, boosted trees, lasso, GAMs, and neural networks (MLP). Results show consistent returns of 8 to 9 percent per additional year of schooling across methods. Simulations reveal that all predictors perform well under linear assumptions if hyperparameters are optimally adjusted, while OLS/Lasso suffer from nonlinearity. Findings suggest that OLS remains robust in low-dimensional, near-linear contexts, offering practical guidance for economists and policymakers balancing model complexity and interpretability in education research.
Returns to Education in the United States: A Comparison of OLS and Double Machine Learning Methods
Keywords
Returns to education, cognitive skills, earnings
Education level
Topics
Document Object Identifier (DOI)
10.26300/tmac-c636
EdWorkingPaper suggested citation:
Helal, Al Mansor, Ryotaro Hiraki, and Harry Anthony Patrinos. (). Returns to Education in the United States: A Comparison of OLS and Double Machine Learning Methods. (EdWorkingPaper: -1473). Retrieved from
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