%0 Report
        %A Helal, Al Mansor
%A Hiraki, Ryotaro
%A Patrinos, Harry Anthony
        %T Returns to Education in the United States: A Comparison of OLS and Double Machine Learning Methods
        %D 2026
        %8 2026 Apr
        %I Institute of Labor Economics (IZA)
        %C Bonn
        %7 IZA Discussion Paper
        %N 18523
        %U https://www.iza.org/publications/dp18523
        %X 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.
        %K returns to education
%K machine learning