TY  - RPRT
        AU  - Helal, Al Mansor
AU  - Hiraki, Ryotaro
AU  - Patrinos, Harry Anthony
        TI  - Returns to Education in the United States: A Comparison of OLS and Double Machine Learning Methods
        PY  - 2026/Apr/
        PB  - Institute of Labor Economics (IZA)
        CY  - Bonn
        T2  - IZA Discussion Paper
        IS  - 18523
        UR  - https://www.iza.org/publications/dp18523
        AB  - 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.
        KW  - returns to education
KW  - machine learning
        ER  -