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Returns to Education in the United States: A Comparison of OLS and Double Machine Learning Methods

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  • Helal, Al Mansor
  • Hiraki, Ryotaro
  • Patrinos, Harry Anthony

Abstract

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.

Suggested Citation

  • Helal, Al Mansor & Hiraki, Ryotaro & Patrinos, Harry Anthony, 2026. "Returns to Education in the United States: A Comparison of OLS and Double Machine Learning Methods," GLO Discussion Paper Series 1733, Global Labor Organization (GLO).
    • Handle: RePEc:zbw:glodps:1733
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    Keywords

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    JEL classification:

    • I20 - Health, Education, and Welfare - - Education - - - General
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • D62 - Microeconomics - - Welfare Economics - - - Externalities
    • O15 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Economic Development: Human Resources; Human Development; Income Distribution; Migration

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