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

Al Mansor Helal, Ryotaro Hiraki and Harry Anthony Patrinos

No 1733, GLO Discussion Paper Series from Global Labor Organization (GLO)

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.

Keywords: Returns to education; Machine learning (search for similar items in EconPapers)
JEL-codes: D62 I20 J24 J31 O15 (search for similar items in EconPapers)
Date: 2026
New Economics Papers: this item is included in nep-big, nep-cmp and nep-lma
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:glodps:1733

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