@EdWorkingPaper{ai26-1473,
title = "Returns to Education in the United States: A Comparison of OLS and Double Machine Learning Methods",
author = "Al Mansor Helal, Ryotaro Hiraki, Harry Anthony Patrinos",
institution = "Annenberg Institute at Brown University",
number = "1473",
year = "2026",
month = "May",
URL = "http://www.edworkingpapers.com/ai26-1473",
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.},
}