Measuring the Returns to Education Nonparametrically
Shawn W. Ulrick
U.S. Federal Trade Commission (FTC)
November 8, 2005
This article uses a nonparametric model of earnings to measure the returns to education. Under very general smoothness conditions, a nonparametric estimator reveals the true shape of the earnings profiles up to random sampling error. Thus, the nonparametric model should provide better predictions than its parametric counterpart. We find that the nonparametric model predicts very different estimated returns than standard Mincer formulations. Depending on the experience and education level, returns measured in log earnings estimated from nonparametric model can be nearly twice those obtained from the Mincer model. Finally, this paper examines what structural features parametric models should include.
This is a preprint of an article whose final and definitive form has been published in Applied Economics Letters © 2006 [copyright Taylor & Francis];
Applied Economics Letters is available online at: http://www.tandf.co.uk/journals/titles/13504851.asp
Number of Pages in PDF File: 27
Keywords: Returns to education, nonparametric models
JEL Classification: C14, J31working papers series
Date posted: August 16, 2005
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