Individual heterogeneity in the returns to schooling: instrumental variables quantile regression using twins data
Considerable effort has been exercised in estimating mean returns to education while carefully considering biases arising from unmeasured ability and measurement error. Recent work has investigated whether there are variations from the “mean” return to ed
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Abstract. Considerable effort has been exercised in estimating mean returns to education while carefully considering biases arising from unmeasured ability and measurement error. Recent work has investigated whether there are variations from the "mean" return to education across the population with mixed results. We use an instrumental variables estimator for quantile regression on a sample of twins to estimate an entire family of returns to education at different quantiles of the conditional distribution of wages while addressing simultaneity and measurement error biases. We test whether there is individual heterogeneity in returns to education and find that: more able individuals obtain more schooling perhaps due to lower marginal costs and/or higher marginal benefits of schooling and that higher ability individuals (those further to the right in the conditional distribution of wages) have higher returns to schooling consistent with a non-trivial interaction between schooling and unobserved abilities in the generation of earnings. The estimated returns are never lower than 9 percent and can be as high as 13 percent at the top of the conditional distribution of wages but they vary significantly only along the
* We are grateful to Michael Boozer, David Card, Kenneth Chay, John DiNardo, Bernd Fitzenberger, James Heckman, William Maloney, Franco Peracchi, and Cecilia Rouse for important discussions and to Bernd Fitzenberger and an anonymous referee for extremely helpful comments and suggestions. We are especially indebted to Roger Koenker for helping us to consider many issues in the paper and for reading several earlier drafts. We are also grateful to Orley Ashenfelter, Alan Krueger, and Cecilia Rouse for access to their data on twins. In addition, we appreciate the comments of seminar participants at the University of Illinois, the University of la Plata, the Centro de Estudios Macroeconomicos de Argentina, the August 1999 meeting of the Latin American Econometric Society in Cancun, Mexico, and at the June 2000 conference on the Economic Applications of Quantile Regression at the University of Konstanz. The programs for the estimation and testing were written in S-plus and are available from the authors upon request. B. Fitzenberger et al. (eds.), Economic Applications of Quantile Regression © Springer-Verlag Berlin Heidelberg 2002
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lower to middle quantiles. Our findings may have meaningful implications for the design of educational policies.
Key words: Returns to Education, Human Capital, Heterogeneity, Quantile Treatment Effects, Instrumental Variables. JEL classification: C14, 12, 124,131 1. Introduction The causal relation between education and earnings has been one of the most heavily and carefully explored subjects in empirical work in labor economics (See Card (1999) for a comprehensive review). The many empirical and theoretical difficulties associated with the analysis of such a relationship have been approached with a remarkable variety of econometric tools on diverse data sets. A
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