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The causal effect of education on earnings
TL;DR: This paper surveys the recent literature on the causal relationship between education and earnings and concludes that the average (or average marginal) return to education is not much below the estimate that emerges from a standard human capital earnings function fit by OLS.
Abstract: This paper surveys the recent literature on the causal relationship between education and earnings. I focus on four areas of work: theoretical and econometric advances in modelling the causal effect of education in the presence of heterogeneous returns to schooling; recent studies that use institutional aspects of the education system to form instrumental variables estimates of the return to schooling; recent studies of the earnings and schooling of twins; and recent attempts to explicitly model sources of heterogeneity in the returns to education. Consistent with earlier surveys of the literature, I conclude that the average (or average marginal) return to education is not much below the estimate that emerges from a standard human capital earnings function fit by OLS. Evidence from the latest studies of identical twins suggests a small upward "ability" bias -- on the order of 10%. A consistent finding among studies using instrumental variables based on institutional changes in the education system is that the estimated returns to schooling are 20-40% above the corresponding OLS estimates. Part of the explanation for this finding may be that marginal returns to schooling for certain subgroups -- particularly relatively disadvantaged groups with low education outcomes -- are higher than the average marginal returns to education in the population as a whole.
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TL;DR: The economic returns to schooling are estimated using comparable microdata in 28 countries, worldwide as mentioned in this paper, and there is no evidence for a worldwide rising rate of return to education from 1985 through 1995.
386 citations
TL;DR: The major contributions of twentieth century econometrics to knowledge were the definition of causal parameters within well-defined economic models in which agents are constrained by resources and markets and causes are interrelated, the analysis of what is required to recover causal parameters from data (the identification problem), and clarification of the role of causal parameter in policy evaluation and in forecasting the effects of policies never previously experienced as discussed by the authors.
Abstract: The major contributions of twentieth century econometrics to knowledge were the definition of causal parameters within well-defined economic models in which agents are constrained by resources and markets and causes are interrelated, the analysis of what is required to recover causal parameters from data (the identification problem), and clarification of the role of causal parameters in policy evaluation and in forecasting the effects of policies never previously experienced. This paper summarizes the development of these ideas by the Cowles Commission, the response to their work by structural econometricians and VAR econometricians, and the response to structural and VAR econometrics by calibrators, advocates of natural and social experiments, and by nonparametric econometricians and statisticians. This paper considers the definition and identification of causal parameters in economics and their role in econometric policy analysis. It assesses different research programs designed to recover causal parameters from data. At the beginning of this century, economic theory was mainly intuitive, and empirical support for it was largely anecdotal. At the end of the century, economics has a rich array of formal models and a high-quality database. Empirical regularities motivate theory in many areas of economics, and data are routinely used to test theory. Many economic theories have been developed as measurement frameworks to suggest what data should be collected and how they should be interpreted. Econometric theory was developed to analyze and interpret economic data. Most econometric theory adapts methods originally developed in statistics. The major exception to this rule is the econometric analysis of the identification problem and the companion analyses of structural equations, causality, and economic policy evaluation. Although an economist did not invent the phrase, ‘‘correlation does not imply causation,’’ 1 economists clari
381 citations
TL;DR: The value of this alternative approach to economic policy analysis is illustrated by making the implicit economics of LATE explicit, thereby extending the interpretability and range of policy questions that LATE can answer.
Abstract: This paper compares the structural approach to economic policy analysis with the program evaluation approach. It offers a third way to do policy analysis that combines the best features of both approaches. We illustrate the value of this alternative approach by making the implicit economics of LATE explicit, thereby extending the interpretability and range of policy questions that LATE can answer.
376 citations
TL;DR: In this article, the effect of education on individual earnings is reviewed for single treatments and sequential multiple treatments with and without heterogeneous returns, and the sensitivity of the estimates once applied to a common data set is explored.
Abstract: Regression, matching, control function and instrumental variables methods for recovering the effect of education on individual earnings are reviewed for single treatments and sequential multiple treatments with and without heterogeneous returns. The sensitivity of the estimates once applied to a common data set is then explored. We show the importance of correcting for detailed test score and family background differences and of allowing for (observable) heterogeneity in returns. We find an average return of 27% for those completing higher education versus anything less. Compared with stopping at 16 years of age without qualifications, we find an average return to O-levels of 18%, to A-levels of 24% and to higher education of 48%.
367 citations
TL;DR: Findings support the importance of socioemotional behaviors both as predictors of later school success and as indicators of school success.
Abstract: In this article we replicate and extend findings from Duncan et al. (2007). The 1st study used Canada-wide data on 1,521 children from the National Longitudinal Survey of Children and Youth (NLSCY) to examine the influence of kindergarten literacy and math skills, mother-reported attention, and mother-reported socioemotional behaviors on 3rd-grade math and reading outcomes. Similar to Duncan et al., (a) math skills were the strongest predictor of later achievement, (b) literacy and attention skills predicted later achievement, and (c) socioemotional behaviors did not significantly predict later school achievement. As part of extending the findings, we incorporated a multiple imputation approach to handle missing predictor variable data. Results paralleled those from the original study in that kindergarten math skills and Peabody Picture Vocabulary Test-Revised scores continued to predict later achievement. However, we also found that kindergarten socioemotional behaviors, specifically hyperactivity/impulsivity, prosocial behavior, and anxiety/depression, were significant predictors of 3rd-grade math and reading. In the 2nd study, we used data from the NLSCY and the Montreal Longitudinal-Experimental Preschool Study (MLEPS), which was included in Duncan et al., to extend previous findings by examining the influence of kindergarten achievement, attention, and socioemotional behaviors on 3rd-grade socioemotional outcomes. Both NLSCY and MLEPS findings indicated that kindergarten math significantly predicted socioemotional behaviors. There were also a number of significant relationships between early and later socioemotional behaviors. Findings support the importance of socioemotional behaviors both as predictors of later school success and as indicators of school success.
364 citations