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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: In this article, Chen et al. used a truncated-sample model to estimate college effects on earnings using both the conventional Mincer-type regression model and the revised truncated sample model that adjusts for the selection mechanisms into college.
Abstract: Taiwan has experienced a rapid expansion in higher education since the 1990s. To gauge changes in earnings returns to higher education caused by this expansion, this paper estimates college effects on earnings using both the conventional Mincer-type regression model and the revised truncated-sample model that adjusts for the selection mechanisms into college. We also apply Xie and Wu's (2005) hierarchical linear model approach to test if the treatment effects of higher education vary as a function of propensity scores strata estimated. Using nationwide data collected in the early 1990s and the early 2000s, we focus on young entrants to the labor market. Our results indicate that average returns to college education remain stable over time. We also find that in both periods, there is a strong negative selection mechanism at work: when workers with a low latent propensity of receiving college education indeed did go to college, they benefit the most from the college attendance.
22 citations
TL;DR: In this paper, the authors report statistically and economically significant sheepskin effects in the tourism industry, with the return to educational degrees clearly exceeding the returns to years of schooling for both male and female employees.
Abstract: This study is the first to report on sheepskin effects in the tourism
industry; that is, on the earnings returns to educational degrees net
of the returns to accumulated years of schooling. The results show
statistically and economically significant sheepskin effects, with the
returns to educational degrees clearly exceeding the returns to years
of schooling for both male and female employees. Both human
capital and sorting explanations of this observation are discussed.
22 citations
16 Dec 2009
TL;DR: In this article, a local linear functional coefficient estimator for stationary time series is proposed, which allows the return to education to depend on other variables, such as experience, experience, and education duration.
Abstract: We propose a local linear functional coefficient estimator that admits a mix of discrete and continuous data for stationary time series. Under weak conditions our estimator is asymptotically normally distributed. A small set of simulation studies is carried out to illustrate the finite sample performance of our estimator. As an application, we estimate a wage determination function that explicitly allows the return to education to depend on other variables. We find evidence of the complex interacting patterns among the regressors in the wage equation, such as increasing returns to education when experience is very low, high return to education for workers with several years of experience, and diminishing returns to education when experience is high. Compared with the commonly used parametric and semiparametric methods, our estimator performs better in both goodness-of-fit and in yielding economically interesting interpretation.
22 citations
TL;DR: In this article, the authors present a general framework and some results for the EU for the private and fiscal returns to schooling and the effect of public policies on private incentives to invest in education.
Abstract: El pdf del articulo es el documento de trabajo publicado con el titulo The private and fiscal returns to schooling and the effect of public policies on private incentives to invest in education: a general framework and some results for the EU.
22 citations
TL;DR: It is illustrated that combining ecological data with subsample data in situations in which a linear model is appropriate provides three main benefits, and that optimal subsampling schemes (conditional on the ecological data) can provide good precision with only a fraction of the observations.
Abstract: In this paper, we illustrate that combining ecological data with subsample data in situations in which a linear model is appropriate provides three main benefits. First, by including the individual level subsample data, the biases associated with linear ecological inference can be eliminated. Second, by supplementing the subsample data with ecological data, the information about parameters will be increased. Third, we can use readily available ecological data to design optimal subsampling schemes, so as to further increase the information about parameters. We present an application of this methodology to the classic problem of estimating the effect of a college degree on wages. We show that combining ecological data with subsample data provides precise estimates of this value, and that optimal subsampling schemes (conditional on the ecological data) can provide good precision with only a fraction of the observations.
22 citations