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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 paper, the authors examined the robustness of the estimate to the inclusion/exclusion of non-fatal risks and other relevant characteristics and found that the coefficient is robust to other changes in the specification.
Abstract: Estimates of the extra earnings for jobs with higher risks of death are used in cost-benefit studies involving risk changes. Because of this use, the magnitude and stability of the estimated coefficient are important. Part of the current study closely reproduces the 1982 study by Marin and Psacharopoulos to check on the stability. We also examine the robustness of the estimate to the inclusion/exclusion of non-fatal risks and other relevant characteristics. While the magnitude of the coefficient has increased from the earlier study, the coefficient is robust to other changes in the specification. This yields a “value of life” figure of £9.7 million.
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TL;DR: The authors found that most of the variance of school achievement at age 15 is explained by family characteristics and observed school inputs explain very little of adolescent performance, and that children from highly educated parents benefit of rich cultural environments in the home and become highly educated adults.
Abstract: Portugal has one of the highest levels of income inequality in Europe, and low wages and unemployment are concentrated among low skill individuals. Education is an important determinant of inequality. However, there are large differences in the educational attainment of different individuals in the population, and the sources of these differences emerge early in the life-cycle when families play a central role in individual development. We estimate that most of the variance of school achievement at age 15 is explained by family characteristics. Observed school inputs explain very little of adolescent performance. Children from highly educated parents benefit of rich cultural environments in the home and become highly educated adults. Education policy needs to be innovative: (1) it needs to explicitly recognize the fundamental long run role of families on child development; (2) it needs to acknowledge the failure of traditional input based policies.
64 citations
TL;DR: This article reviewed the economic literature on the determinants of schooling outcomes and schooling gaps with a focus on static and dynamic household responses to specific policy initiatives, perceived economic returns and other incentives.
Abstract: In developing countries, rising incomes, increased demand for more skilled labor, and government investments of considerable resources on building and equipping schools and paying teachers have contributed to global convergence in enrollment rates and completed years of schooling. Nevertheless, in many countries substantial education gaps persist between rich and poor, between rural and urban households and between males and females. To address these gaps, some governments have introduced school vouchers or cash transfers programs that are targeted to disadvantaged children. Others have initiated programs to attract or retain students by expanding school access or by setting higher teacher eligibility requirements or increasing the number of textbooks per student. While enrollments have increased, there has not been a commensurate improvement in knowledge and skills of students. Establishing the impact of these policies and programs requires an understanding of the incentives and constraints faced by all parties involved, the school providers, the parents and the children. The chapter reviews the economic literature on the determinants of schooling outcomes and schooling gaps with a focus on static and dynamic household responses to specific policy initiatives, perceived economic returns and other incentives. It discusses measurement and estimation issues involved with empirically testing these models and reviews findings. Governments have increasingly adopted the practice of experimentation and evaluation before taking steps to expand new policies. Often pilot programs are initiated in settings that are atypically appropriate for the program, so that the results overstate the likely impact of expanding the program to other settings. Program expansion can also result in general equilibrium feedback effects that do not apply to isolated pilots. These behavioral models provide a useful context within which to frame the likely outcomes of such expansion.
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TL;DR: Moffitt et al. as discussed by the authors estimated marginal treatment effects in heterogeneous populations and used the data from the National Institute of Child Health and Human Development (NICHHD).
Abstract: Estimating Marginal Treatment Effectsin Heterogeneous PopulationsRobert MoffittJohns Hopkins UniversityDecember, 2006Revised, October, 2007The author would like to thank Marc Chan for research assistance and Lorraine Dearden forgenerous help in obtaining and using the data. Comments from Joshua Angrist, JamesHeckman, Guido Imbens, Matthew White, Tiemen Woutersen, and participants at workshops atseveral universities, research institutes, and conferences are appreciated, as well as commentsfrom two anonymous referees, Thierry Magnac, and other participants at the Conference onEconometric Evaluation of Public Policies: Methods and Applications,” Paris, December, 2005on an earlier closely related paper. The author also thanks Steffen Reinhold for correcting twoerrors in the Appendix. Research support from the National Institute of Child Health and HumanDevelopment is gratefully acknowledged. welfls0_v4.wpd10/3/07
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TL;DR: In this paper, the authors compare the properties of human capital returns using a performance measure and by using tests for mean-variance spanning, and identify a range of educations that are efficient in terms of investment goods, and a range that may be chosen for consumption purposes.
64 citations