Estimating the returns to education for Australian youth via rank-order instrumental variables

This paper employs the rank-order instrumental variable (IV) procedure of Vella and Verbeek [Vella, F., Verbeek, M., 1997. Using rank order as an instrumental variable: an application to the return to schooling, CES Discussion Paper 97.10, K.U. Leuven.] to estimate the returns to education for Australian youth. The attraction of this approach is that it can account for the endogeneity of schooling in the wage equation via the use of instrumental variables without the use of exclusion restrictions. We find, after accounting for the endogeneity of schooling, that an additional year of schooling is associated with an increase in wages of approximately 8%. Furthermore, we find that the rank-order IV approach is able to identify the presence of endogeneity in this particular empirical example. However, despite this, the adjusted estimate of how schooling affects wage is close to the ordinary least squares (OLS) estimate.     

Impact of Sibship Size,Birth Order and Sex Composition on School Enrolment in Urban Turkey*

This paper investigates, in a unified framework, the effects of sibship size, birth order and sibling sex composition on children's school enrolment in urban Turkey. We utilize an instrumental variable estimation method to address parents’ joint fertility and schooling decisions using twin births as instruments. We find no causal impact of sibship size on school enrolment. However, there is evidence for a parabolic impact of birth order where middle‐born children fare worse. Sex composition of siblings matters only for female children. Our finding that birth order and sibling sex composition matter more for poorer households suggests that scarce financial resources play an important role in bringing about the sibling composition effects.     

Does Education Matter for Economic Growth?

Empirical growth regressions typically include mean years of schooling as a proxy for human capital. However, empirical research often finds that the sign and significance of schooling depends on the sample of observations or the specification of the model. We use a non‐parametric local‐linear regression estimator and a non‐parametric variable relevance test to conduct a rigorous and systematic search for significance of mean years of schooling by examining five of the most comprehensive schooling databases. Contrary to a few recent articles that have identified significant nonlinearities between education and growth, our results suggest that mean years of schooling is not a statistically relevant variable in growth regressions. However, we do find evidence (within a cross‐sectional framework), that educational achievement, measured by mean test scores, may provide a more reliable measure of human capital than mean years of schooling.     

Matching and semi-parametric IV estimation, a distance-based measure of migration, and the wages of young men

Our paper estimates the effect of US internal migration on wage growth for young men between their first and second job. Our analysis of migration extends previous research by: (i) exploiting the distance-based measures of migration in the National Longitudinal Surveys of Youth 1979 (NLSY79); (ii) allowing the effect of migration to differ by schooling level and (iii) using propensity score matching to estimate the average treatment effect on the treated (ATET) for movers and (iv) using local average treatment effect (LATE) estimators with covariates to estimate the average treatment effect (ATE) and ATET for compliers.We believe the Conditional Independence Assumption (CIA) is reasonable for our matching estimators since the NLSY79 provides a relatively rich array of variables on which to match. Our matching methods are based on local linear, local cubic, and local linear ridge regressions. Local linear and local ridge regression matching produce relatively similar point estimates and standard errors, while local cubic regression matching badly over-fits the data and provides very noisy estimates.We use the bootstrap to calculate standard errors. Since the validity of the bootstrap has not been investigated for the matching estimators we use, and has been shown to be invalid for nearest neighbor matching estimators, we conduct a Monte Carlo study on the appropriateness of using the bootstrap to calculate standard errors for local linear regression matching. The data generating processes in our Monte Carlo study are relatively rich and calibrated to match our empirical models or to test the sensitivity of our results to the choice of parameter values. The estimated standard errors from the bootstrap are very close to those from the Monte Carlo experiments, which lends support to our using the bootstrap to calculate standard errors in our setting.From the matching estimators we find a significant positive effect of migration on the wage growth of college graduates, and a marginally significant negative effect for high school dropouts. We do not find any significant effects for other educational groups or for the overall sample. Our results are generally robust to changes in the model specification and changes in our distance-based measure of migration. We find that better data matters; if we use a measure of migration based on moving across county lines, we overstate the number of moves, while if we use a measure based on moving across state lines, we understate the number of moves. Further, using either the county or state measures leads to much less precise estimates.We also consider semi-parametric LATE estimators with covariates (Frölich 2007), using two sets of instrumental variables. We precisely estimate the proportion of compliers in our data, but because we have a small number of compliers, we cannot obtain precise LATE estimates.     

Returns to Schooling and Bayesian Model Averaging: A Union of Two Literatures

Abstract.  In this paper, we review and unite the literatures on returns to schooling and Bayesian model averaging. We observe that most studies seeking to estimate the returns to education have done so using particular (and often different across researchers) model specifications. Given this, we review Bayesian methods which formally account for uncertainty in the specification of the model itself, and apply these techniques to estimate the economic return to a college education. The approach described in this paper enables us to determine those model specifications which are most favored by the given data, and also enables us to use the predictions obtained from all of the competing regression models to estimate the returns to schooling. The reported precision of such estimates also account for the uncertainty inherent in the model specification. Using U.S. data from the National Longitudinal Survey of Youth (NLSY), we also revisit several 'stylized facts' in the returns to education literature and examine if they continue to hold after formally accounting for model uncertainty.     

Instrumental quantile regression inference for structural and treatment effect models

We introduce a class of instrumental quantile regression methods for heterogeneous treatment effect models and simultaneous equations models with nonadditive errors and offer computable methods for estimation and inference. These methods can be used to evaluate the impact of endogenous variables or treatments on the entire distribution of outcomes. We describe an estimator of the instrumental variable quantile regression process and the set of inference procedures derived from it. We focus our discussion of inference on tests of distributional equality, constancy of effects, conditional dominance, and exogeneity. We apply the procedures to characterize the returns to schooling in the U.S.     

Bayesian inference in a correlated random coefficients model: Modeling causal effect heterogeneity with an application to heterogeneous returns to schooling

We consider the problem of causal effect heterogeneity from a Bayesian point of view. This is accomplished by introducing a three-equation system, similar in spirit to the work of Heckman and Vytlacil (1998), describing the joint determination of a scalar outcome, an endogenous “treatment” variable, and an individual-specific causal return to that treatment. We describe a Bayesian posterior simulator for fitting this model which recovers far more than the average causal effect in the population, the object which has been the focus of most previous work. Parameter identification and generalized methods for flexibly modeling the outcome and return heterogeneity distributions are also discussed.Combining data sets from High School and Beyond (HSB) and the 1980 Census, we illustrate our methods in practice and investigate heterogeneity in returns to education. Our analysis decomposes the impact of key HSB covariates on log wages into three parts: a “direct” effect and two separate indirect effects through educational attainment and returns to education. Our results strongly suggest that the quantity of schooling attained is determined, at least in part, by the individual’s own return to education. Specifically, a one percentage point increase in the return to schooling parameter is associated with the receipt of (approximately) 0.14 more years of schooling. Furthermore, when we control for variation in returns to education across individuals, we find no difference in predicted schooling levels for men and women. However, women are predicted to attain approximately 1/4 of a year more schooling than men on average as a result of higher rates of return to investments in education.     

Are Catholic primary schools more effective than public primary schools?

This paper assesses the causal effects of Catholic primary schooling on student outcomes such as test scores, grade retention, and behavior. Catholic school students have substantially better average outcomes than do public school students throughout the primary years, but we present evidence that selection bias is entirely responsible for these advantages. Estimates based on several empirical strategies, including an approach developed by Altonji et al. (2005a) to use selection on observables to assess the bias arising from selection on unobservables, imply that Catholic schools do not appreciably boost test scores. All of the empirical strategies point to sizeable negative effects of Catholic schooling on mathematics achievement. Similarly, we find very little evidence that Catholic schooling improves behavioral and other non-cognitive outcomes once we account for selection on unobservables.     

Convexity and Sheepskin Effects in the Human Capital Earnings Function: Recent Evidence for Filipino Men

The issue of possible non-linearities in the relationship between log wages and schooling has received a good deal of attention in the literature. This paper uses data from a recent, high quality household survey for the Philippines, the 1998 Annual Poverty Indicator Survey (APIS), to test the fit of the log-linear specification for Filipino men. The results are based on a number of estimation strategies, including spline regressions, and semi-parametric regressions with a large number of dummies for years of schooling and experience. The basic conclusions of the paper are two. First, there appear to be large differences between the rates of return to education across levels in the Philippines. In particular, the returns to both primary and secondary education are lower than those for tertiary education, a difference which persists even after correcting for differences in direct private costs across levels. Second, within a given level, the last year of schooling is disproportionately rewarded in terms of higher wages. That is, there are clear sheepskin effects associated with graduation from primary school, secondary school, and university.     

The external returns to education: UK evidence using repeated cross-sections

Augmenting a Mincerian earnings function with industry level data we estimate the external return to schooling for a repeated cross-section of individuals in the UK over the period 1994–2004. For men age 30–49 we find that a one year increase in the industry average level of schooling is associated with an increase in individual wages of 2.6 to 3.9%, around 2 to 3 fifths of the private return to schooling. We illustrate the sensitivity of external return estimates to industry ICT use and union density, and individuals' own level of schooling.     

Maternal employment,migration, and child development

We analyze the roles of and interrelationships among school inputs and parental inputs in affecting child development through the specification and estimation of a behavioral model of household migration and maternal employment decisions. We integrate information on these decisions with observations on child outcomes over a 13-year period from the National Longitudinal Study of Youth (NLSY). We find that the impact of our school quality measures diminishes by factors of 2 to 4 after accounting for the fact that families may choose where to live in part based on school characteristics and labor market opportunities. The positive statistical relationship between child outcomes and maternal employment reverses sign and remains statistically significant after controlling for its possible endogeneity. Our estimates imply that when parental responses are taken into account, policy changes in school quality end up having only minor impacts on child test scores.     

The Causal Effect of Education on Wages Revisited*

This study estimates the return to education in Britain using two instrumental variable (IV) estimators: one exploits variation in schooling associated with early smoking, the other uses the raising of the school leaving age; both affect a sizeable proportion of the sample. Early smoking is found to be a strong and valid IV and unlike previous IV strategies uses variations in education at numerous points across the distributions of (i) education, and (ii) ability. Thus whilst still a ‘local average treatment effect’ the estimate is closer to the average effect of additional education, akin to least squares but corrected for endogeneity.     

Teacher quality and dropout outcomes in a large,urban school district

Recent research shows that variation in teacher quality has large effects on student performance. However, this research is based entirely on student test scores. Focusing on high-school math teachers, this paper evaluates teacher quality in terms of another educational outcome of great interest—graduation. I use a unique instrumental variables approach to identify teacher effects and find that differences in teacher quality have large effects on graduation outcomes. Because teacher effects on graduation outcomes will be more pronounced for students who are on the graduation margin, the results imply an avenue through which high-quality teachers are more productive with disadvantaged students.     

Teacher quality and dropout outcomes in a large, urban school district

Recent research shows that variation in teacher quality has large effects on student performance. However, this research is based entirely on student test scores. Focusing on high-school math teachers, this paper evaluates teacher quality in terms of another educational outcome of great interest—graduation. I use a unique instrumental variables approach to identify teacher effects and find that differences in teacher quality have large effects on graduation outcomes. Because teacher effects on graduation outcomes will be more pronounced for students who are on the graduation margin, the results imply an avenue through which high-quality teachers are more productive with disadvantaged students.     

Age of Menarche,Adolescent Sexual Intercourse and Schooling Attainment of Women

We use data from the 1970 British Cohort Study to measure the effect of adolescent sexual intercourse on female schooling attainment. We emphasize the appropriate use of menarcheal age as an instrumental variable (IV) for early intercourse. Our analysis suggests that developmental trajectories vary with menarcheal age and, therefore, capturing variations in individual cognitive capacities induced by pubertal timing is crucial for the validity of the IV identification strategy. Our empirical results indicate that adolescent sexuality reduces full‐time education by approximately one year. Given that 37 percent of females in our data exited virginity in adolescence, the aggregate loss of human capital as measured by average years of female schooling could be up to one‐third of a year.     

Multidimensional skills and the returns to schooling: Evidence from an interactive fixed-effects approach and a linked survey-administrative data set

This paper presents new evidence on returns to schooling based on an interactive fixed-effects framework that allows for multiple unobserved skills with potentially time-varying prices as well as individual-level heterogeneity in returns. This constitutes a substantive generalization of most existing approaches. Our empirical analysis employs a unique linked survey-administrative panel data set on education and earnings. We find average marginal returns to schooling of about 2.8–4.4% relative to least squares/instrumental variable estimates between 7.7% and 12.7%. Omitted ability accounts for a larger fraction of the aggregate least squares bias compared to heterogeneity. We also find considerable heterogeneity in individual returns.     

Addressing Unobserved Selection Bias in Accounting Studies: The Bias Minimization Method

This note explains the minimum-biased estimator (MBE), which accounting researchers can use to analyze the robustness of regression or propensity score-matched treatment estimates to unobserved selection (endogeneity) bias. Based on the principles of the Heckman treatment model, the MBE entails estimating matched treatment effects within a range of propensity scores that minimizes unobserved selection bias. A major advantage of the MBE is that an instrumental variable is not required. The potential utility of the MBE in accounting studies is highlighted, and a familiar empirical illustration is provided.     

The return to firm investments in human capital

In this paper we estimate the rate of return to firm investments in human capital in the form of formal job training. We use a panel of large firms with detailed information on the duration of training, the direct costs of training, and several firm characteristics. Our estimates of the return to training are substantial (8.6%) for those providing training. Results suggest that formal job training is a good investment for these firms possibly yielding comparable returns to either investments in physical capital or investments in schooling.