We establish that season of birth is related to educational attainment because of school start age policy and compulsory school attendance laws. Individuals born in the beginning of the year start school at an older age, and can therefore drop out after completing less schooling than individuals born near the end of the year. Roughly 25 percent of potential dropouts remain in school because of compulsory schooling laws. We estimate the impact of compulsory schooling on earnings by using quarter of birth as an instrument for education. The instrumental variables estimate of the return to education is close to the ordinary least squares estimate, suggesting that there is little bias in conventional estimates.

Parents gauge school quality in part by the level of student achievement and a school's racial and socioeconomic mix. The importance of school characteristics in the housing market can be seen in the jump in house prices at school district boundaries where peer characteristics change. The question of whether schools with more attractive peers are really better in a value-added sense remains open, however. This paper uses a fuzzy regression-discontinuity design to evaluate the causal effects of peer characteristics. Our design exploits admissions cutoffs at Boston and New York City's heavily over-subscribed exam schools. Successful applicants near admissions cutoffs for the least selective of these schools move from schools with scores near the bottom of the state SAT score distribution to schools with scores near the median. Successful applicants near admissions cutoffs for the most selective of these schools move from above-average schools to schools with students whose scores fall in the extreme upper tail. Exam school students can also expect to study with fewer nonwhite classmates than unsuccessful applicants. Our estimates suggest that the marked changes in peer characteristics at exam school admissions cutoffs have little causal effect on test scores or college quality.

Research on the labor-supply consequences of childbearing is complicated by the endogeneity of fertility. This study uses parental preferences for a mixed sibling-sex composition to construct instrumental variables (IV) estimates of the effect of childbearing on labor supply. IV estimates for women are significant but smaller than ordinary least-squares estimates. The IV are also smaller for more educated women and show no impact of family size on husbands' labor supply. A comparison of estimates using sibling-sex composition and twins instruments implies that the impact of a third child disappears when the child reaches age 13.

The utility of machine learning (ML) for regression-based causal inference is illustrated by using lasso to select control variables for estimates of college characteristics’ wage effects. Post-double-selection lasso offers a path to data-driven sensitivity analysis. ML also seems useful for an instrumental variables (IV) first stage, since two-stage least squares (2SLS) bias reflects overfitting. While ML-based instrument selection can improve on 2SLS, split-sample IV and limited information maximum likelihood do better. Finally, we use ML to choose IV controls. Here, ML creates artificial exclusion restrictions, generating spurious findings. On balance, ML seems ill-suited to IV applications in labor economics.

Most research on the relationship between teacher characteristics and pupil achievement focuses on salaries, experience, and education. The effect of in‐service training has received less attention. We estimate the effect of in‐service teacher training on achievement in Jerusalem elementary schools using a matched‐comparison design. Differences‐in‐differences, regression, and matching estimates suggest training in secular schools led to an improvement in test scores. The estimates for religious schools are not clear cut, perhaps because training in religious schools started later and was implemented on a smaller scale. Estimates for secular schools suggest teacher training provided a cost‐effective means of increasing test scores.

Until 1983, the language of instruction for most subjects in grades 6 and above in Moroccan public schools was French. Beginning in 1983, the language of instruction for new cohorts of Moroccan sixth graders was switched to Arabic. We use this policy change to estimate the effect of French language skills on test scores and earnings. The estimates suggest that the elimination of compulsory French instruction led to a substantial reduction in the returns to schooling for Moroccans affected by the change. This reduction appears to be largely attributable to a loss of French writing skills.

The problem of when to control for continuous or high-dimensional discrete covariate vectors arises in both experimental and observational studies. Large-cell asymptotic arguments suggest that full control for covariates or stratification variables is always efficient, even if treatment is assigned independently of covariates or strata. Here, we approximate the behavior of different estimators using a panel-data-type asymptotic sequence with fixed cell sizes and the number of cells increasing to infinity. Exact calculations in simple examples and Monte Carlo evidence suggest this generates a substantially improved approximation to actual finite-sample distributions. Under this sequence, full control for covariates is dominated by propensity-score matching when cell sizes are small, the explanatory power of the covariates conditional on the propensity score is low, and/or the probability of treatment is close to 0 or 1. Our panel-asymptotic framework also provides an explanation for why propensity-score matching can dominate covariate matching even when there are no empty cells. Finally, we introduce a random-effects estimator that provides finite-sample efficiency gains over both covariate matching and propensity-score matching.

The Israeli matriculation certificate is a prerequisite for most postsecondary schooling. In a randomized trial, we attempted to increase certification rates among low-achievers with cash incentives. The experiment used a school-based randomization design offering awards to all who passed their exams in treated schools. This led to a substantial increase in certification rates for girls but had no effect on boys. Affected girls had a relatively high ex ante chance of certification. The increase in girls' matriculation rates translated into an increased likelihood of college attendance. Female matriculation rates increased partly because treated girls devoted extra time to exam preparation.

The effect of government programs on the distribution of participants' earnings is important for program evaluation and welfare comparisons.This paper reports es- timates of the effects of JTPA training programs on the distribution of earnings.The estimation uses a new instrumental variable (IV) method that measures program impacts on the quantiles of outcome variables.This quantile treatment effects (QTE) estimator accommodates exogenous covariates and reduces to quantile regres- sion when selection for treatment is exogenously determined.The QTE estimator can be computed as the solution to a convex linear programming problem, although this requires first-step estimation of a nuisance function.We develop distribution theory for the case where the first step is estimated nonparametrically.For women, the empirical results show that the JTPA program had the largest proportional impact at low quantiles.Perhaps surprisingly, however, JTPA training raised the quantiles of earnings for men only in the upper half of the trainee earnings distribution.