Palestinian residents of the West Bank and Gaza Strip who work in Israel generally earn more than Palestinians employed locally, but this wage premium is highly volatile. Beginning with the 1987 Palestinian uprising, changes in wage differentials by work location parallel Palestinian absences from work in Israel. This article interprets changing location differentials in response to exogenous shocks as movements along an Israeli demand curve for migrant workers. Estimates of a model of the West Bank and Gaza Strip labor market are used to evaluate the effect of policies governing Palestinian access to the Israeli labor market.

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.

This article presents evidence on the child-quantity/child-quality trade-off using quasi-experimental variation due to twin births and preferences for a mixed sibling sex composition, as well as ethnic differences in the effects of these variables. Our sample includes groups with very high fertility. An innovation in our econometric approach is the juxtaposition of results from multiple instrumental variables strategies, capturing the effects of fertility over different ranges for different sorts of people. To increase precision, we develop an estimator that combines different instrument sets across partially overlapping parity-specific subsamples. Our results are remarkably consistent in showing no evidence of a quantity-quality trade-off.