We investigate the effect of Head Start on education and wage income for individuals in their 30s in the NLSY79. We contribute to the existing literature by examining effects across outcome distributions, using an approach that relies on two weak stochastic dominance assumptions that can be checked using pre–Head Start cohorts. We find that Head Start has positive and statistically significant effects on years of education and wage income. We also uncover important heterogeneity in the program’s effectiveness; the effects are concentrated at the lower end of the distribution, and the effects are strongest for women, blacks, and Hispanics.

This article estimates peer effects originating from the ability composition of tutorial groups for undergraduate students in economics. We manipulated the composition of groups to achieve a wide range of support, and assigned students—conditional on their prior ability—randomly to these groups. The data support a specification in which the impact of group composition on achievement is captured by the mean and standard deviation of peers’ prior ability, their interaction, and interactions with students’ own prior ability. When we assess the aggregate implications of these peer effects regressions for group assignment, we find that low- and medium-ability students gain on an average 0.19 SD units of achievement by switching from ability mixing to three-way tracking. Their dropout rate is reduced by 12 percentage points (relative to a mean of 0.6). High-ability students are unaffected. Analysis of survey data indicates that in tracked groups, low-ability students have more positive interactions with other students, and are more involved. We find no evidence that teachers adjust their teaching to the composition of groups.

This article examines the labor market payoffs to different types of postsecondary education, including field and institution of study. Instrumental variables (IV) estimation of the payoff to choosing one type of education compared to another is made particularly challenging by individuals choosing between several types of education. Not only does identification require one instrument per alternative, but it is also necessary to deal with the issue that individuals who choose the same education may have different next-best alternatives. We address these difficulties using rich administrative data for Norway’s postsecondary education system. A centralized admission process creates credible instruments from discontinuities that effectively randomize applicants near unpredictable admission cutoffs into different institutions and fields of study. The admission process also provides information on preferred and next-best alternatives from strategy-proof measures of individuals’ ranking of institutions and fields. The results from our IV approach may be summarized with three broad conclusions. First, different fields of study have substantially different labor market payoffs, even after accounting for institution and peer quality. Second, the effect on earnings from attending a more selective institution tends to be relatively small compared to payoffs to field of study. Third, the estimated payoffs to field of study are consistent with individuals choosing fields in which they have a comparative advantage. Comparing our estimates to those obtained from other approaches highlights the importance of using instruments to correct for selection bias and information on individuals’ ranking of institutions and fields to measure their preferred and next-best alternatives.

This paper evaluates the effects of two subsidies targeted at schools with large proportions of disadvantaged pupils. The first scheme gives primary schools with at least 70% disadvantaged minority pupils extra funding for personnel. The second scheme gives primary schools with at least 70% pupils from any disadvantaged group extra funding for computers and software. The cutoffs provide a regression discontinuity design that we exploit in a local difference-in-differences framework. For both subsidies we find negative point estimates, which are for some outcomes significantly different from 0. Extra funding for computers and software seems especially detrimental for girls' achievement. The negative effects of extra funding for computers and software are consistent with results from other recent studies casting doubt on the efficacy of computers in schools.