- John Engberg
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Community schools are an increasingly popular strategy used to improve the performance of students whose learning may be disrupted by non-academic challenges related to poverty. Community schools partner with community based organizations (CBOs) to provide integrated supports such as health and social services, family education, and extended learning opportunities. With over 300 community schools, the New York City Community Schools Initiative (NYC-CS) is the largest of these programs in the country. Using a novel method that combines multiple rating regression discontinuity design (MRRDD) with machine learning (ML) techniques, we estimate the causal effect of NYC-CS on elementary and middle school student attendance and academic achievement. We find an immediate reduction in chronic absenteeism of 5.6 percentage points, which persists over the following three years. We also find large improvements in math and ELA test scores – an increase of 0.26 and 0.16 standard deviations by the third year after implementation – although these effects took longer to manifest than the effects on attendance. Our findings suggest that improved attendance is a leading indicator of success of this model and may be followed by longer-run improvements in academic achievement, which has important implications for how community school programs should be evaluated.
The primary goal of job training programs is to improve employment and earning outcomes of participants. However, effective job training programs may have potential secondary benefits, including in the form of reduced arrests. In this paper, we evaluate the impact of a job training program in New Orleans that was implemented using a randomized controlled trial design. We find that among those who had a prior criminal record, those assigned to the treatment group were two-fifths as likely to get arrested as those assigned to the control group at any time point after randomization. We explore several potential mechanisms for why this effect occurs and find suggestive evidence that the training program’s impact on wages, as well as peer effects from other trainees, can partially explain this effect.
We consider the case in which the number of seats in a program is limited, such as a job training program or a supplemental tutoring program, and explore the implications that peer effects have for which individuals should be assigned to the limited seats. In the frequently-studied case in which all applicants are assigned to a group, the average outcome is not changed by shuffling the group assignments if the peer effect is linear in the average composition of peers. However, when there are fewer seats than applicants, the presence of linear-in-means peer effects can dramatically influence the optimal choice of who gets to participate. We illustrate how peer effects impact optimal seat assignment, both under a general social welfare function and under two commonly used social welfare functions. We next use data from a recent job training RCT to provide evidence of large peer effects in the context of job training for disadvantaged adults. Finally, we combine the two results to show that the program's effectiveness varies greatly depending on whether the assignment choices account for or ignore peer effects.