Programs to encourage labor market activity among youth, including public employment programs and wage subsidies like the Work Opportunity Tax Credit, can be supported by three broad rationales. They may: (1) provide contemporaneous income support to participants; (2) encourage work experience that improves future employment and/or educational outcomes of participants; and/or (3) keep participants “out of trouble.” We study randomized lotteries for access to the New York City (NYC) Summer Youth Employment Program (SYEP), the largest summer youth employment program in the U.S., by merging SYEP administrative data on 294,100 lottery participants to IRS data on the universe of U.S. tax records; to New York State administrative incarceration data; and to NYC administrative cause of death data. In assessing the three rationales, we find that: (1) SYEP participation causes average earnings and the probability of employment to increase in the year of program participation, with modest contemporaneous crowdout of other earnings and employment; (2) SYEP participation causes a modest decrease in average earnings for three years following the program and has no impact on college enrollment; and (3) SYEP participation decreases the probability of incarceration and decreases the probability of mortality, which has important and potentially pivotal implications for analyzing the net benefits of the program. 

We use quantile treatment effects estimation to examine the consequences of the random-assignment New York City School Choice Scholarship Program (NYCSCSP) across the distribution of student achievement. Our analyses suggest that the program had negligible and statistically insignificant effects across the skill distribution. In addition to contributing to the literature on school choice, the paper illustrates several ways in which distributional effects estimation can enrich educational research: First, we demonstrate that moving beyond a focus on mean effects estimation makes it possible to generate and test new hypotheses about the heterogeneity of educational treatment effects that speak to the justification for many interventions. Second, we demonstrate that distributional effects can uncover issues even with well-studied datasets by forcing analysts to view their data in new ways. Finally, such estimators highlight where in the overall national distribution data from particular interventions lie; this is important for exploring the external validity of the intervention’s effects.

Hierarchical models play three important roles in modeling causal effects: (i) accounting for data collection, such as in stratified and split-plot experimental designs; (ii) adjusting for unmeasured covariates, such as in panel studies; and (iii) capturing treatment effect variation, such as in subgroup analyses. Across all three areas, hierarchical models, especially Bayesian hierarchical modeling, offer substantial benefits over classical, non-hierarchical approaches. After discussing each of these topics, we explore some recent developments in the use of hierarchical models for causal inference and conclude with some thoughts on new directions for this research area.

Why wealth is systematically lower in the tropics remains a puzzle. We point out that latitude may have fundamental economic consequence because it plays a key role in how countries experience geophysical processes that have economic implications. We demonstrate that annual fluctuations in the El Nino Southern Oscillation (ENSO) leads to hotter and dryer local weather across tropical countries and subsequently to substantial losses in agricultural yields, output, and value-added. If volatility in agricultural production impedes economic growth, the relatively stronger influence of ENSO on the tropics may offer yet another partial explanation for slower historical growth in the tropics.

It has been proposed that geography influences economic growth for many reasons. Previous analyses of comparative development seem to have sidestepped the question of location-dependent depreciation. However the construction of new measures of tropical cyclone exposure enables us to consider the potential impact of this single source of capital depreciation. Using an estimate of asset destruction due to tropical cyclones, we identify the "sandcastle depreciation" rate, and find support for location-dependent depreciation by looking at average growth rates. This leads us to propose that heterogeneous and geographically-dependent depreciation rates may play an important role in global patterns of economic development.