This paper studies the effect of transaction costs on the take-up and targeting of social safety net programs in the context of multi-program enrollment by exploiting the Combined Application Project (CAP), a widespread state-level policy designed to encourage enrollment in the Supplemental Nutrition Assistance Program (SNAP) among elderly recipients of Supplemental Security Income. I show that the CAP increased SNAP take-up by 8–13 percentage points, or about 17%–24%. The increase was suggestively larger among those with a higher probability of being food insecure. Exploiting heterogeneity in the format of the CAP across states, I find that “auto-enrollment” most effectively increased SNAP take-up.
Using a natural experiment which randomized class times to students, this study reveals that enrolling in early morning classes lowers students’ course grades and the likelihood of future STEM course enrollment. There is a 29% reduction in pursuing the major within the same college and a 21% rise in choosing a low-earning major, predominantly influenced by early morning STEM classes. To understand the mechanism, I conducted a survey of undergraduate students enrolled in an introductory course, some of whom were assigned to a 7:30 a.m. section. I find evidence of a decrease in human capital accumulation and learning quality for early morning sections.
This study examines the impact of residential water-efficiency standards on household water consumption, utilizing billing data from California. I examine both the short- and long-term impacts of the California Green Building Standards Code (CALGreen), comparing homes constructed just before and after its implementation in 2010. The findings show that, on average, CALGreen homes use 11.7% less water than comparable pre-code homes, with savings persisting over time at approximately 10.9%. Notably, engineering-based (ex ante) estimates of water savings are nearly twice as large as the realized reductions, likely reflecting the “offsetting behavior” effect. The analysis further reveals that most water savings occur during warmer months when outdoor water use peaks. In addition, CALGreen homes exhibit lower sensitivity to temperature fluctuations, with smaller increases in use during hotter months and smaller reductions during cooler months. Finally, when accounting for compliance costs and the opportunity cost of water, the results suggest that CALGreen policy passes a cost-effectiveness test. These findings highlight the effectiveness of building codes as a durable policy tool for achieving long-term water conservation in fast-growing, semi-arid regions.
Can reducing administrative burdens increase the take-up of social benefits? This meta-analysis reviews 51 field experimental studies reporting 187 treatment effect sizes. Using the administrative burden framework to compare interventions, I reclassify each intervention by the stage it measures on (application vs. final receipt) and whether it reduces the learning demands by providing information or the compliance demands by providing assistance. The results indicate that it is significantly easier to increase application rates than actual take-up rates. On average, estimated treatment effects are about twice as large when outcomes are measured at the application stage as when they are measured on final benefit receipt. The most effective interventions are the ones reducing compliance demands, as these are estimated to increase actual take-up by 8.31 percentage points on average. Interventions reducing learning demands are estimated to increase actual take-up by 3.39 percentage points on average. These findings consolidate the field experimental evidence on how to improve take-up rates and highlight the need for further research on application stages, treatment compliance, and variation across welfare regimes.
Recruiting teachers via “grow-your-own” (GYO) programs is a popular, yet rarely evaluated, strategy for addressing local workforce shortages and ensuring that incoming teachers resemble, understand, and have strong connections to their communities. We provide novel evidence on the impacts of one such GYO program by exploiting the staggered rollout of the Teacher Academy of Maryland (TAM) Career and Technical Education (CTE) program across public high schools. Exposed students were more likely to become teachers a decade later by 0.6 percentage points (pp), or 45%. Effects were concentrated among White girls (1.4pp/39%) and Black girls (0.7pp/82%), though boys benefitted too (0.2pp/59%). Although White girls induced by the program to become teachers often did so in the same district they attended as students (0.9pp/43%)—a key goal of GYO and localized teacher recruitment programs—this was less common for Black girls. Rather, Black girls induced by the program to become teachers did so in districts with more Black teachers than their home district (0.4pp/143%) and in districts with higher starting salaries (0.5pp/239%). Access to the program also increased wages (5% on average/18% for Black girls), challenging the narrative that such programs cause students to forego more lucrative professions.
As recreational marijuana laws (RML) expanded marijuana access over the last decade, very little is known about the impact of RML on two costly social behaviors that may arise as a consequence of marijuana consumption: unhealthy eating (i.e., munchies) and sedentary lifestyle (i.e., couch-lock). Using NielsenIQ Consumer Panel data, we find that the passage of RML led to an increase in the number of grocery store trips that involved “junk food,” as well as the amount of spending. This effect is particularly driven by an increase in ice cream, chips, and candy. Using the Behavioral Risk Factor Surveillance System (BRFSS) and the American Time Use Survey (ATUS), we find that the passage of RML also led to a decrease in exercise, particularly driven by a reduction in cardio, and suggestive evidence of more time spent at home. These results suggest that RMLs have an adverse effect on health through unhealthy eating and reduced physical activity, posing a significant public health challenge to diet- and lifestyle-related chronic conditions.
Policymakers are often urged to rely on “evidence-based policy” (EBP)—adopting only interventions proven effective (i.e., positive and statistically significant in multisite impact evaluations). EBP clearinghouses chronicle and rate tests of social policy interventions. But EBP clearinghouse standards are based almost entirely on internal validity. They largely ignore whether research findings from multisite trials apply to individual localities, where much of social policy is formulated. We develop a Bayesian model of the probability that the EBP rule is sound advice to local policymakers. The model allows a direct test of the probability of a correct policy decision under the EBP rule, its positive predictive value (PPV), and its negative predictive value (NPV)—the probabilities that an intervention deemed effective by that rule will in fact be effective in a particular site (PPV), and that an intervention deemed ineffective will not be effective in a particular site (NPV), given the true impact of the intervention. These intuitive, easily calculated probabilities are major contributions of this paper. In our illustrative analysis of six multisite randomized trials, we find that under the EBP clearinghouse rule the probability of a correct policy decision, PPV, and NPV are all unacceptably low unless the cross-site impact heterogeneity is quite low.
Substitution of capital in the form of automation and robotics has displaced many American manufacturing workers, especially those without college degrees. However, this need not be the path forward. We argue that now is an opportune time for greater policy emphasis on collaborative robots, or “cobots,” which are designed to complement rather than replace manual labor while raising productivity. To realize this potential, policies must properly align incentives, support training, and address often overlooked issues such as monitoring and privacy. Through an interdisciplinary lens, we examine the current state of cobot deployment and describe how well-designed job tasks, paired with carefully integrated cobots, can preserve employment opportunities while improving ergonomics and enhancing productivity. Strategic policy measures aimed at redirecting investments can balance manufacturers' needs while ensuring stable employment, higher wages, and safer workplaces. With informed and deliberate policy, the adoption of cobots can mark a new phase of technological progress while averting the costly dislocations that accompanied past automation and offshoring.
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