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.
We evaluate the effects of the nationwide transition in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) from paper vouchers to Electronic Benefit Transfer (EBT) cards on store decisions to seek authorization to accept WIC benefits. We combine novel administrative data from “The Integrity Profile” (USDA administrative data on stores participating in WIC and their WIC reimbursements) with new nationwide policy data on WIC EBT implementation. Using a staggered adoption difference-in-differences approach, we find that the transition had heterogeneous and occasionally unanticipated effects across states. The number of WIC-authorized independent vendors declined 10% following WIC EBT implementation. We find no significant effect of WIC EBT implementation on WIC redemptions in a subset of ZIP codes with sufficient vendors for data sharing. Vendors in states that were early adopters of WIC EBT have more negative effects on the probability of WIC authorization, which may be due to learning effects or improvements in technology over time. Past experience with EBT implementation by financial services providers hired by states to help them implement WIC EBT processing (processors) reduces the magnitude of these negative effects of EBT implementation on store participation in WIC.
The United States spends more than $4 trillion per year on health care, largely conducted by private providers and reimbursed by insurers. A major concern in this system is overbilling and fraud by hospitals, who face incentives to misreport their claims to receive higher payments. In this work, we develop novel machine learning tools to identify hospitals that overbill insurers, which can be used to guide investigations and auditing of suspicious hospitals for both public and private health insurance systems. Using large-scale claims data from Medicare, the US federal health insurance program for the elderly and disabled, we identify patterns consistent with fraud among inpatient hospitalizations. Our proposed approach for fraud detection is fully unsupervised, not relying on any labeled training data, and is explainable to end users, providing interpretations for which diagnosis, procedure, and billing codes lead to hospitals being labeled suspicious. Using newly collected data from the Department of Justice on hospitals facing anti-fraud lawsuits, and case studies of suspicious hospitals, we validate our approach and findings. Our method provides a nearly fivefold lift over random targeting of hospitals. We also perform a postanalysis to understand which hospital characteristics, not used for detection, are associated with suspiciousness.
Schools often overlook how structuring student and teacher schedules may impact educational outcomes. We analyze the impact of nontraditional school calendars on student and teacher productivity. These calendars differentially allocate mandated instructional time by choosing (1) the number of hours in the school day, (2) the number of school days each year, and (3) the distribution of school days throughout the year. To do this, we use administrative data on over 2 million students and exploit the staggered elimination of nontraditional school calendars that vary on these three dimensions. We find that while school schedules have little impact on younger children's learning, school schedules with longer and fewer school days have large negative effects on older students that are equivalent to decreasing teacher quality by nearly one standard deviation. Our results appear to be driven by changes in at-home study behavior and school start times rather than how school days are distributed throughout the year. In addition, school schedules with longer and fewer school days increase teacher turnover. Our results suggest an important role of daily schedules on school productivity.
This study examines how officials’ heterogeneous preferences, particularly shaped by traumatic childhood experiences, impact government policy decisions. Leveraging China's Great Famine as a natural experiment, we apply a cohort difference-in-differences (DID) approach to analyze the effect of officials’ exposure to famine on government objectives in their jurisdictions. Results show that famine-exposed officials, particularly those aged five or older during the crisis (a formative period for value formation), systematically set lower economic growth targets, reflecting trauma-induced risk aversion rather than diminished administrative capacity. Two key findings support this interpretation: (1) Jurisdictions led by famine-exposed officials achieve comparable economic outcomes, and (2) these officials allocate greater resources to social stability expenditures. The evidence highlights how early-life adversity embeds risk-averse heuristics in bureaucratic decision-making, with lasting implications for institutional governance.
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