Journal of Policy Analysis and Management最新文献

The broad-based categorical eligibility (BBCE) policy allows states to bypass federal gross income and asset tests for supplemental nutrition assistance program (SNAP) eligibility. Policymakers often propose limiting BBCE's scope or eliminating it altogether. Yet, our understanding of BBCE's impact on SNAP participation has relied solely on static two-way fixed effects (TWFE) estimators, which have been criticized for assuming no treatment effect heterogeneity across states and over time. In this study, using a heterogeneity-robust difference-in-differences estimator, we provide new estimates of BBCE's impact that are more than twice as large as those derived from the static TWFE models. Importantly, our event-study analysis shows that BBCE's effect has increased uniformly over time across state groups defined by their adoption timing, explaining the smaller effects estimated by the static TWFE model. In addition, we find that although BBCE extended eligibility to higher-income households, most of its impact on participation occurred among households already eligible under federal gross income limits. Our counterfactual simulations further show that between 2000 and 2016, extending eligibility to higher-income households accounted for approximately 11.5% of the increase in participation and 3.8% of the rise in program spending resulting from BBCE, with the remainder driven by already income-eligible households.

Low-income housing is often of substandard quality. Health care payers like Medicaid and Medicare are piloting reimbursement for housing quality improvement with the rationale of reducing expenses from acute care use, but little is known about whether housing quality interventions can alter health utilization. I evaluate a large-scale policy mandating remediation of NYC's worst buildings in a regression discontinuity design to estimate the causal effect of housing improvements on health utilization. I do not find evidence of impacts on any metric of short-run acute care use or health expenditures, even among vulnerable subgroups. Exploratory longer-run event studies show some evidence of moderate (10%–15%) reductions in emergency department visits 3–4 years later but no evidence of impacts on expenditures. The policy succeeded at reducing housing violation rates in treated buildings by half. Yet, this only corresponded to a move from the 98th percentile to the 96th percentile of all buildings, underscoring the dire conditions tenants continue to live in—and suggesting one potential explanation for muted findings on health care use. The theory that housing quality interventions can directly translate to reductions in health use and spending may not be borne out empirically, particularly if interventions cannot completely address substandard conditions.

Evaluating the comparative performance of U.S. federal agencies is difficult, particularly since both tasks and missions vary so dramatically. In addition, forces beyond an agency's control (e.g., COVID, an economic downturn, etc.) can determine outcomes even when agencies are performing at a high level. In this paper, we introduce a new approach to measuring organizational performance, something conceptually distinct from, but correlated with, both organizational inputs and outcomes. This measurement approach focuses on how well the internal machinery of agencies is functioning. We analyze a vast trove of subjective and objective performance information and aggregate it using a Bayesian structural equation measurement (BSEM) model. We isolate organizational performance from inputs and outcomes through careful model specification, information from the BSEM models, and model identification through a careful evaluation of different models and diagnostics. Our analysis yields 2479 organizational performance estimates for 135 U.S. federal departments and agencies spanning 19 years between 2002 and 2024. We explore the validity of these estimates by comparing them with other measures of similar or related concepts. We conclude by discussing the implications of our measurement approach and its usefulness for evaluating organizational performance in diverse and changing contexts.

A number of large companies deploy facilities—factories, warehouses, shopping malls—that employ thousands of people directly and indirectly. Local governments have been enticing these companies with various financial incentives. But how much economic growth do these facilities promote, if any? We study this question using Amazon distribution facilities as the case in point. We show evidence of a positive effect of opening Amazon's distribution facilities on counties' economic outcomes. We focus on midsized counties in which Amazon opened facilities in the years 2014–2017 because we have good controls for these counties. We find a selection effect where Amazon locates its facilities, and we address this issue. Our preferred methodology is Callaway–Sant'Anna difference-in-differences combined with matching. After Amazon's entry, in our preferred specification, we find that the employment-to-population ratio in the treated county increased by 0.0087 (+1.46% at the mean), the poverty rate decreased by 0.36 percentage points (−2.69%), and the median household income increased by $1413 (+2.33%). We present evidence to argue that our findings can likely be interpreted as causal and plausible.

In 2015, Michigan increased its Career and Technical Education (CTE) funding and changed its funding formula to reimburse programs based on student progression through program curricula. Although this change nearly doubled program completion rates, student enrollment and persistence were unaffected; instead, administrators accelerated student progress by reorganizing course curricula around notches in the new funding formula. As a result of response heterogeneity, 30% of the funding increase was transferred away from high-poverty districts to more affluent ones, underscoring how supply-side responses to loopholes shape the incidence of public services.