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We describe a framework for empirical welfare analysis that uses the causal estimates of a policy's impact on net government spending. This framework provides guidance for which causal effects are (and are not) needed for empirical welfare analysis of public policies. The key ingredient is the construction of each policy's marginal value of public funds (MVPF). The MVPF is the ratio of beneficiaries' willingness to pay for the policy to the net cost to the government. We discuss how the MVPF relates to \"traditional\" welfare analysis tools such as the marginal excess burden and marginal cost of public funds. We show how the MVPF can be used in practice by applying it to several canonical empirical applications from public finance, labor, development, trade, and industrial organization.

We develop a framework for welfare analysis of interventions designed to increase take-up of social safety net programs in the presence of potential behavioral biases. We calibrate the key parameters using a randomized field experiment in which 30,000 elderly individuals not enrolled in—but likely eligible for—the Supplemental Nutrition Assistance Program (SNAP) are either provided with information that they are likely eligible, provided with this information and offered assistance in applying, or are in a “status quo” control group. Only 6% of the control group enrolls in SNAP over the next nine months, compared to 11% of the Information Only group and 18% of the Information Plus Assistance group. The individuals who apply or enroll in response to either intervention have higher net income and are less sick than the average enrollee in the control group. We present evidence consistent with the existence of optimization frictions that are greater for needier individuals, which suggests that the poor targeting properties of the interventions reduce their welfare benefits.

A Camden, New Jersey, “hotspotting” program is designed to prevent rehospitalizations among “superutilizers” of heath care services through home visits and telephone calls from nurses, social workers, and community health care workers who help coordinate outpatient care. In a randomized, controlled trial, the program did not reduce hospital readmissions.

The impact of the coronavirus disease 2019 (COVID-19) pandemic has been starkly unequal across race and ethnicity. We examined the geographic variation in excess all-cause mortality by race and ethnicity to better understand the impact of the pandemic. We used individual-level administrative data on the US population between January 2011 and April 2020 to estimate the geographic variation in excess all-cause mortality by race and Hispanic origin. All-cause mortality allows a better understanding of the overall impact of the pandemic than mortality attributable to COVID-19 directly. Nationwide, adjusted excess all-cause mortality during that period was 6.8 per 10,000 for Black people, 4.3 for Hispanic people, 2.7 for Asian people, and 1.5 for White people. Nationwide averages mask substantial geographic variation. For example, despite similar excess White mortality, Michigan and Louisiana had markedly different excess Black mortality, as did Pennsylvania compared with Rhode Island. Wisconsin experienced no significant White excess mortality but had significant Black excess mortality. Further work understanding the causes of geographic variation in racial and ethnic disparities-the relevant roles of social and environmental factors relative to comorbidities and of the direct and indirect health effects of the pandemic-is crucial for effective policy making.

We use an event study approach to examine the economic consequences of hospital admissions for adults in two datasets: survey data from the Health and Retirement Study, and hospitalization data linked to credit reports. For non-elderly adults with health insurance, hospital admissions increase out-of-pocket medical spending, unpaid medical bills, and bankruptcy, and reduce earnings, income, access to credit, and consumer borrowing. The earnings decline is substantial compared to the out-of-pocket spending increase, and is minimally insured prior to age-eligibility for Social Security Retirement Income. Relative to the insured non-elderly, the uninsured non-elderly experience much larger increases in unpaid medical bills and bankruptcy rates following a hospital admission. Hospital admissions trigger fewer than 5 percent of all bankruptcies in our sample.

In 2008, Oregon initiated a limited expansion of a Medicaid program for uninsured, low-income adults, drawing names from a waiting list by lottery. This lottery created a rare opportunity to study the effects of Medicaid coverage by using a randomized controlled design. By using the randomization provided by the lottery and emergency-department records from Portland-area hospitals, we studied the emergency department use of about 25,000 lottery participants over about 18 months after the lottery. We found that Medicaid coverage significantly increases overall emergency use by 0.41 visits per person, or 40% relative to an average of 1.02 visits per person in the control group. We found increases in emergency-department visits across a broad range of types of visits, conditions, and subgroups, including increases in visits for conditions that may be most readily treatable in primary care settings.

We study the demand response to nonlinear price schedules using data on insurance contracts and prescription drug purchases in Medicare Part D. We exploit the kink in individuals’ budgets set created by the famous ‘‘donut hole,’’ where insurance becomes discontinuously much less generous on the margin, to provide descriptive evidence of the drug purchase response to a price increase. We then specify and estimate a simple dynamic model of drug use that allows us to quantify the spending response along the entire nonlinear budget set. We use the model for counterfactual analysis of the increase in spending from ‘‘filling’’ the donut hole, as will be required by 2020 under the Affordable Care Act. In our baseline model, which considers spending decisions within a single year, we estimate that filling the donut hole will increase annual drug spending by about $150, or about 8 percent. About one-quarter of this spending increase reflects anticipatory behavior, coming from beneficiaries whose spending prior to the policy change would leave them short of reaching the donut hole. We also present descriptive evidence of cross-year substitution of spending by individuals who reach the kink, which motivates a simple extension to our baseline model that allows—in a highly stylized way—for individuals to engage in such cross-year substitution. Our estimates from this extension suggest that a large share of the $150 drug spending increase could be attributed to cross-year substitution, and the net increase could be as little as $45 a year.

There is a popular perception that insurance coverage will reduce overuse of the emergency department (ED). Both opponents and advocates of expanding insurance coverage under the Affordable Care Act (АСА) have made statements to the effect that EDs have been jammed with the uninsured and that paying for the uninsured population's emergency care has burdened the health care system as a result of the expense of that care. It has therefore been surprising to many to encounter evidence that insurance coverage increases ED use instead of decreasing it. Two facts may help explain this unexpected finding. First, there is a common misperception that the uninsured use the ED more than the insured. In fact, insured and uninsured adults use the ED at very similar rates and in very similar circumstances-and the uninsured use the ED substantially less than the Medicaid population. Second, while the uninsured do not use the ED more than the insured, they do use other types of care much less than the insured.

Between 1974 and 1981, the RAND health insurance experiment provided health insurance to more than 5,800 individuals from about 2,000 households in six different locations across the United States, a sample designed to be representative of families with adults under the age of 62. More than three decades later, the RAND results are still widely held to be the “gold standard” of evidence for predicting the likely impact of health insurance reforms on medical spending, as well as for designing actual insurance policies. On cost grounds alone, we are unlikely to see something like the RAND experiment again. In this essay, we reexamine the core findings of the RAND health insurance experiment in light of the subsequent three decades of work on the analysis of randomized experiments and the economics of moral hazard. First, we re-present the main findings of the RAND experiment in a manner more similar to the way they would be presented today. Second, we reexamine the validity of the experimental treatment effects. Finally, we reconsider the famous RAND estimate that the elasticity of medical spending with respect to its out-of pocket price is – 0.2. We draw a contrast between how this elasticity was originally estimated and how it has been subsequently applied, and more generally we caution against trying to summarize the experimental treatment effects from nonlinear health insurance contracts using a single price elasticity.

We demonstrate the existence of multiple dimensions of private information in the long-term care insurance market. Two types of people purchase insurance: individuals with private information that they are high risk and individuals with private information that they have strong taste for insurance. Ex post, the former are higher risk than insurance companies expect, while the latter are lower risk. In aggregate, those with more insurance are not higher risk. Our results demonstrate that insurance markets may suffer from asymmetric information even absent a positive correlation between insurance coverage and risk occurrence. The results also suggest a general test for asymmetric information.