Listing 1 - 10 of 10 |
Sort by
|
Choose an application
Choose an application
Choose an application
We develop a new nonparametric discrete choice methodology and use it to analyze the demand for health insurance in the California Affordable Care Act marketplace. The methodology allows for endogenous prices and instrumental variables, while avoiding parametric functional form assumptions about the unobserved components of utility. We use the methodology to estimate bounds on the effects of changing premiums or subsidies on coverage choices, consumer surplus, and government spending on subsidies. We find that a $10 decrease in monthly premium subsidies would cause a decline between 1.8% and 6.7% in the proportion of subsidized adults with coverage. The reduction in total annual consumer surplus would be between $63 and $74 million, while the savings in yearly subsidy outlays would be between $209 and $601 million. We estimate the demand impacts of linking subsidies to age, finding that shifting subsidies from older to younger buyers would increase average consumer surplus, with potentially large impacts on enrollment. We also estimate the consumer surplus impact of removing the highly-subsidized plans in the Silver metal tier, where we find that a nonparametric model is consistent with a wide range of possibilities. We find that comparable mixed logit models tend to yield price sensitivity estimates towards the lower end of the nonparametric bounds, while producing consumer surplus impacts that can be both higher and lower than the nonparametric bounds depending on the specification of random coefficients.
Choose an application
In government-sponsored health insurance, subsidy design affects market outcomes. First, holding premiums fixed, subsidies determine insurance uptake and average cost. Insurers then respond to these changes, adjusting premiums. Combining data from the first four years of the California ACA marketplace with a model of insurance demand, cost, and insurers' competition, I quantify the impact of alternative subsidy designs on premiums, enrollment, costs, public spending, and consumer surplus. Younger individuals are more price sensitive and cheaper to cover. Increasing subsidies to this group would make all buyers better off, increase market participation, and lower average costs and average subsidies.
Choose an application
Under the Affordable Care Act, individual states have discretion in how they define coverage regions, within which insurers must charge the same premium to buyers of the same age, family structure, and smoking status. We exploit variation in these definitions to investigate whether the size of the coverage region affects outcomes in the ACA marketplaces. We find large consequences for small and rural markets. When states combine small counties with neighboring urban areas into a single region, the included rural markets see .6 to .8 more active insurers, on average, and savings in annual premiums of between $200 and $300.
Choose an application
We study how the politicization of policies designed to correct market failures can undermine their effectiveness. The Patient Protection and Affordable Care Act (ACA) was among the most politically divisive expansions of the US government. We examine whether partisanship distorted enrollment and market outcomes in the ACA insurance marketplaces. Controlling for observable characteristics and holding fixed plans and premiums available, Republicans enrolled less than Democrats and independents in ACA marketplace plans. Selection out of the ACA marketplaces was strongest among Republicans with lower expected healthcare costs, generating adverse selection. Computing enrollment and average cost with and without partisan differences, we find that this political adverse selection reduced enrollment by around three million people and raised average costs in the marketplaces, increasing the level of public spending necessary to provide subsidies to low-income enrollees by around $105 per enrollee per year. Lower enrollments and higher costs are concentrated in more Republican areas, potentially contributing to polarized views of the ACA.
Choose an application
We develop a new nonparametric approach for discrete choice and use it to analyze the demand for health insurance in the California Affordable Care Act marketplace. The model allows for endogenous prices and instrumental variables, while avoiding parametric functional form assumptions about the unobserved components of utility. We use the approach to estimate bounds on the effects of changing premiums or subsidies on coverage choices, consumer surplus, and government spending on subsidies. We find that a $10 decrease in monthly premium subsidies would cause a decline of between 1.8% and 6.7% in the proportion of subsidized adults with coverage. The reduction in total annual consumer surplus would be between $62 and $74 million, while the savings in yearly subsidy outlays would be between $207 and $602 million. We estimate the demand impacts of linking subsidies to age, finding that shifting subsidies from older to younger buyers would increase average consumer surplus, with potentially large im- pacts on enrollment. We also estimate the consumer surplus impact of removing the highly-subsidized plans in the Silver metal tier, where we find that a nonparametric model is consistent with a wide range of possibilities. We find that comparable mixed logit models tend to yield price sensitivity estimates towards the lower end of the non-parametric bounds, while producing consumer surplus impacts that can be both higher and lower than the nonparametric bounds depending on the specification of random coefficients.
Choose an application
Under the Affordable Care Act, individual states have discretion in how they define coverage regions, within which insurers must charge the same premium to buyers of the same age, family structure, and smoking status. We exploit variation in these definitions to investigate whether the size of the coverage region affects outcomes in the ACA marketplaces. We find large consequences for small and rural markets. When states combine small counties with neighboring urban areas into a single region, the included rural markets see .6 to .8 more active insurers, on average, and savings in annual premiums of between $200 and $300.
Choose an application
Who bears the consequences of administrative problems in healthcare? We use data on repeated interactions between a large sample of U.S. physicians and many different insurers to document the complexity of healthcare billing, and estimate its economic costs for doctors and consequences for patients. Observing the back-and-forth sequences of claim denials and resubmissions for past visits, we can estimate physicians' costs of haggling with insurers to collect payments. Combining these costs with the revenue never collected, we estimate that physicians lose 18% of Medicaid revenue to billing problems, compared with 4.7% for Medicare and 2.4% for commercial insurers. Identifying off of physician movers and practices that span state boundaries, we find that physicians respond to billing problems by refusing to accept Medicaid patients in states with more severe billing hurdles. These hurdles are quantitatively just as important as payment rates for explaining variation in physicians' willingness to treat Medicaid patients. We conclude that administrative frictions have first-order costs for doctors, patients, and equality of access to healthcare. We quantify the potential economic gains--in terms of reduced public spending or increased access to physicians--if these frictions could be reduced, and find them to be sizable.
Choose an application
We develop a heterogeneous-agents network-based model to analyze alternative policies during a pandemic outbreak, accounting for health and economic trade-offs within the same empirical framework. We leverage a variety of data sources, including data on individuals' mobility and encounters across metropolitan areas, health records, and measures of the possibility to be productively working from home. This combination of data sources allows us to build a framework in which the severity of a disease outbreak varies across locations and industries, and across individuals who differ by age, occupation, and preexisting health conditions. We use this framework to analyze the impact of different social distancing policies in the context of the COVID-19 outbreaks across US metropolitan areas. Our results highlight how outcomes vary across areas in relation to the underlying heterogeneity in population density, social network structures, population health, and employment characteristics. We find that policies by which individuals who can work from home continue to do so, or in which schools and firms alternate schedules across different groups of students and employees, can be effective in limiting the health and healthcare costs of the pandemic outbreak while also reducing employment losses.
Listing 1 - 10 of 10 |
Sort by
|