Updates coming soon.
Between 2000 and 2020, the share of US bed capacity at independent hospitals dropped from 42% to 19% – a rapid corporatization of care. We study whether this restructuring of the industry in favor of larger firms improved hospital profitability and quality. We combine novel, patient-level data on transaction prices from one of the largest commercial insurers in the US, Medicare claims, and New York hospital discharge files to study 116 hospital deals between 2012 and 2018. Systems increase the target hospital’s profitability by increasing prices and procedure intensity while reducing operating costs, primarily by reducing labor inputs. Cost reductions contribute more than the price increases. Patient demand remains unaffected despite these changes. However, readmission rates meaningfully worsen, with suggestive evidence of a trade-off between cost and quality.
There is significant interest in understanding the labor market consequences of the opioid epidemic, but little is known about how opioid use impacts on-the-job productivity. We analyze the impact of opioid initiation in the emergency department (ED) on workforce outcomes in the Military using linked medical and administrative personnel data for active duty service members from 2008 to 2017. Exploiting quasi-random assignment of patients to physicians in the ED, we find that assignment to a high-intensity opioid prescribing physician increases the probability of long-term opioid use and leads to subsequent negative effects on work capacity, job performance, and productivity. We also analyze the mechanisms underlying these negative workforce outcomes. While opioid use does not negatively affect measures of physical job performance, we find large increases in behavioral problems which lead to disciplinary actions and job separation.
How do "Must Access" Prescription Drug Monitoring Programs Address Opioid Misuse
The opioid epidemic led to the creation of state Prescription Drug Monitoring Programs (PDMPs) that eventually mandated access. We examine how these “must-access” PDMPs influenced prescribing after an emergency department (ED) visit and in the long term for the working-age population. By using data from a large multistate commercial insurance database from 2010 to 2014 and estimating difference-in-differences models, we show that only the broadest must-access PDMPs reduced opioid prescribing after an ED visit and in the long term. We then compared changes in prescribing rates for opioid naïve relative to non–opioid naïve individuals to disentangle the influence of information from administration costs on prescriber behavior. Findings suggest that hassle cost explains the majority of the decline in initial prescribing, and that the information value drives most of the reduction in long-term outcomes.
(with Charles Courtemanche, James Marton, Aaron Yelowitz, and Daniela Zapata)
The Affordable Care Act (ACA) aimed to achieve nearly universal health insurance coverage in the United States through a combination of insurance market reforms, mandates, subsidies, health insurance exchanges, and Medicaid expansions, most of which took effect in 2014. This paper estimates the causal effects of the ACA on health insurance coverage in 2014 using data from the American Community Survey. We utilize difference-in-difference-in-differences models that exploit cross-sectional variation in the intensity of treatment arising from state participation in the Medicaid expansion and local area pre-ACA uninsured rates. This strategy allows us to identify the effects of the ACA in both Medicaid expansion and non-expansion states. Our preferred specification suggests that, at the average pre-treatment uninsured rate, the full ACA increased the proportion of residents with insurance by 5.9 percentage points compared to 2.8 percentage points in states that did not expand Medicaid. Private insurance expansions from the ACA were due to increases in both employer-provided and non-group coverage. The coverage gains from the full ACA were largest for those without a college degree, non-whites, young adults, unmarried individuals, and those without children in the home. We find no evidence that the Medicaid expansion crowded out private coverage.
This paper examines the short- and long-term effects of quitting smoking on alcohol consumption using the Lung Health Study, a randomized smoking cessation program. The paper estimates the relationship between smoking and alcohol consumption using several self-reported and objective smoking measures, while also implementing a two-stage least squares estimation strategy that utilizes the randomized smoking cessation program assignment as an instrument for smoking. The analysis leads to three salient findings. First, self-reported and clinically verified smoking measures provide mixed evidence on the short term impact of quitting smoking on alcohol consumption. Second, the long-term impact of smoking on alcohol consumption, measured with the historic 5 years smoking behavior, suggests that those with the highest average cigarette consumption and those with the longest smoking history see the largest increase in alcohol consumption. Specifically, abstaining from smoking or reducing the average cigarette consumption to the mean level lowers alcohol consumption by roughly 25% per week. As a result, these findings present comprehensive evidence that smoking and drinking are complements in the long-term and that the public health and finance benefits in smoking cessations treatments are undervalued.
(with Charles Courtemanche and Rusty Tchernis)
This paper aims to identify the causal effect of smoking on body mass index (BMI) using data from the Lung Health Study, a randomized trial of smoking cessation treatments. Since nicotine is a metabolic stimulant and appetite suppressant, quitting or reducing smoking could lead to weight gain. We find evidence of an inverse relationship between smoking and weight that strengthens after accounting for endogeneity. Our preferred estimates suggest that quitting smoking leads to an average weight gain of around 1.9 BMI units, or 11.9 pounds at the average height. The results are similar using both self-reported smoking behavior and clinically-measured carbon monoxide levels. We then estimate semi-parametric models that provide evidence of a diminishing marginal effect of smoking on BMI, with nearly all of the weight gain from reduced smoking occurring at levels below a pack of cigarettes per day. Next, subsample regressions show that the impact of smoking on BMI is largest for younger individuals, females, those with no college degree, and those with healthy baseline BMI levels. Finally, we show that the effect is slightly stronger in the long run than the short run, which contradicts prior associational evidence that some of the weight gain after smoking cessation is temporary.