Quarterly Journal of Economics最新文献

Overdose deaths involving opioids have increased dramatically since the 1990s, leading to the worst drug overdose epidemic in U.S. history, but there is limited empirical evidence about the initial causes. In this article, we examine the role of the 1996 introduction and marketing of OxyContin as a potential leading cause of the opioid crisis. We leverage cross-state variation in exposure to OxyContin's introduction due to a state policy that substantially limited the drug's early entry and marketing in select states. Recently unsealed court documents involving Purdue Pharma show that state-based triplicate prescription programs posed a major obstacle to sales of OxyContin and suggest that less marketing was targeted to states with these programs. We find that OxyContin distribution was more than 50% lower in "triplicate states" in the years after the drug's launch. Although triplicate states had higher rates of overdose deaths prior to 1996, this relationship flipped shortly after the launch and triplicate states saw substantially slower growth in overdose deaths, continuing even 20 years after OxyContin's introduction. Our results show that the introduction and marketing of OxyContin explain a substantial share of overdose deaths over the past two decades.

Physicians, judges, teachers, and agents in many other settings differ systematically in the decisions they make when faced with similar cases. Standard approaches to interpreting and exploiting such differences assume they arise solely from variation in preferences. We develop an alternative framework that allows variation in preferences and diagnostic skill and show that both dimensions may be partially identified in standard settings under quasi-random assignment. We apply this framework to study pneumonia diagnoses by radiologists. Diagnosis rates vary widely among radiologists, and descriptive evidence suggests that a large component of this variation is due to differences in diagnostic skill. Our estimated model suggests that radiologists view failing to diagnose a patient with pneumonia as more costly than incorrectly diagnosing one without, and that this leads less skilled radiologists to optimally choose lower diagnostic thresholds. Variation in skill can explain 39% of the variation in diagnostic decisions, and policies that improve skill perform better than uniform decision guidelines. Failing to account for skill variation can lead to highly misleading results in research designs that use agent assignments as instruments.

Competition in health insurance markets may fail to improve health outcomes if consumers are not able to identify high quality plans. We develop and apply a novel instrumental variables framework to quantify the variation in causal mortality effects across plans and how much consumers attend to this variation. We first document large differences in the observed mortality rates of Medicare Advantage plans within local markets. We then show that when plans with high (low) mortality rates exit these markets, enrollees tend to switch to more typical plans and subsequently experience lower (higher) mortality. We derive and validate a novel "fallback condition" governing the subsequent choices of those affected by plan exits. When the fallback condition is satisfied, plan terminations can be used to estimate the relationship between observed plan mortality rates and causal mortality effects. Applying the framework, we find that mortality rates unbiasedly predict causal mortality effects. We then extend our framework to study other predictors of plan mortality effects and estimate consumer willingness to pay. Higher spending plans tend to reduce enrollee mortality, but existing quality ratings are uncorrelated with plan mortality effects. Consumers place little weight on mortality effects when choosing plans. Good insurance plans dramatically reduce mortality, and redirecting consumers to such plans could improve beneficiary health.