Health Insurance and Mortality: Experimental Evidence from Taxpayer Outreach

Abstract

We evaluate a randomized outreach study in which the IRS sent informational letters to 3.9 million households that paid a tax penalty for lacking health insurance coverage under the Affordable Care Act. Drawing on administrative data, we study the effect of this intervention on taxpayers subsequent health insurance enrollment and mortality. We find the intervention led to increased coverage during the subsequent two years and reduced mortality among middle-aged adults over the same time period. The results provide experimental evidence that health insurance coverage can reduce mortality in the United States.

The Health Costs of Cost-Sharing

“…Most closely related to our study is Huh and Reif (2017), who estimate effect of introduction of Medicare Part D on mortality by comparing eligible 66 year-olds to ineligible 64 year-olds, and find annual mortality reductions among compliers of 8.2%. Other studies by Miller et al (2019) (9.4%) and Goldin et al (2020), (10.1%) exploring the effects of any insurance and Medicaid respectively, likewise find effects of a similar magnitude.…”

“…However, given the presence and large magnitude of the coexisting errors, the price elasticity of demand for medical care is not a sufficient statistic for welfare: seemingly rational price responses are outweighed by 'sub-optimal' health response (Baicker et al, 2015;. Our results thus contribute to a growing literature in economics documenting the mortality consequences of plan choice (Abaluck et al, 2020), receipt of health insurance (Goldin et al, 2020;Huh and Reif, 2017;Miller et al, 2019), income (Gross et al, 2020), all of which point to taking a more complete view of welfare, beyond demand elasticities. The magnitudes of both the utilization and health responses are needed to understand welfare and design insurance contracts.…”

Generating Evidence That Influences Policy

“…Even where there may be barriers to conducting traditional RCTs, such as a perceived lack of equipoise in the research or policy question at hand, a discomfort with treating people differently (even if in a randomized manner), or a general aversion to experimentation (Meyer et al, 2019), it is still possible to prioritize causal inference in the design of program evaluations. In the case where there is a perceived lack of equipoise or an aversion to treating people differently, an alternate approach may be a research design in which everyone is eligible for the program, but a randomly selected portion are further encouraged to participate (Goldin, Lurie, & McCubbin, 2020). Similarly, stepped-wedge trial designs-in which successive groups of individuals or communities randomly receive the intervention (Hemming et al, 2015)-can be an effective way to address policymaker concerns around leaving some groups out of an intervention that they believe may be beneficial.…”

An investigation of health insurance policy and behavior in a virtual environment

“…The assessment of long term impacts is limited by the fact that all are relatively recent reforms, in comparison to the decades over which chronic diseases develop. More recently, Miller et al [ 16 ], Borgschulte and Vogler [ 17 ], and Goldin et al [ 18 ] all find that the ACA reduced mortality.…”