Impacts of the Affordable Care Act dependent coverage provision on health-related outcomes of young adults
Introduction
The Patient Protection and Affordable Care Act (ACA) of March 2010 aimed to achieve nearly universal coverage in the United States through a combination of mandates, subsidies, Medicaid expansions, and health insurance exchanges (Gruber, 2011). Although the majority of the ACA's provisions just took effect in 2014, one important component of the law – a dependent coverage provision – was implemented on September 23rd, 2010. This provision allows dependents to remain on a parent's private health insurance plan until the start of the first plan year after they turn 26 years old. Previously, private insurers often dropped non-student dependents at age 19 and student dependents at age 23 (Anderson et al., 2012, Anderson et al., 2014).
Many states already had some form of dependent coverage mandate before the ACA, but the state laws are typically weaker. Most state laws have an age threshold below 26 or require additional criteria, such as being a full-time student, living with one's parents, or not being married. Moreover, state laws do not apply to self-funded benefit programs, and more than half of private sector workers with employer-provided health insurance are in self-funded plans (Monheit et al., 2011). Perhaps because of these limitations, Monheit et al. (2011) and Levine et al. (2011) find that state dependent coverage mandates only lead to small increases in dependent coverage that are offset by a decline in young adults holding their own policies. In contrast, the ACA provision applies to all young adults under age 26 and all private plans. It therefore has the potential to dramatically affect young adults across the country, including in states with a pre-existing dependent coverage provision.
The ACA dependent coverage expansion provides a unique opportunity to study the impacts of a health insurance intervention specific to young adults, the age group with the highest uninsured rate (Levine et al., 2011). Prior to the ACA, the uninsured rate was 29% among individuals ages 18–24 and 27% among those 25–34, compared to 19% for 35–44 year olds and 14% for 45–64 year olds (DeNavas-Walt et al., 2010). Since any attempt to obtain universal coverage necessarily involves large coverage expansions among young adults, it is important to understand the effects of insurance on this group. It is unclear the extent to which results from other contexts – such as Medicaid, Medicare, or the Massachusetts health care reform of 2006 – are applicable. Young adults are generally healthier than the populations covered by these programs, and therefore may experience smaller gains from health insurance. Alternatively, young adults may be relatively poor and therefore respond strongly to reduced out-of-pocket costs of medical care.1
Given the short amount of time since its implementation, researchers are only beginning to study the impacts of the ACA dependent coverage provision. Cantor et al. (2012) and Sommers and Kronick (2012) show that the mandate increased health insurance coverage for young adults across all racial groups and regardless of employment status. Sommers et al. (2013) find that the provision increased insurance coverage among young adults, while reducing delays in getting care and care foregone because of cost. Akosa Antwi et al. (2013) again find an increase in insurance coverage, but they also present evidence of labor market consequences such as young adults shifting from full-time to part-time jobs. Akosa Antwi et al. (2014) show that the mandate increased young adults’ utilization of inpatient care, particularly for mental illness. Chua and Sommers (2014) do not find any evidence that the provision affected health care use, but they do find a reduction in out-of-pocket medical expenses and increases in excellent self-reported physical and mental health.
These papers all share a common general research design: comparing changes in outcomes among the treated age range 19–25 to those of other young adults. The age range used for the control group varies across these studies, with some including individuals up to 34 years old (Sommers and Kronick, 2012, Sommers et al., 2013, Chua and Sommers, 2014). Slusky (2013) questions the validity of this approach, arguing that different age groups are often subject to different economic shocks. He runs placebo tests using data from before the mandate and artificial “treatment” dates, finding that the same specification estimates significant “effects” more often than could be attributed to chance. He suggests narrowing the age bandwidths of the treatment and control groups as a possible solution.
We contribute to this literature on the ACA dependent coverage provision in four ways. First, we consider a number of new outcomes. Using data from the Behavioral Risk Factor Surveillance System (BRFSS), we investigate 18 outcomes related to health care access, utilization of preventive care, risky health behaviors, and self-assessed health. The health care access measures include having insurance, a primary care doctor, and any foregone care because of cost. Our preventive care measures are dummies for recent flu vaccinations, well-patient checkups, and pap tests. The health behavior outcomes reflect smoking, drinking, body mass index, exercise, and pregnancy. The self-assessed health variables relate to overall, mental, and physical health as well as health-related functional limitations. Of these outcomes, only insurance coverage, foregone care because of cost, and self-assessed physical and mental health are studied in other papers in the literature. To our knowledge we are the first to investigate the ACA dependent coverage provision's impact on preventive care or health behaviors. Moreover, although Chua and Sommers (2014) examine self-assessed physical and mental health, their measures and ours are meaningfully different. They use dummies for self-reporting excellent physical and mental health, so their estimates only capture changes at the upper end of the health distribution. In contrast, we utilize five measures that should together capture changes at various parts of the distribution. A dummy for excellent overall health reflects the high end, a dummy for very good or excellent health reflects a somewhat lower portion, and three more severe outcomes – number of days of the past 30 not in good physical health, not in good mental health, and with health-related limitations – reflect an even lower portion. This distinction will prove critical to the results.
Our second contribution is to push further than prior studies toward addressing the methodological concerns raised by Slusky (2013), both by using narrow age ranges for the treatment and control groups and by validating these selections through placebo testing. Our treatment group consists of individuals ages 23–25, slightly below the dependent coverage provision's age cutoff, and our control group consists of those slightly above the cutoff at ages 27–29. We run placebo tests checking for “effects” of artificial interventions in the pre-treatment period. Our classifications perform well in the placebo tests, whereas the wider age ranges commonly used in the literature prove more problematic.
Another contribution is that we use over three full years of post-treatment data (2011 through 2013, plus a few months after implementation at the end of 2010). To our knowledge, none of the prior papers in the ACA dependent coverage provision literature have used more than one full year of post-treatment data, which leaves the estimates susceptible to confounding from temporary age-specific shocks and fluctuations. If estimated effects persist with three years of post-treatment data, we can be more confident that they are not driven by transitory movements in unobserved characteristics.
Finally, we contribute to the literature by testing for heterogeneous effects. Of the outcomes included in our paper, heterogeneity in the effects of the ACA dependent coverage provision has only previously been evaluated for insurance coverage (Akosa Antwi et al., 2013, Sommers et al., 2013) and cost being a barrier to care (Sommers et al., 2013). We will find important heterogeneous effects on other outcomes as well, such as self-assessed health. Moreover, although Akosa Antwi et al. (2013) and Sommers et al. (2013) evaluate whether effects differ by certain demographic characteristics, neither paper tests for heterogeneous effects by socioeconomic status.2 We will find that the effects of the dependent coverage provision vary considerably by education level.
Our difference-in-differences results from the full sample suggest that the ACA dependent coverage provision improved health care access for young adults, had little effect on preventive care use, had mixed effects on risky health behaviors, and improved self-assessed health at the high end of the distribution. Specifically, we document improvements in four of the eighteen outcomes: health insurance coverage, access to a primary care doctor, excellent self-assessed health, and body mass index. However, we find evidence of an increase in risky drinking, and no clear effects in either direction on the remaining thirteen outcomes.
We evaluate heterogeneity in the effects of the mandate through subsample analyses, finding the greatest improvements in outcomes for men and college graduates. The increase in health insurance coverage was greater for men than women, and only men experienced statistically significant gains in any outcomes beyond health insurance: primary care access, exercise, and overall self-assessed health. Stratifying by education reveals that the insurance expansions were similar for college graduates and non-college graduates. However, only college graduates experienced significant gains in any other outcomes besides insurance – specifically, primary care access, cost being a barrier to care, body mass index (BMI), obesity, and overall self-assessed health. Young adults with different education levels therefore appear to respond differently to exogenously obtaining health insurance.
Section snippets
Health insurance and health-related outcomes
The most obvious theoretical implication of health insurance is that by lowering the effective price of health care, health insurance should increase its utilization. However, increased health care utilization does not necessarily improve health. Diminishing marginal returns suggest that health care can only improve health up to a certain level (e.g. Grossman, 1972). Whether the additional consumption of medical care induced by insurance generates substantial gains in health therefore depends
Data
Our main data source is the BRFSS, a telephone survey conducted by state health departments in conjunction with the U.S. Centers for Disease Control and Prevention to collect information on health and health behaviors. The survey is conducted monthly through a random digit dialing method that selects a representative sample of respondents from the non-institutionalized population of adults at least 18 years old. The BRFSS provides several advantages for our analyses. First, it contains a wide
Baseline model
We estimate the effects of the ACA dependent coverage provision on the eighteen health-related outcomes using reduced-form difference-in-differences regressions. While it is tempting to estimate instrumental variables models using the mandate as an instrument for having insurance coverage, we are not confident that the exclusion restriction would hold in such models because there are several other mechanisms through which the mandate could affect health-related outcomes besides the extensive
Placebo tests
We next provide a series of placebo tests to evaluate whether the previous results can credibly be interpreted as causal effects of the ACA dependent coverage provision. Following Slusky (2013), we estimate variants of equation (1) that test for “effects” of artificially-timed “treatments” during pre-treatment years. We estimate models for three different seven-year windows of pre-treatment data (to match the seven years used in our main 2007–2013 analyses): 2003–2009, 2002–2008, and 2001–2007.
Heterogeneity
Having established our baseline results and assessed the validity of our model, we next turn to an examination of heterogeneity in the treatment effects. We considered stratifications by sex, race/ethnicity, education, and state pre-ACA dependent coverage law status, but we did not observe any statistically significant differences in effects across the subgroups for race/ethnicity and pre-ACA law, so we only report the results for the stratifications by sex and education. For education, we
Discussion
The first major insurance expansion under the ACA – a provision requiring insurers to allow young adults to remain on their parents’ health insurance until turning 26 – was implemented in September 2010. This paper uses data from the BRFSS to examine the effects of this mandate on various outcomes related to health care access, preventive care utilization, risky health behaviors, and self-assessed health. We implement a difference-in-differences model with individuals slightly below the
Acknowledgments
We thank the editor, two anonymous referees, Yaa Akosa Antwi, Kitt Carpenter, Jim Marton, Melinda Pitts, Anne Royalty, Chris Ruhm, Kosali Simon, Erdal Tekin, and audiences at the American Society of Health Economists Biennial Conference, University of Virginia, and Vanderbilt University for valuable comments.
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