Unintended consequences of health insurance: Affordable Care Act's free contraception mandate and risky sex

Abstract

Health insurance is a primary driver of rising medical expenditures. Economic theory suggests that insurance induces an increase in risky behaviors, but previous empirical evidence is mixed. I use a mandate in the Affordable Care Act in which contraceptives were covered at zero cost to consumers to test for unintended effects of insurance on risky sex. Leveraging mandated zero cost-sharing for contraception and pre-policy insured rates as a measure of treatment intensity, I provide evidence that this 2012 policy reduced fertility but caused unintended consequences: a decline in condom use and a subsequent increase in sexually transmitted infections (STIs). I discuss shortcomings of controlling for nonparallel pre-trends using state-trends, and I suggest an alternative to control for pre-trends directly in the context of dose-response difference-indifferences. Finally, estimates based on the 2010 dependent coverage mandate indicate health insurance provides an overall net positive effect on insurance and STI prevention.

The effects of public health insurance on health behaviors: Evidence from the fifth year of Medicaid expansion

“…The majority of studies find no detectable effects of either private or public health insurance on risky behaviors (Baicker et al, 2013; Barbaresco et al, 2015; Brook et al, 1983; Cawley et al, 2018; Courtemanche, Marton, Ukert, Yelowitz, & Zapata, 2018; Courtemanche et al, 2019; Donahoe et al, 2019; Simon et al, 2017; Yörük, 2017). However, some find evidence that insurance coverage leads to some ex ante moral hazard, such as increases in BMI and obesity (Asfaw, 2019; Bhattacharya, Bundorf, Pace, & Sood, 2011; Kelly & Markowitz, 2009; Klick & Stratmann, 2007; Stanciole, 2008), increases in smoking and risky drinking (Barbaresco et al, 2015; Dave & Kaestner, 2009; Dave, Kaestner, & Wehby, 2019; Stanciole, 2008), reductions in exercise (Asfaw, 2019; Dave & Kaestner, 2009; Stanciole, 2008), and increases in risky sexual behaviors (Willage, 2019). Meanwhile, other studies find evidence of improved healthy behaviors, such as increased exercise (Courbage & de Coulon, 2004), reduced tobacco and alcohol purchases (Cotti et al, 2019), and reduced BMI (Courtemanche & Zapata, 2014).…”

Impact of a Provider Tele-mentoring Learning Model on the Care of Medicaid-enrolled Patients With Diabetes

“…While the use of a matched comparison group assures the accuracy of estimators even if preintervention trends are not parallel, 35 , 36 we tested for nonparallel trends and, where appropriate, adjusted estimators for differential pretrends. 37 We conducted linear probability modeling for binary outcomes such as annual tests or ordinary least square for continuous and count variables. Standard errors are clustered at the provider level to account for non-independence of observations.…”

Behavioral responses to the 2015 MERS epidemic in Korea

“…Identification in the above model requires that persons having lived in cities ever exposed to the epidemic and persons in non-exposed cities would have had similar trends in outcomes without the epidemic in 2015. To examine whether there were differential trends in outcome measures before the epidemic, I additionally report event study coefficients where smoking and drinking measures are regressed on interactions between the exposure group indicator and the full set of year dummies ( Hoynes et al, 2015 ; Willage, 2020 ). The survey year 2014 is treated as the omitted category, so coefficients on the interactions can be interpreted as changes in outcomes relative to this reference period.…”