Early Evidence on the Impact of the Affordable Care Act on Prisoner Recidivism

Wednesday, June 15, 2016: 10:35 AM
G55 (Huntsman Hall)

Author(s): Sharmini Radakrishnan; Dr. Lauren E.W. Olsho

Discussant: Kathryn McCollister

The Affordable Care Act (ACA) expanded Medicaid eligibility to 138% of the federal poverty line, irrespective of disability, age, pregnancy status, or dependent children. As many ex-offenders are low-income adults without insurance coverage, many would have gained Medicaid coverage under the ACA in states adopting the expansion. There is a high need for health services among ex-offenders due to the high prevalence of health conditions that require ongoing care. In particular, ex-offenders experience higher rates of mental illness, substance abuse disorders, HIV, and Hepatitis C than in the general population. Better access to healthcare through Medicaid may improve ex-offenders’ health and their chances of successfully re-entering society, thereby reducing their likelihood of returning to prison. This paper studies the impact of state ACA Medicaid expansions on recidivism among state prisoners using data from the National Corrections Reporting Program (NCRP). To our knowledge, this is the first study to empirically examine the ACA’s impact on prisoner recidivism. We employ a difference-in-differences identification strategy that uses within-state variation in adoption of the ACA Medicaid expansion. We estimate the impact of a state’s adoption of the Medicaid expansion on ex-offenders’ likelihood of returning to prison by  estimating a separate Weibull survival model for each state, then combining these using Weighted Least Squares to get the second difference. Preliminary estimates using data from 2013 (just before the Medicaid expansion) and 2014 (just after the Medicaid expansion) indicate that there was no statistically significant change in recidivism associated with the ACA Medicaid expansion. While the point estimate is negative (-0.028), it is imprecisely estimated. Using more years of data will likely improve the precision of the estimates. Next steps include expanding the data window, controlling for flexible time trends and additional robustness checks.