Does Obamacare Care? A Fuzzy Difference-in-discontinuities Approach

This paper explores the use of fuzzy regression-discontinuity design in the context where multiple treatments are applied at the threshold. It derives the conditions for identification of the effects of one of the treatments. The identification result shows that, under the strong assumption that the change in the probability of treatment at the cut off is equal across treatments, a difference-in-discontinuities estimator identifies the treatment effect of interest. Point identification of the treatment effect using fuzzy difference-in-discontinuities is impossible if the changes in the treatment probabilities are not equal across treatments. Estimable bounds of the treatment effect and a modification of the fuzzy difference-in-discontinuities are proposed under milder assumptions. These results suggest some caution when applying before-and-after methods in presence of fuzzy discontinuities. Using data from the National Health Interview Survey (NHIS), we apply this new identification strategy to evaluate the causal effect of the Affordable Care Act (ACA) on older Americans’ health care access and utilization. Our results suggest that the implementation of the Affordable Care Act has (1) led to 5% increase in the hospitalization rate of elderly Americans, (2) increased by 3.6% the probability of delaying care for cost reasons, and (3) exacerbated cost-related barriers to follow-up and continuity of care–7% more elderly could not afford prescriptions, 7% more could not see a specialist and, 5.5% more could not afford a follow-up visit- as a result of the ACA.