Publicly funded health insurance schemes and demand for health services: evidence from an Indian state using a matching estimator approach

Abstract Using Demographic and Health Survey data (2015–16) from the state of Andhra Pradesh, we estimate the differential probability of hysterectomy (removal of uterus) for women (aged 15–49 years) covered under publicly funded health insurance (PFHI) schemes relative to those not covered. To reduce the extent of selection bias into treatment assignment (PFHI coverage) we use matching methods, propensity score matching, and coarsened exact matching, achieving a comparable treatment and control group. We find that PFHI coverage increases the probability of undergoing a hysterectomy by 7–11 percentage points in our study sample. Sub-sample analysis indicates that the observed increase is significant for women with lower education levels and higher order parity. Additionally, we perform a test of no-hidden bias by estimating the treatment effect on placebo outcomes (doctor's visit, health check-up). The robustness of the results is established using different matching specifications and sensitivity analysis. The study results are indicative of increased demand for surgical intervention associated with PFHI coverage in our study sample, suggesting a need for critical evaluation of the PFHI scheme design and delivery in the context of increasing reliance on PFHI schemes for delivering specialised care to poor people, neglect of preventive and primary care, and the prevailing fiscal constraints in the healthcare sector.