"Insurance Risk, Capability Deprivation and the Middle-Income Gap: A Monte Carlo Simulation Calibrated to NFHS-5 with a Critical Analysis of PM-JAY"

India's health insurance landscape is characterised by extreme inequality in access, catastrophic out-of-pocket (OOP) expenditure and a structurally incomplete government insurance scheme. This paper presents a Monte Carlo simulation — calibrated to the National Family Health Survey 2019-21 (NFHS-5) and the NSS 75th Round health survey — to model insurance risk and welfare outcomes across five income quintiles of the Indian population. Six insurance scenarios are evaluated: Uninsured, Private Insurance (Rs. 6,000/yr), PM-JAY Ideal, PM-JAY with 50% Enrollment Gap, Graduated Fix Ideal, and Graduated Fix Realistic. The simulation introduces the Capability Deprivation Index (CDI) — a novel metric operationalising Amartya Sen's Capability Approach (1999) as a formal simulation output — measuring the fraction of the population whose wealth depletion precludes continued healthcare access. Healthcare inflation (14% per annum), chronic disease burden (15-20% of population) and PM-JAY claim rejection rates (20%, CAG 2022) are modelled using real government data. Key findings: private insurance at market rates produces a CDI of 40.0% — worse than being uninsured (34.1%) — due to widespread policy lapsing; PM-JAY reduces CDI to 23.4% under ideal conditions but 28.8% under realistic enrollment assumptions; a proposed graduated premium subsidy reduces CDI to 19.1% under conservative assumptions and remains superior to PM-JAY at enrollment rates as low as 40%. The paper argues that PM-JAY's fixed Rs. 5 lakh coverage cap constitutes silent benefit erosion under sustained medical inflation, and that a graduated scheme extending proportional coverage to all quintiles represents a robust, evidence-based policy improvement grounded in Sen's principle of progressive universalism.