Impact of Health Insurance Coverage on Out-of-Pocket Healthcare Expenditure and Equity in India
DOI:
https://doi.org/10.66635/x2fgez24Keywords:
Health Insurance, Out-of-Pocket Expenditure, Propensity Score Matching, Equity, Concentration Index, Kakwani Index, Catastrophic Health Expenditure, Financial Protection, AB-PMJAY, India, NSS 80th RoundAbstract
This study examines the effect of health insurance coverage on household out-of-pocket healthcare expenditure (OOPE) and assesses the equity of healthcare financing in India, using nationally representative unit-level microdata from the 80th round of the National Sample Survey (NSS), Household Social Consumption: Health (January–December 2025). Analysis is based on 650,842 individuals (139,732 households) after cleaning. A binary logistic regression identifies the socio-economic determinants of insurance enrolment, and one-to-one nearest-neighbour Propensity Score Matching (PSM) with caliper restriction is used to reduce observable selection bias when estimating the causal effect of insurance on OOPE. Equity in healthcare financing is assessed using the Concentration Index, the Gini coefficient, and the Kakwani Progressivity Index. Age, education, household size, monthly per-capita consumption expenditure (MPCE), religion, and social group are all significant predictors of insurance enrolment. Matching achieved excellent covariate balance (mean absolute standardized mean difference falling from 0.102 to 0.007). On the matched sample, insured individuals incurred approximately 29.8% higher out-of-pocket expenditure than comparable uninsured individuals (β = 0.2609, p < 0.001), suggesting that insurance is associated with greater healthcare utilization rather than a reduction in direct payments. The Concentration Index (0.274) and Gini coefficient (0.291) indicate that OOPE and consumption are both concentrated among relatively better-off households, while a small negative Kakwani Index (−0.017) indicates that healthcare financing is mildly regressive, close to proportional, but still imposing a marginally larger relative burden on poorer households. Coverage is overwhelmingly publicly financed: the two large public schemes, AB-PMJAY and the state health insurance schemes, together account for 88.9% of all reported coverage. Assessed at the household level, 5.99% of households incur catastrophic health expenditure at the 10% threshold of annual consumption, and both the incidence and the intensity of these payments fall steadily as household consumption rises, so that the burden of catastrophic spending is concentrated among poorer households.
References
1.Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 46(3), 399–424. https://doi.org/10.1080/00273171.2011.568786
2.Gini, C. (1912). Variabilità e mutabilità. Tipografia di Paolo Cuppini.
3.Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (3rd ed.). John Wiley & Sons. https://doi.org/10.1002/9781118548387
4.Imbens, G. W. (2004). Nonparametric estimation of average treatment effects under exogeneity: A review. Review of Economics and Statistics, 86(1), 4–29. https://doi.org/10.1162/003465304323023651
5.Joe, W. (2015). Distressed financing of household out-of-pocket health care payments in India: Incidence and correlates. Health Policy and Planning, 30(6), 728–741. https://doi.org/10.1093/heapol/czu050
6.Kakwani, N. C. (1977). Measurement of tax progressivity: An international comparison. The Economic Journal, 87(345), 71–80. https://doi.org/10.2307/2231833
7.King, G., Gakidou, E., Imai, K., Lakin, J., Moore, R. T., Nall, C., Ravishankar, N., Vargas, M., Téllez-Rojo, M. M., Ávila, J. E. H., Ávila, M. H., & Llamas, H. H. (2009). Public policy for the poor? A randomised assessment of the Mexican universal health insurance programme. The Lancet, 373(9673), 1447–1454. https://doi.org/10.1016/S0140-6736(09)60239-7
8.Manning, W. G., & Mullahy, J. (2001). Estimating log models: To transform or not to transform? Journal of Health Economics, 20(4), 461–494. https://doi.org/10.1016/S0167-6296(01)00086-8
9.National Health Authority. (2023). Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (AB-PMJAY): Annual report 2022–23. Government of India.
10.National Health Systems Resource Centre (NHSRC). (2022). National health accounts estimates for India, 2018–19. Ministry of Health and Family Welfare, Government of India.
11.National Statistical Office (NSO). (2025). Household social consumption: Health, NSS 80th round (January–December 2025): Unit-level data, Schedule 25.0. Ministry of Statistics and Programme Implementation, Government of India.
12.O’Donnell, O., van Doorslaer, E., Wagstaff, A., & Lindelow, M. (2008). Analyzing health equity using household survey data: A guide to techniques and their implementation. The World Bank. https://doi.org/10.1596/978-0-8213-6933-3
13.Prinja, S., Jagnoor, J., Sharma, D., Aggarwal, S., Katoch, S., Lakshmi, P. V. M., & Ivers, R. (2019). Out-of-pocket expenditure and catastrophic health expenditure for hospitalization due to injuries in public sector hospitals in North India. PLOS ONE, 14(11), e0224721. https://doi.org/10.1371/journal.pone.0224721
14.Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55. https://doi.org/10.1093/biomet/70.1.41
15.Selvaraj, S., & Karan, A. K. (2012). Why publicly-financed health insurance schemes are ineffective in providing financial risk protection. Economic & Political Weekly, 47(11), 60–68.
16.Stuart, E. A. (2010). Matching methods for causal inference: A review and a look forward. Statistical Science, 25(1), 1–21. https://doi.org/10.1214/09-STS313
17.United Nations. (2015). Transforming our world: The 2030 Agenda for Sustainable Development (A/RES/70/1). United Nations.
18.Wagstaff, A., Lindelow, M., Jun, G., Ling, X., & Juncheng, Q. (2009). Extending health insurance to the rural population: An impact evaluation of China’s new cooperative medical scheme. Journal of Health Economics, 28(1), 1–19. https://doi.org/10.1016/j.jhealeco.2008.10.007
19.Wagstaff, A., Paci, P., & van Doorslaer, E. (1991). On the measurement of inequalities in health. Social Science & Medicine, 33(5), 545–557. https://doi.org/10.1016/0277-9536(91)90212-U
20.Wagstaff, A., & van Doorslaer, E. (2003). Catastrophe and impoverishment in paying for health care: With applications to Vietnam 1993–1998. Health Economics, 12(11), 921–933. https://doi.org/10.1002/hec.776
21.World Bank. (2023). Walking the talk: Reimagining primary health care after COVID-19. World Bank.
22.World Health Organization (WHO). (2010). The world health report 2010: Health systems financing: The path to universal coverage. World Health Organization.
23.World Health Organization (WHO), & World Bank. (2023). Tracking universal health coverage: 2023 global monitoring report. World Health Organization.
24.Xu, K., Evans, D. B., Kawabata, K., Zeramdini, R., Klavus, J., & Murray, C. J. L. (2003). Household catastrophic health expenditure: A multicountry analysis. The Lancet, 362(9378), 111–117. https://doi.org/10.1016/S0140-6736(03)13861-5





