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Working Paper 225 - Measuring the Impact of Micro-Health Insurance on Healthcare Utilization: A Bayesian Potential Outcomes Approach

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Abstract

One of the primary reasons for low healthcare utilization rates in low-income countries is lack of affordable health insurance coverage. In recent years, Community-Based Health Insurance programs are widely implemented across developing countries aimed at increasing healthcare utilization and providing financial protection. This study investigates the causal effects of the program on utilization of healthcare services in Rwanda using nonrandomized household survey data. In a Bayesian potential outcomes framework with Markov Chain Monte Carlo simulation techniques, we address issues of selection bias on observable and unobservable dimensions. In addition, we address heterogeneity by estimating treatment effects at the individual-level. We find that Community-Based Health Insurance schemes significantly increase the likelihood of utilizing medical consultation and screening services but not utilization of drugs. We also find considerable heterogeneity in treatment effects with married women and under-five children benefiting the most.

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  • Shimeles Abebe & Andinet Woldemichael, 2015. "Working Paper 225 - Measuring the Impact of Micro-Health Insurance on Healthcare Utilization: A Bayesian Potential Outcomes Approach," Working Paper Series 2166, African Development Bank.
  • Handle: RePEc:adb:adbwps:2166
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    1. Binagwaho, Agnes & Hartwig, Renate & Ingeri, Denyse & Makaka, Andrew, 2012. "Mutual health insurance and its contribution to improving child health in Rwanda," Passauer Diskussionspapiere, Volkswirtschaftliche Reihe V-66-12, University of Passau, Faculty of Business and Economics.
    2. James J. Heckman, 2008. "Econometric Causality," International Statistical Review, International Statistical Institute, vol. 76(1), pages 1-27, April.
    3. Poirier, Dale J & Tobias, Justin L, 2003. "On the Predictive Distributions of Outcome Gains in the Presence of an Unidentified Parameter," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(2), pages 258-268, April.
    4. Aradhna Aggarwal, 2010. "Impact evaluation of India's ‘Yeshasvini’ community‐based health insurance programme," Health Economics, John Wiley & Sons, Ltd., vol. 19(S1), pages 5-35, September.
    5. Philipp Eisenhauer & James J. Heckman & Edward Vytlacil, 2015. "The Generalized Roy Model and the Cost-Benefit Analysis of Social Programs," Journal of Political Economy, University of Chicago Press, vol. 123(2), pages 413-443.
    6. A. D. Roy, 1951. "Some Thoughts On The Distribution Of Earnings," Oxford Economic Papers, Oxford University Press, vol. 3(2), pages 135-146.
    7. Shimeles, Abebe, 2010. "Community based health insurance schemes in Africa: The case of Rwanda," Working Papers in Economics 463, University of Gothenburg, Department of Economics.
    8. James H. Albert & Siddhartha Chib, 2001. "Sequential Ordinal Modeling with Applications to Survival Data," Biometrics, The International Biometric Society, vol. 57(3), pages 829-836, September.
    9. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
    10. Poirier, Dale J., 1998. "Revising Beliefs In Nonidentified Models," Econometric Theory, Cambridge University Press, vol. 14(4), pages 483-509, August.
    11. James J. Heckman & Hedibert F. Lopes & Rémi Piatek, 2014. "Treatment Effects: A Bayesian Perspective," Econometric Reviews, Taylor & Francis Journals, vol. 33(1-4), pages 36-67, June.
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