Skip to main content

Advertisement

SpringerLink
Log in

The Impact of Subsidized Health Insurance for the Poor: Evaluating the Colombian Experience Using Propensity Score Matching

  • Published:
International Journal of Health Care Finance and Economics Aims and scope Submit manuscript

Abstract

This paper evaluates the impact of Colombia’s subsidized health insurance program (SUBS) on medical care utilization. Colombia’s SUBS program is a demand-side subsidy intended for low-income families, where the screening of beneficiaries takes place in decentralized locations across the country. Due to the self-selection problems associated with non-experimental data, we implement Propensity Score Matching (PSM) methods to measure the impact of this subsidy on medical care utilization. By combining unique household survey data with community and regional data, we are able to compute propensity scores in a way that is consistent with both the local government’s decision to offer the subsidy, and with the individual’s decision to accept the subsidy. Although the application of PSM using these rich datasets helps to achieve a balance between the treatment and control groups along observable dimensions, we also present instrumental variable estimates to control for the potential endogeneity of program participation. Using both methods, we find that Colombia’s subsidized insurance program greatly increased medical care utilization among the country’s poor and uninsured. This evidence supports the case for other Latin American countries implementing similar subsidy programs for health insurance for the poor.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  1. Baker, J. (2000). Evaluating the Impact of Development Projects on Poverty: A Handbook for Practitioners. The World Bank.

  2. Becker, S. O. and A. Ichino. (2002). “Estimation of Average Treatment Effects Based on Propensity Scores.” Stata Journal 2, 358–377.

    Google Scholar 

  3. Bitran & Asociados-Econometria S.A. -ESAP (2003). “Evaluacion y Restructuracion de los Procesos, Estrategias y Organismos Publicos y Privados encargados de la Afiliacion, Pago y Recaudo de Aportes al SGSS. Informe final.

  4. Bloom, E., L. Mina and C. Olivo. (1998). Eficiencia y Equidad en el Sector Salud Colombiano. Informe final.

  5. Blundell, R. and M. Costa Dias. (2000). “Evaluation Methods for Non-Experimental Data.” Fiscal Studies 21(4), 427–468.

    Google Scholar 

  6. Brook, R., Ware, W. Rogers, E. Keeler, A. Davies, Cathy Donald, G. Golberg, K. Lohr, P. Masthay and J. Newhouse. (1983). “Does Free Care Improve Adults’ Health?” The New England Journal of Medicine 309, 1426–1434.

    PubMed  Google Scholar 

  7. Conning, J. and M. Kevane. (2002). “Community Based Targeting Mechanisms for Social Safety Nets: A Critical Review.” World Development 30(3).

  8. Davison, R. and J. G. MacKinnon. (1993). Estimation and Inference in Econometrics. New York: Oxford University Press.

  9. Dehejia, R. H. and S. Wahba. (1999). “Causal Effects in Non-experimental Studies: Reevaluating the Evaluation of Training Programs.” Journal of the American Statistical Association 94, 1053–1062.

    Google Scholar 

  10. Departamento Nacional de Planeacion-DPN, Unidad de Desarrollo Social. Sistema de Seleccion de Beneficiarios para Programas Sociales (SISBEN).

  11. Diaz, J. J. and S. Handa. (2004). “An Assessment of Propensity Score Matching as a Non-Experimental Impact Estimator: Evidence from a Mexican Poverty Program.” Unpublished Manuscript.

  12. Friedlander, D. and P. K. Robins. (1995). “Evaluating Program Evaluations: New Evidence on Commonly Used Non-experimental Methods.” American Economic Review 85(2), 923–937.

    Google Scholar 

  13. Gertler, P. (2004). “Do Conditional Cash Transfers Improve Child Health? Evidence from PROGRESA’s Control Randomized Experiment.” American Economic Review 94(2), 336–341.

    Article  Google Scholar 

  14. Heckman, J. (1996). “Randomization as an Instrumental Variable Estimator.” Review of Economics and Statistics 56, 336–341.

    Google Scholar 

  15. Heckman, J. (1990). “Varieties of Selection Bias.” American Economic Review 80, 313–318.

    Google Scholar 

  16. Heckman, J. J. and V. J. Hotz (1989). “Choosing Among Alternative Non-experimental Methods for Estimating the Impact of Social Programs: The Case of Manpower Training.” Journal of the American Statistical Association 84, 862–874.

    Google Scholar 

  17. Heckman J. J., H. Ichimura and P. E. Todd. (1997). “Matching As An Econometric Evaluation Estimator: Evidence From Evaluating a Job Training Program.” Review of Economics and Statistics 64, 605–654.

    Google Scholar 

  18. Heckman, J. J. and J. A. Smith. (1995). “Assessing the Case for Social Experiments.” Journal of Economic Perspectives 9, 85–110.

    Google Scholar 

  19. Ichimura, H. and C. Taber. (2001). “Propensity-Score Matching With Instrumental Variables.” AEA Papers and Proceedings 91, 119–124.

    Google Scholar 

  20. Jack, W. (2000). “The Evolution of Health Insurance Institutions: Theory and Four Examples From Latin America.” World Bank. Working Paper.

  21. Jalan, J. and M. Ravallion. (2003). “Estimating the Benefit Incidence of an Antipoverty Program by Propensity Score Matching.” Journal of Business and Economics Statistics 21, 19–31.

    Article  Google Scholar 

  22. Jaramillo, I. (2001). “Evaluación de la descentralización en salud en Colombia.” Archivos de Economia, # 170, Departamento Nacional de Planeación.

  23. Joffe, M. M. and P. R. Rosenbaum. (1999). “Propensity Scores.” American Journal of Epidemiology 150, 327–333.

    PubMed  Google Scholar 

  24. LaLonde, R. J. (1986). “Evaluating the Econometric Evaluations of Training Programs With Experimental Data.” American Economic Review 76, 604–620.

    Google Scholar 

  25. Londoño, B., I. Jaramillo and J. P. Uribe. (2001). “Descentralizacion y Reforma en los Servicios de Salud: El caso Colombiano.” Working Paper. World Bank.

  26. McKay, A. (2002). “Should the Survey Measure Total Household Income?” In M Grosh and P. Glewwe (eds), Designing Household Survey Questionnaires for Developing Countries: Lessons from 15 years of the LSMS.

  27. McGee, R. (1999). “Technical, Objective, Equitable and Uniform? A Critique of the Colombian System for the Selection of the Beneficiaries of Social Programs SISBEN.” IDPM Discussion Papers, University of Sussex, Working Paper 57, 1–34.

  28. Minkin, A. (2002). “Nonparametric Propensity Score Matching Method for Treatment Effect Estimation in Observational Studies.” Working Paper. Department of Economics, University of Wisconsin.

  29. Moffitt, R. (1983). “An Economic Model of Welfare Stigma.” The American Economic Review 73, 1023–1035.

    Google Scholar 

  30. Moffitt, R. (1991). “Program Evaluation With Non-Experimental Data.” Evaluation Review 15(3), 291–314.

    Google Scholar 

  31. Panopoulus, G. and C. E. Velez. (2002). “Subsidized Health Insurance, Proxy Means Testing and the Demand for Health Care among the Poor in Colombia.” Working Paper, 214–241.

  32. Pauly, Mark V. (1968). “The Economics of Moral Hazard: Comment.” American Economic Review 3, 531–537.

    Google Scholar 

  33. Peñaloza, E. and E. Londoño. (2002). “Regimen Subsidiado de Salud en Colombia: Evaluacion y Propuestas de Ajuste. Fundacion Corona, Banco Interamericano de Desarrollo.” CENDEX, Potificia Universidad Javeriana.

  34. Remler, D. and S. Glied (2003). “What other Programs can Teach Us: Increasing Participation in Health Insurance Programs.” American Journal of Public Health 93, 67–74.

    PubMed  Google Scholar 

  35. Rosenbaum, P. R. and D. B. Rubin. (1983). “Central Role of the Propensity Score in Observational Studies for Causal Effects.” Biometrika 70, 41–55.

    Google Scholar 

  36. Rosenbaum, P. R. and D. B. Rubin. (1984). “Reducing Bias in Observational Studies Using Sub-classification on the Propensity Score.” Journal of the American Statistical Association 79, 516–524.

    Google Scholar 

  37. Rosenbaum, P. R. and D. B. Rubin. (1985). “Bias Due to Incomplete Matching.” Biometrics 41, 103–116.

    PubMed  MathSciNet  Google Scholar 

  38. Sianesi, B. (2001). “An Evaluation of the Active Labor Market Programmes in Sweden Office of Labor Market Policy Evaluation.” Institute for Labor Market Policy Evaluation. Working Paper.

  39. Sianesi, B. (2001). “PSMATCH: Stata Module to Perform Various Types of Propensity Score Matching.” http://ideaas.repc.org/p/boc/bocode/s418602.html.

  40. Smith, J. A. and P. E. Todd. (2001). “Reconciling Conflicting Evidence on the Performance of Propensity-Score Matching Methods.” American Economic Review 91, 112–124.

    Google Scholar 

  41. Trujillo, A. (2003). “Medical Care Use and Selection in a Social Health Insurance with an Equalization Fund: Evidence from Colombia.” Health Economics 12, 231–246.

    Article  PubMed  Google Scholar 

  42. Velez, C. E., E. Castano and R. Deutsch. (1999). “An Economic Interpretation of Targeting Systems for Social Programs: The Case of Colombia’s SISBEN.” World Bank. Working Papers.

  43. Velez, C. E. and V. Foster. (2000). “Colombia Poverty Study. The Impact of Public Social Expenditure.” Latin American Caribbean Region, World Bank, Washington D.C.

Download references

Author information

Authors and Affiliations

  1. College of Health and Public Affairs, University of Central Florida, 14140 Cheval Vineyard Way, Apt. 105, Orlando, FL, 32828, USA

    Antonio J. Trujillo

  2. Economists Incorporated, Washington DC

    Jorge E. Portillo

  3. School of Business, University of Connecticut, USA

    John A. Vernon

Authors
  1. Antonio J. Trujillo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jorge E. Portillo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. John A. Vernon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Antonio J. Trujillo.

Additional information

JEL classification:

Rights and permissions

Reprints and permissions

About this article

Cite this article

Trujillo, A.J., Portillo, J.E. & Vernon, J.A. The Impact of Subsidized Health Insurance for the Poor: Evaluating the Colombian Experience Using Propensity Score Matching. Int J Health Care Finance Econ 5, 211–239 (2005). https://doi.org/10.1007/s10754-005-1792-5

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10754-005-1792-5

Keywords

Search

Navigation