The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

Health Econometrics

  • Andrew M. Jones
Reference work entry


The term health econometrics has been adopted to describe the development and application of econometric methods within health economics. This article outlines the distinctive issues that arise in applying econometrics to health data and how these applications have helped to shape the broader literature.


Econometrics Evaluation Health economics Microeconometrics 

JEL Classifications

C1 I1 
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  1. Aakvik, A., J.J. Heckman, and E.J. Vytlacil. 2005. Estimating treatment effects for discrete outcomes when responses to treatment vary: An application to Norwegian vocational rehabilitation programs. Journal of Econometrics 125: 15–51.CrossRefGoogle Scholar
  2. Arcidiacono, P., H. Sieg, and F. Sloan. 2007. Living rationally under the volcano? An empirical analysis of heavy drinking and smoking. International Economic Review 48: 37–65.CrossRefGoogle Scholar
  3. Auld, M.C. 2006. Using observational data to identify the causal effects of health-related behaviour. In The Elgar companion to health economics, ed. A.M. Jones. Cheltenham: Edward Elgar.Google Scholar
  4. Auster, R., I. Leveson, and D. Sarachek. 1969. The production of health an exploratory study. Journal of Human Resources 15: 411–436.CrossRefGoogle Scholar
  5. Bago d’Uva, T. 2006. Latent class models for utilisation of health care. Health Economics 15: 329–343.CrossRefGoogle Scholar
  6. Basu, A., and P.J. Rathouz. 2005. Estimating marginal and incremental effects on health outcomes using flexible link and variance function models. Biostatistics 6: 93–109.CrossRefGoogle Scholar
  7. Basu, A., J. Heckman, S. Navarro, and S. Urzua. 2007. Use of instrumental variables in the presence of heterogeneity and self-selection: An application to treatments of breast cancer patients. Health Economics 16: 1133–1157.CrossRefGoogle Scholar
  8. Black, S., P. Devereux, and K. Salvanes. 2007. From the cradle to the labour market? The effect of birth weight on adult outcomes. The Quarterly Journal of Economics 122: 409–439.CrossRefGoogle Scholar
  9. Blough, D.K., C.W. Madden, and M.C. Hornbrook. 1999. Modeling risk using generalized linear models. Journal of Health Economics 18: 153–171.CrossRefGoogle Scholar
  10. Cameron, A.C., and P.K. Trivedi. 1986. Econometric models based on count data: Comparisons and applications of some estimators and tests. Journal of Applied Econometrics 1: 29–53.CrossRefGoogle Scholar
  11. Cameron, A.C., P.K. Trivedi, F. Milne, and J. Piggott. 1988. A microeconometric model of demand for health care and health insurance in Australia. Review of Economic Studies 55: 85–106.CrossRefGoogle Scholar
  12. Contoyannis, P., A.M. Jones, and N. Rice. 2003. Simulation-based inference in dynamic panel probit models: An application to health. Empirical Economics 28: 1–29.CrossRefGoogle Scholar
  13. Contoyannis, P., A.M. Jones, and R. Leon-Gonzalez. 2004a. Using simulation-based inference with panel data in health economics. Health Economics 13: 101–122.CrossRefGoogle Scholar
  14. Contoyannis, P., A.M. Jones, and N. Rice. 2004b. The dynamics of health in the British household panel survey. Journal of Applied Econometrics 19: 473–503.CrossRefGoogle Scholar
  15. Conway, K.S., and P. Deb. 2005. Is prenatal care really ineffective? Or is the ‘devil’ in the distribution? Journal of Health Economics 24: 489–513.CrossRefGoogle Scholar
  16. Deb, P. 2001. A discrete random effects probit model with application to the demand for preventive care. Health Economics 10: 371–383.CrossRefGoogle Scholar
  17. Deb, P., and P.K. Trivedi. 1997. Demand for medical care by the elderly: A finite mixture approach. Journal of Applied Econometrics 12: 313–336.CrossRefGoogle Scholar
  18. Deb, P., and P.K. Trivedi. 2006. Specification and simulated likelihood estimation of a non-normal treatment-outcome model with selection: Application to health care utilization. Econometrics Journal 9: 307–331.CrossRefGoogle Scholar
  19. Deb, P., M.K. Munkin, and P.K. Trivedi. 2006. Bayesian analysis of the two-part model with endogeneity: Application to health care expenditure. Journal of Applied Econometrics 21: 1081–1099.CrossRefGoogle Scholar
  20. Dor, A., P. Gertler, and J. van der Gaag. 1987. Non-price rationing and the choice of medical care providers in rural Cote D’Ivoire. Journal of Health Economics 6: 291–304.CrossRefGoogle Scholar
  21. Dowd, B., R. Feldman, S. Cassou, and M. Finch. 1991. Health plan choice and the utilization of health care services. Review of Economics and Statistics 73: 85–93.CrossRefGoogle Scholar
  22. Duan, N. 1983. Smearing estimate: A nonparametric retransformation method. Journal of the American Statistical Association 78: 605–610.CrossRefGoogle Scholar
  23. Duan, N., W.G. Manning, C.N. Morris, and J.P. Newhouse. 1983. A comparison of alternative models for the demand for health care. Journal of Business and Economic Statistics 1: 115–126.Google Scholar
  24. Feldstein, M.S. 1967. Economic analysis for health service efficiency: Econometric studies of the British national health service. Amsterdam: North-Holland.Google Scholar
  25. Gilleskie, D.B., and T.A. Mroz. 2004. A flexible approach for estimating the effects of covariates on health expenditures. Journal of Health Economics 23: 391–418.CrossRefGoogle Scholar
  26. Grootendorst, P.V. 1997. Health care policy evaluation using longitudinal insurance claims data: An application of the panel tobit estimator. Health Economics 6: 365–382.CrossRefGoogle Scholar
  27. Grossman, M. 1972. The demand for health: A theoretical and empirical investigation. New York: Columbia University Press for the National Bureau of Economic Research.Google Scholar
  28. Hamilton, B. 1999. HMO selection and medicare costs: Bayesian MCMC estimation of a robust panel data tobit model with survival. Health Economics 8: 403–414.CrossRefGoogle Scholar
  29. Hay, J., and R.J. Olsen. 1984. Let them eat cake: A note on comparing alternative models of the demand for health care. Journal of Business and Economic Statistics 2: 279–282.Google Scholar
  30. Heckman, J.J. 2012. The developmental origins of health. Health Economics 21: 24–29.CrossRefGoogle Scholar
  31. Hoch, J.S., A.H. Briggs, and A.R. Willan. 2002. Something old, something new, something borrowed, something blue: A framework for the marriage of health econometrics and cost-effectiveness analysis. Health Economics 11: 415–430.CrossRefGoogle Scholar
  32. Jochmann, M., and R. Leon-Gonzalez. 2004. Estimating the demand for health care with panel data: A semiparametric Bayesian approach. Health Economics 13: 1003–1014.CrossRefGoogle Scholar
  33. Jones, A.M. 2000. Health econometrics. In Handbook of health economics, ed. A.J. Culyer and J.P. Newhouse. Amsterdam: Elsevier.Google Scholar
  34. Jones, A.M. 2007. Applied econometrics for health economists: A practical guide. Oxford: Radcliffe Medical Publishing.Google Scholar
  35. Jones, A.M. 2009. Panel data methods and applications to health economics. In Palgrave handbook of econometrics, vol. II: Applied econometrics, ed. T.C. Mills and K. Patterson. Basingstoke: Palgrave Macmillan.Google Scholar
  36. Jones, A.M. 2011. Models for health care. In Oxford handbook of economic forecasting, ed. D. Hendry and M. Clements. Oxford: Oxford University Press.Google Scholar
  37. Jones, A.M., and N. Rice. 2011. Econometric evaluation of health policies. In Oxford handbook of health economics, ed. S. Glied and P.C. Smith. Oxford: Oxford University Press.Google Scholar
  38. Jones, A.M., N. Rice, T. Bago d’Uva, and S. Balia. 2007. Applied health economics. London: Routledge.Google Scholar
  39. Keeler, E.B., W.G. Manning, and R.B. Wells. 1988. The demand for episodes of mental health services. Journal of Health Economics 7: 369–392.CrossRefGoogle Scholar
  40. Kerkhofs, M., and M. Lindeboom. 1995. Subjective health measures and state dependent reporting errors. Health Economics 4: 221–235.CrossRefGoogle Scholar
  41. Manning, W. 1998. The logged dependent variable, heteroscedasticity, and the retransformation problem. Journal of Health Economics 17: 283–295.CrossRefGoogle Scholar
  42. Manning, W. 2006. Dealing with skewed data on costs and expenditure. In The Elgar companion to health economics, ed. A.M. Jones. Cheltenham: Edward Elgar.Google Scholar
  43. Manning, W.G., and J. Mullahy. 2001. Estimating log models: To transform or not to transform? Journal of Health Economics 20: 461–494.CrossRefGoogle Scholar
  44. Manning, W.G., N. Duan, and W.H. Rogers. 1987a. Monte Carlo evidence on the choice between sample selection and two-part models. Journal of Econometrics 35: 59–82.CrossRefGoogle Scholar
  45. Manning, W., J.P. Newhouse, N. Duan, E. Keeler, A. Leibowitz, and M.S. Marquis. 1987b. Health insurance and the demand for medical care: Evidence from a randomized experiment. American Economic Review 77: 251–277.Google Scholar
  46. Manning, W.G., A. Basu, and J. Mullahy. 2005. Generalized modelling approaches to risk adjustment of skewed outcomes data. Journal of Health Economics 24: 465–488.CrossRefGoogle Scholar
  47. McClellan, M., and J.P. Newhouse. 1997. The marginal cost-effectiveness of medical technology: A panel instrumental variables approach. Journal of Econometrics 77: 39–64.CrossRefGoogle Scholar
  48. McClellan, M., J.P. Newhouse, and B. McNeil. 1994. Does more intensive treatment of acute myocardial infarction in the elderly reduce mortality? Journal of the American Medical Association 272: 859–866.CrossRefGoogle Scholar
  49. Miguel, E., and M. Kremer. 2004. Worms: Identifying impacts on education and health in the presence of treatment externalities. Econometrica 72: 159–217.CrossRefGoogle Scholar
  50. Mullahy, J. 1986. Specification and testing of some modified count data models. Journal of Econometrics 33: 341–365.CrossRefGoogle Scholar
  51. Mullahy, J. 1997a. Instrumental variable estimation of count data models. Applications to models of cigarette smoking behaviour. Review of Economics and Statistics 79: 586–593.CrossRefGoogle Scholar
  52. Mullahy, J. 1997b. Heterogeneity, excess zeros, and the structure of count data models. Journal of Applied Econometrics 12: 337–350.CrossRefGoogle Scholar
  53. Mullahy, J. 1998. Much ado about two: Reconsidering retransformation and the two-part model in health econometrics. Journal of Health Economics 17: 247–281.CrossRefGoogle Scholar
  54. Newhouse, J.P. 1987. Health economics and econometrics. American Economic Review 77: 269–274.Google Scholar
  55. Pohlmeier, W., and V. Ulrich. 1995. An econometric model of the two-part decision making process in the demand for health care. Journal of Human Resources 30: 339–360.CrossRefGoogle Scholar
  56. Risa Hole, A. 2008. Modelling heterogeneity in patients’ preferences for the attributes of a general practitioner appointment. Journal of Health Economics 27: 1078–1094.CrossRefGoogle Scholar
  57. Rosenzweig, M.R., and T.P. Schultz. 1983. Estimating a household production function: Heterogeneity, the demand for health inputs, and their effects on birth weight. Journal of Political Economy 91: 723–746.CrossRefGoogle Scholar
  58. Santos Silva, J.M.C., and F. Windmeijer. 2001. Two-part multiple spell models for health care demand. Journal of Econometrics 104: 67–89.CrossRefGoogle Scholar
  59. Smith, P.C., N. Rice, and R. Carr-Hill. 2001. Capitation funding in the public sector. Journal of the Royal Statistical Society A 164: 217–257.CrossRefGoogle Scholar
  60. Terza, J.V. 1998. Estimating count data models with endogenous switching: Sample selection and endogenous treatment effects. Journal of Econometrics 84: 93–127.CrossRefGoogle Scholar
  61. Van de Ven, W., and R.P. Ellis. 2000. Risk adjustment in competitive health plan markets. In Handbook of health economics, ed. A.J. Culyer and J.P. Newhouse. Amsterdam: Elsevier.Google Scholar
  62. van de Ven, W.P.M.M., and J. van der Gaag. 1982. Health as an unobservable: A MIMIC model for health care demand. Journal of Health Economics 1: 157–183.CrossRefGoogle Scholar
  63. van Doorslaer, E.K.A. 1987. Health, knowledge and the demand for medical care. Assen/Maasricht: Van Gorcum.Google Scholar
  64. Van Ourti, T. 2004. Measuring horizontal inequity in belgian health care using a gaussian random effects two part count data model. Health Economics 13: 705–724.CrossRefGoogle Scholar
  65. Wagstaff, A. 1989. Econometric studies in health economics. Journal of Health Economics 8: 1–51.CrossRefGoogle Scholar
  66. Windmeijer, F.A.G., and J.M.C. Santos Silva. 1997. Endogeneity in count data models; An application to demand for health care. Journal of Applied Econometrics 12: 281–294.CrossRefGoogle Scholar
  67. Wolfe, B., and J. van der Gaag. 1981. A new health status index for children. In Health, economics, and health economics, ed. J. van der Gaag and M. Perlman. Amsterdam: North-Holland.Google Scholar
  68. Zimmer, D.M., and P.K. Trivedi. 2006. Using trivariate copulas to model sample selection and treatment effects: Application to family health care demand. Journal of Business and Economic Statistics 24: 63–76.CrossRefGoogle Scholar

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© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • Andrew M. Jones
    • 1
  1. 1.