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Panel data methods and applications to health economics

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  • Andrew M. Jones

Abstract

Much of the empirical analysis done by health economists seeks to estimate the impact of specific health policies and the greatest challenge for successful applied work is to find appropriate sources of variation to identify the treatment effects of interest. Estimation can be prone to selection bias, when the assignment to treatments is associated with the potential outcomes of the treatment. Overcoming this bias requires variation in the assignment of treatments that is independent of the outcomes. One source of independent variation comes from randomised controlled experiments. But, in practice, most economic studies have to draw on non-experimental data. Many studies seek to use variation across time and events that takes the form of a quasi-experimental design, or “natural experiment”, that mimics the features of a genuine experiment. This chapter reviews the data and methods that are used in applied health economics with a particular emphasis on the use of panel data. The focus is on nonlinear models and methods that can accommodate unobserved heterogeneity. These include conditional estimators, maximum simulated likelihood, Bayesian MCMC, finite mixtures and copulas.

Suggested Citation

  • Andrew M. Jones, 2007. "Panel data methods and applications to health economics," Health, Econometrics and Data Group (HEDG) Working Papers 07/18, HEDG, c/o Department of Economics, University of York.
  • Handle: RePEc:yor:hectdg:07/18
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    Cited by:

    1. Pierre Koning & Dinand Webbink & Nicholas Martin, 2015. "The effect of education on smoking behavior: new evidence from smoking durations of a sample of twins," Empirical Economics, Springer, vol. 48(4), pages 1479-1497, June.
    2. Laudicella, Mauro & Cookson, Richard & Jones, Andrew M. & Rice, Nigel, 2009. "Health care deprivation profiles in the measurement of inequality and inequity: An application to GP fundholding in the English NHS," Journal of Health Economics, Elsevier, vol. 28(6), pages 1048-1061, December.
    3. Schneider Brit S. & Schneider Udo & Ulrich Volker, 2007. "Health and the Decision to Invest in Education," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 227(5-6), pages 725-746, October.
    4. Terence C. Cheng & Pravin K. Trivedi, 2015. "Attrition Bias in Panel Data: A Sheep in Wolf's Clothing? A Case Study Based on the Mabel Survey," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1101-1117, September.
    5. Moran, Valerie & Jacobs, Rowena, 2013. "An international comparison of efficiency of inpatient mental health care systems," Health Policy, Elsevier, vol. 112(1), pages 88-99.
    6. Udo Schneider & Jürgen Zerth, 2011. "Improving Prevention Compliance through Appropriate Incentives: Theoretical Modelling and Empirical Evidence," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 147(I), pages 71-106, March.
    7. Schneider, Udo & Zerth, Jürgen, 2008. "Improving prevention compliance through appropriate incentives," MPRA Paper 8280, University Library of Munich, Germany.

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