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Modelling socioeconomic and health determinants of health care use: A semiparametric approach

  • Jürgen Maurer

    ()

    (Munich Center for the Economics of Aging (MEA))

This paper suggests bivariate semiparametric index models as a tool for modelling the interplay of socioeconomic and health characteristics in determining health care utilisation. These models allow for a fully nonparametric relationship between socioeconomic status, health care need and care utilisation. The only parametric restriction imposed is that multiple socioeconomic and health indicators can be aggregated into two distinct indices that measure the broader concepts of socioeconomic status and health care need, respectively. We demonstrate the usefulness of this class of models based on an illustrative empirical example. The estimations highlight complex interactions of socioeconomic status and health care need in determining care use, which may be difficult to grasp via standard parametric modelling approaches.

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Paper provided by Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy in its series MEA discussion paper series with number 07145.

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Date of creation: 17 Jul 2007
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Handle: RePEc:mea:meawpa:07145
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  1. Farré, Lídia & Vella, Francis, 2006. "Macroeconomic Conditions and the Distribution of Income in Spain," IZA Discussion Papers 2512, Institute for the Study of Labor (IZA).
  2. James P. Smith, 2003. "Consequences and Predictors of New Health Events," NBER Working Papers 10063, National Bureau of Economic Research, Inc.
  3. Härdle, W.K., 1992. "Applied Nonparametric Methods," Discussion Paper 1992-6, Tilburg University, Center for Economic Research.
  4. Jürgen Maurer, 2007. "Socioeconomic and Health Determinants of Health Care Utilization Among Elderly Europeans: A Semiparametric Assessment of Equity, Intensity and Responsiveness for Ten European Countries," MEA discussion paper series 07144, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
  5. Oliver LINTON, . "Applied nonparametric methods," Statistic und Oekonometrie 9312, Humboldt Universitaet Berlin.
  6. Ichimura, H., 1991. "Semiparametric Least Squares (sls) and Weighted SLS Estimation of Single- Index Models," Papers 264, Minnesota - Center for Economic Research.
  7. Jones, Andrew M., 2000. "Health econometrics," Handbook of Health Economics, in: A. J. Culyer & J. P. Newhouse (ed.), Handbook of Health Economics, edition 1, volume 1, chapter 6, pages 265-344 Elsevier.
  8. repec:cup:cbooks:9780521012263 is not listed on IDEAS
  9. repec:cup:cbooks:9780521812832 is not listed on IDEAS
  10. Ignacio Abasolo & Rob Manning & Andrew Jones, 2001. "Equity in utilization of and access to public-sector GPs in Spain," Applied Economics, Taylor & Francis Journals, vol. 33(3), pages 349-364.
  11. James P. Smith, 2004. "Unravelling the SES health connection," IFS Working Papers W04/02, Institute for Fiscal Studies.
  12. Susan Vos, 2005. "Indicating Socioeconomic Status among Elderly People in Developing Societies: An Example from Brazil," Social Indicators Research, Springer, vol. 73(1), pages 87-108, 08.
  13. Stern, Steven, 1996. "Semiparametric estimates of the supply and demand effects of disability on labor force participation," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 49-70.
  14. Agar Brugiavini & Tullio Jappelli & Guglielmo Weber, 2002. "The Survey on Health, Aging and Wealth," CSEF Working Papers 86, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
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