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Socioeconomic and Health Determinants of Health Care Utilization Among Elderly Europeans: A Semiparametric Assessment of Equity, Intensity and Responsiveness for Ten European Countries

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  • Jürgen Maurer

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

Abstract

This paper investigates the interplay of socioeconomic and medical determinants of health care utilization among elderly Europeans from ten countries. Using novel strictly comparable cross-national data from the Survey of Health, Ageing and Retirement in Europe (SHARE), the study exploits recent semi- and nonparametric estimation methods to illustrate how individual socioeconomic status and health determine health care utilization in different institutional settings. Our flexible estimation method allows for the use of multiple health measures to adjust for individual differences in health care need without sacrificing cross-national comparability of the resulting estimates. Within countries, we find only a small, if any, socioeconomic gradient. Moreover, all health systems appear to be reasonably responsive to differences in care need. At the same time, we find considerable variation in treatment intensity across countries, which we cannot fully explain by differences in health care need.

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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 07144.

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Date of creation: 17 Jul 2007
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Handle: RePEc:mea:meawpa:07144

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Postal: Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy, Amalienstraße 33, 80799 München, Germany
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  1. Richard Blundell & James Powell, 2001. "Endogeneity in semiparametric binary response models," CeMMAP working papers CWP05/01, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  2. Anne Case & Angus S. Deaton, 2005. "Broken Down by Work and Sex: How Our Health Declines," NBER Chapters, in: Analyses in the Economics of Aging, pages 185-212 National Bureau of Economic Research, Inc.
  3. 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.
  4. Klein, Roger W & Spady, Richard H, 1993. "An Efficient Semiparametric Estimator for Binary Response Models," Econometrica, Econometric Society, vol. 61(2), pages 387-421, March.
  5. David M. Cutler & Adriana Lleras-Muney, 2006. "Education and Health: Evaluating Theories and Evidence," NBER Working Papers 12352, National Bureau of Economic Research, Inc.
  6. Dana P. Goldman & James P. Smith, 2004. "Can Patient Self-Management Help Explain the SES Health Gradient?," HEW 0403004, EconWPA.
  7. Richard Blundell & James Powell, 2001. "Endogeneity in nonparametric and semiparametric regression models," CeMMAP working papers CWP09/01, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  8. Chamberlain, Gary, 1984. "Panel data," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 22, pages 1247-1318 Elsevier.
  9. Roger W. Klein & Robert P. Sherman, 2002. "Shift Restrictions and Semiparametric Estimation in Ordered Response Models," Econometrica, Econometric Society, vol. 70(2), pages 663-691, March.
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Cited by:
  1. Lambrelli D & O’Donnell O, 2009. "Why Does the Utilization of Pharmaceuticals Vary So Much Across Europe? Evidence from Micro Data on Older Europeans," Health, Econometrics and Data Group (HEDG) Working Papers 09/06, HEDG, c/o Department of Economics, University of York.
  2. Jürgen Maurer, 2007. "Modelling socioeconomic and health determinants of health care use: A semiparametric approach," MEA discussion paper series 07145, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.

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