Skip to main content

Advertisement

SpringerLink
Log in

The sustainability of European health care systems: beyond income and aging

  • Original Paper
  • Published:
The European Journal of Health Economics Aims and scope Submit manuscript

Abstract

During the last 30 years, health care expenditure (HCE) has been growing much more rapidly than GDP in OECD countries. In this paper, we review the determinants of HCE dynamics in Europe, taking into account the role of income, aging population, technological progress, female labor participation and public budgetary variables. We show that HCE is a multifaceted phenomenon where demographic, social, economic, technological and institutional factors all play an important role. The comparison of total, public and private HCE reveals an imbalance of European welfare toward the care of the elderly. European Governments should increasingly rely on pluralistic systems to balance sustainability and access and equilibrate the distribution of resources across the functions of the public welfare system.

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

Fig. 1

Similar content being viewed by others

Notes

  1. As a tentative explanation, micro-level studies have shown that it is not age per se that is relevant in explaining HCE, rather remaining lifetime.

  2. Even though OECD data on health care expenditures cover a longer time span, information on the dependent variable is available for a limited set of countries before 1980, and the deflators provided by EU KLEMS database is available up to the year 2007. The list of EU-15 Countries follows: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, and United Kingdom.

  3. OECD Health Data available at http://www.ecosante.org/oecd.htm (edition 2010).

  4. Data are available at http://www.euklems.net (November 2009 edition). Data are available over the period 1970–2007. See O’Mahony and Timmer [38] for information about the methodology and construction of the database. See also [24]

  5. In principle, life habits should also be considered. However, due to lack of internationally comparable data and strong correlation with countries’ GDP we did not find any significant effect on HCE.

  6. See also [39] for a review.

  7. PubMed is a service maintained by the US National Library of Medicine, covering over 17 million citations from MEDLINE and other life science journals for biomedical articles back to the 1950s. We queried PubMed for publications in the countries and time periods considered in the analysis to proxy the extent of informed adoption of medical technologies (accessed on March 2011).

  8. We defined a dummy variable for health care reforms as listed by the European Observatory on Health Systems and Policies (http://www.euro.who.int/en/home/projects/observatory). The reform dummy variable has been dropped in our regressions since it was not statistically significant.

  9. The index is computed as the sum of shares (squared) of expenditures in all areas reported in OECD data (excluding health): pensions and services for the elderly; pensions and services for survivors; incapacity-related benefits; family support; active labor market policies; unemployment; housing allowances and rent subsidies; and a residual category (other social policy areas). The index ranges from 1/8 (if all social policy areas have the same allocated resources) to 1 (when only one area exhibits a positive allocation, whereas all the other areas have an allocation equal to zero).

  10. Due to the high incidence of missing data, Belgium and Luxemburg are excluded from the analysis.

  11. Expenditure and GDP values have been converted into current US dollars using the deflators provided by EU KLEMS database (2009). The “Health and social work” (ISIC rev. 3, class 85) deflator is considered for health expenditure, whereas the figure for “all industries” is applied in the case of GDP data. Log-values (natural) have been used for all regressed variables.

  12. Results are broadly consistent when a restricted time period is considered. Particularly we run the regressions using the last available ten years (1998–2007). Due to missing data, UK is also excluded in the analysis of private and public HCE. The following changes are detected in estimated coefficients: (regression on total HCE) the coefficient of DEBT becomes positive and statistically significant; (regression on public HCE) the coefficient of CONC becomes negative and statistically significant; (regression on private HCE) the coefficient of SC.PUB becomes negative and statistically significant.

  13. We considered both the KPSS stationarity test and the (augmented) Dickey-Fuller unit root test.

  14. Carrion-i-Silvestre [9] provides evidence that HCE and GDP series can be characterized as stationary processes evolving around a broken trend. Similar results are reported in Jewell et al. [26]. A throughout investigation of the pattern of series stationarity is carried trough only as a preliminary step to the regression analysis. The empirical size and power of the unit root tests largely depend on the available data. Therefore, we prefer to employ an estimation strategy that is “robust” to stationarity patterns.

  15. Under the null hypothesis (equal long-run coefficients), the difference between PMG and MG should be insignificant and the PMG is efficient. On the contrary, under the alternative (different long-run coefficients), the PMG estimator is inconsistent and the MG estimator should be considered. In the Tables, when the Hausman test is statistically significant at the 5% level, the MG estimator is reported.

  16. Country fixed effects are considered in order to allow for different trends across countries. A two-stage approach is considered where GDP is treated as an endogenous regressor. The instruments considered for estimation are energy use (kg of oil equivalent per capita) and an index of openness to trade, computed as the sum of imports and exports of goods and services (as a share of GDP). Data are provided by the World Bank database (World Development Indicators). The validity of the selected variables is assessed with the Hansen test.

  17. In our baseline specification, FD-IV estimation confirms the results of PMG. The latter estimator is then preferred because better suited to estimation on our data.

  18. Either the new technology is used in addition to the old ones, or it replaces the old ones with an expansion in the treatable conditions. We thank one referee for raising the issue. Unfortunately, our data do not allow to discriminate between the two effects.

  19. No account is made in our analysis of the benefits associated with medical technology improvements; therefore no conclusions can be drawn on the issue of the net value accrued to patients from innovation.

  20. Unreported regressions include a dummy variable identifying the years when health care reforms came into force. The variable has been built on the basis of the information provided by the European Observatory on Health Systems and Policies (http://www.euro.who.int/en/home/projects/observatory). The reform dummy variable was not statistically significant in all our regressions.

  21. Needless to say, the benefits associated with longer healthy life are not easily accounted for in this type of regressions, nonetheless being an important implication of technological progress.

References

  1. Arellano, M., Bond, S.: Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev. Econ. Stat. 58, 277–297 (1991)

    Article  Google Scholar 

  2. Anderson, T., Hsiao, C.: Estimation of dynamic models with error components. J. Am. Stat. Assoc. 76, 598–606 (1981)

    Article  Google Scholar 

  3. Anderson, T., Hsiao, C.: Formulation and estimation of dynamic models using panel data. J. Econom. 18, 47–82 (1982)

    Article  Google Scholar 

  4. Bassanini, A., Scarpetta, S.: Does human capital matter for growth in OECD countries? Evidence from pooled mean-group estimates. Economics Department Working Paper No. 282, OECD (2001)

  5. Baumol, W.J.: Macroeconomics of unbalanced growth: the anatomy of urban crisis. Am. Econ. Rev. 57(3), 415–426 (1967)

    Google Scholar 

  6. Baumol, W.J.: Health care, education and the cost disease: a looming crisis for public choice. Public Choice 77(1), 17–28 (1993)

    Article  Google Scholar 

  7. Blomqvist, A., Carter, R.A.: Is health care really a luxury? J. Health Econ. 16, 207–229 (1997)

    Article  PubMed  CAS  Google Scholar 

  8. Blundell, S., Bond, S.: Initial conditions and moment restrictions in dynamic panel data models. J. Econom. 87(1), 115–143 (1998)

    Article  Google Scholar 

  9. Carrion-i-Silvestre, J.L.: Health care expenditure and GDP: are they broken stationary? J. Health Econ. 24, 839–854 (2005)

    Article  PubMed  Google Scholar 

  10. Christiansen, T., Lauridsen, J., Nielsen, P.: Demographic changes and aggregate health-care expenditure in Europe. ENEPRI Research Report, No. 32 (2006)

  11. Del Boca, D., Vuri, D.: The mismatch between employment and child care in Italy: the impact of rationing. J. Popul. Econ. 20, 805–832 (2007)

    Article  Google Scholar 

  12. Docteur, E., Oxley, H.: Health-care systems: lessons from the reform experience. OECD Health Working Papers, No. 9 (2003)

  13. Dormont, B., Martins, J.O., Pelgrin, F., Suhrcke, M.: Health expenditure, longevity and growth. Paper prepared for the IX European conference of the Fondazione Rodolfo Debenedetti on “Health, Longevity and Productivity”, Limone sul Garda (2007)

  14. European Commission (2006): The impact of ageing and public expenditure: projections for the EU25 member states on pensions, health care, long-term care, education and unemployment transfers, European economy special report 01, European Commission, Bruxelles (2006)

  15. Ford, E.S., Ajani, U.A., Croft, J.B., Critchley, J.A., Labarthe, D.R., Kottke, T.E., Giles, W.H., Capewell, S.: Explaining the decrease in U.S. deaths from coronary disease, 1980–2000. N. Engl. J. Med. 356, 2388–2398 (2007)

    Article  PubMed  CAS  Google Scholar 

  16. Freeman, R.B., Schettkat, R.: Marketization of household production and the EU-US gap in work. Econ. Policy 20(41), 6–50 (2005)

    Google Scholar 

  17. Gelijns, A., Rosenberg, N.: The dynamics of technological change in medicine. Health Aff. 13(3), 28–46 (1994)

    Article  CAS  Google Scholar 

  18. Gerdtham, U.G., Jönsson, B.: International comparisons of health expenditure: theory, data and econometric analysis. In: Culyer, A.J., Newhouse, J.P. (eds.) Handbook of Health Economics, vol. 1. Elsevier, Amsterdam (2000)

  19. Getzen, T.E.: Health care is an individual necessity and a national luxury: applying multilevel decision models to the analysis of health care expenditure. J. Health Econ. 19, 259–270 (2000)

    Article  PubMed  CAS  Google Scholar 

  20. Haberkeern, K., Szydlik, M.: State care provision, societal option and children’s care of older parents in 11 European countries. Ageing Soc 30, 299–323 (2010)

    Article  Google Scholar 

  21. Hall, R.E., Jones, C.I.: The value of life and the rise in health spending. Quart. J. Econ. 122(1), 39–72 (2007)

    Article  Google Scholar 

  22. Han, Y.: Measuring industrial knowledge stocks with patents and papers. J. Informetr. 1(4), 269–276 (2007)

    Article  Google Scholar 

  23. Hartwig, J.: What drives health care expenditure? Baumol’s model of ‘unbalanced growth’ revisited. J. Health Econ. 27(3), 603–623 (2008)

    Article  PubMed  Google Scholar 

  24. Hartwig, J.: Testing the Baumol-Nordhaus model with EU Klems data. Rev. Income Wealth (forthcoming) (2010)

  25. Jaffe, A.B., Trajtenberg, M.: Patents, citations and innovations: a window on the knowledge economy. MIT Press, Cambridge (2002)

  26. Jewell, T., Lee, J., Tieslau, M., Strazicich, M.C.: Stationarity of health expenditures and GDP: evidence from panel unit root tests with heterogeneous structural breaks. J. Health Econ. 22, 313–323 (2003)

    Article  PubMed  Google Scholar 

  27. Kahn, A.: The dynamics of medical device innovation: an innovator’s perspective. In: Gelijns, A.C., Halm, A. (eds.) The changing economics of medical technology. National Academy Press, Washington (1991)

    Google Scholar 

  28. Kohler, H.P., Billari, F.C., Ortega, J.A.: The emergence of lowest-low fertility in Europe during the 1990s. Popul. Dev. Rev. 28(4), 641–680 (2002)

    Article  Google Scholar 

  29. Krugman, P.: The coscience of a liberal. W.W. Norton and Company Inc., New York (2007)

    Google Scholar 

  30. Lichtenberg, F.: The impact of new drugs on U.S. longevity and medical expenditure, 1990–2003. Am. Econ. Rev. 97(2), 438–443 (2007)

    Article  Google Scholar 

  31. Martin, J.J.M., Puerto Lopez del Amo Gonzalez, M., Dolores Cano Garcia, M.: Review of the literature on the determinants of healthcare expenditure. Appl. Econ. 43(1), 19–46 (2011)

    Article  Google Scholar 

  32. Murphy, M.K., Topel, R.H.: Measuring the gains from medical research: an economic approach. University of Chicago Press, Chicago (2003)

  33. Murthy, N.R., Okunade, A.A.: Managed care, deficit financing, and aggregate health care expenditure in the United States: a cointegration analysis. Health Care Manag Sci 3, 279–285 (2000)

    Article  PubMed  CAS  Google Scholar 

  34. Newhouse, J.P.: Medical care expenditure: a cross-national survey. J. Hum. Res. 12, 115–125 (1977)

    Article  CAS  Google Scholar 

  35. Newhouse, J.P.: Medical care costs: how much welfare loss? J. Econ. Pers. 6(3), 3–21 (1992)

    Article  CAS  Google Scholar 

  36. Nickell, S.: Biases in dynamic models with fixed effects. Econometrica 49, 1417–1426 (1981)

    Article  Google Scholar 

  37. Okunade, A.A., Murthy, V.N.R.: Technology as ‘major driver’ of health care costs: a cointegration analysis of the newhouse conjecture. J. Health Econ. 21(1), 147–159 (2002)

    Google Scholar 

  38. O’Mahony, M., Timmer, M.P.: Output, input and productivity measures at the industry level: the EU KLEMS database. Econ. J. 119(538), 374–403 (2009)

    Article  Google Scholar 

  39. Pammolli, F., Riccaboni, M., Oglialoro, C., Magazzini, L., Baio, G., Salerno, N.C.: Medical devices competitiveness and impact on public health expenditure. Study prepared for the directorate enterprise of the European commission (2005)

  40. Pesaran, M.H., Shin, Y., Smith, R.P.: Pooled mean group estimation of dynamic heterogeneous panels. J. Am. Stat. Assoc. 94(446), 621–634 (1999)

    Article  Google Scholar 

  41. Rogerson, R.: Understanding differences in hours worked. Rev. Econ. Dyn. 9(3) 365–409 (2006)

    Google Scholar 

  42. Rogerson, R.: Structural transformation and the deterioration of European labor market outcomes. NBER Working Paper 12889, NBER, Boston, Ma (2007)

  43. Thomson, S., Foubister, T., Mossialos, E.: Financing health care in the European union: challenges and policy responses. WHO Europe on behalf of the European observatory on health systems and policies, Copenhagen (2009)

  44. Zweifel, P., Felder, S., Meiers, M.: Ageing of the population and health care expenditure: a red herring? Health Econ. 8(6), 485–496 (1999)

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

The authors wish to thank Bengt Jönsson, Jochen Hartwig, Giuseppe Nicoletti, Alessandro Petretto, Nicola C. Salerno, Mark Dincecco, Niklas Potrafke, Catarina Goulão, for comments and suggestions on previous versions of the paper, and Guido Borà and Anna Horodok for skillful research assistance. A previous version of the paper was presented at the “7th European Conference on Health Economics” (Rome 23–26 July 2008), comments from conference participants are gratefully acknowledged.

Author information

Authors and Affiliations

  1. IMT Lucca Institute for Advanced Studies, Piazza San Ponziano, 6, 55100, Lucca, Italy

    Fabio Pammolli

  2. Fondazione CERM, via Poli 29, 00187, Rome, Italy

    Fabio Pammolli

  3. DISA, University of Trento, Via Inama, 5, 38122, Trento, Italy

    Massimo Riccaboni

  4. Department of Economics, University of Verona, viale dell’Università 3, 37129, Verona, Italy

    Laura Magazzini

Authors
  1. Fabio Pammolli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Massimo Riccaboni
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laura Magazzini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fabio Pammolli.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pammolli, F., Riccaboni, M. & Magazzini, L. The sustainability of European health care systems: beyond income and aging. Eur J Health Econ 13, 623–634 (2012). https://doi.org/10.1007/s10198-011-0337-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10198-011-0337-8

Keywords

JEL Classification

Search

Navigation