The European Journal of Health Economics

, Volume 16, Issue 5, pp 543–559 | Cite as

Health inequalities in the European Union: an empirical analysis of the dynamics of regional differences

  • Laia Maynou
  • Marc Saez
  • Jordi Bacaria
  • Guillem Lopez-Casasnovas
Original Paper

Abstract

In a panel setting, we analyse the speed of (beta) convergence of (cause-specific) mortality and life expectancy at birth in EU countries between 1995 and 2009. Our contribution is threefold. First, in contrast to earlier literature, we allow the convergence rate to vary, and thereby uncover significant differences in the speed of convergence across time and regions. Second, we control for spatial correlations across regions. Third, we estimate convergence among regions, rather than countries, and thereby highlight noteworthy variations within a country. Although we find (beta) convergence on average, we also identify significant differences in the catching-up process across both time and regions. Moreover, we use the coefficient of variation to measure the dynamics of dispersion levels of mortality and life expectancy (sigma convergence) and, surprisingly, find no reduction, on average, in dispersion levels. Consequently, if the reduction of dispersion is the ultimate measure of convergence, then, to the best of our knowledge, our study is the first that shows a lack of convergence in health across EU regions.

Keywords

Health convergence Beta convergence Sigma convergence Catching-up Spatiotemporal modelling Bayesian models Integrated nested Laplace approximation 

JEL Classification

I14 I15 C33 C11 

References

  1. 1.
    Durlauf, S.N., Johnson, P.A., Temple, J.R.W.: Growth econometrics. In: Aghion, P., Durlauf, S.N. (eds.) Handbook of Economic Growth, pp. 555–677. Elsevier, Amsterdam (2005)Google Scholar
  2. 2.
    Kenny, C.: Why are we worried about income? Nearly everything that matters is converging. World Dev. 33(1), 1–19 (2005)CrossRefGoogle Scholar
  3. 3.
    Sen, A.: Mortality as an Indicator of economic success and failure. Econ. J. 108(446), 1–25 (1998)CrossRefGoogle Scholar
  4. 4.
    Sen, A.: Development as Freedom. Random House, New York (1999)Google Scholar
  5. 5.
    Maslow, A.: A theory of human motivation. Psychol. Rev. 50, 370–396 (1943)CrossRefGoogle Scholar
  6. 6.
    Becker, G., Phillipson, T., Soares, R.: The quantity and quality of life and the evolution of world inequality. NBER working paper series, no. 9765 (2003)Google Scholar
  7. 7.
    Mayer, D.: Convergence clubs in cross-country life expectancy dynamics. In: van der Hoeven, R., Shorrocks, A. (eds.) Perspectives on Growth and Poverty, pp. 144–171. United Nations University Press, Tokyo (2003)Google Scholar
  8. 8.
    Wagstaff, A., Paci, P., van Doorslaer, E.: On the measurement of inequalities in health. Soc. Sci. Med. 33(5), 545–557 (1991)CrossRefPubMedGoogle Scholar
  9. 9.
    Van Doorslaer, E., Wagstaff, A., Bleichrodt, H., Calonge, S., Gerdtham, U., Gerfin, M., Geurts, J., Gross, L., Häkkinen, U., Leu, R., O’Donell, O., Propper, C., Puffer, F., Rodríguez, M., Sundberg, G., Winkelhake, O.: Income-related inequalities in health: some international comparisons. J. Health Econ. 16(1), 93–112 (1997)CrossRefPubMedGoogle Scholar
  10. 10.
    Clarke, P.M., Gerdtham, U.G., Johannesson, M., Bingefors, K., Smith, L.: On the measurement of relative and absolute income-related health inequality. Soc. Sci. Med. 55, 1923–1928 (2002)CrossRefPubMedGoogle Scholar
  11. 11.
    Oliver, A., Healey, A., Le Grand, J.: Addressing health inequalities. Lancet 360, 565–567 (2002)CrossRefPubMedGoogle Scholar
  12. 12.
    Wagstaff, A.: Inequality aversion, health inequalities, and health achievement. J. Health Econ. 21, 627–641 (2002)CrossRefPubMedGoogle Scholar
  13. 13.
    Van Ourti, T.: Socio-economic inequality in ill-health amongst the elderly. Should one use current or permanent income? J. Health Econ. 22(2), 219–241 (2003)CrossRefPubMedGoogle Scholar
  14. 14.
    Van Doorslaer, E., Jones, A.M.: Inequalities in self-reported health: validation of a new approach to measurement. J. Health Econ. 22, 61–87 (2003)CrossRefPubMedGoogle Scholar
  15. 15.
    O’Donnell, O., van Doorslaer, E., Wagstaff, A., Lindelöw, M.: Analyzing Health Equity Using Household Survey Data: A Guide to Techniques and Their Implementation. The World Bank, Washington (2008)Google Scholar
  16. 16.
    Fleurbaey, M., Schokkaert, E.: Unfair inequalities in health and health care. J. Health Econ. 28(1), 73–90 (2009)CrossRefPubMedGoogle Scholar
  17. 17.
    Erreygers, G., Van Ourti, T.: Measuring socioeconomic inequality in health, health care and health financing by means of rank-dependent indices: a recipe for good practice. J. Health Econ. 30(4), 685–694 (2011)CrossRefPubMedCentralPubMedGoogle Scholar
  18. 18.
    Frick, J., Zeibarth, N.: Welfare-related health inequality: does the choice of measure matter? Eur. J. Health Econ. 14(3), 431–442 (2013)CrossRefPubMedGoogle Scholar
  19. 19.
    Wagstaff, A.: Poverty and health sector inequalities. Bull. WTO 80(2), 97–102 (2002)Google Scholar
  20. 20.
    Wagstaff, A.: The bounds of the concentration index when the variable of interest is binary, with an application to immunization inequality. Health Econ. 14(4), 429–432 (2005)CrossRefPubMedGoogle Scholar
  21. 21.
    Erreygers, G.: Correcting the concentration index. J. Health Econ. 28, 504–515 (2009)CrossRefPubMedGoogle Scholar
  22. 22.
    Erreygers, G.: Correcting the concentration index: a reply to Wagstaff. J. Health Econ. 28, 521–524 (2009)CrossRefGoogle Scholar
  23. 23.
    Wagstaff, A.: Correcting the concentration index: a comment. J. Health Econ. 28, 516–520 (2009)CrossRefPubMedGoogle Scholar
  24. 24.
    Erreygers, G., Van Ourti, T.: Putting the cart before the horse. Comment on “The concentration index of a binary outcome revisited”. Health Econ. 20, 1161–1165 (2011)CrossRefPubMedCentralPubMedGoogle Scholar
  25. 25.
    Wagstaff, A.: The concentration of a binary outcome revisited. Health Econ. 20, 1155–1160 (2011)CrossRefPubMedGoogle Scholar
  26. 26.
    Wagstaff, A.: Reply to Guido Erreygers and Tom Van Ourti’s comment on “The concentration index of a binary outcome revisited”. Health Econ. 20, 1166–1168 (2011)CrossRefPubMedGoogle Scholar
  27. 27.
    Erreygers, G., Clarke, P., Van Ourti, T.: “Mirror, mirror, on the wall, who in this land is fairest of all?”—distributional sensitivity in the measurement of socioeconomic inequality of health. J. Health Econ. 31, 257–270 (2012)CrossRefPubMedCentralPubMedGoogle Scholar
  28. 28.
    Wennberg, J., Gittelsohn, A.: Small area variations in health care delivery. Science 182(4117), 1102–1108 (1973)CrossRefPubMedGoogle Scholar
  29. 29.
    Myrdal, G.: Economic Theory and Underdeveloped Regions. Duckworth, London (1957)Google Scholar
  30. 30.
    Friedmman, J.: Regional Development Policy: A Case Study of Venezuela. MIT Press, Cambridge (1966)Google Scholar
  31. 31.
    Keeble, D., Oxford, J., Walker, S.: Periphery Regions in a Community of Twelve Member States. EC Official Publications, Luxembourg (1988)Google Scholar
  32. 32.
    Krugman, P.: Increasing returns and economic geography. J. Polit. Econ. 99(3), 483–499 (1991)CrossRefGoogle Scholar
  33. 33.
    Felder, S., Tauchmann, H.: Federal state differentials in the efficiency of health production in Germany: an artefact of spatial dependence? Eur. J. Health Econ. 14, 21–39 (2013)CrossRefPubMedGoogle Scholar
  34. 34.
    Paas, T., Kuusk, A., Schlitte, F., Võrk, A.: Econometric Analysis of Income Convergence in Selected EU Countries and Their NUTS 3 Level Regions. University of Tartu, Tartu (2007)Google Scholar
  35. 35.
    Barro, R., Sala-i-Martin, X.: Economic Growth. MIT Press, Boston (1991)Google Scholar
  36. 36.
    Sala-i-Martin, X.: The classical approach to convergence analysis. Econ. J. 106(437), 1019–1036 (1996)CrossRefGoogle Scholar
  37. 37.
    Sala-i-Martin, X.: Regional cohesion: evidence and theories of regional growth and convergence. Eur. Econ. Rev. 40(6), 1325–1352 (1996)CrossRefGoogle Scholar
  38. 38.
    Baumol, W.: Productivity growth, convergence, and welfare: what the long run data show. Am. Econ. Rev. 76(5), 1072–1085 (1986)Google Scholar
  39. 39.
    Barro, R., Sala-i-Martin, X.: Convergence. J. Polit. Econ. 100(2), 223–251 (1992)CrossRefGoogle Scholar
  40. 40.
    Fischer, M., Stirböck, C.: Regional income convergence in the enlarged Europe, 1995–2000: a spatial econometric perspective. ZEW discussion paper no. 04–42 (2004). http://www.econstor.eu/bitstream/10419/24051/1/dp0442.pdf. Accessed 19 Jan 2013
  41. 41.
    Quah, D.: Galton’s fallacy and the convergence hypothesis. Scand. J. Econ. 95, 427–443 (1993)CrossRefGoogle Scholar
  42. 42.
    Preston, S.: The changing relation between mortality and level of economic development. Popul. Stud. 29, 231–248 (1975)CrossRefGoogle Scholar
  43. 43.
    Barro, R.: Economic growth in a cross section of countries. Q. J. Econ. 106(2), 407–443 (1991)CrossRefGoogle Scholar
  44. 44.
    Pritchett, L., Summers, L.: Wealthier is healthier. J. Hum. Resour. 31(4), 842–868 (1996)CrossRefGoogle Scholar
  45. 45.
    Anand, S., Ravallion, M.: Human development in poor countries: on the role of private incomes and public services. J. Econ. Perspect. 7(1), 133–150 (1993)CrossRefGoogle Scholar
  46. 46.
    Wilson, C.: On the scale of global demographic convergence 1950-2000. Popul. Dev. Rev. 27(1), 155–171 (2011)CrossRefGoogle Scholar
  47. 47.
    Glei, D.A., Meslé, F., Vallin, J.: Diverging trends in life expectancy at age 50: A look at causes of death. In: Eileen, M., Crimins, S.H.P., Cohen, B. (eds.) International Differences in Mortality at Older Ages: Dimensions and Sources, pp. 103–151. National Academies Press, Washington (2010)Google Scholar
  48. 48.
    Edwards, R.D.: Changes in world inequality in length of life: 1970–2000. Popul. Dev. Rev. 37(3), 499–528 (2011)CrossRefPubMedGoogle Scholar
  49. 49.
    Clark, R.: World health inequality: convergence, divergence, and development. Soc. Sci. Med. 72(4), 617–624 (2011)CrossRefPubMedGoogle Scholar
  50. 50.
    Eggleston, K.N., Fuchs, V.R.: The new demographic transition: most gains in life expectancy now realized late in life. J. Econ. Perspect. 26(3), 137–156 (2012)CrossRefPubMedCentralPubMedGoogle Scholar
  51. 51.
    Edwards, R.D., Tuljapurkar, S.: Inequality in life spans and a new perspective on mortality convergence across industrialized countries. Popul. Dev. Rev. 31(4), 645–674 (2005)CrossRefGoogle Scholar
  52. 52.
    d’Albis, H., Esso, L.J., Pifarré, H.: Mortality convergence across high-income countries: An econometric approach. Documents de Travail du Centre d’Economie de la Sorbonne, Université Paris 1, 2012.76 (2012)Google Scholar
  53. 53.
    Monfort, P.: Convergence of EU regions. Measures and evolution. European Union, regional policy working paper 01/2008 (2008)Google Scholar
  54. 54.
    Eckey, H.F., Türk, M.: Convergence of EU-Regions: A literature review. Discussion paper at the Economic Department of the University of Kassel, 86/06, Kassel (2006)Google Scholar
  55. 55.
    Maynou, L., Saez, M., Bacaria, J.: Analysis of regional convergence in the euro area (1990–2010) (in Spanish). Ekonomiaz 82, 200–217 (2013)Google Scholar
  56. 56.
  57. 57.
    Baumont, C., Ertur, C., Le Gallo, J.: The European Regional Convergence Process, 1980–1995: Do Spatial Regimes and Spatial Dependence Matter? EconWPA series on econometrics, number 0207002 (2002). http://econpapers.repec.org/paper/wpawuwpem/0207002.htm. Accessed 19 Jan 2013
  58. 58.
    Borrell, C., Marí-Dell’olmo, M., Serral, G., Martínez-Beneito, M., Gotsens, M., MEDEA Members: Inequalities in mortality in small areas of eleven Spanish cities (the multicenter MEDEA project). Health Place 16(4), 703–711 (2010)CrossRefPubMedGoogle Scholar
  59. 59.
    Puigpinós-Riera, R., Marí-Dell’Olmo, M., Gotsens, M., Borrell, C., Serral, G., Ascaso, C., Calvo, M., Daponte, A., Domínguez-Berjón, F.M., Esnaola, S., Gandarillas, A., López-Abente, G., Martos, C.M., Martínez-Beneito, M.A., Montes-Martínez, A., Montoya, I., Nolasco, A., Pasarín, I.M., Rodríguez-Sanz, M., Saez, M., Sánchez-Villegas, P.: Cancer mortality inequalities in urban areas: a Bayesian small area analysis in Spanish cities. Int. J. Health Geogr. 10, 6 (2011)CrossRefPubMedCentralPubMedGoogle Scholar
  60. 60.
    Salcedo, N., Saez, M., Bragulat, B., Saurina, C.: Does the effect of gender modify the relationship between deprivation and mortality? BMC Public Health 12, 574 (2012)CrossRefPubMedCentralPubMedGoogle Scholar
  61. 61.
    Kondo, N., Sembajwe, G., Kawachi, I., van Dam, R., Subramanian, S., Yamagata, Z.: ncome inequality, mortality, and self rated health: meta-analysis of multilevel studies. Br. Med. J. 339, b4471 (2009)CrossRefGoogle Scholar
  62. 62.
    Hsiao, C., Pesaran, M.H.: Random coefficient panel data models. In: Mátyás, L., Sevestre, P. (eds.) The Econometrics of Panel Data. Advances Studies in Theoretical and Applied Econometrics, vol. 46, pp. 185–213. Springer, Berlin (2008)Google Scholar
  63. 63.
    Pinheiro, J.C., Bates, D.: Mixed-Effects Models in S and S-Plus. Springer, New York (2000)CrossRefGoogle Scholar
  64. 64.
    R-INLA project http://www.r-inla.org/. Accessed 2 Aug 2013
  65. 65.
    Lawson, A.B., Browne, W.J., Vidal-Rodeiro, C.L.: Disease Mapping with WinBUGS and MLwiN. Wiley, Chichester (2003)CrossRefGoogle Scholar
  66. 66.
    Barceló, M.A., Saez, M., Saurina, C.: Spatial variability in mortality inequalities, socioeconomic deprivation, and air pollution in small areas of the Barcelona Metropolitan Region, Spain. Sci. Total Environ. 407(21), 5501–5523 (2009)CrossRefPubMedGoogle Scholar
  67. 67.
    Lindgren, F., Rue, H., Lindström, J.: An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach (with discussion). J. R. Stat. Soc. Ser. B 73(4), 423–498 (2011). http://www.math.ntnu.no/~hrue/spde-jrssb.pdf. Accessed 19 Jan 2013
  68. 68.
    Stein, M.L.: Statistical Interpolation of Spatial Data: Some Theory for Kriging. Springer, New York (1999)CrossRefGoogle Scholar
  69. 69.
    Nickell, S.: Biases in dynamic models with fixed effects. Econometrica 49(6), 1417–1426 (1981)CrossRefGoogle Scholar
  70. 70.
    Anderson, T.W., Hsiao, C.: Estimation of dynamic models with error components. J. Am. Stat. Soc. 76, 598–606 (1981)CrossRefGoogle Scholar
  71. 71.
    Anderson, T.W., Hsiao, C.: Formulation and estimation of dynamic models using panel data. J. Econom. 18, 47–82 (1982)CrossRefGoogle Scholar
  72. 72.
    Arellano, M., Bond, S.: Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev. Econ. Stud. 58, 277–297 (1991)CrossRefGoogle Scholar
  73. 73.
    Holtz-Eakin, D., Newey, W., Rosen, H.S.: Estimating vector autoregressions with panel data. Econometrica 56, 1371–1395 (1988)CrossRefGoogle Scholar
  74. 74.
    Blundell, R., Bond, S.: Initial conditions and moment restrictions in dynamic panel data models. J. Econom. 87, 115–143 (1998)CrossRefGoogle Scholar
  75. 75.
    Hsiao, C., Pesaran, M.H., Tahmiscioglu, A.K.: Bayes estimation of short-run coefficients in dynamic panel data models. In: Hsiao, C., Lee, L.F., Lahiri, K., Pesaran, M.H. (eds.) Analysis of Panels and Limited Dependent Variables Models, pp. 268–296. Cambridge University Press, Cambridge (1999)CrossRefGoogle Scholar
  76. 76.
    Heckman, J.: Heterogeneity and state dependence. In: Rosen, S. (ed.) Studies in Labor Markets. University of Chicago Press, Chicago (1981)Google Scholar
  77. 77.
    Heckman, J.: Statistical models for discrete panel data. In: Manski, C.F., McFadden, D. (eds.) Structural Analysis of Discrete Data with Econometric Applications. MIT Press, Cambridge (1981)Google Scholar
  78. 78.
    Zhang, P., Small, D.: Bayesian inference for random coefficient dynamic panel data models. Department of Statistics, The Wharton School, University of Pennsylvania (2006). http://www-stat.wharton.upenn.edu/~dsmall/randomcoefficientmodel_submittedversion.pdf. Accessed 26 Jul 2013
  79. 79.
    Maynou, L., Saez, M.: Bayesian Estimation of Small Dynamic Panel Data Models. Research Group on Statistics, Econometrics and Health (GRECS), University of Girona, Girona (2013)Google Scholar
  80. 80.
    Raftery, A.: Bayesian model selection in social research. Sociol. Methodol. 25, 111–163 (1995)CrossRefGoogle Scholar
  81. 81.
    Fernández, C., Ley, E., Steel, M.: Model uncertainty in cross-country growth regressions. J. Appl. Econom. 16, 563–576 (2001)CrossRefGoogle Scholar
  82. 82.
    Sala-i-Martin, X., Doppelhofer, G., Miller, R.: Determinants of long-term growth: a Bayesian averaging of classical estimates (BACE) approach. Am. Econ. Rev. 94(4), 813–835 (2004)CrossRefGoogle Scholar
  83. 83.
    Moral-Benito, E.: Determinants of economic growth: a Bayesian panel data approach. Documento de trabajo 1031. Banco de España, Madrid (2010)Google Scholar
  84. 84.
    Rendon, S.R.: Fixed and random effects in classical and Bayesian regression. Oxf. Bull. Econ. Stat. 75(3), 460–476 (2012)CrossRefGoogle Scholar
  85. 85.
    Rue, H., Martino, S., Chopin, N.: Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations (with discussion). J. R. Stat. Soc. Ser. B 71, 319–392 (2009). http://www.math.ntnu.no/~hrue/r-inla.org/papers/inla-rss.pdf. Accessed 19 Jan 2013
  86. 86.
    Blangiardo, M., Cameletti, M., Baio, G., Rue, H.: Spatial and spatio-temporal models with R-INLA. Spat Spatiotemporal Epidemiol. 4, 33–49 (2013)CrossRefPubMedGoogle Scholar
  87. 87.
    R Development Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna (2012). http://www.R-project.org. Accessed 9 Dec 2012
  88. 88.
    Šlander, S., Ogorevc, M.: Labour costs convergence in the EU: spatial econometrics approach. Privred. Kret. Ekon. Polit, 122, 27–51 (2010). http://www.hrcak.srce.hr/file/80468. Accessed 26 Jan 2013
  89. 89.
    Besag, J.: Spatial interaction and the statistical analysis of lattice systems (with discussion). J. R. Stat. Soc. Ser. B 36, 192–236 (1974)Google Scholar
  90. 90.
    Cressie, N.A.: Statistics for Spatial Data. Wiley, New York (1993)CrossRefGoogle Scholar
  91. 91.
    Kelsall, J., Wakefield, J.: Modeling spatial variation in disease risk: a geostatistical approach. J. Am. Stat. Assoc. 97(459), 692–701 (2002)CrossRefGoogle Scholar
  92. 92.
    Simpson, D., Illian, J., Lindgren, F., Sørbye, S.H., Rue, H.: Going off grid: Computationally efficient inference for log-Gaussian Cox processes. Preprint statistics no. 10/2011. Norwegian University of Science and Technology, Trondheim (2011). http://www.r-inla.org/papers. Accessed 23 Mar 2013
  93. 93.
    Rue H, Held L. Gaussian Markov Random Fields. Chapman & Hall/CRC, Boca Raton (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Laia Maynou
    • 1
    • 2
    • 3
  • Marc Saez
    • 1
    • 2
    • 4
  • Jordi Bacaria
    • 3
    • 5
  • Guillem Lopez-Casasnovas
    • 6
    • 4
    • 7
  1. 1.Research Group on Statistics, Econometrics and Health (GRECS)University of GironaGironaSpain
  2. 2.CIBER of Epidemiology and Public Health (CIBERESP)MadridSpain
  3. 3.London School of Hygiene and Tropical MedicineLondonUK
  4. 4.Center for Research in Health and Economics (CRES)Universitat Pompeu FabraBarcelonaSpain
  5. 5.Instituto Tecnológico Autónomo de México (ITAM)MexicoMexico
  6. 6.Department of Economics and BusinessUniversitat Pompeu FabraBarcelonaSpain
  7. 7.Barcelona Graduate School (BSGE)Universitat Pompeu FabraBarcelonaSpain

Personalised recommendations