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

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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.

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Notes

  1. This was pointed out by one of the anonymous reviewers.

  2. We appreciate this definition from the other anonymous reviewers.

  3. We have a preliminary estimation of all models allowing variation on the three levels (country/time) for all coefficients. In the specification shown, we have provided only the best final models. Results not shown can be requested from the authors.

  4. \(\frac{ - \ln (1 - \beta )}{T} \times 100\).

  5. That is, \(CV = {{E\left( {y_{ijt} } \right)} \mathord{\left/ {\vphantom {{E\left( {y_{ijt} } \right)} {\left( {Var\left( {y_{ijt} } \right)} \right)^{{\tfrac{1}{2}}} }}} \right. \kern-0pt} {\left( {Var\left( {y_{ijt} } \right)} \right)^{{\tfrac{1}{2}}} }},\) where both the numerator and the denominator are estimated in model (3). Also note that this calculation can be done easily only following the Bayesian approach, where it is easier to make inferences about functions of parameters and/or predictions, in particular when the function is non-linear, as in our case [i.e. the dependent variables in (3) were non-linear functions of the health variables].

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Acknowledgments

This work was partly funded by the Short Term Grant Abroad for PhD European, CIBER of Epidemiology and Public Health (CIBERESP), Spain, benefiting Laia Maynou, who is also a beneficiary of the Grant for Universities and Research Centres for the Recruitment of New Research Personnel (FI-DGR 2012), AGAUR, Government of Catalonia (Generalitat de Catalunya). We appreciate the comments of the attendees at The Health Economists’ Study Group Summer 2013 Conference on 26–28 June 2013 at the University of Warwick, UK, at the New Directions in Welfare III 2013 OECD-Universities Joint Conference on 3–5 July 2013 in Paris, France, and at the 53rd European Regional Science Association on 27–31 August 2013, Palermo, Italy, where a preliminary version of this work was presented. We also appreciate the very valuable comments by the members of the Department of Economics, City University London, UK, and in particular Mireia Jofre-Bonet on a previous version of this article. We appreciate the comments of two anonymous reviewers that, without doubt, helped us improve our work.

Conflict of interest

There are no conflicts of interest for any of the authors. All authors freely disclose any actual or potential conflict of interest including any financial, personal or other relationships with other people or organizations within 3 years of beginning the submitted work that could inappropriately influence, or be perceived to influence, their work.

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Authors and Affiliations

  1. Research Group on Statistics, Econometrics and Health (GRECS), University of Girona, Campus de Montilivi, 17071, Girona, Spain

    Laia Maynou & Marc Saez

  2. CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain

    Laia Maynou & Marc Saez

  3. London School of Hygiene and Tropical Medicine, London, UK

    Laia Maynou & Jordi Bacaria

  4. Center for Research in Health and Economics (CRES), Universitat Pompeu Fabra, Barcelona, Spain

    Marc Saez & Guillem Lopez-Casasnovas

  5. Instituto Tecnológico Autónomo de México (ITAM), Mexico, Mexico

    Jordi Bacaria

  6. Department of Economics and Business, Universitat Pompeu Fabra, Barcelona, Spain

    Guillem Lopez-Casasnovas

  7. Barcelona Graduate School (BSGE), Universitat Pompeu Fabra, Barcelona, Spain

    Guillem Lopez-Casasnovas

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  1. Laia Maynou
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Maynou, L., Saez, M., Bacaria, J. et al. Health inequalities in the European Union: an empirical analysis of the dynamics of regional differences. Eur J Health Econ 16, 543–559 (2015). https://doi.org/10.1007/s10198-014-0609-1

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