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The European Journal of Health Economics
Published in: The European Journal of Health Economics 5/2015

01-06-2015 | Original Paper

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

Authors: Laia Maynou, Marc Saez, Jordi Bacaria, Guillem Lopez-Casasnovas

Published in: The European Journal of Health Economics | Issue 5/2015

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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.
Footnotes
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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Metadata
Title
Health inequalities in the European Union: an empirical analysis of the dynamics of regional differences
Authors
Laia Maynou
Marc Saez
Jordi Bacaria
Guillem Lopez-Casasnovas
Publication date
01-06-2015
Publisher
Springer Berlin Heidelberg
Published in
The European Journal of Health Economics / Issue 5/2015
Print ISSN: 1618-7598
Electronic ISSN: 1618-7601
DOI
https://doi.org/10.1007/s10198-014-0609-1

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