The above-mentioned article reflects the increasing economic, political and social integration of the EU member states that is resulting in the emergence of transnational norms of equality and inequality, transforming economic disparities into inequalities. A major result of the article is that the income quintile share ratio (S80/S20)1 has been calculated by Eurostat as the weighted average of the national means and not as the ratio of the total income received by the 20% of Europe's population with the highest income compared to the income received by the 20% of the European population with the lowest income.2 Such a calculation can only be justified by assuming that norms of equality are basically national norms, an assumption which is at the core of the methodological nationalism of inequality research. The authors rightly challenge this assumption and implicitly assume the emergence of transnational norms of inequality, especially in the economically and politically integrated EU. Therefore, the authors rightly insist on also using transnational reference groups for inequality research, a factor which should also be reflected in the calculation of transnational inequality measures.
However, we do not understand why the authors estimate the income inequality of the EU based on the national income quintile share ratios instead of calculating it directly on the basis of the available microdata, especially given that the authors insist on the usefulness of individual data sets (p. 44 and p. 46) which can be easily obtained from Eurostat. The method chosen by the authors is often employed for the calculation of global disparities. In the case of the EU, however, comprehensive, comparable and up-to-date microdata sets are available (especially the European Community Household Panel (ECHP) from 1995-2001 and the Survey on Income and Living Conditions (EU-SILC) since 2005). Even if the EU-SILC data still harbours major problems concerning representativeness, data accuracy, comparability and coherence3, it is based on an ex ante harmonised framework and can be used for the analysis of European inequalities. In any case, the methodological problems of EU-SILC cannot be the reason why this data was not used by Dauderstädt and Keltek given that the aggregated figures they use are also based on the EU-SILC data.4 A calculation based on the original microdata is more reliable than estimating an indicator on the basis of other aggregated indicators - even if the estimation of the authors (2008: 6.79) is very good in comparison to our own calculations on the basis of the EU-SILC data (2008: 6.98; cf. Table 1). Besides greater reliability, the use of microdata also obliges the authors to explain and justify the decisions every user has to make concerning the following topics: population coverage, the chosen income concept, the employed equivalence scale, the weighting procedures, the treatment of outliers, e.g. by bottom- and top-coding, the treatment of missing values and zero incomes, the employed conversion rates from national currencies and the employed purchasing power parities (PPP). Dauderstädt/Keltek had to accept the decisions of Eurostat as taken for granted because they used aggregated data. To summarise this point so far, the available databases for analysing income inequalities in the EU are better than in any other supranational region. The bold statement of "immeasurable inequality" has not been proved convincingly.
Table 1 (back to the text)
Different Measures for the "Immeasurable" Inequality of Income in the EU-27 (PPS, 2008)
|Median||Gini||Rich (200%)||Poor |
A second critical point is that the authors use only one inequality indicator, the quintile share ratio, without discussing its implications. This is highly problematic because inequality and the underlying notion of social welfare are always based on normative considerations. A complex distribution (see Figure 1), however, cannot be described by one indicator. Therefore, different inequality measures focus on different forms and aspects of inequality. The quintile share ratio chosen by Dauderstädt/Keltek, for example, is sensitive to very high and very low incomes but is insensitive to changes in the middle of the distribution. In contrast, decile (P90/P10) and quintile ratios (P80/P20) ignore very high and very low incomes, focusing instead on the middle of the distribution. The Gini index assumes that each deviation from an equal distribution is equally important and is thus especially sensitive to income differences around the mode of the distribution. In contrast, the Atkinson index A(ε) is able to take into account different degrees of aversion to inequality, with typical values of ε ranging from 0.5 to 2. A higher ε implies that income increases at the bottom of the income distribution have a higher social utility than transfers at the top income levels. The Theil index is especially sensitive to income differences at the top of the distribution, while the mean logarithmic deviation (MLD) is more sensitive towards income differences at the lower end of the distribution. The share of the rich (200% and more of the median income) and the poor (60% and less of the median income) ignores the middle of the distribution and the differences among the group of rich or poor people.5 It is therefore very important that the chosen indicator is explained and justified (see Table 1 for the current values of these indicators in the EU6). This is not the case in the article of Dauderstädt/Keltek, which focuses strongly on the choice between the disposable income measured in "purchasing power standards" (PPS) and in euros - even if the latter can hardly be justified for the comparative analysis of income inequalities.
Figure 1 (back to the text)
Distribution of Disposable Income (PPS; EU-27 without Malta, 2008)
Thirdly, in an international perspective measurement of income distributions should not only examine dispersion but also take into account measures of location (see for example the median in Table 1). Dauderstädt/Kelek state that income inequality in India is even lower than in the EU-27. According to the indicators provided by the World Bank, this is true because the S80/S20-ratio for India (2005) is 45.3/8.1=5.6 and for the EU-27 (2008) is 40.5/5.8=7.0: the poor in India are relatively better off than the poor in Europe. Nevertheless, it should at least be mentioned that in India as well as in the other 22 countries whose quintile share ratios are lower than in the EU (Bangladesh, Belarus, Egypt, Indonesia, Russia, Ukraine ...), the income situation of the population measured by other indicators (for example by the median income or rates of absolute poverty) is much worse.
Table 2 (back to the text)
Income Inequalities Within and Between Nations in the EU (1995-2008, PPS, MLD)
EU 25 (without Malta)
EU 27 (without Malta)
|Within nations||Between nations||Total||Within nations||Between nations||Total||Within nations||Between nations||Total|
a French values for 2007.
Fourthly, the authors insist on the necessity of distinguishing and analysing within- and between-country inequalities - without, however, providing corresponding indicators, such as the MLD, which can be easily decomposed with the Stata module ineqdeco (in contrast to the Gini index which cannot be additively decomposed even if the authors expect this of Brandolini on p. 47). On this basis it can be shown that only 7% of the income inequalities in the EU-15 are between-country inequalities - in contrast to a quarter in the EU-25 and more than a third in the EU-27 (2008: 37%; see Table 2). For the EU-15 and the EU-25, the within- as well as the between-country inequalities have been shrinking since 1995 and 2005 respectively.7
In conclusion, the authors do not use the available microdata sets (ECHP; EU-SILC) and indicators for measuring income inequality in the EU directly and criticise Eurostat for these decisions. Unintentionally, however, they demonstrate that the methods applied by Milanovic, Sala-i-Martin et al. for calculating global disparities lead to convincing results also in comparison to the direct calculation on the basis of microdata.
Martin Heidenreich and Marco Härpfer, University of Oldenburg, Germany.