The comment by Martin Heidenreich and Marco Härpfer has been a pleasant surprise to us. After reading an article of his on inequalities of income in Europe1 in January 2011, Michael Dauderstädt provided Heidenreich with our then unpublished article and asked him to explain why his estimates of European inequality (D9/D1 = 3.8 for 2005 on p. 434,) were much lower than our own estimates. Heidenreich considered our estimates widely inaccurate and strongly recommended a withdrawal of the article. When he asked the editors of Intereconomics in an e-mail on 26 January 2011 for an opportunity to comment on our article, he wrote "...I challenge the assumption that the EU-27 is much more unequal than other large economies, for example India. Instead of a quintile ratio of 6.79 (PPS) we calculate for 2008 a ratio of 3.3 which is even much lower than the figure provided by EUROSTAT (5.0 for the EU27)." Given this previous intellectual exchange, we were looking forward to their critique.
Table 1 (back to the text)
Income Distribution in Europe, 1998-2009 (official EU statistics)
|European Union (27 countries)||:||:||:||:||:||:||:||5.0||4.9||5.0||5.0||4.9|
|European Union (25 countries)||4.6||4.6||4.5||4.5||:||:||:||:||:||:||:||:|
|European Union (15 countries)||4.6||4.6||4.5||4.5||:||4.6||4.8||4.8||4.7||4.9||4.9||4.9|
|New Member States |
|New Member States |
|Germany (including former GDR from 1991)||3.6||3.6||3.5||3.6||:||:||:||3.8||4.1||4.9||4.8||4.5|
Source: Eurostat (http://epp.eurostat.ec.europa.eu/portal/page/portal/statistics/search_database; [ilc_di11] - inequality of income distribution (income quintile share ratio) (accessed on 28.2.2011).
Their present comment, however, seems largely to confirm our findings whilst not fitting Heidenreich's aforementioned 2010 estimate. Furthermore, their commentary contributes additional relevance to our analysis as an important element of the study of European inequality by drawing attention to Atkinson et al.: "For the EU-27 as a whole, the S80/S20 ratio is 5. It should be noted that the latter is the weighted average of the 27 national ratios, in which each country ratio is weighted by the country's population size; it is thus not the same as the ratio of the top to bottom quintile shares in the EU-27 as a whole, which can be expected to be higher."2 Our analysis confirms Atkinson's expectations and provides an estimate for the higher value. Furthermore, our results are confirmed by Branko Milanovic's new book in which he compares the EU-27 and the USA.3
Instead, Heidenreich and Härpfer have raised four rather technical and trivial points to which we reply briefly:
- Database: We did not use the original SILC microdata but rather aggregated data provided to us by Eurostat. Indeed, the most reliable and best way of obtaining those numbers would be to use microdata as a direct source, which we rightly acknowledged in our article. However, although available, the process of obtaining the data is very tedious. It involves a contractual agreement with Eurostat, costs several thousand euros depending on how much data is ordered and, most importantly, requires the agreement of every single member state. This process may take several months before ordered data are available for research. In this context, we consider our approach much more efficient.
- Choice of indicator: There are, as Heidenreich and Härpfer show, a plethora of possible indicators. We chose the S80/S20 ratio because, again, it was available and is intuitively understandable. In particular, the construction of European quintiles according to our method is very transparent. We still think that the use of exchange rates (which exacerbate the level of inequality in the EU) makes sense for several reasons. An indicator based on exchange rates is better suited for the purpose of international comparison because within single-state economies like China, household incomes are not adjusted for different regional price levels and hence purchasing power. Furthermore, the closing of the gap between PPP and exchange rates is an important element of catching-up growth, which reduces between-country inequality. The gap has certain important economic implications in integrated markets, too. Potential investors will compare labour costs, which are strongly correlated with income, in particular in the poorer quintiles, at exchange rates. Potential migrants will calculate the value of their expected remittances at exchange rates.
- Poverty: Heidenreich and Härpfer point out the obvious when they mention that the poor in India are poorer than the poor in Europe. But we attempted to measure and compare income inequality, not poverty.
- Within- and between-country inequalities: We have not dwelled much on within-country inequality as these data are available on Eurostat in the same table (see above) from which we took our Table 1. We gave information on between-country inequality on p. 46. However, given the international concerns about rising inequality, we find the assertion by Heidenreich and Härpfer that within-country inequalities have declined in recent years baffling. The EU data (see Table 1) and the OECD and IMF sources we quoted in our footnote 32 all show that within-country inequality has increased in many EU member states, in particular the bigger and richer ones, albeit with some oscillations.