Abstract
What explains cross-national variation in wage inequality? Research in comparative political economy stresses the importance of the welfare state and wage coordination in reducing not only disposable income inequality but also gross earnings inequality. However, the cross-national variation in gross earnings inequality between median- and low-income workers is at odds with this conventional wisdom: the German coordinated market economy is now more unequal in this type of inequality than the United Kingdom, a liberal market economy. To solve this puzzle, I argue that non-inclusive coordination benefits median but not bottom-income workers and is as a result associated with higher – rather than lower – wage inequality. I find support for this argument using a large N quantitative analysis of wage inequality in a panel of Western European countries. Results are robust to the inclusion of numerous controls, country fixed effects, and also hold in a larger sample of OECD countries. Taken together these findings force us to reconsider the relationship between coordination and wage inequality at the bottom of the income distribution.
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Notes
This shift was the result of changes in the prevailing technology (Freeman and Katz, 1995; Goldin and Katz, 1996; Acemoglu, 2002), the structure of employment (Levy and Murnane, 1992), rising trade competition (Wood, 1994; Burtless, 1995; Freeman, 1995) and immigration (Borjas et al, 1997; Rueda and Pontusson, 2000, p. 357).
The figures for France are net (see Supplementary Appendix) so they are not directly comparable (many thanks to an anonymous reviewer for pointing this out to me). However note that France has one of the highest minimum wage legislation in Western Europe, and other studies have shown that the ratio of median to bottom income earnings is indeed very low. I have also recalculated my results without France and they are unchanged (results available from the author).
‘A full-time employee is defined as a low-wage earner when his/her annual gross earnings are less than two thirds of the annual full-time median gross earnings’ (Eurostat, 2010, p. 6).
These figures are taken from the Eurostat statistic in focus, see endnote 3.
See Visser’s codebook, endnote 6.
One variable which is not captured here but that could affect wage inequality is trade union ideology – for which there is no reliable data for my whole sample over time. This is only a problem if there is a very strong correlation between union density and egalitarian ideology. For instance, an egalitarian ideology seems more prevalent in Sweden than in Germany (Dolvik and Stokke, 1998; Jacobi et al, 1998, p. 218) and union density is higher in Sweden than in Germany (I thank a reviewer for raising this point). However, it is not obvious that there is necessarily a statistical correlation between the two. Indeed, elsewhere I have created a variable capturing the working class ideology of unions (arguably not a perfect proxy) and union density for a sample of Western European countries after 2000 (Benassi and Vlandas, 2016) and the correlation between the two variables is <0.1, so an omitted variable bias is unlikely to be a problem.
This has the additional advantage of allowing us to be sure that inclusiveness is what matters for wage inequality. In the power resource approach, union density is often construed as an indicator of union strength rather than inclusiveness. It is therefore difficult to rule out that it is union strength rather than inclusiveness that explains the negative correlation between union density and wage inequality (I thank a reviewer for raising this point). However, there is no a priori reason that strength should mediate the effect of coordination on wage inequality.
While the replacement rate after the first year of unemployment could also matter, there are two problems with it. First, the replacement rate after the first year also captures the duration of benefits rather than their level. Second, most workers exit unemployment within 1 year. In any case, data availability for the replacement rate after the first year is very limited.
See Supplementary Appendix 3 for details on years available for each variable.
The results are reported in Supplementary Appendix 3.
My results do not change if the trend that I include in certain models is excluded, nor if GDP growth is excluded. I have further rerun my key regressions without France – which has a different measure of wage inequality that is net rather than gross – and my key results are unchanged. I am grateful to an anonymous reviewer for suggesting these robustness checks.
Note that data for these variables only starts in the mid-1980s so the number of observations drops slightly.
The original 14 Western European countries plus Australia, Canada, New Zealand, Switzerland, Japan and the United States.
See Supplementary Table A2.2 for details on the time period by country available for each independent variable.
I am grateful to an anonymous reviewer for suggesting this interpretation of this finding.
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Acknowledgements
The author is grateful to David Marsden, Marco Simoni, Bob Hancke, David Rueda, Silja Häusermann, Bruno Amable, Julie Valentin, David Soskice, Peter Hall, Chiara Benassi, Alison Johnston, Abel Bojar, Philippe Pochet, Baptiste Francon, Michael Zemmour, Elvire Guillaud, Jonathan Golub and the participants of the seminar co-organized by PSE (Paris School of Economics) and MSE (University Paris 1 Pantheon-Sorbonne) as well as the 2013 ECPR joint session in Mainz for comments and discussion on previous versions of this article. The author accepts responsibility for any errors remaining in the article.
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Vlandas, T. Coordination, inclusiveness and wage inequality between median- and bottom-income workers. Comp Eur Polit 16, 482–510 (2018). https://doi.org/10.1057/cep.2016.25
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DOI: https://doi.org/10.1057/cep.2016.25