Abstract
This paper presents an analysis of two processes of convergence between European countries, in wage levels and wage distributions, and the extent to which they explain recent trends in wage inequality for the EU as a whole. The results show that wage convergence was the main driver behind wage inequality trends for the EU as a whole in the last decade, which was significantly reduced prior to the crisis as a result of wage catch-up growth mainly in Eastern Europe, a process which was interrupted during the crisis but reactivated again in the most recent period. On the other hand, the contribution of within-country wage developments to explain changes in wage inequality for the EU as whole over the last decade was much more limited, although it is interesting to note as well a process of convergence in wage distributions between European countries towards intermediate inequality levels. Policies directed at reducing wage disparities within countries offer the best prospect to tackle wage inequalities both at the national and EU-wide level, as illustrated by the introduction of the German statutory minimum wage in 2015.
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Source: EU-SILC
Source: EU-SILC
Source: EU-SILC
Source: EU-SILC
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Notes
Wages represent a large share of total value added (around 63% according to AMECO database) and a very strong correlation exists in European countries between hourly wages (data from Structure of Earnings Survey) and GDP per capita (Eurostat data based on national accounts).
Nevertheless, the final impact of the increase in trade on wage inequality is open to certain degree of indeterminacy since the above-mentioned predictions depend on a set of assumptions that, if altered, can lead to different outcomes. For example, Feenstra and Hanson (1996) show that when the increase in market integration leads to the transfer of production activities from high to low skilled countries via Foreign Direct Investment (FDI), wage increases among higher-skilled workers in low-skilled countries would occur due to the increase in labour demand of the former workers derived from the FDI, which would compensate the increase in wage levels among low-skilled workers predicted by the Stopler-Samuelson theorem.
A detailed methodology explaining how this full-time equivalent measure of gross monthly wages is constructed can be found in the Annex. There is a 1-year lag in the wage data, which means the EU-SILC waves corresponding to the period 2005–2016 have been used.
Information on wage levels at the top 1% should be interpreted with care and probably as lower bound estimates. The very highest wage levels are most likely underestimated due to the typically poor coverage of the top of the distribution by income surveys, which explains why most recent research on inequality is using tax data instead (Piketty 2014).
The within-countries component of the Theil index reflects the average trends in wage inequality in all countries, but the weight of each country depends on the share it represents over the total EU wage mass. The bumpy yearly variations in this component are largely due to the UK.
EU-SILC data on average wages must be treated with some care in certain cases. Apart from the general limitations of EU-SILC (see Annex), the data seems less reliable in the first waves of the survey, which may result in unrealistic growth rates in some countries during the early period. Moreover, currency depreciation/appreciation in those countries not using the Euro may have had an influence in changes in wage levels expressed in euros, although in theory this should not be the case since the PPP used to correct the wage levels across countries are supposed to control for it. In any case, the picture provided in here using EU-SILC data is broadly similar to the one emerging when using national accounts data from AMECO, which includes all EU-28 countries from 1996 and refers to compensation of employees, including wages and salaries and employer’s social contributions (as well expressed in PPP-adjusted euros). The total compensation of employees is divided by the total number of employees across countries to obtain the final measure of average compensation per employee, which is comparable but not identical to our wage measure based in EU-SILC.
Some caution is needed when interpreting these results since developments in full-time equivalent wages cannot be used for a general evaluation of the impact of the crisis on the welfare of Europeans, since they only reflect the earnings of those that remained employed throughout the period, while the main impact of the crisis was on unemployment levels. Moreover, it should be noted that data on average wage levels could be biased upwards in those countries most affected by the crisis, due to lower-paid employees being more likely to lose their jobs (Vacas-Soriano 2018). This compositional effect could mask the real extent of the wage correction across some countries in the European periphery strongly affected by declining employment during the recession. Moreover, emigration is another factor which must be taken into account, since its associated reduction in the size of the labour supply in the sender country would have an upwards impact on average wages. Although EU-SILC does not allow for an adequate estimation of this, Labour Force Survey data (on number of emigrants relative to the population of the sender countries) shows that in could have been a factor to consider in several countries, such as in Lithuania, Latvia Romania and, from the onset of the crisis, in Spain, Greece, Malta and Cyprus. This emigration could have had a certain effect in pushing wages upwards, contributing to wage convergence prior to the crisis and acting as a break to divergence forces during the crisis.
When using data from national accounts (AMECO, see footnote 8), the upwards wage convergence process between European countries has similar characteristics. Nevertheless, the main difference is that it is almost entirely due to CEE countries, because Mediterranean countries generally fail to converge both before and after the crisis (the period starts in 1996 in this case).
If the whole period is considered (2004–2015), wage inequality grew in all the countries that initially had lower levels of wage inequality, Scandinavian and several Mediterranean (Italy, Greece and Spain) and Continental countries (Austria, Belgium and Netherlands). Trends among those countries that were most unequal at the beginning of the period were more mixed (inequalities moving upwards in Ireland, Luxembourg, Estonia or Portugal; and going downwards in Lithuania, Latvia and, especially, Poland and Hungary).
Another interesting insight emerging from Fig. 3 refers to the business cycle dynamics of wage inequalities, which have expanded in around two-thirds of European countries over the whole period but tend to describe a pro-cyclical behaviour over the different sub-periods. Prior to the crisis (left panel of the figure), there were more cases of inequality expansions (and of a larger magnitude) than reductions among European countries. Then, when the crisis emerged, wage inequality decreased in more than two-thirds of the countries between 2008 and 2011 (centre panel). Last, as the recovery set in among most European countries, wage inequality increases become more common again in the most recent period 2011–2015 (right panel).
This trend may be found as well in some Mediterranean countries (Portugal, Spain and Italy to a lower extent), although it is less clear, since the relative improvement of lower-educated workers was rather the result of wages among higher-educated employees being more affected by the crisis, while wage inequality declined during several years but only did so over the whole period in Portugal. Moreover, as explained previously (see footnote 9), a note of caution is needed, since compositional shifts due to declining employment levels for the low educated could partially explain declining wage inequality level over the period in some of these countries.
This happened as well in Latvia and Finland during the first years of the period, after which their wage inequality levels started to decline, possibly due to the existence of compositional effects resulting from lower-educated/lower-paid employees being relatively more affected by exists from employment.
Between 2014 and 2015, the ratio between the 90th and the 50th wage percentile (p90p50) remained rather unchanged in Germany (at around 1.86), while that between the 10th and the 50th wage percentile (p10p50) went from 0.374 to 0.451. That wage developments were relatively more positive at the bottom of the wage distribution is confirmed by trends in average wages among lower-educated as compared with those among higher-educated employees: average wages registered a larger expansion among lower-educated employees (5.3% compared to 3.1%).
In the UK case, wage developments between 2014 and 2015 were as well relatively more positive at the bottom of the wage distribution, but less so than in Germany: the decline in the p90p50 ratio (from 2.243 to 2.208) is more modest that the expansion observed in the p10p50 ratio (from 0.503 to 0.514), while average wages among lower-educated employees registered a lower reduction than among their higher-educated counterparts (− 7.2% as compared to − 13.4%).
Some researchers conducting independent evaluations have found that the cohesions policies implemented by the EU via the regional developments funds have promoted catch-up in less developed Member States (Rodríguez-Pose and Garcilazo 2013).
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Vacas-Soriano, C., Fernández-Macías, E. & Muñoz de Bustillo, R. Recent trends in wage inequality from an EU perspective: a tale of two convergences. Empirica 47, 523–542 (2020). https://doi.org/10.1007/s10663-019-09436-7
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DOI: https://doi.org/10.1007/s10663-019-09436-7