Abstract
Wages in Eastern Europe are much lower than wages in Western Europe. The large differences remain also if we adjust for differences in price levels. This chapter assesses the extent to which wage differences between East European countries and Germany can be explained by differences in productivity and other economic fundamentals in individual countries. We compare the residual country effects on wages, including also effects of wage-setting institutions that remain once we control for labour force compositions and differences in economic structures. The negative country-wage effects in Eastern European countries, in fact, increase once we control for labour force compositions and differences in economic structures. We also decompose the returns effect for occupational groups and sectors, distinguishing the interaction between occupation and sectors and countries.
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Notes
- 1.
The comparison is only provisional, given that wage-adjusted productivity is an inversion of wage share. Differences in the latter might be related to structural differences in the economy, such as capital intensity.
- 2.
Efficiency wage models, for instance, relax the neoclassical assumption that productivity is exogenous and allow for a reverse causation in which higher wages lead to higher productivity by, for instance, inducing greater work effort or better work organisation (Shapiro and Stiglitz 1984).
- 3.
Rents refer to returns in conditions of imperfect competition. The latter might be the result of quasi-monopolies, labour unionisation, or social policies which change workers’ external options through, for example, benefit payments.
- 4.
Behr and Pötter (2010) decomposed wage differences between EU countries in different quintiles, using a proportional hazard model to analyse the 2001 European Community Household Panel (ECHP) dataset, which included 13 European countries. Brandolini et al. (2011) analysed the distribution of earnings using the 2007 dataset of the European Union Statistics on Income and Living Conditions (EUSILC).
- 5.
Behr and Pötter (2010) use education, tenure with current employer, and general working experience as measures of skills. Brandolini et al. (2011) relied on education (secondary and university) and worker age. Pereira and Galego (2016) conducted their analysis using education, supervisory responsibility, and being a native worker as explanatory variables.
- 6.
The EWCS has been conducted by Eurofound every five years since 1990. Its samples are representative of persons in employment, both employees and self-employed, working for at least one hour a week, who are 15 years of age or older (16 or older in Spain and the UK). The interviews are conducted face-to-face. Sample sizes, with a few exceptions, are around 1000 workers per country. The response rate for questions on income was 83 per cent.
- 7.
Measurements of educational attainment are based on the ISCED classification (seven groups).
- 8.
Further control variables used in this analysis include occupational groups according to ISCO and 21 economic sectors based on one-digit NACE.
- 9.
The differences between the two means of measurement also include a different sectoral structure (SES does not include agriculture, public administration, defence, or compulsory social security) and different age brackets.
- 10.
Lithuania reported much lower wages in the 2010 wave of the EWCS. In 2015, the response rate in this country was relatively high (90.7 per cent), which makes it unlikely that this factor was the source of this distortion.
- 11.
Response rates lower than 70 per cent were found in Hungary (46.1 per cent), Italy (56.1 per cent), Czechia (59.2 per cent), Poland (60.8 per cent), Greece (61.1 per cent), Portugal (65.1 per cent), Estonia (65.5 per cent), Croatia (67.1 per cent), and Romania (68.4 per cent).
- 12.
See the tax wedge on labour in 2015 in the European Commission’s tax and benefits database, based on OECD data, http://europa.eu/economy_finance/db_indicators/tab/
- 13.
The small negative effect in Finland is not statistically significant (i.e., essentially zero).
- 14.
The presence of a significant interaction indicates that the effect of one predictor variable on the independent variable differs when the other predictor variable varies—the latter variable being sectors and occupations in this analysis.
- 15.
We dropped Malta and Cyprus from this analysis.
- 16.
Our regression analysis controls for these differences.
- 17.
Non-significant values should thus be considered as indicating the same wage penalty as manufacturing in the given country cluster.
- 18.
Source: OECD International Direct Investment statistics database.
- 19.
Non-significant values should thus be considered as indicating the same wage penalty as professionals in the given country.
- 20.
The capital-productivity method leads to somewhat more conservative estimates of wage gaps, but these might be related to a reliance on observed differences in value added that reflect differences in wages rather than differences in actual labour productivity. Moreover, as indicated by the significant undervaluation of wages in Ireland and Luxembourg, the results are distorted by the recording of profits in favourable tax jurisdictions (hence the large recorded return on capital stock in these countries).
- 21.
See the tax wedge on labour in 2015 in the European Commission’s tax and benefits database, based on OECD data, http://europa.eu/economy_finance/db_indicators/tab/
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Drahokoupil, J., Piasna, A. (2019). Dependent Market Economies and Wage Competition in Central and Eastern Europe. In: Gerőcs, T., Szanyi, M. (eds) Market Liberalism and Economic Patriotism in the Capitalist World-System. International Political Economy Series. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-05186-0_4
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