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ABSTRACT
We examine sources of income and wage inequality in three transition countries. Our results reveal that much lower inequality of disposable income in Slovenia than in Croatia and Serbia can mainly be explained by more equally distributed net wages across households. This is partly attributed to the lower fraction of households with very low work intensity in Slovenia than in the other two countries, combined with a higher redistributive capacity of taxes and social transfers. We find that educational qualifications and both quantity and quality of work, play an important role in explaining differences in wage inequality in all three countries.
Acknowledgments
This research was funded by the London School of Economics’ International Inequality Institute (LSE III) (http://www.lse.ac.uk/International-Inequalities), the Atlantic Fellowship Program for Social and Economic Equity (https://www.atlanticfellows.org/first-cohorts) within the project titled “Goodbye
Tito: The role of diverging welfare state trajectories on income inequality in four former Yugoslav republics.” The paper has benefited from the comments of participants at the two LSE III Seminars (January and June 2018), the ESPAnet Conference 2018 (13–15 September, Florence) and the LSE FREN workshop 2018 (22 October, Belgrade). Authors is also grateful to Peter Sanfey and Will Bartlett, anonymous reviewers and the editor for useful comments on an earlier draft on this paper.
Disclosure Statement
No potential conflict of interest was reported by the author.
Notes
1. Unemployment rate according to: https://www.theglobaleconomy.com/rankings/Unemployment_rate/
2. Unemployment rate (15–74 years old) for 2015 according to Eurostat database: https://ec.europa.eu/eurostat/databrowser/view/une_rt_a/default/table?lang=en
3. Income in-kind was not included in total income following Eurostat methodology.
4. Hoffman, Bićanić, and Vukoja (Citation2012) examined wage inequality and wage differentials in Croatia from 1970 to 2008 and showed that income inequality increased primarily due to increased wage differentials within educational and vocational groups.
5. A recent study which compares sources of income inequality in Lithuania with other EU countries in 2015 shows that taxes have higher and social transfers have lower redistributive capacity in old EU countries than in new EU countries (Černiauskas and Čiginas Citation2019).
6. The Gini correlation is a form of rank correlation coefficient, similar to Pearson’s rank correlation. It measures the extent to which the relationship between Yk and the cumulative rank distribution of Y coincides with the relationship between Yk and its own cumulative rank distribution: Rk = cov(yk, F)/cov(yk, Fk). Rk assumes the range [−1, 1].
7. The first adult member of a household is given a weight of 1, other adult members of a household are given a weight of 0.5, while a weight of 0.3 is given to each child under the age of 14.
8. The proportion of excluded individuals is highest in Serbia but it does not exceed 0.4% of the sample.
9. Lower shares of public pensions in total income in Slovenia and Croatia (14.3% and 18.9% respectively) is partly explained by the fact that pensions from private pension plans are included in capital incomes, while private pensions (Pillar II) in Serbia do not exist.
10. The same result is obtained when decomposing gross income inequality by income sources. These results are available upon request.
11. It should be noted that redistributive effect of taxes is largest in Slovenia.
12. These are the persons under the age of 60, living in households in which adults (aged 18–59) worked, on average, less than 20% of the total number of months in which they could have worked during the reference period (Eurostat Citation2012).
13. Eurostat database based on 2016 SILC data: http://appsso.eurostat.ec.europa.eu/nui/submitViewTableAction.do.
14. We find that disposable income inequality is much lower than wage inequality. Wage inequality in turn is much lower than self-employment and capital income inequality and the inequality of private transfers (García-Peñalosa and Orgiazzi Citation2013). High inequality of these three components are both due to a large fraction of the population having no income from these sources, but also due to the large inequality that prevails for those with positive incomes.
15. Author calculations. Data on total spending on social assistance and child allowance for Slovenia based on Ministry of Labor, Family, Social Affairs and Equal Opportunities, for Croatia based on Inchauste and Rubil (Citation2017); for Serbia based on Ministry of Finance. Data on social assistance per capita as % of average wage for Slovenia based on Ministry of Labor; for Serbia based on the World Bank (Citation2015b).
16. World Bank ASPIRE database: http://datatopics.worldbank.org/aspire/indicator/social-assistance. Data for Croatia refers to 2014, for Serbia to 2015. Data for Slovenia is not available.
17. The mean log deviation (GE(0)) is sensitive to earnings gaps at the bottom end of wage distribution, the 1/2CV2 measure (GE(2)) is sensitive to gaps at the top end, while the Theil index (GE(1)) is equally responsive across the whole distribution.
18. Differences can also be explained by varying age groups (all ages for income inequality vs. 16–64 for wage inequality decomposition), treatment of tax and social security contribution (gross wages in income inequality and net wages in wage inequality decomposition) and the use of equivalent scales in income decomposition.
19. For data on collective bargaining coverage rates in Slovenia and Croatia see https://ilostat.ilo.org/topics/collective-bargaining/. For Serbia see Annual Review 2018 of Labor Relations and Social Dialog (Friedrich-Ebert-Stiftung Citation2018). For data on union density see European Foundation for the Improvement of Living and Working Conditions (EUROFOUND Citation2020) European Working Conditions Survey 2020, online data: https://www.eurofound.europa.eu/data/european-working-conditions-survey.
20. It is worth mentioning that our estimates of returns to education are not fully comparable with other studies due to a different set of variables included in regressions. For Croatia, estimates of returns to tertiary education are available only for years of schooling. The only exception is a report by Nestić (Citation2005) which shows that university graduates earn 28% more than those with completed general secondary education.
21. It should be noted that, although the same method is applied (Fields Citation2003), these results are not comparable with ours as the authors decompose disposable income and used different regression factors.
22. OECD, PISA 2018 Database, Table II.B1.2.6; Available at: https://www.oecd.org/pisa/data/. The index ranges from −1 to 1. A negative value indicates that low performers are more prevalent amongst the most disadvantaged students; a positive value indicates that low performers are more prevalent amongst the most advantaged students. It is null in the absence of socio-economic inequalities in the probability of being a low/high performer.