Labor Market Outcomes of Unemployed Czech and Polish Workers: Catching-Up with Austria?

ABSTRACT

This paper examines the micro-determinants of unemployment durations and exits in Czechia, Poland and Austria. Our hazard estimates utilize EU-SILC data and identify national specificities in which individual, household and regional characteristics affect labor market outcomes. This concerns particularly the effects of education on job-finding probability, which are nearly absent in Poland, as opposed to Austria and, even more so, Czechia. However, the key results are common across countries: Unemployed women are less employable than unemployed men, even after controlling for explanatory covariates and the disproportionally high female selection to inactivity. The analogous findings apply to the elderly and those in poor health.

KEYWORDS:

• Central Eastern Europe
• hazard model for competing risks
• survival and cumulative incidence functions
• unemployment

JEL CLASSIFICATION:

• C34
• C41
• J01
• J64

Acknowledgments

The longitudinal datasets of the European Union Statistics on Income and Living Conditions (EU-SILC LT UDB 2008–2020, version of 2022-03) were made available to the authors on the basis of research project proposal No. 126/2022-EU-SILC, granted to ŠKODA AUTO University by the European Commission, Eurostat. The responsibility for all results and conclusions drawn from the data lies entirely with the authors.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. Unlike Czechia and Poland, the net immigration for the EU as a whole is steadily positive - see World Bank databases (https://data.worldbank.org/indicator/SM.POP.NETM?locations=EU-CZ-PL). Further, Poland records the highest share of temporary jobs in the European Union (over 20% of total employment). Previously jobless people with the lowest education levels are overrepresented among temporary workers (Lewandowski and Magda 2018). The expansion of temporary jobs contributes to the fluidity of the Polish labor market (measured by worker flow rates between employment, unemployment and inactivity), which is persistently higher than in Czechia (Garda 2016; Galuščák, Šolc, and Strzelecki 2021).

2. The inflow into the disability pension system represented for decades an increasing fiscal burden for Austria. In 2014, temporary disability pensions were abolished for younger cohorts and the principle of rehabilitation before pension was enforced. Most persons affected by the reform started to receive rehabilitation benefits instead of disability pension, but they did not work anyway (Fuchs, Zolyomi, Danaj, and Scoppetta, 2018). See also Staubli (2011); OECD (2015); Kuhn, Staubli, Wuellrich, and Zweimüller (2020) for more discussion on the adverse effects of disability insurance programs on labor market participation in Austria.

3. Tax incentives to support employment of older Austrian workers were abolished in 2009. The government instead attempted to smooth their transition into retirement by offering compensations for a drop in earnings in case of reduced working hours (for women aged over 53 and men aged over 58). Nevertheless, the problem of their long-term unemployment persisted and might be even greater than captured by the data on low aggregate unemployment, as many of them gradually exhausted their unemployment benefits and were no longer listed as recipients of unemployment insurance - see, e.g., Boheim (2017); FMSA (2021) for more discussion.

4. The Czech health variable is burdened by a high item non-response, so we excluded an additional 22% of unemployment spells from the Czech sample.

5. We cannot distinguish Austrian and Polish unemployed homeowners from those unemployed paying a mortgage over the 2005–2006 period. For that period, the HH financial burden in Poland and Austria includes only the unemployment spells of rent payers.

6. We consider the national median of equivalized household disposable income (total disposable household income equivalized by the OECD-modified scale) as a benchmark. The national medians are obtained from the cross-sectional EU-SILC.

7. See Kalbfleisch and Prentice (2002) for generalized formulation of the cause-specific model and Putter, Schumacher, and van Houwelingen (2020) for the treatment of the sub-distribution model.

8. Suppose that the sub-hazard ratio for a woman’s unemployment spell terminating in an exit to employment is 0.65. The accurate interpretation should first refer to the termination of a female unemployment spell via employment relative to the female risk set. Second, one would have to refer in an analogous manner to the termination of a male unemployment spell with the same individual, household and regional characteristics. Relating these two results then yields the odds of 0.65:1. We do not apply such interpretation throughout the text. We would instead report (somewhat inaccurately) that the odds of terminating a female unemployment spell via employment are 35% lower relative to a male unemployment spell. Or we would say that the female’s exit odds from unemployment to employment are 35% lower relative to an unemployed man.

9. For instance, if the value of a male CIF is F₁(6)=0.4, this would mean that the estimated probability of a male unemployment spell terminating in an exit to employment during the first six months of an elapsed unemployment duration is 0.4. Or, for simplicity, we would say that 40% of unemployed men exit until t=6 via employment. The results for survival functions indicate analogously the probability of an unemployment spell continuing.

10. Each child diminishes the odds of an unemployment spell terminating in an exit to employment by precisely 5.9%.

11. The countries are nonetheless similar in the absence of any disadvantage in the job-finding odds of an unemployed person living in a thinly populated area, probably due to commuting to work.

12. These results do not exclude that Austrian unemployed workers suffer, in some cases, relatively more from long-term unemployment: Specifically, this concerns the relative exposure of those aged 60–65 to extremely long unemployment: In Austria, 25% of their unemployment spells remain open longer than 18 months, which is a much worse result compared to Czechia (see Table 3).

13. Table 3 involves many additional insights into the specificity of unemployment durations among the various socioeconomic groups. Some of them are striking: For instance, more than 25% of unemployment spells of the least educated Czechs do not terminate over the entire observation period. In Austria, one-quarter of unemployment spells in the age band 50–59 remain open longer than 19 months. Nearly the same result applies to female unemployment spells in Poland.

14. The gender gap in the probability of remaining unemployed is controlled for the impacts of the explanatory covariates and corresponds to the space between the female and male survival functions. The remaining gaps to which we refer in the text are derived analogously.

15. These figures also yield results for the reference categories of unemployed individuals (tertiary education, age 35–49, good health). We do not comment on them here due to space limitations.

Additional information

Notes on contributors

Vladislav Flek

Vladislav Flek is Associate Professor in Economics at ŠKODA AUTO University. He obtained Ph.D. in Economics from the Czechoslovak Academy of Sciences. From 1997 to 2020 cooperated with Charles University in Prague as Associate Professor in Labor Economics. Between 1992 and 2007 he worked in the Czech National Bank as an economic research coordinator and an advisor to the Bank Board. Afterwards he moved to the Ministry of Finance as Head of the Organizational Committee for Euro Adoption, a post he held until 2011. Research Fellow at the Catholic University of Leuven, LICOS (1992), Stanford University, GSB (1995–1996), and University of Oxford, Pembroke College (1996).

Martin Hála

Martin Hála is Research Fellow at ŠKODA AUTO University. He received his Ph.D. in Statistics at the Charles University in Prague, Faculty of Mathematics and Physics. He teaches at the University of New York in Prague, the Czech Technical University, and the Metropolitan University, Prague.

Martina Mysíková

Martina Mysíková is Research Fellow at ŠKODA AUTO University. She obtained Ph.D. in Economics from the Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague. Since 2008, she has been affiliated with the Institute of Sociology of the Czech Academy of Sciences. She worked in the Czech Statistical Office (2004–2008) and participated in the methodology and coordination of the international household survey “Statistics on Income and Living Conditions”.