Do low minimum wages disserve workers? A case study of the Czech and Slovak Republics

ABSTRACT

This article analyses the effects of minimum wage on employment in the Czech and Slovak Republics based on 2005–17 EU-SILC data. Our results contribute to the scant literature on minimum wage effects in the Central and Eastern European (CEE) region. While prior empirical findings concurred with the effects of minimum wage on labour market outcomes in CEE countries when the minimum wage is relatively high, there is ambiguity when the minimum wage is relatively low. In Slovakia we find that regular minimum wage hikes had insignificant effects on employment. Similarly, we find no negative employment consequences from irregular hikes in the comparatively low minimum wage (MW) of the Czech Republic. Moreover, the groups assumed to be most affected by MW hikes did not experience greater negative consequences following hikes when compared to the overall population of workers in either country.

KEYWORDS:

• Minimum wage
• employment
• unemployment
• EU-SILC data
• Central and Eastern European region

Disclosure statement

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

Notes

1 Sub-minimum wage tariffs for young and disabled workers were used to supplement the system in both countries; these rates were introduced in 1991 and were valid until 2007 in SK and 2012 in CZ. The sub-minima for disabled workers were reintroduced in 2015 in CZ and cancelled again in 2017.

2 Zero growth in MW has roughly corresponded with more right-wing oriented governments. Rises prior to 2008 occurred under social democratic governments and, after 2013, under a primarily social democratic government followed by a caretaker government appointed by President Miloš Zeman, who previously belonged to the Czech Social Democratic Party, and a populist government without a clear right/left orientation headed by Andrej Babiš.

3 We use data from Eurostat, cross-sectional EU-SILC – Cross UDB 2005–17 and longitudinal EU-SILC – Long 2008–17 (2017 is available for CZ only), March 2019 version.

4 The effects of MW increases are assumed to be driven by the demand side, for which the gross value rather than the net value is relevant. Moreover, the data does not provide information on net wages for SK.

5 Months worked part-time were counted at a weight of 0.5—the modal value of hours worked part-time is 20.

6 Movements into disability were excluded as this is obviously independent of labour market conditions.

7 In the Czech Republic, MW increased in a month other than January twice: in 2006, both in January and in July, and in 2013 in August but not in January. To account for this irregularity, the calculations of minimum wage status groups use the weighted-average values of the MW in these years.

8 The thresholds were adjusted for youth according to their sub-minimum wage tariffs in relevant years.

9 At-risk-of-poverty rate is an official indicator used by Eurostat. Inability to make ends meet is the share of respondents living in households reporting (great) difficulty making ends meet (the two lowest categories). Low work intensity is defined differently than in Eurostat; here, the indicator captures the share of respondents living in a household where members aged 16–64 worked on average less than half the year in period t.

10 Even without MW hikes, low-wage earners have lower subsequent employment probabilities than workers who are higher in the wage distribution (see, e.g., Stewart, 2007). Low-wage workers are more likely to be high-turnover workers for unobserved reasons, probably the same reasons they earn low wages.

11 It takes the value of 1 if the individual remained employed full-time and 0 if she/he changed to part-time employment, self-employment, unemployment or dropped out of the labour force in t+1.

12 We have not estimated a logit or probit model for employment effects as the number of observations for each individual is small and the estimations would yield inconsistent results (Heckman, 1981, p. 134). A further disadvantage of logit fixed-effects (FE) estimates is that they ignore individuals for whom the dependent variable does not change through the sample period. The appropriateness of the FE model stems from the nature of the data and the rationale behind the subject. From the methodological point of view, our data fails to meet the basic assumption of random-effects (RE) models, that unobserved random variables and observed explanatory variables are not correlated. The feasibility of the FE model was tested using the Durbin-Wu-Hausman test; the results rejected the null hypothesis of consistency among both the FE and RE estimators at the 1% significance level.

13 Table 3 shows only the coefficients for the main MW variables of interest. The coefficients for the full list of independent variables of the baseline regressions are presented in online supplement 2.

14 Note the very small sample sizes in the case of workers with only primary education and workers aged 15–24.

15 The intervals are defined as follows: $MWS{T}_t^{1a}$ for w_it < MW_t and $MWS{T}_t^{1b}$ for MW_t ≤w_it ≤ MW_t+1. We relax the downward restriction on the sample (0.5MW_t) so that the category MWST^1a covers all full-time workers in t. Note the low number of observations in both MWST^1a (568 in CZ, 960 in SK) and MWST^1b (200 in CZ, 379 in SK).

Additional information

Funding

The work was supported by the Czech Science Foundation under grant number 18-07036S. The EU-SILC datasets were made available on the basis of contract no. 247/2019-EU-SILC-HBS between the European Commission, Eurostat, and the Institute of Sociology of the Czech Academy of Sciences. The responsibility for all conclusions drawn from the data lies entirely with the authors.

Notes on contributors

Kamila Fialová

Kamila Fialová is a researcher at the Institute of Sociology of the Czech Academy of Sciences. Her specialisation covers macroeconomic and labour market research and her main academic interest is the labour market institutional environment, informal employment, low-wage employment, part-time work, poverty, inequality and well-being.

Martina Mysíková

Martina Mysíková is a senior researcher at the Institute of Sociology of the Czech Academy of Sciences. Her field of specialisation is poverty and income inequality, well-being and job satisfaction, household economics, and labour economics. She participated in various national and European research projects focused mainly on poverty, living conditions and satisfaction of individuals and households, work and job values, or (un)employment.