density of wages close to the increased minimum wage was slightly greater than at other points of the

wage distribution. The sensitivity analysis calculations with 75% and 125% of the minimum wage threshold

(Figures 14 and 15 in the appendix) show that the shares of workers who earned less than both the current

and the previous year’s (proportionally adjusted) minimum wage were higher than in the benchmark case:

on average, the shares were 85% (78%) and 73% (76%), respectively.

Figure 9: Decomposition of the average (over 2004-2012) incidence of violations into violations of both

the current and the previous year’s minimum wage, and the current minimum wage only

Monthly violation (V m 0 ) Hourly violation (Vh 0 )

Source: Own calculations on the EU-SILC data.

4.2 Individual and workplace characteristics of workers affected by non-compliance

In this subsection we seek to identify the individual and the workplace features related to minimum wage

violations. To this end we estimate a probit regression for the probability of non-compliance in hourly wage

terms, v h 0

, on a pooled dataset with country and time controls. We also estimate separate models for each

country. The results are presented in Table 10 in the appendix, and in most cases the relative importance

of various regressors is preserved.

The set of significant categorical independent variables is similar to those found in the Mincerian wage

regressions, which are broadly used in the literature to study determinants of wages. The marginal effects

obtained from the pooled regression (see Figure 10) show that the youngest workers (aged 25-30) faced

the highest, and workers aged 41-50 faced the lowest probability of being affected by non-compliance (respectively

1.3 pp. higher and 0.4 pp. lower than for workers aged 31-40). However, the relationship between

the probability of being affected by non-compliance and age is not monotonic: i.e., workers aged 55 and

above were by 0.2 pp. more likely to have experienced it than workers aged 31-40. Importantly, women were

significantly more likely to been affected by non-compliance than men: the marginal effect for women was

2.1 pp., which we think is relatively strong. Our results also point to the importance of education and skills.

Across the CEE10, workers with medium education had a 1.8 pp. lower probability of being affected by

non-compliance than workers with low education, while the effect for workers with tertiary education was

twice as high (3.5 pp.). 17 Moreover, workers in high-skilled occupations (ISCO 1-3) were much less likely (by

6.3 pp.) than workers in elementary occupations to be affected by non-compliance (the strongest marginal

effect in the model). Negative and noticeable marginal effects are also found for machine operators (2.7

pp.); clerks, sales, and service workers (2.7 pp.); and craft workers (2.6 pp.).

17 We define low education as levels 1-2, medium education as levels 3-4, and high education as levels 5-6 of the

ISCED classification.


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