Analogue to the previous section analysing training of employees of higher education, in this section, institutional determinants for the gender training gap among employees with- out university education are examined. Table 11 shows that a multilevel approach is ap- propriate for the data of this subsample as well, and that model fit improves with increas- ing complexity from Models 1 to 4. Model 1 shows that among the lower educated, only about 46 employees train for every 100 employees that do not take part in training. This shows that employees without a degree train considerably less than employees with univer- sity education where 124 employees train for every 100 employees who do not train.
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Table 11: Model development for employees with no degree – level 1
1 2 3 4 FIXED PART Female 0.846*** 0.886** 0.885** Level-1 controls Constant 0.462*** 0.717 0.700 0.721 RANDOM PART SD slope 0.205*** 0.205*** SD intercept 0.776*** 0.726*** 0.773*** 0.796*** Corr. slope-int. -0.681*** MODEL STATISTICS Log likelihood -34626.3 -32641.8 -32603.6 -32598.9 Parameters 2 41 42 43 LR test *** *** *** *** Individuals 62,218 62,218 62,218 62,218 Countries 22 22 22 22
*** p<0.01, ** p<0.05, * p<0.1; means that level-1 controls are included; reference model for the likelihood-ratio test of Model 1: conventional logit model, reference model for Models 2 to 4: always the previous model.
Contrary to employees holding a degree, among lower educated employees, females are less likely to train than men. Model 4 indicates women’s training odds are 0.885 times lower than the odds of men. The statistically significant parameters in the random part of the model further suggest that in this employee group, overall training levels as well as the gender training gap significantly vary between countries. In contrast to the results for high- ly educated employees, both random measures are negatively correlated. This suggests that, for this specific employee group, countries with higher overall levels of training tend to show lower training odds for women compared to men. Taking into account the random and the fixed part of the “Female” dummy leads to the conclusion that for this employee group, at country-level, 95% of the odds ratios for women lie between 0.593 and 1.324. These results are slightly more pessimistic for women than the results displayed in Figure 3, which indicate the lowest female odds ratio to be 0.652 for the Czech Republic and highest to be 1.368 for Latvia.
Table 12 shows the results of the models including the institutional variables.32 It appears that labour market institutions, while being related to overall levels of training among the lower educated do not have an impact on gender differences in training. High mean tenure appears to be strongly negatively related to training of employees without university edu- cation: For every year that mean tenure rises, training odds are lowered by the factor 0.852; for every change in one standard deviation women’s training is lowered by the factor 0.8522.504=0.670. High union density seems to be related to higher overall training levels
32 The complete tables documenting the process of the level-2 model development for employees without a
91 among employees without university education: For every standard deviation union density increases, training odds increase by the factor 1.01321.652=1.325. The strong effects of mean tenure and union density are reflected in the random part of the models. The consid- erable drops in the intercepts’ standard deviation suggest that differences in mean tenure and union density account for cross-country differences in training levels. While bargain- ing coverage is not related to training of the lower educated, a high share of female union members is. Although the model including this institutional measure reveals that overall levels of training do not significantly increase with high shares of female unionists, the cross-level interaction with the “Female” dummy – although only significant at 11% – supports the assumption that a high rate of women in unions has a positive impact on fe- male training participation: For every standard deviation the share of female unionist rises, training odds for women increase by the factor 1.0108.718=1.090, which can be interpreted as an effect of moderate size.
When comparing these findings to the results for highly educated employees in Table 10, it becomes obvious that correlations between training and labour market characteristics vary for employees of different educational backgrounds. High mean tenure, which is generally related to lower training levels, appears to present an additional disadvantage for women of higher education compared to men, while it does not have significantly differing effects on men and women of lower education. While for the lower educated, a high union density is consistently related to a higher training probability, among the highly educated, union strength basically favours men. Moreover, while a high share of women in trade unions is positively related to female training participation among the lower educated, it is neither significantly related to training levels nor gender differences among university graduates. The results on the educational system reveal that the importance of the university system is not related to training participation of employees without a degree. On the contrary, the importance of the vocational system appears to have an effect on both the overall level of training and the gender training gap, as indicated by the statistically significant odds ratios of the institutional coefficient and the interaction term as well as the reduction of the ran- dom parameters. For every percentage point the share of pupils in vocational programmes rises, male training odds rise by the factor 1.052; for every standard deviation by the factor 1.0529.983=1.659. However, women benefit significantly less from vocational systems as their training odds compared to men are lowered by the factor 0.990. Still, the overall rela- tionship between the vocational system and training is strongly positive for women as well: With every increase in standard deviation women’s training increases by (1.052*0.990)9.983=1.500. Overall, the results on the educational system are similar to the results for employees with a degree displayed in Table 10.
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Table 12: Summary of the level-2 model development for employees with no degree
Labour Market Educ. System Support for Women
Model M. ten. U. dens. Bar cov. F. u. m. Uni Vocat. Chc. <3 Chc. ≥3 M. leave Ch. ben. Empl. e. Deg. e. Pay e.
FIXED PART Female 0.886** 0.887** 0.884** 0.894** 0.885** 0.884*** 0.885** 0.886** 0.886** 0.885** 0.883** 0.884** 0.885** Institution 0.852*** 1.013* 1.000 1.002 1.002 1.052*** 1.014 1.012 1.027*** 0.927 1.037 0.997 0.971 Female x Inst. 1.010 0.990** 1.010 1.003 RANDOM PART SD slope 0.206*** 0.203*** 0.209*** 0.196*** 0.205*** 0.179*** 0.204*** 0.205*** 0.205*** 0.204*** 0.191*** 0.191*** 0.206*** SD intercept 0.657*** 0.770*** 0.695*** 0.805*** 0.801*** 0.655*** 0.786*** 0.747*** 0.676*** 0.798*** 0.777*** 0.788*** 0.751* Corr. slope-int.
*** p<0.01, ** p<0.05, * p<0.1; means that a correlation between slope and intercept is considered; models control for previous education, age (raw and squared), tenure, part-time employment, occupation, industry, size of the local unit, degree of urbanization, year and interview method; the complete level-2 model development is displayed in Tables 36-39 in the Appendix.
93 The measures of support for women do not show many statistically significant relation- ships to training. Maternity leave seems to raise overall levels of training considerably, without differing in the effects on men and women. For every standard deviation paid ma- ternity leave increases, training odds of lower educated employees increase by the factor 1.02713.232=1.423.
When included without a cross-level interaction term, employment equality appears to raise overall levels of training participation (see Table 39 in the Appendix). However, as model fit improves with the interaction term for females, the model including the interac- tion is preferred and thus displayed in Table 12. The odds ratios indicate a strongly positive relationship for men (significant at 13%) but an even stronger positive effect for women (significant at 12%): With every change in standard deviation, men’s training odds in- crease by the factor 1.03726.663=2.635 while women’s training odds even increase by the factor (1.037*1.010)26.663=3.435. Degree equality is not significantly related to training levels. The interaction between the “Female” dummy and degree equality is significant at 12% error probability, indicating that in countries where the level of women holding a uni- versity degree is high, training odds for women without a degree rise in comparison to equally qualified men. The effect appears moderate in size (1.0036.265=1.019). Still, it re- flects the result on this indicator from the country-level analyses shown in Table 6.
Overall, the result on the indicators for women’s support slightly differ to the ones for em- ployees with university education displayed in Table 10: While childcare for children un- der the age of three is related to a comparative training disadvantage of women with uni- versity education, it does not have an impact on gender equality among the lower educated. However, for employees of lower education, childcare for children aged three and older appear to raise overall training levels while it does not seem to have an impact on training levels of the highly educated. Child benefits appear to be negatively related to relative training participation of highly educated women but not of lower educated women.