Based on analyses of the 383 districts as units of analyses, the Pearson correlation of urbanisation with non-

employment is 0.136 (which is with 99.3% certainty above 0) and with economic development it is 0.643 (with more than 99.9% certainty above 0).

� �� �� �� �� �� �� ��% manufacturing ��% manufacturing at least some tertiary secondary completed primary completed no completed education

figure 7.9 Women’s employment predicted, varying education and the number of light manufacturing jobs

Women's probability of employment (%)

7.1 To the extent that the male labour supply in a community is more depleted, the greater the effect of a woman’s education ✓ on her probability of being gainfully non-agriculturally employed in that community.

7.2 To the extent that the number of jobs in the service sector in a community increases, the smaller the effect of awoman’s

✓ education on her probability of being gainfully non-agriculturally employed in that community.

7.3 To the extent that the number of jobs in the light manufacturing sector in a community increases, the larger the x difference in women’s probability of being gainfully non-agriculturally employed between primary educated women and

women without education, and the smaller the difference between primary educated women and women with a higher education.

7.4 To the extent that the urbanisation of a woman’s living environment increases, the smaller the effect of a woman’s ✓ education on her probability of being gainfully non-agriculturally employed.

Box 7.5

are active in one of the following three trades: ‘spinners, weavers, knitters, dyers and related workers’, ‘food & beverage processors’, and ‘tailors, dress makers, sewers, upholsterers & related workers’.

differs. The difference in service sector size is however much more relevant for women with lower educational levels. They seem to be harmed most by the absence of service sector jobs.

The results for the embedding effect of light manufacturing do not support the expectations. Where it was expected that the size of this sector would have a positive effect on the chances of women with primary education, it mainly affects higher-educated women. The more light manufacturing jobs, the larger the effect of education for those with tertiary education, especially when compared with the smaller effects enjoyed by lower education levels. Given the main coefficients (see Appendix 7.2), this means that the employment level is similar for women with no, primary or secondary education regardless of the number of light- manufacturing jobs, but women with tertiary education have a considerably higher likelihood of being employed in districts with more manufacturing jobs (see Figure 7.9). This indicates that employers in the manufacturing industry draw from a group of tertiary-educated women who are not employed in the service sector. Possibly, this group consists of tertiary-educated women who want to work, but will not or cannot work outside the house, for instance because of the presence of children or restrictive values and norms. In those cases the manufacturing industry offers opportunities to work at home in the food and textile industry (see Section 4.7.2, were the operationalisation is discussed).96

The multivariate model also shows the expected negative interaction of the degree of urbanisation with the effect of education. That this effect was not found in the bivariate model is not really surprising, because the degree of urbanisation is not only positively correlated to the presence of suitable jobs, but also, for example, to non-employment and economic development,97 _{which show opposite effects. In the bivariate model, these are not filtered out.}

Overall, the analyses in this section tend to support three of the hypotheses formulated above (see Box 7.5 for the hypotheses and outcomes). Only Hypothesis 7.3 was not supported by the results. It seems that the underlying assumption about preference formation has to be adjusted. Given social restrictions, some tertiary-educated women might choose a manufacturing job at home (or not at all) instead of one in the service sector, because manufacturing gives them the opportunity to work from home. These women might only enter the labour market if such possibilities exist. Simultaneously, the results for the service sector and urbanisation support the idea that employers in these sectors prefer higher-educated women over women (and men) with a lower education level. For light manufacturing this seems to hold as well: when tertiary-educated women want to work in manufacturing, it seems they are able to find a job. Generally, tertiary-educated women who want to work outside the home are relatively immune to changes in the non-employment of men and the presence of service sector jobs, as long as there is a shortage of people with tertiary education. Lower-educated people are affected more by changes in labour market structures. That employers prefer to employ relatively higher-educated people underscores the importance of receiving education in finding a job and gaining employment.

7.5.4 CULTURAL NORMS

With regard to gendered public presence norms, the effect of education does indeed seem to be less strong in districts more favourable to women entering the labour market (Table 7.1). However, once translated and ‘delinearised’ to probabilities, the relationship between education and employment shows more similarities than differences between districts with less or more restrictive norms. It seems that the norms on entering the public sphere also matter for women with tertiary education. However, the effect is proportionally larger for women with lower education levels: comparing districts with a low public presence (one standard deviation below average: 0.05) and with a higher one (one standard deviation above average: 0.37) shows that women’s chances of being employed increase by more than 100% for women without education or with primary education, while for women with secondary and tertiary education the chance increases by 44% and 19% respectively.98

The care role norm variable shows a more mixed result. Before controlling for other factors, the effect is opposite to what was expected, with especially the higher employment of tertiary- educated women in more traditional areas found to be remarkable (Figure 7.10a). Once the

99 For Figure 7.10a the average scores are used on all