In Model 4, without the living-in-a-city variable, the logged odds of the district-level urbanisation variable are

0.756, the s.e. 0.168.

3.3 To the extent the economic development of a community/country increases, the greater the probability of awoman

✓/x being gainfully non-agriculturally employed in that community/country.

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

3.5 To the extent that the relative number of light manufacturing jobs in a community increases, the greater the probability ?✓ of a woman being gainfully non-agriculturally employed in that community.

3.6 To the extent that the urbanisation of place a woman lives increases, the greater the probability of a woman being ✓ gainfully non-agriculturally employed.

3.7 To the extent the foreign direct investments in a country increase, the greater the woman’s probability of being gainfully ? x non-agriculturally employed in that country.

Box 6.2 Demand for labour Gender equality norms Educational attainment Wealth: Economic need Women’s Employment Economic Development

larger effect was found. In the bivariate analysis the increase in logged odds on employment per standard deviation increase in district wealth is 1.53. This positive effect, however, completely disappears after control for the district-level norms and the labour market structure variables.63

Considering the multiple impacts economic development can have helps to understand this disappearing act (see Figure 6.2). Economic development has an effect on women’s employment because of the creation of more jobs in industry and services, as well as a concurrent shift in values away from traditionalism, towards the acceptance of women in the public sphere. However, the positive effect not only disappears taking into account district-level norms and the labour market structure variables, in Model 2 and Model 4 economic development even has a negative effect, which in particularly grows after the inclusion of education.64 _{Thus after}

controlling for effect through the factors mentioned above, economic development tends to decrease the need for women to enter the labour market: the (male) breadwinners seem to then be able to provide a sufficient income for the household without women earning additional income.

The three variables discussed next focus on the structure of the labour market. The male labour supply was measured by the degree of men’s non-employment and it was expected that the more men are non-employed, the lower employment likelihoods of women. In all models a strong and highly significant negative effect is found for this variable. After control (Model 4), the effect is smallest, but still the odds on employment for women living in the district with the highest non-employment are less than a quarter of those of a woman in the district with the lowest non-employment. The results show that the demand for female labour, and thus women’s employment likelihood, is clearly lower if more men are not employed.

The size of the light-manufacturing sector might be mediating between economic development and women’s employment, but the most important sector seems to be the service sector (see Section, 6.5). In Model 4, the variable’s effect is not significant (Table 6.2). Additional models, however, tend to support the expected effect of the presence of light manufacturing jobs.65 _{Furthermore, the distinction I made theoretically between heavy and light manufacturing}

labour seems justified. The former clearly has no effect on women’s (non-agricultural) employment, whereas the latter seems to do have effect (Table 6.3).

The demand for labour and the availability of jobs is also measured by urbanisation. Based on the bivariate models, I conclude that there is a large difference between women living in more rural areas and women living in more urban areas. At the micro-level, women living in a city have employment odds that are 312% higher than those of women not living in a city. After control for other micro-level differences, the effect at the household level decreases, but is still present and rather strong: women living in a city have employment odds that are 125% higher than those of women not living in a city. The effects of higher education and of having a partner employed outside agriculture have been filtered from that figure. Part of the decline in the coefficient can be indirect. Girls living in cities probably have more educational opportunities (see Huisman & Smits, 2009; Kazeem, Jensen & Stokes, 2010; Smits, 2007), and consequently higher likelihoods of employment.

In the final model, living in a more urbanised district was found to have no effect. Though economic development and the size of the service sector take away the influence of urbanisation at the district level, a reasonable effect still remains. However, when living in a city is included, the district-level urbanisation becomes irrelevant.66 _{The results for the two urbanisation}

variables strongly support the argument about the importance of proximity of women to opportunities. To a large extent the effect of urbanisation at the district level is compositional: it is not the number of cities in the vicinity, but living in the city itself that counts.

The influence of foreign direct investment is often linked to labour market structures, but it does not seem to work through them. After controlling for other factors the effect of FDI disappears, while in the bivariate analysis it makes a maximum difference of an 81% increase in women’s employment odds. Additional test show that it is not economic development or the labour market structure, as might be expected, are responsible for the decline in FDI effect. In these data, the institutionalisation of conservative Islam rather is the variable that has led to this result. It seems that the overlap between low FDI and a higher level of institutionalised

67 While the analyses here cannot fully support the idea of