Mensajes de estado e información de diagnóstico

different effects across contexts. 86 Although the review of Pettit & Hook focuses on industrialised (OECD) countries, their conclusion can be easily extended to women’s employment in Muslim countries or developing economies, a smaller and also fragmented literature.

% of all women in the country

Darker parts of the bar represent the percentage of the women being employed, the total bar the percentage of women with the indicated education level: no education completed or tertiary education

� �� �� �� �� ���

Kyrgyz Republic - Tert.Kyrgyz Republic - No Kazakhstan - Tert.Kazakhstan - No Azerbaijan - Tert.Azerbaijan - No Malaysia - Tert.Malaysia - No Jordan - Tert.Jordan - No Lebanon - Tert.Lebanon - No Turkey - Tert.Turkey - No Indonesia - Tert.Indonesia - No Syria - Tert.Syria - No Algeria - Tert.Algeria - No Zanzibar - Tert.Zanzibar - No Egypt - Tert.Egypt - No Nigeria - Tert.Nigeria - No Bangladesh - Tert.Bangladesh - No Tunisia - Tert.Tunisia - No Djibouti - Tert.Djibouti - No Pakistan - Tert.Pakistan - No Morocco - Tert.Morocco - No Sierra Leone - Tert.Sierra Leone - No Eritrea - Tert.Eritrea - No Mauritania - Tert.Mauritania - No Senegal - Tert.Senegal - No Yemen - Tert.Yemen - No Burkina Faso - Tert.Burkina Faso - No Mali - Tert.Mali - No Guinea - Tert.Guinea - No Niger - Tert.Niger - No Chad - Tert.Chad - No

figure 7.2 Employed women by educational group and country

results of this are graphed. The figures are thus controlled for all other variables in the model (see Chapter 6).

levels should then be similar across countries. For instance, the relative size of the darker bar would for instance triple when comparing uneducated and tertiary-educated women. Such a consistent pattern is not to be found, indicating that the effect of education at least differs across countries. Take Nigeria and Yemen. In Nigeria, relatively many women without education are employed (>40%) and for tertiary-educated women this is about two thirds, while in Yemen hardly any woman without education is employed, but about half of the small number of women with tertiary education is employed in Yemen. This indicates that the effect of education is relatively small in Nigeria when that country is compared to Yemen.

These figures are based on statistics that are not controlled for other important

characteristics the women might have, which might (partly) explain these differences, and these percentages only present country-level differences. One way to assess whether education effects differ per macro-level context (after control for other relevant factors) is to re-run the final model as presented in the previous chapter, but now with random slopes for education at the district or country level. The results of these analyses are shown in Table 7.1, and it is clear that all the variances are statistically significant. The effects of each level of education on women’s employment differ substantially between countries as well as districts. The positive coefficients, which become larger for each level of education, furthermore indicate that the effect is fanning out (see Jones, 2007): the further the educational level from the reference category, the larger the differences in effect between areas.84 _{This is also one of the conclusions}

that can be derived from Figure 7.3, which graphs the employment likelihoods (in logged odds) by educational level, per district and per country.85 _{It shows that though a few countries and}

districts show some (minor) negative effects of education, the effects are positive in almost all areas. The maximum difference between tertiary and no education is about 5 logged odds at the country level and somewhat over 6 at the district level.

So far, I have shown that education has a strong and positive effect on women’s employment in general, and that the effect is not the same everywhere. Interesting as this finding might be, it is plainly not informative. To transcend killjoy remarks, such as ‘the effects of education differ’, it is pivotal to understand why the effects of education differ, and the subsequent parts of this chapter therefore focus on the question: What factors might explain the variance in the slopes of education?

7.3 LIterature overvIew

In their 2005 review of the literature on women’s employment, Pettit & Hook aptly summarise the condition of the field (780): “The polarization of research on women’s employment into traditions emphasising either micro or macro determinants of women’s employment ignores the relationship between individual employment decisions and institutional conditions”.86

Consequently, there is hardly any systematic assessment of, or theorisation on, how the effect of education depends on the macro-level context, something already observed by Pampel & Tanaka (1986: 601) in their classic study about the relationships between economic development and female labour force participation: “Even though empirical models have not tested for interactions, there are several reasons for supposing they exist”. A closer look at the studies discussing women’s employment in Muslim countries lays bare some of these reasons, these ideas about embedded effects.

At least four authors link the effects of education to the cultural context,87 _{more specifically}

the strength of a woman’s role as mother and housewife, but each in a different way. More stringent cultural norms are related to the influence of education through employer preferences, through household restrictions, and what is taught at school. In what can be called the first line of reasoning that connects education to culture, Abu-Lughod (1998) and Shakry (1998) refer to the hybridisation of modernisation, which signifies that modernisation can accommodate more traditional ideas in different combinations. Shakry (128–9) discusses colonial Egypt specifically, where the education of women was seen as important to elevate their moral and material position. However, the domain of women was not reconstructed and women remained confined to the private sphere. Translated, this means that even if women’s educational level improves, society (including employers) may still uphold the idea that a woman’s place is in the private

variable Slope coefficients of education variances of slopes

B-coeff. s.e. variance s.e.

Country-level random slopes

Education (ref = less than primary completed)

primary completed, secondary not 0.478 *** 0.079 0.261 *** 0.080

secondary completed, no tertiary 0.934 *** 0.135 1.161 *** 0.347

at least some tertiary 1.828 *** 0.165 2.145 *** 0.669

district-level random slopes

Education (ref = less than

primary completed, secondary not 0.639 *** 0.039 0.338 *** 0.042

secondary completed, no tertiary 1.493 *** 0.078 1.256 *** 0.129

at least some tertiary 2.925 *** 0.107 2.394 *** 0.226

Notes: (1) * p<0.05 ** p<0.01 *** p<0.001; (2) These coefficients presented here are based on models of which all specifications are the same as Model 4 in Table 6.2. The only difference is that for the three education dummies the slopes were allowed to vary at the macro-level context. The variances of these slopes for the district and country level are derived from two separate models.

table 7.1 Variance coefficients of the effects of education on women’s employment

-� -� � � � � � � � � no education A

primary secondary tertiary

figure 7.3 Effects of education on employment

District level

no education

B

primary secondary tertiary -� � � � � � � � Country level

87 These authors do not use the terms of interactions or