Dirección de caudal (menú C1.3.1)

religiosity of the state (all religions) and finds negative effects of both, with a somewhat stronger effect of the first. The sample in that study however includes 135 countries and the political-religious variable included more levels of conservatism captured under one heading. For instance, Norway and Germany are coded as religious governments because they identify with Protestantism, but in my reasoning they would get a low score because conservative religious restrictions are relatively absent from the state structure.

(1) table 6.2, Model 4 (1) + % Muslims

variable Coeff. Coeff. full range one s.e.

State institutionalisation of conservative Islam -0.428 *** -0.208 ** -1.24 -0.38

Percentage of people being Muslim - -0.014 *** -0.88 -0.28

Country-level variance 1.062 *** 1.059 *** - -

Note: These coefficients are based on a model of which all specifications are the same as Model 4 in Table 6.2. The only difference is that in the second model an additional variable is included.

variable that measures the institutionalisation of Islam. This variable appeared to be an important explanatory factor and the results supported the idea of the importance of values. Including a population-based variable (percentage of Muslims) in the models used here also shows a statistically significant relationship. However, state Islamisation seems to be greater (see Table 6.6).80 _{Moreover, the theoretical argument for using the percentage of Muslims}

is still underspecified. Ideas about what these values concern fall back on humdrum, such as “Islam is more conservative”, and how this influence is exerted not specified beyond “through culture”. In addition, there are serious methodological problems with the usage of the ‘Muslim percentage’ variable at the country level, because the values on this variable are highly clustered in geographical patterns, which means that the variable might measure many other things related to this geographic clustering (e.g. colonial history, Arab culture, postcolonial relations, and the amount of sand in a country). For more discussions and tests on the influence of religion, see Chapter 9.

Several of the other results in this study do not refine existing knowledge, but simply contradict it. Firstly, based on ideas of rational cost-benefit reasoning on the part of women and their families, several scholars expect primary education to have a negative effect on women’s employment, because the opportunity costs of employment rise more with attaining at least primary education than the benefits of a job do. The analyses of Aromolaran (2004) and Kuepie, Nordman & Roubaud (2009) tend to support these ideas for Nigeria and Sub-Saharan West Africa (see also Lincove, 2008). I have found a very clear positive effect of primary education in all 28 countries. In terms of needs, opportunities and values, the positive net result of primary education is to be expected. Additional education does not increase the care needs of the household and working can help to meet the economic needs of the household, while a higher education level simply creates more opportunities on the labour market. For instance, primary education opens the door to jobs in which literacy is required. The empirical results in this study seem to confirm this. Secondly, in the analyses performed here, several indications were found that economic development might have a negative effect on women’s employment. In the literature, however, it is generally accepted that economic development only has a positive impact on women’s (non-agricultural) employment (Abu-Lughod, 1998; Inglehart, 1997; Jaquette, 1982; Lenski & Nolan, 1984; Lerner, 1958; Mernissi, 1987; Moghadam, 1998, 2003, 2007; Rukhsana, 2004). Applying the needs, opportunities and values framework logically leads to disentangling different processes influencing women’s employment that stem from economic development. One of these processes does entail a negative influence because economic household needs decline when a country and its households become richer. My framework and analyses have led to a better understanding of the mechanism behind ‘old results’, in which the effects of economic development are collapsed.

6.9.2 ROADS TO FOLLOW

The framework and multilevel modelling have highlighted some avenues for future work on data and methods beyond the scope of this present thesis, and the results logically lead to the themes of the four upcoming chapters.

QUALITATIVE AND QUANTITATIVE DATA ENRICHMENT

For some factors, the analyses in this chapter have resulted in less robust conclusions. In most cases this is directly related to the measurement of the variables. At the country level, the least strong results were found for FDI, public sector size, and the two policies variables. These variables were also the ones for which the weakest measurements have been used. This is a problem for many cross-sectional country-comparative studies that have to make use of existing data. In this study, I have used several new measurements at the country level (state Islamisation and the allowances variable), and the results suggest that this is the start of a fruitful endeavour. Secondly, the less robust and less clear result at the micro level might also be traced to data issues. At this level, there are for example variables that were expected to tap into values, which show important differences between estimation techniques (Appendix 6.1). These variables are behaviour-based proxies for values. Obviously, there is a concept-measure inconsistency and

81 All results do point in the expected direction, but the size