relative to men’s non-agricultural employment. Therefore it can be included in the models in which women’s paid non-agricultural employment is the dependent variable. more detailed information about occupation (only a few do). In Azerbaijan, for example, the larger part of women coded as being active in light-manufacturing are active in ‘food processing and related trades workers’ and ‘textile & garment & related trades workers’. In Burkina Faso it are mostly ‘crafts & production of goods’ workers. For Nigeria almost all women in this category 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’.
4.7.2 DISTRICT LEVEL
At the district and country levels, the models include economic development. At the district level, I include a variable that measures the district’s level of wealth. By aggregation I derived the number of people in a district with a TV, a telephone, a refrigerator, a car, and access to running water and to electricity. Exploratory factor analysis (EFA) with these six factors clearly showed that they load on one factor (see Appendix 4.2).39 The factor loadings are applied as weights in
averaging the scores on household assets. This takes into account the relative extent to which an item is related to the underlying concept (factor). The districts with missing data on one or more assets are included by calculating the same weighted average, leaving out the missing asset.40
The average of these weighted variables is taken as an indicator of the economic development level in the district. Conceptually the scale runs from 0 to 1. The depletion of the male labour supply is measured by taking the proportion of men that were not employed. To measure the level of non-employment, I aggregated the information on the partners’ – who are all men in the data sets – employment. All partners who were not involved in a job were considered not- employed. If more men are not-employed, the supply of male labour is larger. This is measured on a scale ranging from 0 to 1.
Next, both service-sector jobs and light-manufacturing jobs focus on the labour market structures and the types of jobs available. The labour market structure in a district can be charted by aggregating the proportion of people active in a certain occupation compared to all people active on the labour market. The occupation variables provided in the survey do not fully overlap with the two theoretical variables, but I can create two variables that approximate these labour market structures. For service sector jobs, I take the proportion of all women and their partners if they are aged 25 and over and had a white collar job, and divide this by the total number of working women and partners. This age limitation was chosen since many service-sector jobs require secondary or tertiary education. For ‘light-manufacturing jobs’, I focus on the blue-collar jobs, but not all. All datasets make a distinction between skilled and unskilled manual labour.41
The latter includes rougher labour such as ‘mining’, ‘construction’ and ‘production workers’; jobs that are general considered to be part of the heavy industries. ‘Skilled manual labour’ on the other hand includes jobs like ‘assemblers’, ‘paper/plastic workers’ ‘drivers’ and ‘confectioners’, and for women mainly jobs in the food and textile industry.42 In other words, there is
considerable overlap between ‘skilled-manual labour’ and ‘light-manufacturing jobs’,43 which
is generally considered to include the manufacture of small electronics and home appliances; textiles, cloths and shoes; food; and small furniture. Clearly not all light-manufacturing jobs are captured by the category ‘skilled-manual labour’ and not all harder manufacturing by ‘unskilled- manual labour’, but a decent indication of the labour market structure can be derived from these data. I therefore take the proportion of all women and partners that had a skilled manual-labour job against the total number of working women and partners. In addition to these labour market structure variables, I include a more general measurement of job opportunities: the degree of urbanisation in the district (in addition to ‘living in a city’ at the household level). The assumption underlying this variable is that in more urban areas the number of service-sector and light manufacturing jobs is higher than those of other manufacturing and agricultural jobs. Therefore the degree of urbanisation captures these two aspects as well. The degree of urbanisation is measured by the proportion of people living in an urban area of the district. All three discussed variables range from 0 to 1.
Finally, two values variables will be included at the district level. The women in public sphere variable is based on the extent to which the number of women present in the non- agricultural labour market compares to men and the same for higher education.44 Per district,
the total number of women who were non-agriculturally employed or had finished a secondary or tertiary education is divided by the total number of women and men (i.e. partners) that were non-agriculturally employed or had finished a secondary or tertiary education. For education, I only used ages 25 through 49, when higher education has definitely been concluded and in order to focus on people who have finished education in the last decades and not long ago. For labour market participation the focus is on the current situation at the labour market, and all women (15 through 49) and their partners are included. The average of the figures for the
45 In the factor analysis including the extended-family item,