men that have no job often become active in some family business or informal job. Consequently, very few men indicate to be unemployed. Real unemployment can be considered much higher than what is derived from these surveys. This would bias the results against finding an effect, because men who are classified as employed are actually not really employed or decently paid.
807 Aside from your own housework, have you done any work in the last seven days?
808 As you know, some women take up jobs for which they are paid in cash or kind. Others sell things, have a small business or work on the family farm or in the family business. In the last seven days, have you done any of these things or any other work?
811 What is your occupation, that is, what kind of work do you mainly do?
Source: Measure DHS. (2006) Model Questionnaire. With Commentary. Basic Documentation Number 2. Section 8.
Box 4.3 DHS economic activities questions
variable values variable values
Occupation1 0 Not working2 Type of earnings3 0 Not paid
1 Prof., Tech., Manag. 1 Cash only
2 Clerical 2 Cash and kind
3 Sales 3 In kind only
4 Agricultural self-employed School attendance4 0 Not attended during current school year
5 Agricultural employee 1 Attended at some time during current school year 6 Household & domestic
7 Services 8 Skilled manual 9 Unskilled manual
Notes: (1) There is some variation in the answer labels for the different countries. But the distinction between not working, agricultural work and other work is always clearly made; (2) For Azerbaijan, Bangladesh and Burkina Faso a more refined coding was present for ‘Not working’ women, including being a student and being disabled; (3) In three countries the data is conflated and this variables only tells if at least some cash is earned by the women, since that is basically the same as the selection of category 1 and 2 I use, this is no problem whatsoever; (4) For Bangladesh, Egypt, Pakistan and Zanzibar, this variable includes a considerable number of missings; based on age of entry, age of the women, and their educational attainment a proxy for being still in school was created and used (see text).
the surveys for these countries, they were excluded as well. In total, I coded 15,078 women from the DHS surveys to be still in school. For Kazakhstan it was furthermore known if women did not work because of a disability. Taking this into account led not to the exclusion of more women.
Of the over 200,000 selected women, all women that had no agricultural job, were not ‘not working’, and were paid at least partly in cash are considered to be gainfully employed outside agriculture. For Jordan no information on payment was available. However, other variables showed that of the non-agriculturally employed women, all except four were self-employed or employees, and that in this country almost 90% of the non-agriculturally employed women had a professional or clerical occupation. Consequently, I decided to treat all women with a non- agricultural occupation as being gainfully employed. For Pakistan, the ‘type of earnings’ variable shows a considerable number of missing data. In cases where the data were present, however, the women with a non-agricultural job were almost all paid at least some cash. Based on this I decided to code the women without an entry on earnings but with a non-agricultural job as being gainfully employed.
IPUMS
The data in the IPUMS for Malaysia was highly similar to those in PAPFam and the same procedure was followed.
4.7 Independent varIaBLeS
In this section I will discuss how the factors mentioned in the hypotheses in Chapter 3 are measured (see also Figure 3.1). This will first be done for the micro-level hypotheses (individual and household levels), then the district level and finally the country level. Statistically, some of the micro-level variables are included at the individual level of the model (e.g. education) and others at the household level (e.g. living in an urban area). If women in the same household can have different values on it, it is an individual-level variable. This statistical distinction, however, should not be confused with the theoretical distinction made in Chapters 2 and 3. Theoretically it is important to understand the embeddedness of individuals in a household, because it draws attention, for instance, to the importance of the partner and household structure (see Chapter 8). Some of the household characteristics (e.g. having a partner and having children), however, may vary statistically at the individual level. Of two women living in the same household, one might be married and the other might be single. However, in an attempt to prevent too much confusion, I will discuss the micro-level variables at once. Nevertheless, Figure 3.1 shows the theoretical levels of each factor, while in Table 4.2 the statistical levels are leading. Table 4.2 provides an overview of the variables, including descriptives and information on the missing data.
4.7.1 INDIVIDUAL & HOUSEHOLD LEVEL
Related to the ‘needs hypotheses’ (the left quadrant of Figure 3.1), three variables are operationalised. To measure whether a male breadwinner is present, I include two variables. Firstly, a dummy is used to indicates whether (1) or not (0) the household head is male and between the ages of 18 and 65. Secondly, I include a dummy for the presence of a male partner based on the marital status of the women: (0) no partner is present; (1) a partner is present. Both dummies focus on the presence/absence of a male person in the household position of being responsible for fulfilling the economic needs of the household. Another possible indicator comes from a variable used to measure socio-economic status (see below): the partner’s employment status. If a partner is indicated having no job, this signals that the chance is higher someone else needs to provide the household with an income.36 The presence of children is measured for
each individual woman. The number of children (aged 14 and under) is taken and divided in four groups: no children, 1 or 2, 3 or 4, and more than 4. Because young children are expected to have higher care needs, I distinguish women that have at least one child below the age of 6 and women without children below that age. This means that the presence of children is measured in seven categories: (1) no children, (2) 1–2 children, all older than 5, (3) 3–4 children, all older than 5, (4) 5 or more children, all older than 5, (5) 1–2 children, at least one younger than 6, (6) 3–4 children, at least one younger than 6, (7) 5 or more children, at least one younger than 6.
factor (Hypo) variable values1 average (s.e.)2 Missings3
Individual level (n=316,958)
male breadwinner partner present4 (1) yes 0.768 49
presence of children children (0) no children 0.343 0
(1) 1or 2, older than 5 0.137 (2) 1or 2, at least 1 younger than 6 0.239 (3) 3 or 4, older than 5 0.041 (4) 3 or 4, at least 1 younger than 6 0.173 (5) 5 or more, older than 5 0.003 (6) 5 or more, at least 1 younger
than 6
0.064
human capital education (1) No primary completed 0.538 445
(2) primary completed, secondary not
0.276 (3) secondary completed, no
tertiary
0.127 (4) at least some tertiary 0.060
socio-economic network partner’s occupation3 (1) agriculture 0.312 8,184
(2) blue collar 0.277
(3) lower white collar 0.188 (4) upper white collar 0.179
(5) unemployed 0.044
traditional values partner’s education3 (1) No primary completed 0.468 11,315
(2) primary completed, secondary not
0.283 (3) secondary completed, no
tertiary
0.153 (4) at least some tertiary 0.095 age at birth first child min: 6
max: 49
20.30 (4.35) 207 age difference with partner
(partner-women)
min: -35 max: 67
8.07 (7.29) 4,536
control variable age min: 7
max: 49
30.97 (9.27) 0
Household level (n=242,410)
male breadwinner Household head is male aged 18–65
(1) yes 0.802 251
urban area living in a city (1) yes 0.453 0
traditional values Traditional family structure min: 0 max: 2
0.33 (0.50) 923
factor (Hypo) variable values1 average (s.e.)2 Missings3
district level (n=383)
economic development Wealth level min: 0.01
max: 0.87
0.47 (0.27) 66
male labour supply Male non-employment min: 0.00 max: 0.71
0.06 (0.10) 0
service sector jobs Proportion white collar jobs min: 0.03 max: 0.81
0.44 (0.15) 0
Light manufacturing Proportion skilled labour min: 0.01 max: 0.48
0.21 (0.10) 0
Service + light manufacturing5 Degree of urbanisation min: 0.00
max: 1.00
0.44 (0.28) 0
Women in public sphere Proportion of women in higher education and the labour market
min: 0.00 max: 0.79
0.21 (0.16) 0
Traditional care roles Prevalence of traditional household
min: 0.08 max: 0.76
0.33 (0.15) 0
Country level (n=28)
Economic development GDP per capita in thousands min: 0.49 max: 8.16
2.84 (2.29) 0
Foreign direct investment Ln (FDI three years average (% GDP))
min: -1.11 max: 3.50
0.92 (1.10) 0
Democracy Freedom House civil liberties and political rights
min: 1 max: 5
2.41 (1.24) 0
State institutionalisation of conservative Islam
Codification of Islam in constitu- tion
min: 0 max: 6
1.64 (1.83) 0
Traditional family policies Gender unequal policies and laws (based on OECD GID)
min: 0.06 max: 0.71
0.41 (0.17) 0
Public sector size Government expenditures (% GDP)
min: 5 max: 41
14.68 (7.67) 0
Social safety net Family Allowance policies (dummy)
(1) yes 0.51 0
Notes: (1) the presented minimums and maximums are the extremes as they are present in the data, not the conceptual/possible extremes; (2) these figures are based on the unweighted data; in the case of nominal/categorical variables no standard deviation is reported and the average represents the proportion of cases falling in the designated category; (3) ‘impossible missings’ (see Section 4.4.3) are not included as missing here; (4) these variables are theoretically part of the household level, but measured at the individual level, the missing should therefore be interpreted related to the n at the individual level; (5) the degree of urbanisation is expected to measure both the presence of light manufacturing and service sector jobs, as explained in the text; (6) all these cases were given scores as described in the text, only cases with missing on the individual and household level were deleted.