_Table 3. Poverty rates by location and employment status, prime working age (25‐54)_

||||Femal<br>|e<br>||||Male<br>|<br>||
|---|---|---|---|---|---|---|---|---|---|---|
||Paid<br>worker|Unpaid<br>worker|Self‐<br>empl.|Unempl.|Out of<br>labor<br>force|Paid<br>worker|Unpaid<br>worker|Self<br>empl.|Unempl.|Out of<br>labor<br>force|
|East Asia and<br>Pacific|1.1|6.2|3.0|2.6|4.5|1.6|6.4|4.0|7.4|4.0|
|Europe and<br>Central Asia|0.1|2.2|0.4|1.1|0.4|0.1|3.0|0.6|1.3|0.6|
|Latin<br>America and<br>Caribbean|0.6|12.8|3.0|9.8|6.1|0.7|24.3|4.6|15.9|9.7|
|South Asia|16.6|14.8|10.4|7.0|13.3|14.0|16.5|11.8|6.7|9.5|
|Sub‐Saharan<br>Africa|28.8|54.2|46.7|32.1|40.9|25.6|54.2|41.3|33.8|42.0|
|Total|3.2|21.6|11.2|7.9|11.1|5.2|21.9|11.6|10.4|13.2|

Note: Sample of 71 countries Source: WB Staff’s calculations based on GMD.

What explains gender differences in poverty rates through the lifecycle?

An initial look at the data indicates that gender differences in poverty appear to relate to age, in as much as age relates to life events such as marriage and to participation in economic activity. The previous analysis examined unconditional correlations between gender differences in poverty and demographic and economic characteristics. We now turn to conditional correlations, estimated from probit regressions of poverty status among adults 15‐64 and demographic and economic characteristics. Since the relationship between poverty and those characteristics is complex and interwoven, the results should not be interpreted as causal relationships. However, they can provide useful information to explore the factors that help explain observed gender differences in poverty among different age groups. The regression uses individual‐level data and regresses the poverty status (whether the individual lives in an extremely poor household or not) on a set of household and individual characteristics.

The analysis compares the unconditional results with three specifications. The first baseline regression includes as control variables a set of country dummy variables, an urban dummy, and the highest category of education attained by a household member<sup>27</sup> . Educational attainment is divided into four categories, no education, primary, secondary and tertiary education completed. The second specification adds the dependency ratio of the household (presence of children and elderly), as well as a dummy for the presence of adults, as controls. The third adds the individual’s economic activity category, which distinguishes between out of the labor force, unemployed, paid employee, or self‐employed. In each case, regressions are estimated separately for women and men to generate estimated average predicted probabilities holding the control variables constant.

What we find is that girls and young women face a poverty penalty that cannot be explained solely by the characteristics that are controlled for, or by the presence of children in the household. Figure 9 shows how the female poverty penalty, defined as the simple difference between predicted female and

> 27 As education is age‐dependent, and we are including individuals who might still be in the process of completing their education

> (e.g. those younger than 25), we choose the highest level of education at the household level instead of individual education.

19