Bridging the Gap : Identifying What is Holding Self-Employed Women Back in Ghana, Rwanda, Tanzania, the Republic of Congo, and Uganda
This paper explores the determinants of the gender gap in income earnings in five Sub-Saharan countries: the Republic of Congo, Ghana, Rwanda, Uganda, and Tanzania. It shows that first, self-employment tends to provide marginally lower average income (with the exception of Ghana and men in Rwan...
| Main Authors: | , , |
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| Format: | Policy Research Working Paper |
| Language: | English en_US |
| Published: |
World Bank, Washington, DC
2014
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| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/2014/06/19724505/bridging-gap-identifying-holding-self-employed-women-back-ghana-rwanda-tanzania-republic-congo-uganda http://hdl.handle.net/10986/19378 |
| Summary: | This paper explores the determinants of the gender
gap in income earnings in five Sub-Saharan countries:
the Republic of Congo, Ghana, Rwanda, Uganda, and
Tanzania. It shows that first, self-employment tends
to provide marginally lower average income (with the
exception of Ghana and men in Rwanda) and much
higher variability in income compared with wage work.
Women on average earn less than men when they
are self-employed and in wage employment, but also
have less volatile earnings. The analysis uses quantile
decomposition methods and finds that the differences in
observable choices and endowments explain the gender
gap in earnings for the self-employed who earn the least
while the gap for the most successful male and female
entrepreneurs is largely driven by differences in returns
to observable covariates in the majority of the countries.
These results suggest a glass ceiling effect, wherein a
large portion of the income gaps between high-earning
men and women cannot be explained by observable
characteristics. The paper concludes by looking at the
variables that account for a larger portion of the gender
gap explained by observable characteristics and finds that
hours of work and industry explain a higher fraction
compared with standard human capital and demographic
factors such as age and education. |
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