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  • 标题:Estimating the Likelihood of Women Working in the Service Sector in Formal Enterprises: Evidence from Sub Saharan African Countries
  • 本地全文:下载
  • 作者:Ernest Ngeh Tingum
  • 期刊名称:Journal of Economics and Sustainable Development
  • 印刷版ISSN:2222-2855
  • 电子版ISSN:2222-2855
  • 出版年度:2016
  • 卷号:7
  • 期号:2
  • 页码:51-64
  • 语种:English
  • 出版社:The International Institute for Science, Technology and Education (IISTE)
  • 摘要:The paper uses individual data for 9,957 female employees (drawn from a total sample of 29,332 individuals) in formal enterprises from 16 Sub-Saharan African (SSA) countries to analyse the likelihood of women in the service sector. A well-structured questionnaire was used in all the countries to collect the data required for the analysis. The data reveal that there is a significant higher presence of women (81.56 percent) working in services as compared to the manufacturing and agricultural sectors; indicating that the service sector is more favourable for women employment compared with men. This indicates that female employment not only in the service sector is a driver of growth, and thus high female employment rates indicate a country’s potential to grow more rapidly. More so, in many developing countries women’s employment is sometimes considered as a coping mechanism in response to economic shocks that hit the household. With regards to methodology, both demographic and household variables are used in the probit model. The findings indicate that there is a significant and positive participation of the female labor force in most of the countries. Age, household size, and tertiary education levels emerged as the most important and positive determinants in the model. Contrary to a priori expectation, the results show that marital status reduces the likelihood of a woman to be employed in the service sector. These findings could serve as useful inputs for the design of optimal sectorial employment policy measures aimed at promoting gender equality in SSA countries.
  • 关键词:women; service sector; likelihood; probit model; Sub Saharan Africa
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