Statistical Modeling of Women Employment Status at Harari Region Urban Districts: Bayesian Approach

Habtamu Kiros () and Alebachew Abebe ()
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Habtamu Kiros: Haramaya University
Alebachew Abebe: Haramaya University

Annals of Data Science, 2020, vol. 7, issue 1, No 5, 63-76

Abstract: Abstract Women have always faced a number of disadvantageous gaps in the labour market; the status of women at the labour markets throughout the world has not substantially narrowed gender gaps in the workplace. Many women in developing countries are domestic workers or informal factory workers, while others are unpaid workers in family enterprises and family farms. Agriculture is the primary sector of women’s employment; Sub-Saharan Africa is among regions with the highest proportion of women employment in the agriculture sector. This research was conducted on 274 sampled households with the objective to determine the factors associated with women’s employment status and to examine whether the estimated parameters for logistic regression model adopting Bayesian and maximum likelihood estimation approaches are similar or not. The research revealed that about 144 (52.6%) of sampled women were unemployed that is, they were not involved in any activity for earning during the data collection. The inferential analysis using both Bayesian and Maximum likelihood estimation schemes indicated that, pregnancy, age, education level, husband/partner occupation, marital status, family size, training opportunity and a child less than 5 years old had statistically significant (p

Keywords: Bayesian; Maximum likelihood; Posterior distribution; Gibbs sampling (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s40745-019-00215-6

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