Determinant Factor of Women's Unemployment Status in Ethiopia Using Multilevel Analysis Approach

Background: Unemployment as a state in which, people who can work are without jobs and are seeking for pay or profit. Unemployment is one of the main challenges of the modern era in both developed and developing countries. Unemployment gives rise to private and social problems in the society such as increased crimes, suicides, poverty, alcoholism and prostitution. The main objective of this study was designed to describe the variation in unemployment among women in Ethiopia. Methodology: The dataset for this study was taken from the EDHS 2016. The data included basic demographic and social characteristics. A Binary logistic regression model was used to estimate determinants of unemployment status. The Multilevel random coefficient model has a best-fitted model and estimates the variation of unemployment status across the regions of Ethiopia. Results: A total of 15683 women were included in this study, among this study 10011(63.8% ) women are unemployed and 5672(36.2%) women are employed in Ethiopia. according to 2016 EDHS data. The results from the multilevel reveal that to the random coefficient model the variable Women education level ware secondary and above, parent education level ware primary, parent economic status was richest, women who reside urban, marital status ware separated, and the current age of women ware less than or equal to 25 years, less likely to be that the women were unemployed. While pregnancy women, Women's household size greater than 5 and where the Sex of the Head of Household is male were more likely to be unemployed. The effect of these significant variables is the same for each region in Ethiopia. Conclusion: This study indicated significant evidence of variation among regions clusters and found that the variation within regions was much higher in regions. This study recommends that continued programmatic and policy initiatives should be directed to improve education levels as a means of improving women's unemployment.