Spatial Distribution and Associated Factors of Youth unemployment in  Ethiopia: A Spatial and Multilevel Analysis

Awol Musa

dc.contributor.author	Awol Musa
dc.date.accessioned	2022-12-22T09:08:41Z
dc.date.available	2022-12-22T09:08:41Z
dc.date.issued	2022-12
dc.identifier.uri	http://ir.bdu.edu.et/handle/123456789/14729
dc.description.abstract	Background: The high population of youth unemployment is one of the most critical issues at the global level. Which is caused by socio-economic and demographic factors. Exploring the spatial distribution and identifying associated factors is important to design effective policy and programs to reduce youth unemployment. Thus, this study aimed to explore the spatial distribution and associated factors of youth unemployment in Ethiopia using the 2021 National labor force survey. Method: This study used the national labor force survey 2021 data as a data source. The data was collected using a two-stage sampling method. To analyze our data we employed spatial and multilevel analysis. ArcGIS version 10.8 and SAS version 9.4 statistical software were used for spatial and multilevel analysis respectively. Results: A total of 19803 youth were included in this study, among those 5007(25.3) youths are unemployed and 14796(74.7) youths are employed in Ethiopia according to 2021 national labor force survey data. This study revealed that the spatial distribution of youth unemployment was clustered or non-random in the Ethiopian administrative zones with Moran’s index of 0.0618 (P value=0.0278). The high unemployment of youth was observed in almost all zones of the Afar region, north Gondar, central Gondar, Weg Hamra, and the north Shewa zone of the Amhara region. Based on AIC, and BIC criteria, the random intercept model with level one and level two predictors was preferred and the result shows sex, age, marital status, education level, the field of study, relation to the household, region, and size of a household were found to be a statistically significant variable for the unemployment of youth. Conclusions: Spatial distribution of youth unemployment varies across the Ethiopian administrative zones. Based on the selected multilevel model result we summarize that females, illiterate, spouses of household, household size more than four, engineering fields of study, and married youth were more likely to be unemployed. Therefore, the government and concerned bod y should give attention to risk areas and identified factors, and also create a labor market that works better for youth employment. Keywords: Youth unemployment, spatial analysis, multilevel analysis, Ethiopia	en_US
dc.language.iso	en_US	en_US
dc.subject	Statistics	en_US
dc.title	Spatial Distribution and Associated Factors of Youth unemployment in Ethiopia: A Spatial and Multilevel Analysis	en_US
dc.type	Thesis	en_US