Multilevel Modelling of Determinants of Youth Unemployment in Urban Ethiopia: Bayesian Estimation Approach

Abstract

The main objective of this study was to identify and explain the effects of the Demographic and Socio-economic determinant factors of Youth unemployment in urban of Ethiopia. The data used for this study is the 2016 Ethiopian Urban Employment Unemployment Survey (UEUS) which was conducted by Central Statistical Agency (CSA) of Ethiopia. The statistical methods of data analysis are multilevel logistic regression models and Bayesian multilevel models and the parameters are estimated by using maximum likelihood estimation method and Bayesian estimation method by Stata and WinBUGS software. The analysis result revealed that Out of the 3870 youth considered in the analysis, 1,757 (45.4%) youth were unemployed, while 2113 (54.6%) youth were employed at the time of data collection. Region, Sex of youth, Age of youth, Literacy status, marital status, Type of Training, Steps taken to search work, Household size and Educational level are found to be the significant determinants of youth unemployment in urban Ethiopia. The multilevel logistic model revealed that the random intercept is better fit than null and random coefficient multilevel models. The intra correlation coefficient suggests that there is clear variation of youth unemployment status across the region of urban Ethiopia. The result of classical and Bayesian multilevel shows high prevalence of unemployment among youth and the probability of being unemployed for youth was found to decline with increasing age, literacy level, training, educational level and household size.