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

Teshita Uke Chikako

doi:doi:10.11648/j.ijdst.20180402.15

| Peer-Reviewed

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

Teshita Uke Chikako

Published in International Journal on Data Science and Technology (Volume 4, Issue 2)

Received: 20 May 2018 Accepted: 5 June 2018 Published: 4 July 2018

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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.

Published in	International Journal on Data Science and Technology (Volume 4, Issue 2)
DOI	10.11648/j.ijdst.20180402.15
Page(s)	67-78
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Youth Unemployment, Regional Variations, Multilevel Logistic Regression, Bayesian Multilevel

References

[1]	Asif M, Arshad I. A and Ali, N. (2015). A Statistical Analysis of Factors Affecting the Women Employment in Pakistan. Islamabad, Pakistan, 27(1)791-794.
[2]	ILO (2010). Growth-Employment-Poverty Reduction linkages: a Framework for Recovery and Accelerated Progress towards the Millennium Development Goals, Economic Report on Africa.
[3]	Economic Commission for Africa (ECA 2005). Economic Report on Africa 2005 Meeting the Challenges of Unemployment and Poverty in Africa. ECA Publication Cluster.
[4]	Therese F. Azeng Thierry U. Yogo (2013) Youth Unemployment and Political Instability in Selected Developing Countries.
[5]	F. Nazir, M. A. Cheema, M. I. Zafar and Z. Batool (2009). Socio-Economic Impacts of Unemployment in Urban Faisalabad, Pakistan Journal of Social Science, 18(3): 183-188. Department of Rural Sociology, University of Agriculture, Faisalabad 38040, Pakistan.
[6]	Ministry of Youth, Sport and Culture (2004). The Federal Democratic Republic of Ethiopia National Youth Policy, Addis Ababa, Ethiopia.
[7]	Snijders, T. A. B. and Roel J. Bosker (1999). An Introduction to Basic and Advanced Multilevel Modeling, First ed. Department of Statistics, University of Poone.
[8]	Kynn, M. (2005). Elicting Expert Knowledge for Bayesian Logistic Regression in Species Habitat Modelling.
[9]	Howson, C. and Urbach, P. (1993), Scientific Reasoning: the Bayesian Approach, 2. ed. Open Court, Chicago.
[10]	Dezfuli. H, Smith. C, Galyean. W, (2009). Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis.
[11]	Gelman, A., Carlin, J. C., Stern, H. and Rubin, D. B, (1984). Bayesian Data Analysis. Chapman and Hall, New York.
[12]	Merkle, E., Shev, C. and Trisha, G. (2005). Simulation Based Bayesian Inference Using Winbugs. Winbugs Tutorial Outline.
[13]	Walsh, B. (2004). Markov Chain Monte Carlo and Gibbs Sampling.
[14]	Hox, J.(2002). Multilevel Analysis: Techniques and Applications. Mahwah, N. J: Lawrence Erlbaum.
[15]	Abebe Fikre Kasa (2011). Unemployment in Urban Ethiopia: Determinant and Impacts on Household Welfare. School of business, economic and law, University of Gothenburg.
[16]	Hongyu Yang, (1992). Female Labor Force Participation and Wages: A Case Study of Panama. In Case Studies on Women’s Employment and Pay in Latin America.
[17]	Mesfin Mulu (2012). Determinants of Women Unemployment in Ethiopia: A Multilevel Model Approach. Master Thesis. Addis Ababa University, Addis Ababa, Ethiopia.
[18]	Bhorat, H. (2007). Unemployment in South Africa: Descriptors and Determinants. Paper Presented To The Commission On Growth And Development, World Bank, Washington Dc.
[19]	Pieter Serneels (2007) The Nature of Unemployment Among Young Men in Urban Ethiopia.

Cite This Article

Plain Text BibTeX RIS

APA Style

Teshita Uke Chikako. (2018). Multilevel Modelling of Determinants of Youth Unemployment in Urban Ethiopia: Bayesian Estimation Approach. International Journal on Data Science and Technology, 4(2), 67-78. https://doi.org/10.11648/j.ijdst.20180402.15

Copy | Download

ACS Style

Teshita Uke Chikako. Multilevel Modelling of Determinants of Youth Unemployment in Urban Ethiopia: Bayesian Estimation Approach. Int. J. Data Sci. Technol. 2018, 4(2), 67-78. doi: 10.11648/j.ijdst.20180402.15

Copy | Download

AMA Style

Teshita Uke Chikako. Multilevel Modelling of Determinants of Youth Unemployment in Urban Ethiopia: Bayesian Estimation Approach. Int J Data Sci Technol. 2018;4(2):67-78. doi: 10.11648/j.ijdst.20180402.15

Copy | Download

@article{10.11648/j.ijdst.20180402.15,
  author = {Teshita Uke Chikako},
  title = {Multilevel Modelling of Determinants of Youth Unemployment in Urban Ethiopia: Bayesian Estimation Approach},
  journal = {International Journal on Data Science and Technology},
  volume = {4},
  number = {2},
  pages = {67-78},
  doi = {10.11648/j.ijdst.20180402.15},
  url = {https://doi.org/10.11648/j.ijdst.20180402.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20180402.15},
  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.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Multilevel Modelling of Determinants of Youth Unemployment in Urban Ethiopia: Bayesian Estimation Approach
AU  - Teshita Uke Chikako
Y1  - 2018/07/04
PY  - 2018
N1  - https://doi.org/10.11648/j.ijdst.20180402.15
DO  - 10.11648/j.ijdst.20180402.15
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 67
EP  - 78
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20180402.15
AB  - 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.
VL  - 4
IS  - 2
ER  -

Copy | Download

Author Information

Teshita Uke Chikako

Department of Statistics, College of Natural and Computational Science, Bule Hora University, Bule Hora, Ethiopia

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Teshita Uke Chikako. (2018). Multilevel Modelling of Determinants of Youth Unemployment in Urban Ethiopia: Bayesian Estimation Approach. International Journal on Data Science and Technology, 4(2), 67-78. https://doi.org/10.11648/j.ijdst.20180402.15

Copy | Download

ACS Style

Teshita Uke Chikako. Multilevel Modelling of Determinants of Youth Unemployment in Urban Ethiopia: Bayesian Estimation Approach. Int. J. Data Sci. Technol. 2018, 4(2), 67-78. doi: 10.11648/j.ijdst.20180402.15

Copy | Download

AMA Style

Teshita Uke Chikako. Multilevel Modelling of Determinants of Youth Unemployment in Urban Ethiopia: Bayesian Estimation Approach. Int J Data Sci Technol. 2018;4(2):67-78. doi: 10.11648/j.ijdst.20180402.15

Copy | Download

@article{10.11648/j.ijdst.20180402.15,
  author = {Teshita Uke Chikako},
  title = {Multilevel Modelling of Determinants of Youth Unemployment in Urban Ethiopia: Bayesian Estimation Approach},
  journal = {International Journal on Data Science and Technology},
  volume = {4},
  number = {2},
  pages = {67-78},
  doi = {10.11648/j.ijdst.20180402.15},
  url = {https://doi.org/10.11648/j.ijdst.20180402.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20180402.15},
  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.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Multilevel Modelling of Determinants of Youth Unemployment in Urban Ethiopia: Bayesian Estimation Approach
AU  - Teshita Uke Chikako
Y1  - 2018/07/04
PY  - 2018
N1  - https://doi.org/10.11648/j.ijdst.20180402.15
DO  - 10.11648/j.ijdst.20180402.15
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 67
EP  - 78
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20180402.15
AB  - 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.
VL  - 4
IS  - 2
ER  -

Copy | Download