A Bayesian multilevel model for women unemployment in South Africa

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dc.contributor.advisor Bere, A.
dc.contributor.advisor Sigauke, Caston
dc.contributor.author Ramarumo, V. P.
dc.date 2021
dc.date.accessioned 2021-12-12T00:55:46Z
dc.date.available 2021-12-12T00:55:46Z
dc.date.issued 2021-08
dc.identifier.citation Ramarumo, V. P. (2021) A Bayesian multilevel model for women unemployment in South Africa. University of Venda, South Africa.<http://hdl.handle.net/11602/1814>.
dc.identifier.uri http://hdl.handle.net/11602/1814
dc.description MSc (Statistics) en_ZA
dc.description Department of Statistics
dc.description.abstract The study is aimed at investigating and explaining the demographic and socio-economic determinants components a ecting women unemployment in South Africa. The classical and the Bayesian estimation approach were applied to a multilevel logistic regression (MLR) model. Secondary data acquired from the Demographic and Health survey (DHS) held in South Africa in 2016 was used in the study. Information criteria revealed that the random intercept model outperformed the MLR model of the null and random coe cient multilevel models. The Intraclass Correlation Coe cient (ICC) proposes that there is an understandable di erence in women unemployment level over various provinces of South Africa. The results of the classical MLR and the Bayesian MLR indicate in ated commonness for women unemployment and the chance of being without employment for women was established to decrease with an increase of age, wealth index, and educational attainment. en_ZA
dc.description.sponsorship NRF en_ZA
dc.format.extent 1 online resource (xi, 82 leaves)
dc.language.iso en en_ZA
dc.rights University of Venda
dc.subject A Bayesian inference en_ZA
dc.subject A multilevel logistic regression en_ZA
dc.subject Provincial Variations en_ZA
dc.subject Unemployment en_ZA
dc.title A Bayesian multilevel model for women unemployment in South Africa en_ZA
dc.type Dissertation en_ZA

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