A bayesian hierarchical modelling of small area variation in youth unemployment in Namibia
| dc.contributor.author | Shitenga, Linda Vute | |
| dc.date.accessioned | 2023-10-26T07:37:38Z | |
| dc.date.available | 2023-10-26T07:37:38Z | |
| dc.date.issued | 2023 | |
| dc.description | A thesis submitted in partial fulfilment of the requirements for the degree of master of science in applied statistics and demography | en_US |
| dc.description.abstract | Youth unemployment has been one of Namibia's socio-economic problems, which has the potential to have significant and serious social repercussions on economic growth and development and could cause social exclusion and unrest in the affected country. Youth unemployment rates estimates are only available at the national (46.1 percent in 2018) and regional levels in Namibia; however, the Namibia Labour Force survey (NLFS) does not provide such statistics at small area e.g., at constituency level. The census data could be used to provide estimates of youth unemployment at constituency level; however, the data is only obtained every 10 years which in most cases the time frame is too long given the developmental changes that may take place during the 10- year periods. In view of these challenges, it is paramount to estimate the variation in unemployment rates at constituency level for possible targeted interventions within regions. In comparison to conventional small area estimation (SAE) models, the hierarchical Bayesian approach to SAE problems has several benefits, one of which is the ability to properly account for the kind of surveyed variable. For this reason, the main objective of this study was to estimate the risk of youth unemployment at constituency level using the 2018 NLFS data. The likelihood was estimated using a hierarchical Bayesian model and results from the study showed that the chance of youths being unemployed was very high among male youths than female youths in urban areas with OR=1.35 (1.10, 1.66) and OR=0.79 (0.65, 0.96) respectively. Several models were fitted, and the best model was used to estimate the probability of being unemployed amongst the male and female youths (with the DIC values of 4900.90 for the males and 5719.48 for females). The best model considered the fixed effects together with the unstructured spatial effects at constituency and regional levels. Even though employment is the result of aggregate demographic and socio-economic factors, the study recommends that employment opportunities specifically targeting youths should be created by either government or private sectors in constituencies especially those in the rural constituencies. Furthermore, special attention should be paid to integrating the youth into the labour market by improving their educational levels | en_US |
| dc.identifier.uri | http://hdl.handle.net/11070/3738 | |
| dc.language.iso | en | en_US |
| dc.publisher | University of Namibia | en_US |
| dc.subject | Unemployment rate | en_US |
| dc.subject | Youth | en_US |
| dc.subject | Small area estimation | en_US |
| dc.subject | Hierarchical Bayesian | en_US |
| dc.title | A bayesian hierarchical modelling of small area variation in youth unemployment in Namibia | en_US |
| dc.type | Thesis | en_US |