Where Are the Ugandan Youth? Socio-Economic Characteristics and Implications for Youth Employment in Uganda
Bibliographic record
Abstract
This paper undertook a structural analysis of the youth labor force in Uganda by documenting their location in the Ugandan economy by residence, region, sector, and employment status. The data were obtained from the Uganda National Household Surveys 2002/03, 2005/06 and 2009/10. We find that the youth population structure of the country poses a big challenge in an effort to create employment, particular for the youth. The majority of the youth labor force is located in the rural compared to the urban areas of the country. Whereas we find quite impressive labor force participation rates and the employment population ratio, they do not necessarily reflect more and better employment opportunities because of the high degree of informality where most of the youth are underemployed. Over 90% of the youth were employed in the informal sector outside agriculture with slightly more female youth than their male counterparts. The quality of the youth labor in terms of education is improving though will lower attainment rates at postsecondary level, however, the quality of the youth labor force is higher in urban than in rural areas. An increase in self-employment is observed which is an indication of high rate of job creation is in the informal economy. Consequently, there is a limited participation of the youth in professional and technical occupations and in paid employment. By gender, it’s surprising to observe that there more female youth employed as professionals compared to their male counterparts. However, it is noteworthy that the percentage of both male and female youth employed as professionals is on average less than 1%. Connected to this is the finding that there is a higher proportion of the youth labor force employed in agriculture despite its dismal contribution to GDP. Whereas the country experienced sectoral shifts in GDP composition, with the services and manufacturing sector becoming more important than agriculture, there are no sectoral shifts in employment with agriculture remaining the main employer of the youth labor force. Very low youth unemployment rates are observed suggesting a significant time underemployment. Additionally, there is a significant proportion of the youth that are skill underemployed; their educational attainment was higher than the educational level required by their current main jobs. Youth unemployment problem is more of an urban phenomenon with a higher proportion of the youth unemployed in Kampala compared to other regions. Our findings suggest that increasing labor productivity in agricultural as well as in the non-agricultural informal sector where the majority of the youth are located might help to solve the youth unemployment and underemployment predicaments. There is also need to ensure access to cheap finance by the youth, practical education, secure premises for informal businesses, and design policies to slow down the current population growth rates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".