Leveraging Youth Employment in the Tanzania Tourism Sector: The Role of MSMEs
Bibliographic record
Abstract
Despite the employment potential of Tanzania’s tourism sector, the sector is not absorbing the youth sufficiently, who remain unemployed after graduating from various institutions. This study examines the extent of youth employment in the sector and factors associated with their increased employment. Using cross-sectional data from 445 micro, small and medium enterprises (MSMEs) collected between September and November 2018, the study reveals that the youth form the largest proportion of wage employees. Furthermore, using econometric techniques, the study finds that factors influencing enterprises to employ more youths include duration of business operation, formal access to capital, provision of employment contracts, non-networking recruitment channels and number of customers. Hence, the potential of MSMEs to employ more youths depends internally on reliable access to capital and adherence to best employment practices, such as providing employment contracts and ensuring equal opportunity to applicants. Both the survival of MSMEs and increased youth employment depend externally on attracting more tourists. JEL Classifications: M51, J21, E24
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".