Skills mismatch in the agricultural labour market in Benin: vertical and horizontal mismatch
Bibliographic record
Abstract
This study investigates skills mismatch in the agricultural labour market. Therefore, 336 agriculture employers and 654 agriculture employees were surveyed in Benin. Data were analysed using descriptive statistics and non-parametric tests . The findings showed that even though there is a good educational match for most employees, overeducation is more substantial than under education for upper agricultural high school diploma holders (DEAT) and agricultural tertiary education diploma holders. In addition, about 2% of agricultural high education diploma holders and 6.38% of DEAT holders had a job irrelevant to their field of study. The study further showed that agriculture graduates were under-skilled for the soft and digital skills under review. Moreover, training providers do not equip students with job search skills. These findings imply that vertical mismatch is more pronounced than horizontal mismatch. The study suggested a prioritisation of the implementation of training programmes based on the demand in terms of study level and field of study, an update of curricula by integrating the lacking soft, digital and job search skills, a settlement of a collaborative network between employers and training institutions, an implementation of mentoring programmes, and an investment of enterprises in the adequate training of youth as social responsability.
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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 | 0.002 | 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".