The State, Determinants, and Consequences of Skills Mismatch in the Ethiopian Labour Market
Bibliographic record
Abstract
The study analyses the incidence of labour market mismatch, identifies the correlates of skills mismatch that shed light on the causes of the problem, and investigates its consequences on well-being. It is the first attempt to formally study skills mismatch in the urban labour market in Ethiopia. Using several indicators of qualification mismatch, we find that about a quarter of employees are mismatched with over-qualification being the more prevalent problem. In comport with findings for developed countries, our analysis reveals overqualified worker report lower job satisfaction compared to the well-matched. We also find that skill-mismatch, particularly over education lowers wages; while education is positively and significantly associated with wage, overeducated workers earn less than those well-matched for their level of education. This implies a wage penalty associated with over-qualification even in a developing country context. Our study highlights that labour market mismatch is not only a phenomenon of the developed world but also the developing countries. Hence, skills mismatch needs to be a key aspect of labour market policy making along with issues of decent and productive work.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".