The impact of the PDEF on the labour market outcomes of «high-skilled» workers in Senegal
Bibliographic record
Abstract
It is through a United-Nations initiative and as part of the Millennium Goals that Senegal launched in 2000 the Development Program for Education and Formation (PDEF). Among others, this educational reform affected the sector of higher education. The purpose of this paper is to evaluate the impact of the PDEF on the labour market outcomes of those individuals who reached this level of education. Specifically, we seek to determine whether individuals who have benefited from improvements from the reform are employed more often. Using data from two surveys on the Senegalese population, we use the method of difference-indifferences to estimate the effect of the reform. The main contribution of this paper is therefore to give a first overview of the impact attributable to the PDEF on the sample selected. Our approach determines a range of values for the true effect. Thus, our results show that the PDEF allows to benefit a positive advantage between 4.3 and 38.96 percentage points in the probability of being in employment.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".