Impact of internship programs offered by public employment service on labour market indicators in sub-Saharan Africa
Bibliographic record
Abstract
The objective of this study is to assess the impact of the internship programs offered by the Public Employment Services (PES) on access to employment as well as on earnings. Data collected from 8,492 job seekers in the public employment services of sub-Saharan African countries made it possible to compare applicants who took part in internship programs and those who did not. Using the double differences method, we found that internship programs have positive impacts on access to employment and on wages, both in the short term and in the long term. The estimated treatment effects at the time of the survey are 4.1 percentage points for employment access and $103.4 for wages. In addition, the effects are more pronounced in men and young people under 35. Country analyses show that these positive impacts are more profound in Cameroon and Congo regarding access to employment and in Cameroon and Senegal regarding wages. The insignificant impacts in Ivory Coast and Chad can be explained by political instability in these countries and the youthfulness of PES compared to those in other countries. PES can therefore be used as an instrument that can improve labour market outcomes and help in the achievement of the eighth objective of the UN SDG.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".