Impact of employment support programs on the quality of youth employment: Evidence from Senegal's internship program
Bibliographic record
Abstract
Abstract Youth unemployment is major policy concern in Senegal. The country has in recent years, implemented several programs to combat unemployment and the precariousness of youth employment in the labor market. However, the results of these programs are to date hardly perceptible. The objective of this work is to assess the impact of employment support programs on the quality of youth employment. We provide empirical evidence of the effect of the apprenticeship program implemented by the National State‐Employer Convention in facilitating youth access to quality employment. Job quality is determined using an index that captures multiple wage and non‐wage dimensions of job quality. Using survey data on the improvement of employment policies from 2746 individuals, we use the endogenous switching regression method and the propensity score matching method to assess and compare the impact of the apprenticeship program on the quality of jobs held by young men and women. The results show that the apprenticeship program has a positive and significant impact on job quality. Indeed, we find that the quality of employment is better for young men and women who benefited from the internship program than for those who did not. We find, however, that there is a difference in job quality between males and females who received the program. The differences in job quality are explained more by differences in job characteristics but are not directly related to gender or age.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".