Does working while studying affect education mismatch among youth? Evidence from Zambia
Bibliographic record
Abstract
Purpose This paper aims to study the relationship between working while studying in college/university and education mismatch among employed youth in the Zambian labour market. Design/methodology/approach The study uses data from the 2014 School-to-Work Transition Survey and a multinomial logit model to examine three education-mismatch categories: undereducated, matched and overeducated. The paper also examines heterogeneities by education level and gender and uses empirical and subjective approaches of education mismatch. Findings The evidence shows that employed youth who worked while studying have a higher likelihood of having well-matched jobs. The subgroup analysis by education level reveals no significant relationship between working while studying among employed youth with higher education (secondary and above). However, employed youth with lower education (primary and lower) are less likely to be mismatched for the job. The linkage between the education system and the labour market needs to be strengthened to support a smoother school-to-work transition for youth. Additional support to enable exposure to the right type of work during youth's college or university studies could increase job match and reduce labour market inefficiencies. Originality/value The paper provides insights into a significant challenge faced by youth in developing countries, i.e. finding a suitable job for youth's level of education.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".