Labor-Market Scars When Youth Unemployment Is Extremely High: Evidence from Macedonia
Bibliographic record
Abstract
The objective of this study is to assess how the duration of the unemployment spell of Macedonia youth affects later employment (the employment 'scarring' effect) and wage outcomes (the wage 'scarring' effect). To that end, we first devise a model in which the unemployment spell is determined by individual and household characteristics and work attitudes and preferences. Discrete-time duration method is used to estimate this model. Then, we rely on a standard employment and Mincer earnings functions. We repeatedly impute missing wages to address the selection on observables, and use the regional unemployment rate when individual finished school as an instrument to mitigate the selection on unobservables. The School to Work Transition Survey 2012 is used. Results robustly suggest a presence of employment scar as those young persons who stay unemployed over a longer period of time were found to have lower chances to find a job afterwards. On the other hand, the study does not provide evidence for the existence of the wage scar.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".