How do employment effects of job creation schemes differ with respect to the foregoing unemployment duration
Bibliographic record
Abstract
Based on new administrative data for Germany covering entrances into job creation schemes between July 2000 and May 2001, we evaluate the effects of this active labour market policy programme on the employability of the participating individuals. The programme effects are estimated considering the timing of treatment in the individual unemployment spell. Applying propensity score matching in a dynamic setting where the time until treatment in the unemployment spell is stratified into quarters, regional (East and West Germany) as well as gender differences are considered in the estimation. As matching is concerned with selection on observables only, we test the robustness of the estimates against possible unobserved influences. The results in terms of employment present a mixed picture. For West Germany, most of the estimates are insignificant at the end of the observation period, but positive exceptions are found for persons starting in the fifth or ninth quarter of the unemployment spell. For East Germany, none of the groups\nexperiences an improvement of the labour market situation. Instead, the majority of the estimates establish negative employment effects until the end of the observation period (30 months after start of programmes). Hence, job creation schemes decrease the employment chances of the participating individuals.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".