MEASURING THE GENEROSITY OF UNEMPLOYMENT BENEFIT SYSTEMS: EVIDENCE FROM HUNGARY AND ELSEWHERE IN CENTRAL EUROPE
Why this work is in the frame
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Bibliographic record
Abstract
The paper considers two aspects of the targeting of unemployment benefit systems: (a) the probability that benefit is received in the population of those unemployed on standard international criteria of search and availability, and (b) the probability in the population of benefit recipients that search is conducted. The focus is on Hungary but stylised facts for a range of Central European countries and two EU comparators are derived in the first part of the paper. The second part of the paper finds that most of the large decline in coverage of the Hungarian unemployed by insurance benefit (received by only a quarter of the searching stock in 1997) cannot be explained by changes in the composition of unemployment observable in labour force survey data (including unemployment duration). The probability of active search (search other than through a state employment office) is found to be very similar for those receiving insurance and assistance benefit.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it