Welfare benefits and the rate of unemployment: some evidence from the European Union in the last thirty years
Bibliographic record
Abstract
Our objective in this paper is to re-examine the hypothesis that welfare benefits may be responsi-ble for the observed differences in cross- country unemployment rates and test its validity by using panel data from 19 countries over the 1970-2000 period. For this purpose, we set up a general equi-librium model encompassing the private and public sectors of the economy, where the government comes to the relief of the unemployed by increasing the welfare benefits per man. From this model, we extract an unemployment rate equation. The results that emerge from the empirical analysis sug-gest that social benefits per man may indeed adversely influence the rate of unemployment in EU-15. But the results change significantly when the EU member states are classified as high-, low- and average unemployment countries. In particular, we find that, whereas unemployment benefits exert perceptible positive influences in the high and average unemployment sub-groups, their influence in the low unemployment sub-group is nil. This finding, in conjunction with the evi-dence that the unemployment rate is invariant with respect to social benefits in USA and Canada, leads us to the conclusion that some EU countries may have to restructure their welfare systems, so as to reduce welfare benefits in favour of greater labour market flexibility and self-reliance on the part of workers.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".