Business Cycle Dependent Unemployment Benefits -- A Theoretical Investigation
Bibliographic record
Abstract
This thesis considers the optimality of business cycle dependent unemployment benefits. In light of the current financial and economic crisis it is highly relevant to investigate whether the automatic stabilisers can be strengthened without causing further distortionary effects on incentives. Intuitively, business cycle dependent unemployment insurance (UI), e.g. countercyclical unemployment benefits, is an obvious way to strengthen the automatic stabilisers and improve the trade-off between incentives and insurance. Incentives are strengthened in booms, where job search is more efficient, whereas insurance is strengthened in recessions, where job search is less efficient and where involuntary unemployment is higher. A business cycle dependent UI system is not only a theoretical abstraction, as there already exists such systems in practice, e.g. in the US and Canada. Recently, the Danish Labour Market Commission appointed by the Danish Government has suggested to make the benefit duration business cycle dependent. However, surprisingly there exist neither a
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".