Critiquing the OECD’s Employment Protection Legislation Index for individual dismissals: The importance of procedural requirements
Bibliographic record
Abstract
Key EU agencies have successfully urged member states to scale back employment protection legislation as a solution to unemployment. The economic arguments for this reform are mixed, with recent empirical evidence largely unsupportive. Critics have also raised doubts about the accuracy of the OECD’s Employment Protection Legislation Index, which is the principal method EU agencies use to target so-called high-protection regimes. This article supplements existing criticisms of the OECD index by arguing that it fails to account for procedural requirements in assessing the difficulties and costs of carrying out individual dismissals. Evidence from New Zealand, ostensibly a low-protection country, demonstrates procedural requirements can pose the main impediments to carrying out individual dismissals. This suggests the need for revision of the OECD Employment Protection Legislation Index or the use of other indices instead.
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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 | 0.001 | 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".