Job and Workers Flows in Europe and the US: Specific Skills or Employment Protection?
Bibliographic record
Abstract
There is more resistance to layoffs in continental Europe than in the U.S. At the same\ntime, there is some evidence that employed European workers are more productive than their American counterparts. We reconcile these two facts by proposing that some institutions, such as Employment Protection Legislation (EPL), induce workers to invest in and develop job specific skills, making them more productive and leading to costly displacement as these types of skills are lost upon separation from the employer. It is also well established that mobility patterns -flows in and out of unemployment or even movements from job to job, are reduced in continental Europe relative to the U.S. The possibility to invest in skill improvement introduces a complementarity between EPL and the investment decision: more stable matches increase the incentive to accumulate specific skills; but also more productive matches are broken less frequently; hence there is a “mutliplier” effect arising from this complementarity. To quantitatively assess all these propositions, we built a tractable asymmetric information matching model featuring all types of transitions out of employment: layoffs, quits to unemployment and job-to-job transitions. We find that EPL does induce workers to invest more in human capital and may help explain greater resistance to layoffs in Europe. We find that flows out of employment are indeed reduced by EPL. However, allowing for skill investment does not generate any strong multiplier due to the fact several new effects are at play keeping unemployment duration at a low level and thus putting downward pressure on the multiplier. The conclusion of all this may be that EPL matters for explaining specialization and low movements out of jobs, but that low movements out of unemployment may be better explained by other institutions such as unemployment benefits.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".