Unemployment clusters across Europe's regions and countries
Bibliographic record
Abstract
Regional unemployment clusters Nearness matters within and across Europe–s borders High unemployment and regional inequalities are major concerns for European policy–makers, but so far connections between policies dealing with unemployment and regional inequalities have been few and weak. We think that this should change. This paper documents a regional and transnational dimension to unemployment – i.e., geographical unemployment clusters that do not respect national boundaries. Since the mid 1980s, regions with high or low initial unemployment rates saw little change, while regions with intermediate unemployment moved towards extreme values. During this polarization, nearby regions tended to share similar outcomes due, we argue, to spatially related changes in labour demand. These spatially correlated demand shifts were due in part to initial clustering of low–skilled regions and badly performing industries, but a significant neighbour effect remains even after controlling for these, and the effect is as strong within as it is between nations. We believe this reflects agglomeration effects of economic integration. The new economic geography literature shows how integration fosters employment clusters that need not respect national borders. If regional labour forces do not adjust, regional unemployment polarization with neighbour effects can result. To account for these ‘neighbour effects’ a cross–regional and transnational dimension should be added to national anti–unemployment policies. Nations should consider policies that encourage regional wage setting, and short distance mobility, and the EU should consider including transnational considerations in its regional policy, since neighbour effects on unemployment mean that an anti–unemployment policy paid for by one region will benefit neighbouring regions. Since local politicians gain no votes or tax revenues from these ‘spillovers’, they are likely to underestimate the true benefit of the policy and thus tend to undertake too little of it. – Henry G. Overman and Diego Puga
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".