City size and the risk of being unemployed. Job pooling vs. job competition
Bibliographic record
Abstract
We study the relationship between city size and the risk of being unemployed. We introduce a new mechanism, job pooling , as a source of agglomeration economies in a model of risk sharing with an imperfect labor market and risk-neutral agents. Despite competition across workers for jobs in the largest cities ( job competition ), workers tend to be located in large cities because tight labor markets yield income gains from the sharing of firms among workers that do not know ex ante the negotiated wage rates ( job pooling ). The covariance between the probability of being unemployed and firm-specific wage shocks decreases with the number of workers. This explains why larger cities exhibit a higher unemployment rate on average and weaker fluctuations. From French data, we find that the positive covariance between the unemployment rate and local wages decreases with city size. We also show that moving from an area in the first quartile of density to the last quartile translates into an increase in unemployment of 14%. In contrast, the elasticity of unemployment fluctuations with respect to employment density is negative: moving from an area in the first quartile of density to the last quartile translates to a decrease in our unemployment fluctuation index of 25%.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".