Bibliographic record
Abstract
A clear consensus has emerged that agglomeration economies are an important factor explaining why firms cluster next to each other. Yet, because of non-trivial measurement challenges, disagreement remains over the sources of these agglomeration effects. This paper is the first to present direct evidence showing how localized knowledge spillovers arise from workers changing jobs within the same local labor market. Specifically, I assess the extent to which firm-to-firm labor mobility enhances the productivity of firms located near highly productive firms. Using a unique dataset combining Social Security earnings records and balance sheet information for Veneto, a region in Italy with many successful industrial clusters, I first identify a set of highly productive firms, then show that hiring workers with experience at these firms significantly increases the productivity of other firms. To address identification threats arising from both contemporaneous and future unobservable firm-level productivity shocks correlated with hiring, I use control function methods drawn from the productivity literature and a novel instrumental variable strategy, which exploits downsizing events at highly productive firms. My findings imply that worker flows can explain around 10 percent of the productivity gains experienced by incumbent firms when new highly productive firms are added to a local labor market.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".