Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A consensus has emerged that agglomeration economies are an important factor explaining why firms cluster next to each other. Yet disagreement remains over the sources of these agglomeration effects, given non-trivial measurement challenges. 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 as-sess 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 of 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, primarily due to unobservable firm-level productivity shocks correlated with hiring, I use a novel instrumental vari- able strategy, which exploits downsizing events at highly productive firms, in addition to control function methods in the spirit of the productivity literature. My findings from both approaches imply that worker flows can explain around 10 percent of the productivity gains experienced by other firms when new highly productive firms are added to a local labor market.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it