Gone But Not Forgotten: Labor Flows, Knowledge Spillovers, and Enduring Social Capital
Bibliographic record
Abstract
It is well known that patent citations occur disproportionately between patents issued to inventors living in the same location, which has been taken as evidence of geographically localized knowledge spillovers.In this study, we find that patent citations also occur disproportionately often in locations where the cited inventor was living prior to being issued the patent in question, which we interpret as evidence of a significant role played by social capital in promoting knowledge spillovers.We first develop a model of purposeful investments in social capital by co-located inventors that incorporates the effect of expected mobility.Using patent and citation data, we then test two hypotheses motivated by the model.First, we find strong evidence in support of the enduring social capital hypothesis; social ties that facilitate knowledge transfer persist even after formerly co-located individuals are separated.Consistent with the model, we find that individuals with higher ex ante mobility are somewhat less likely to invest in location-specific social relationships, but the pattern of spillovers implied by patent citations is consistent with them investing in those social relationships that survive subsequent geographic separation.Second, we find strong evidence that the social ties associated with co-location are particularly important for facilitating knowledge spillovers across technology fields or communities of practice where alternative mechanisms for transferring knowledge are more costly.
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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 | 0.009 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".