University Research, Industrial R&D, and the Anchor Tenant Hypothesis
Bibliographic record
Abstract
We examine geographic concentration, agglomeration, and co-location of university research and industrial R&D in three technological areas: medical imaging, neural networks, and signal processing.Using data on scientific publications and patents as indicators of university research and industrial R&D, we find strong evidence of geographic concentration in both activities at the level of MSAs.While evidence for agglomeration (in the sense of "excess" concentration relative to the size of MSAs and the size distribution of research labs) of research in these fields is mixed, we do find strong evidence of co-location of upstream and downstream activity.We view such co-located vertically connected activities as constituents of a "local innovation system," and these appear to vary markedly in their ability to convert local academic research into local commercial innovation.We develop and test the hypothesis that the presence of a large, local, R&D-intensive firm -an "anchor tenant" -enhances the productivity of local innovation systems by making local university research more likely to be absorbed by and to stimulate local industrial R&D.Presence of anchor tenant firms may be an important factor in stimulating both the demand and supply sides of local markets for innovation and may be an important channel for transmission of spillovers.While our empirical results are preliminary, they indicate that anchor tenant technology firms may be an economically important aspect of the institutional structure of local economies.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".