Innovation, patents and trade: A firm‐level analysis
Bibliographic record
Abstract
Abstract Using microdata of firm exports and international patent activity, we find that Greek innovative exporters, identified by their patent filing activity, have substantially higher export revenues by selling higher quantities rather than charging higher prices. To account for this evidence, we set up a horizontally differentiated product model in which an innovative exporter competes for market share in a destination against many non‐innovative rivals. We argue that as the competition among the exporters of the non‐innovative product becomes more intense, the innovative firm exports more compared with its non‐innovative rivals in more distant markets, a prediction that is empirically confirmed in the dataset for Greek innovative exporters. Résumé Innovation, brevets et commerce : analyse au niveau de l’entreprise. À l’aide de microdonnées d’entreprises relatives aux exportations et aux activités de brevetage international, nous montrons que les exportateurs grecs innovants, identifiés par leurs dépôts de brevets, réalisent des gains à l’exportation nettement supérieurs en misant davantage sur les volumes de vente que sur l’augmentation des prix. Pour expliquer cette situation, nous avons élaboré un modèle de différenciation horizontale de produits dans lequel les exportateurs novateurs sont en compétition avec de nombreux concurrents non innovants afin de gagner des parts de marché. Nous montrons qu’à mesure que la compétition s’intensifie entre les exportateurs de produits non innovants, l’entreprise innovante exporte davantage que ses concurrents vers les marchés plus éloignés ; cette prédiction se vérifie de façon empirique grâce aux données relatives aux exportateurs grecs innovants.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".