Gravity for cross‐border licensing and the impact of deep trade agreements: Theory and evidence
Bibliographic record
Abstract
Abstract We examine whether deep regional trade agreements facilitate cross‐border licensing. A micro‐founded gravity equation for each supply mode is derived from a model in which heterogeneous firms choose to supply their goods to foreign markets through export, foreign direct investment or licensing. We present several comparative statics results regarding the effects of changes in the fixed costs of serving the destination country, the freeness of trade, and the strength of intellectual property rights protection on bilateral flows of licensing revenues. We then empirically test our theoretical predictions using data on the cross‐border flows of royalties and licence fees for 49 countries in the period 1995–2012. In addition to variables that capture the impact of shallow and deep regional trade agreements, we construct dummy variables that represent subcategories of IP rights‐related provisions. Consistent with our theoretical predictions, we find that improved access to the destination market through a deep regional trade agreement and stronger IP rights protection through a regional trade agreement with legally enforceable IP rights and technology‐related provisions increase bilateral flows of licensing revenues. Among IP rights‐related provisions, the accession to or ratification of existing international IP agreements and the protection of trademarks, patents, or industrial designs are important for facilitating cross‐border licensing.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".