Patent protection and the composition of multinational activity: Evidence from US multinational firms
Bibliographic record
Abstract
This article examines how patent protection in developing countries affects the technology licensing strategy of US multinational firms and the associated technology transfer flows. Strengthening patent rights lowers appropriability hazards and so reduces the firms’ reliance on affiliated licensing as the more secure means of transfer (the internalization effect). However lower appropriability hazards also encourage the firms to increase the volume of technology transfer via licensing both within and outside the firm (the appropriability effect). Which effect prevails depends on the underlying technological complexity of the firms’ product. We find that a strengthening of patent protection in the host country increases the incentive to license innovations to unaffiliated parties. While unaffiliated licensing rises among all firms, the volume of affiliated licensing falls among complex-technology firms but rises among simple-technology firms. The positive appropriability effect on affiliated licensing is strong enough among simple-technology firms that the entire composition of their licensing further shifts towards affiliated parties. The results are significant for recent work on the internalization theories of multinational firms and the interaction between firm strategy and the institutional environment, as well as for patent policy in the developing world, where access to knowledge is critical.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".