Competition, Licensing, and Innovation Strategy
Bibliographic record
Abstract
We consider an upstream innovator and two downstream competitors; and examine the impact of product market competition on the innovator's R&D strategy, when she can license her innovations to either one downstream competitor (targeted licensing) or both (market-wide licensing). We show that downstream competition unambiguously increases the appeal of targeted licensing over market-wide licensing. Moreover, competition increases the innovator's incentives to innovate under targeted licensing, but decreases these incentives under market-wide licensing. Thus, a threshold level of competition may exist such that above (below) that threshold, targeted (market-wide) licensing is optimal and innovation is increasing (decreasing) in competition. Using U.S. data across all industries over the period 1976--2006, we then empirically investigate the impact of downstream competition on upstream innovation. We find that there is an U-shaped relationship between downstream competition and upstream innovation. Furthermore, using import tariff rates as a quasi-natural experiment, our identification tests suggest that downstream competition has a non-linear causal effect on upstream innovation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".