Bibliographic record
Abstract
This thesis contains three papers about how imperfections in the intellectual property trade and enforcement may undermine incentives for innovation. In Chapter 1, I discuss Patent Assertion Entities (PAE) which are firms specialized in patent enforcement, and whose effect on innovation are theoretically ambiguous. I exploit a Supreme Court decision affecting PAEs incentives to file patent infringement lawsuits and cross-industry differences in exposure to empirically estimate the casual effect of PAE activity on innovative firms’ performance. I find that the Supreme Court’s ruling effectively discouraged PAEs’ litigation and I show that sectors exposed to PAEs experienced a remarkable increase in their successful patent applications in response to the reduction of PAEsactivity. Chapter 2 investigates the mechanism linking patent litigation and innovation. I develop a dynamic model of litigation here the defendant screens out heterogeneous plaintiffs through a series of out-of-court settlement offers. Employing granular information on patent infringement cases filed in the US from 2007-2021, I estimate my model. I find that patent litigation costs over $48.8 billion a year to listed defendants, $24.7 billion of which from settlement transfers. Furthermore, cases filed by PAEs have smaller stakes, lower quality, and are more likely to settle for smaller amounts. I consider two counterfactuals. First, I introduce a conditional fee-shifting rule, finding it effective in discouraging frivolous litigation, but likely to induce costly delays. Then, I turn to forum shopping, finding that restricting plaintiffs’ discretionality in the choice of venue would dramatically reduce the incentives to file low-quality cases. Finally, Chapter 3 investigates systematic acquisitions of startups, which are prevalent in digital markets. I introduce a model of competition where demand depends substantially on the quality ranking of incumbents, which can be improved by R&D or acquisitions. I find that the prospect of acquisitions incentivizes startups investments when the incumbents are engaged in a neck-and-neck technological race, however these incentives fade as the leader escapes competition. Hence, when network externalities are strong enough, a forward looking industry leader has significant incentives to lock the industry in a state of stagnation, where startups’ investments are low, and killer acquisitions are prevalent.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".