Product Liability Litigation and Innovation: Evidence from Medical Devices
Bibliographic record
Abstract
We examine the relationship between product liability litigation and innovation by systematically combining data on product liability lawsuits with data on new product introductions in a panel dataset of leading medical device firms.We first document a decline in the propensity to introduce new products for both defendant firms and other firms operating in litigated device categories.This decline, however, does not spill over to other device categories, and we also do not find any slowing down in firms' patenting activities.We then show that changes in two features of the regulatory environment---(1) the availability of public information regarding adverse events and (2) federal law taking precedence over state law---substantially affect the likelihood of litigation.These changes also provide quasi-exogenous variations in litigation that confirm our baseline findings.Finally, we show that litigation appears to induce firms to develop safer devices.Overall, our findings suggest that product liability litigation affects the rate and direction of technological progress, and that safety regulation and liability regimes interact with one another in significant ways.
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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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".