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Record W3143095439 · doi:10.1257/pol.20190757

When Does Product Liability Risk Chill Innovation? Evidence from Medical Implants

2022· article· en· W3143095439 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAmerican Economic Journal Economic Policy · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLiabilityProduct liabilityUpstream (networking)Downstream (manufacturing)BusinessProduct (mathematics)Significant differenceAffect (linguistics)Product innovationIndustrial organizationActuarial scienceMarketingFinanceMedicineComputer sciencePsychologyInternal medicine

Abstract

fetched live from OpenAlex

Liability laws designed to compensate for harms caused by defective products may also affect innovation. We examine this issue by exploiting a major quasi-exogenous increase in liability risk faced by US suppliers of polymers used to manufacture medical implants. Difference-in-difference analyses show that this surge in suppliers’ liability risk had a large and negative impact on downstream innovation in medical implants, but it had no signifi-cant effect on upstream polymer patenting. Our findings suggest that liability risk can percolate throughout a vertical chain and may have a significant chilling effect on downstream innovation. (JEL K13, L64, L65, O31, O34)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0170.002

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.

Opus teacher head0.052
GPT teacher head0.258
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it