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Record W4404378702 · doi:10.1080/10438599.2024.2424866

Industrial designs and firm performance: evidence from publicly traded Canadian companies, 1990–2014

2024· article· en· W4404378702 on OpenAlex
Robert J. D. Embree, Elias Collette, Diego eduardo Santilli

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEconomics of Innovation and New Technology · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBusinessIndustrial organizationEconomicsEconometricsFinancial economics

Abstract

fetched live from OpenAlex

Industrial designs (IDs) are a specialized type of intellectual property that protects a product's form and presentational features, giving it uniqueness that differentiates it from other products. This paper estimates the effect on firm revenue and profitability of holding IDs. Using a unique data set linking Canadian ID holdings with Canadian publicly traded firms over the years 1990-2014, the authors use two methods to identify a positive association between holding IDs and firm revenue per employee and net income per employee. We contribute to the literature on IDs by introducing controls for patenting, R&D spending, and time-varying sector effects. To determine the marginal effect of each additional ID held, we use a fixed effects regression and find that a 1% increase in the stock of IDs increases revenue per employee by 0.19%. With a nearest-neighbor matching approach, we find a 10% total premium in revenue per employee and a 20% premium in net income per employee among firms holding at least one ID, compared to those with none.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.126
GPT teacher head0.240
Teacher spread0.114 · 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