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Record W1638787498 · doi:10.1108/jes-12-2015-0226

Competition and innovation with horizontal R&D spillovers

2017· article· en· W1638787498 on OpenAlex
Massoud Khazabi, Nguyen Xuan Quyen

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

VenueJournal of Economic Studies · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInnovation Policy and R&D
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSpillover effectCompetition (biology)EconomicsOriginalityWelfareMicroeconomicsSocial WelfareProduction (economics)Value (mathematics)MathematicsMarket economyEcology

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to extend a theoretical framework for analyzing competition and innovation in the presence of horizontal spillovers. Design/methodology/approach A theoretical analysis approach is adopted to drive the paper’s findings. Findings It is shown that when firms behave non-cooperatively in both the R&D and production stages, the degree of spillover has a negative relationship with the effective and respective R&D expenditures of each firm as well as the level of social welfare. An inverted-U relationship between competition and social welfare also holds. When firms behave cooperatively in the R&D stage, and non-cooperatively in the production stage the relationship between the R&D expenditure of the joint research lab and the number of firms in the market is negative. Originality/value In the literature on R&D spillovers and process innovation, efforts are mostly focused on the comparative R&D expenditures and the relative social welfare between non-cooperative and cooperative R&D. The question of the effectiveness of R&D technology on the optimal number of firm, however, is not explicitly addressed. The paper is intended to address this lacuna.

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 score0.663
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.122
GPT teacher head0.318
Teacher spread0.197 · 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