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Record W3123399380

Evaluating the Impact of R&D Tax Credits on Innovation: A Microeconometric Study on Canadian Firms

2004· preprint· en· W3123399380 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueMADOC (University of Mannheim) · 2004
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicInnovation Policy and R&D
Canadian institutionsInnovation, Science and Economic Development CanadaUniversité de Sherbrooke
Fundersnot available
KeywordsTax creditBusinessIncentiveTax incentiveMatching (statistics)Monetary economicsIndustrial organizationEconomicsPublic economicsMicroeconomics
DOInot available

Abstract

fetched live from OpenAlex

This study examines the effect of R&D tax credits on innovation activities of Canadian manufacturing firms. Over the 1997-1999 period the Federal and Provincial R&D tax credit programs were used by more than one third of all manufacturing firms and by close to two thirds of firms in high-technology sectors. We investigate the average effect of R&D tax credits on a series of innovation indicators such as number of new products, sales with new products, originality of innovation etc. using a non-parametric matching approach. Compared to a hypothetical situation in the absence of R&D tax credits, recipients of tax credits show significantly better scores on most but not all performance indicators. We therefore conclude that tax credits increase the R&D engagement at the firm level and that the R&D activities induced by fiscal incentives lead to additional innovation output.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.804
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.168
GPT teacher head0.323
Teacher spread0.155 · 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