Innovation Activities and Export Performance of Canadian Small and Medium-Sized Agri-Food Firms
Bibliographic record
Abstract
Canadian small and medium-sized firms face two major challenges, namely, that of innovation in supporting their growth and improving their competitiveness and that of access to international markets. The objective of this study is to analyze the impact of research and Development (R&D) investment on the export performance of Canadian agri-food companies and on that of related sectors, namely, the textile and clothing sector and the manufacture of leather goods and similar products. We used impact assessment methods to analyze the effects of firms' innovation activities on their export performance. First, we analyzed explanatory factors for R&D expenses; second, we analyzed the impact of R&D on extensive (market access) and intensive (trade value) margins of trade. In doing so, we used Statistics Canada's National Accounts Longitudinal Microdata File (NALMF) for 2010 to 2015, which is coupled with the Trade by Exporter Characteristics (TEC) database. The size of firms and their support from the Canadian government affect their propensity to invest in R&D, the value of R&D expenses and their intensity, as measured from the ratio of R&D to sales of goods and services. Overall, our results show that investment in R&D has a positive impact on the export performance of agri-food SMEs. Les petites et moyennes entreprises (PME) canadiennes font face à deux grands enjeux soit celui de l’innovation afin notamment de soutenir leur croissance et améliorer leur compétitivité et celui de l’accès aux marchés internationaux. Le présent projet de recherche a pour objectif d’analyser l’impact des investissements en recherche et développement (R&D) sur les performances à l’exportation des entreprises agroalimentaires canadiennes et de celles de secteurs connexes soit les industries du textile et des vêtements et de la fabrication de produits du cuir et produits analogues. Les méthodes d’évaluation d’impact seront utilisées pour analyser les effets des activités d’innovation des entreprises sur leurs performances à l’exportation. Dans un premier temps, les facteurs explicatifs des investissements en R&D sont analysé. Puis nous analysons les effets des investissements en R&D sur les marges extensive (accès aux marchés) et intensive (valeur du commerce). Nous utilisons le Fichier de micro données longitudinales des comptes nationaux (NALMF) de Statistique Canada pour la période de 2010 à 2015 qui est couplé au fichier du programme de Commerce selon les caractéristiques des exportateurs (TEC). La taille des entreprises et l’appui du gouvernement canadien sont déterminants dans la probabilité d’investir dans la R&D ainsi que le montant de ces investissements et son intensité mesurée par le ratio du montant investit sur les ventes totales de biens et services des PME agroalimentaires.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".