The impact of private R&D on the performance of food-processing firms: Evidence from Europe, Japan and North America
Bibliographic record
Abstract
This report investigates the impact of corporate research and development (R&D) on firm performance in the food-processing industry. The agro-food industry is usually considered to be a low-tech sector (the share of total output that is attributable to R&D is around 0.27% in the EU). However, the agro-food industry is very heterogeneous. On the one hand, there are many highly innovative food-processing firms with intensive R&D activity and, on the other hand, many food-processing firms derive and adopt innovations from other sectors such as machinery, packaging and other manufacturing suppliers. We perform data envelopment analysis (DEA) with two-step bootstrapping, which allows us to correct the bias in (in)efficiency and generate unbiased estimates for (in)efficiencies. We use a corporate dataset of 307 companies from agriculture and food-processing industries from the EU, the USA, Canada and Japan for the period 1991–2009. The estimates suggest that R&D has a positive effect on firms’ performance, with marginal gains decreasing at the R&D level, and performance differences detected across regions and food sectors. General public expenditure in R&D is also associated with a positive impact on firm performance. As a result, policy support for this type of non-high-tech innovative sector is expected to generate growth. However, results that suggest heterogeneity in R&D effects across EU Member States may point to differences in the implications of innovation policies across EU regions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".