The impact of private R&D on the performance of food-processing firms: Evidence from Europe, Japan and North America
Why this work is in the frame
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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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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| 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 it