Bibliographic record
Abstract
Although the econometric evaluation of R&D has attracted wide interest in many countries, it has not attracted much in the UK. The main objective of this paper is to fill this void, i.e., to estimate the impact of R&D on productivity growth of the UK manufacturing sector. However, there are some additional objectives. Firstly, we estimate the impact of R&D on productivity growth of large and small firms and we discuss a number of theoretical arguments regarding the role of firm size. Secondly, given that the technological infrastructure influences the innovative capacity of a firm, we compare the impact of R&D on productivity growth of high-tech firms with the corresponding impact on productivity growth of low-tech firms. Thirdly, we investigate whether the contribution of R&D to productivity growth has changed over time. Based on firm-level data (78 firms, 1989–2002), we find that the contribution of R&D is approximately 0.04. Although the R&D-elasticity of large firms (0.044) is higher than the corresponding elasticity of small firms (0.035), the difference is small. In contrast, the R&D-elasticity is considerably high for high-tech sectors (0.11), but statistically insignificant for low-tech sectors. Finally, the investigation of the elasticity of R&D over time revealed an interesting discontinuity showing that although until 1995 the R&D-elasticity was approximately zero, after 1995 it increased dramatically to 0.09. We investigate the potential causes of such non-linearity and we suggest a number of possible explanations.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".