Determines of Sectoral R&D Investment in the UK: A Dymanic Panel Approach
Bibliographic record
Abstract
The study estimates the determinants of R&D expenditure in the UK by using sectoral data of eight large scale industries over time. We examined both traditional and exceptional however empirically plausible determinants of R&D using two alternative dynamic models. Pooled OLS, the Fixed Effects and the Random Effects models are used for estimation. We find that the size of R&D expenditure in the UK is smaller than many other industrial countries.. The largest amount of R&D expenditure in the UK, takes place for ‘machineries’ industry followed by ‘communication equipment’ and ‘post and telecommunication’ industries. Estimated results demonstrate that the market size, ratio of skilled to unskilled workers, and macroeconomic policies significantly affect R&D investment in the UK. Working hours of low, medium and high skilled workers’ significantly affect the R&D expenditure with different size effects. We also find that R&D expenditure is an industry specific phenomenon.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".