Understanding the Nexus of R&D, Innovation and Economic Growth in Nigeria
Bibliographic record
Abstract
R&D and Innovation activities, which lead to technological progress, are considered as important factors contributing to stable and continuous economic growth. Total Factor Productivity accounts for the proportion of economic growth that is not captured by labour and capital inputs, and is measured by R&D and innovation in this paper. The paper investigates the impact of R&D and innovation, labour and capital on economic growth in Nigeria using Least Square Method. The result of the thirty one (31) years (1977-2007) reviewed shows that Gross Expenditure on R&D (GERD) has significant impact on economic growth. The coefficient of R&D which is negatively related to economic growth implies that it is not enough to increase spending on R&D and innovation when there are weak institutions, high corruption practices, low interaction between the academia and the industry, uncoordinated industrial clusters, among others. The result also shows that both labour and capital are directly related to economic growth, though the former plays a significant role while the latter does not. This paper concludes that government must be committed to R&D and innovation funding, developing strong institutions, enhancing the academia-industrial linkage as well as implementing a workable science, technology and innovation policy in order to bolster and diversify the economy. Government should also provide various fiscal incentives for the industrial firms in their various clusters so as to encourage them to engage in R&D and innovation activities, either through reverse engineering or inventing new ones, as this will not only lead to economic growth but also raise the global competitiveness of Nigeria.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".