Impact of Research and Development Expenditures on Economic Growth: Evidence from Industrial Development in Pakistan and A Comparison from Developed Countries
Bibliographic record
Abstract
This study examines the impact of research and development (R&D) expenditures on economic growth, with a focus on industrial development in Pakistan and a comparative analysis with developed countries. Despite being the sixth most populous country, Pakistan faces significant challenges in achieving sustained growth. Using a panel least squares regression model, this study analyzes the data of eight countries over a period of 25 years, including Pakistan and seven G7 nations i.e. France, the United States, the United Kingdom, Germany, Japan, Italy, and Canada. The correlation results reveal that R&D expenditures positively and significantly impact GDP across these countries. The GDPs of the G7 countries are significantly higher to Pakistan, highlighting the potential for substantial economic gains through increased R&D investment. The model shows a high R² and adjusted R², explaining 88.79% of the variation in GDP, with significant predictors including research expenditures and the lagged GDP into R&D expenditure. These findings suggest that for Pakistan, increasing R&D expenditure could lead to notable improvements in GDP and overall economic performance. Recommendations to increase government allocation to R&D, focusing on key industries such as textiles, automobile, electronics, technological advancement and infrastructure, and implementing policies that encourage private sector investment in R&D. Additionally, enhancing academic research and providing a roadmap for policymakers to foster industrial and economic development in Pakistan.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.002 | 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".