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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".