Terms of trade and economic growth in developing country
Bibliographic record
Abstract
Purpose The purpose of this study is to investigate the first time ever the effects of overall terms of trade, bilateral terms of trade and main commodity groups’ terms of trade on economic growth. Design/methodology/approach Augmented Dickey Duller and Philips Perron unit root tests and Johensan cointegration test have been applied by using annual time series data from 1974 to 2017. Dynamic ordinary least square and fully modified ordinary least square have also been used to perform sensitivity analysis. Findings The cointegration test confirm the positive long-run relationship between overall terms of trade (ToT) and economic growth. Country-wise results show that ToT with Australia, Bangladesh, Canada, Hong Kong, Japan, Kuwait, Malaysia, Singapore, Sri Lanka, UK and the USA have significant positive effect on economic growth. Conversely, ToT with China and UAE has significant negative effect on economic growth. In contrast, ToT with India, Norway, Saudi Arabia and Switzerland has insignificant effect on the economic growth of Pakistan. Product-wise results indicate that the product group namely, Chemical, Crude Material inedible except fuels, Manufactured and Minerals fuels and Lubricant found to be a significant positive effect on economic growth. However, Beverages and Tobacco, and Machinery and Transport product groups found to be significant negative impact on economic, while Food and Live animals found to be insignificant. Practical implications In general, it is suggested that the beneficial terms of trade are favorable for economic growth. The study suggested export promotion policy for which relationship between ToT and economic growth found positive and import substitution policy is suggested the products found a negative relationship between the said variables. Originality/value This paper is a pioneer attempt to investigate the effect of overall ToT, bilateral terms of trade and the main commodity group’s ToT on economic growth 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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".