Does the Gravity Model Explain Bangladesh’s Direction of Trade? A Panel Data Approach
Bibliographic record
Abstract
The goal of this article is to investigate the determinants of bilateral trade flows of Bangladesh with her fifty two major trading partners with the use of trade gravity model approach. The gravity model has been estimated using pooled OLS, fixed effects, random effects estimation technique with the help of panel data for the period 1975-2005. Our estimation results show that trade volume of Bangladesh responds more than proportionally to per capita GDP and distance for OECD and NON -OECD trading partner countries separately. Bangladesh's direction of trade pattern is also strongly governed by geographical characteristics, such as Area implying Bangladesh has a tendency to trade with larger countries. Membership in OECD and GSP dummy has significant impact on trade. The results of gravity models have also been applied to calculate the trade potentials indicating that Bangladesh has unexploited trade potentials with countries like UK, Singapore, Netherlands, Germany, UAE, Canada, India, China, Italy, Australia, Germany, Switzerland & Pakistan. We have found that the actual trade is converging towards equilibrium level of trade using average speed of convergence measure. Therefore, identifying & utilizing unexploited trade potentials among some of Bangladesh's trading partners should stimulate growth to alleviate unemployment & poverty. Keywords: Gravity Model, Panel data, Fixed effects Model, Bangladesh's trade
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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; both teacher heads agree on what is shown here.
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".