THE DETERMINANTS OF THE G7 COUNTRIES INTRA TRADE: A GRAVITY MODEL APPROACH
Bibliographic record
Abstract
The aim of this paper is to evaluate the bilateral trade between G7 countries (Canada, France, Germany, Italy, Japan, United Kingdom, United States) in the period 2005-2020. The methodology is based on gravity model, using panel data and Ordinary Least Squares Method in EViews. The Pearson Correlation, the Unit Root Test and the Hausman test were performed. Three different gravity models were performed, with the exports, the imports and the bilateral trade flows as the dependent variables. The findings are: GDP of the exporters, GDP per capita of the importers, the export openness and the export market penetration are major predictors with a positive impact on the bilateral trade, while the distance between countries, the tariff costs and the non-tariff costs have a negative impact. There were some unexpected results too. The distance and the dummies are not statistically significant. The market penetration had an unexpected negative sign for export flows and the similarity distance had a negative sign too for import flows. The originality is replacing the distance with tariff trade costs and non tariff trade costs, as well as replacing trade openness with export openness and non trade openness, which are all better predictors. Further research should be conducted using a different estimator, like PPML.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".