Regionalization, Changes in Home Bias, and the Growth of World Trade
Bibliographic record
Abstract
In this paper we use numerical modeling methods to quantitatively assess the impacts of changes in home bias within regions on the growth of world trade among major blocs over the last three decades.Existing work focuses on the impacts of trade barrier, transport cost and income changes on trade growth, rather than preferences.Removing changes in home bias over the last three decades from our global general equilibrium model reduces world trade by 27% compared to actual world trade in 2004 in our central case scenario.These results support the view that world trade among major blocs has became more regionalized rather than internationalized which we suggest may be due to a proliferation of free trade agreements.We calibrate a simple global trade model of inter bloc trade to both 1975 and 2004 data and substitute different calibrated parameters from the two data sets between model parameterizations.Our results suggest that if changes over time in home bias involving different regionally sourced goods in a multi-region multi product model are removed, substantial effects follow for the growth of world trade in the last three decades.Home bias changes in developed and developing economies reduce world trade by 8% and 19% respectively, suggesting that regionalization is more pronounced in developing country trade.Our results also indicate that income growth, income convergence, and falling trade costs explain 76%, 4%, and 7% respectively of the growth of world trade over the last three decades.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".