Home and Regional Biases and Border Effects in Armington Type Models
Bibliographic record
Abstract
We discuss biases in preferences and their trade effects in terms of impacts on non-neutral trade flows motivated by recent literature on both home bias and the border effect.These terms take on multiple definitions in the literature and are often used interchangeably even though they differ.The border effect refers to a higher proclivity to trade behind rather than across national borders and is usually defined by the coefficients of regional dummies from an estimated gravity model.It can be present both in data and in counterfactual model solutions.Sometimes the reduced form of the gravity model used is asserted to reflect an Armington type model.For the border effect to occur as a model outcome, a structural model with at least 2 home regions and 1 country abroad is needed.In contrast to current literature, we offer a characterization of various forms of preference bias in trade models and measures of their associated trade effects based on a concept we term trade neutrality.These effects go beyond conventional border effects, and can be both across and within borders.Home bias is typically specified as an Armington preference for domestic over comparable foreign products in a trade model where goods are heterogeneous across countries.It is reflected in both model structure and parameterization, but defined in several different ways in the literature.We assess the contribution of each form of bias to the set of possible trade effects using a calibrated model with 3 Canadian regions, the U.S., and the rest of the world using 2001 data.We also evaluate how much of the conventional border effect is accounted for when model biases are modified in various ways.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".