Can Multi-Stage Production Explain the Home Bias in Trade?
Why this work is in the frame
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Bibliographic record
Abstract
A large empirical literature finds that there is too little international trade, and too much intra-national trade to be rationalized by observed international trade costs such as tariffs and transport costs. The literature uses frameworks in which the nature of production is assumed to be unaffected by trade costs. This paper investigates whether a model in which the nature of production can change in response to trade costs -a framework with multi-stage production -can better explain the home bias in trade. I find that the model can explain about 2/5 of the Canada border effect; this is about two-and-one-half times what a model with one stage of production can explain. The model also explains a significant fraction of a key dimension of Canada-U.S. trade, the high degree of "back-and-forth" trade or vertical specialization.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it