Decompositional Analysis Using Numerical Equilibrium Models: Illustrations from Trade Literature
Bibliographic record
Abstract
INTRODUCTION This chapter discusses recent applications of general equilibrium computational methods in the international trade area which are backwards (ex post) rather than forwards (ex ante) focused. The example chosen is the trade and wages debate, where the issue is disentangling the relative importance of multiple influences on wage inequality change (trade surges from low-wage countries and skill-biased technical change). The relevant literature includes Abrego and Whalley (2000, 2001). In the past, most applied general equilibrium modelling (AGM) of international trade has been forward focussed, attempting to provide some basis of assessment as to what might happen if particular measures are adopted. This includes assessing the effects of NAFTA in advance of its enactment (Francois and Shiells, 1994) and ex ante assessment of the impacts of the Uruguay Round as agreed in the World Trade Organisation (WTO) (Martin and Winters, 1996; Whalley, 2000). In such exercises, calibration usually takes place around benchmark equilibrium data set for a reference year; counterfactual computations then shed light on what this reference year equilibrium might have looked like if a policy or other change, not yet enacted, had been in place. In ex post analysis, it is typically the case that calibration to two or more years is needed. The issue is, given a model consistent with observations in both years X and Y , how its equilibrium might look like were only one (or a subset) of the changes actually occurring between the years actually to occur.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.008 | 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".