Goods market responses to trade shocks and trade and wages decompositions
Bibliographic record
Abstract
Abstract. Trade and wages literature asks whether trade or technology has been the major factor behind increases in wage inequality in OECD countries since the 1980s. In this literature, little attention has been paid to how goods market responses to trade shocks affect conclusions. Using an Armington heterogeneous goods trade model we capture demand side effects, and show how trade shocks affecting the price of unskilled‐intensive importable goods can be absorbed on the demand side of goods markets, with little or no impact on relative wage rates. No wage impact occurs if the elasticity of substitution in preferences between imports and import substitutes is one. As this elasticity increases, trade plays an ever larger role in explaining wage inequality changes, and as the elasticity goes below one the sign of the effect changes. We present some results of general equilibrium decompositions of total wage change into trade and technology components using UK data. We suggest that since many import demand elasticity estimates are in the neighbourhood of one, there is a prima facie case that goods market considerations further lower the significance of trade as an explanation of recent trends in OECD wage inequality beyond that claimed in the literature.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".