Short and long run decompositions of OECD wage in[e]quality changes
Bibliographic record
Abstract
This paper focuses on the causes of increased wage inequality in OECD countries in recent years and its decomposition into the component factors of trade surges in low wage products and technological change that has preoccupied the trade and wages literature. It argues that the length of production run and degree of fixity of factors is crucial in such analyses. In particular, if the observed wage inequality response to price and technology shocks reflects a short-run response in which factors and output have not adjusted fully across industries, then decomposition analysis of the causes of the observed increases in inequality is substantially altered relative to a long-run factors mobile world. This conclusion applies both when one type of labour has mobility costs and in the Ricardo-Viner case where there is an additional, sectorally immobile factor. Furthermore, only small departures from the fully mobile model can greatly change decompositions. This finding is important because most data used in earlier work are interpreted as reflective of a long-run full mobility response, when this may not be the case. Incorrect conclusions as to how trade surges and technology contribute to wage inequality can be easily drawn, if the data are in fact generated by a short-run adjustment process.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".