Decomposing Wage Inequality Change Using General Equilibrium Models
Bibliographic record
Abstract
This paper presents ex post decomposition analysis of wage inequality change using multisector general equilibrium models.The analytical structure used is a specific-factors model of trade, which we calibrate to UK data for the two years 1979 and 1975.We first calibrate our general equilibrium trade model to observations on wage inequality, trade, production and consumption spanning these years, capturing the separate influences of trade, technology and demographics on inequality.Between these years wage inequality changed, but multiple changes in exogenous variables occurred (world prices, technology, endowments).We use calibration techniques to determine parameter values consistent with both the equilibria and the changes in exogenous variables contributing to the wage inequality change being decomposed.We then compute counterfactual equilibria in which only some of the changes in exogenous variables are present to allow us to assess what portion of the observed change is attributable to the various contributing factors.Our findings are that the roles of trade and factor-biased technological change are relatively larger than in earlier literature.We also find that changes in factor endowments to offset increased inequality generated by trade and skilled-biased technological changes, a feature that seems to have gone relatively unnoticed in earlier literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".