Bibliographic record
Abstract
Over the last fifteen years, many researchers have attempted to explain the determinants and changes of wage inequality. I propose a simple procedure to decompose changes in the distribution of wages or in other distributions into three factors: changes in regression coefficients; the distribution of covariates, and residuals. The procedure requires only estimating standard OLS regressions augmented by a logit or probit model. It can be extended by modelling residuals as a function of unmeasured skills and skill prices. Two empirical examples showing how the procedure works in practice are considered. In the first example, sources of differences in the wage distribution in Alberta and British Columbia are considered; in the second, sources of change in overall wage inequality in the United States, 1973–99, are re–examined. Finally, the proposed procedure is compared with existing procedures. JEL classification: J3 La décomposition des changements dans les distributions de salaires : une approche unifée. Au cours des quinze dernières années, nombre d’études se sont penchées sur les déterminants et les changements de la distribution des salaires. Ce mémoire propose une procédure pour décomposer les changements de la distribution des salaires en trois éléments: les changements dans les coefficients de régression, la distribution des regresseurs et les changements résiduels. Cette procédure ne nécessite que l’estimation de regressions par moindre carrés ordinaires et d’un modèle probit ou logit. L’auteur montre aussi comment modéliser les résidus en fonction de compétences non mesurées. La procédure proposée est mise en application dans le contexte de deux exemples: la distribution des salaires en Alberta et en Colombie–Britannique et les changements dans la distribution des salaires de 1973 à 1999 aux Etats–Unis. Le mémoire examine aussi comment cette procédure se compare aux méthodes proposées par d’autres chercheurs.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".