Union bargaining power, relative wages, and efficiency in Canada
Bibliographic record
Abstract
We use a computable general equilibrium model incorporating trade unions, efficient Nash contracts, existing distortions, and international trade to measure the deadweight loss in Canada arising from the ability of unions to raise wages above competitive levels. The model incorporates two features new to CGE analysis: parameterization of union bargaining power and variations in union preferences. Estimates indicate the deadweight loss to be no more than 0.04 per cent of GNP. However, the small aggregate effect masks considerable adjustments at the industry level, in imports and exports, and in the distribution of income. Adjustments are also larger with employment‐oriented unions. Pouvoir de négociation des syndicats, salaires relatifs et efficacité au Canada. Les auteurs utilisent un modèle d'équilibre général calculable qui prend en compte les syndicats ouvriers, des contrats efficients à la Nash, les distorsions existantes, et le commerce international pour mesurer les pertes de bien‐être au Canada attribuables au fait que les syndicats engendrent des niveaux de salaires au dessus des niveaux concurrentiels. Le modèle incorpore deux éléments inédits: la paramétrisation du pouvoir de négociation des syndicats et les variations dans les préférences des syndicats. Les résultats indiquent que les pertes de bien‐être ne dépassent pas 0,04 pourcent du PIB. Cependant, ce petit effet agrégé masque des ajustements substantiels au niveau de l'industrie, dans les importations et exportations, et dans la répartition des revenus. Les ajustements sont aussi plus importants quand les syndicats mettent l'accent sur l'emploi.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".