Power Resources Theory and Inequality in the Canadian Provinces∗
Bibliographic record
Abstract
In this paper, I demonstrate that higher levels of union membership and NDP provincial governments are associated with lower post-tax-and-transfer inequality in Canadian provinces. These results are consistent with the power resources theory of inequality and the welfare state first advanced by Korpi (1983) and Stephens (1979), which claims that differences in organizational resources such as unions and left po-litical parties are responsible for differences in distributional outcomes. While many studies have found this association using cross-national data from rich democracies, the repeated use of data from the same set of countries raises the possibility that the relationship is due to unobserved country-specific characteristics. Using a pooled cross-sectional time series dataset from 1980 to 2003 and focusing on within-province variation in Canada, I find evidence consistent with the power resources model. Over the past twenty-five years, power resources theory has provided one of the most influential accounts of variation in the size, characteristics, and outcomes of the welfare state. At its core, it asserts that working class power, achieved through organization by labor unions or left political parties, produces more egalitarian distributional outcomes (Ko-rpi 1983, Stephens 1979). Relationships have been found between these variables and a number of measures of inequality and redistribution. In one recent contribution from the power resources school, Bradley et al. (2003) examine household income inequality before
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".