SHARING THE WEALTH FROM GROWTH: COMPARING THE CANADIAN AND US EXPERIENCES By
Bibliographic record
Abstract
The purpose of this paper is to compare Canadian and US performances with respect to economic growth and inequality. We find that Canada, with little economic growth in the past two decades (in fact, negative in the most recent decade), has had an increase in inequality of market income (in the absence of government taxes and money transfers), but almost no increase in inequality of after-tax incomes (after deducting taxes and adding money transfers). This experience is remarkably different than that of the United States. The United States has grown more quickly than Canada but inequality in income in that country has increased on both market income and an after-tax income basis. In fact, the increase in inequality in market income was similar in the United States as in Canada, even though inequality is greater in the United States. We find that after accounting for time trends, average market income and inequality are negatively correlated in both Canada and the US, while after-tax income and inequality are negatively related in Canada and positively related in the US. Tests suggest that changes in market income are “Granger causing ” changes in market income inequality in Canada Although the above suggests that one of the major differences in Canadian and US experiences is that Canadian governments have been more “equalizing ” than US governments, a number of factors should be considered when analyzing the data. We discuss how policy and non-policy factors need to be explored further to understand better the relationship between economic growth and inequality. 2 I.
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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 | 0.002 | 0.006 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| 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".