Bibliographic record
Abstract
Inequality is on the rise in Canada and this state of affairs has provoked outrage and demands for redistribution at a time when governments at every level are searching for reliable long-term growth. This paper examines the links between income inequality and economic growth and whether there is a trade-off between redistributive policies, and economic growth, or whether income redistribution can enable faster growth. The authors survey the existing literature on the impact of inequality on economic growth, and then conduct an econometric analysis of the association between provincial economic growth in Canada and three different measures of income inequality, finding no statistically significant relationships. One measure of income redistribution, the difference between the market income Gini coefficient and the disposable (after-tax, after-transfer) income Gini is positively associated with provincial growth rates — but since the largest transfer programs in Canada are federal programs financed out of nation-wide taxes, it is unlikely that this association carries over to the national level. Much of the growth in income disparity has been driven by innovation that places a premium on highly trained workers. With that in mind, the Goldin-Katz model, used to explain the rising earnings differentials of highly skilled workers in the US, can be combined with the Aghion-Bolton model of capital market imperfections to develop a framework for examining the impact of education spending, and the taxes that finance it, on earnings inequality and economic growth. The authors then review evidence that raising marginal tax rates on high-income individuals would not raise additional tax revenues, but impose substantial costs on the economy, as would higher corporate income taxes. Punishing high earners is a self-defeating choice, although improvements to the social safety net would give more Canadians the chance to join their ranks.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".