Exploring the Causes and Consequences of Regional Income Inequality in Canada
Bibliographic record
Abstract
The recent surge in populist movements sweeping many countries has brought into focus the issue of regional inequality. In this article, we develop a panel data set for Canada that includes information on 284 regions observed at 5-year intervals (from 1981 to 2011) and estimate a series of spatial econometric models to study the causes and consequences of regional inequality. Our results draw attention to the fact that the rise in inequality at the national level has been accompanied by greater cross-regional inequality. Differences in the level of economic development, precariousness of labor market conditions, and socioeconomic factors are among the key drivers of these regional patterns of inequality. We also find that the industrial mix of a region plays an important role in shaping its distribution of income: regions with high concentrations of manufacturing activities typically have lower levels of inequality, whereas regions with high concentrations of tertiary services, arts, and entertainment, as well as knowledge-intensive business services tend to have higher levels of inequality. In terms of the consequences of inequality, the growth/equity trade-off across Canadian regions varies significantly over the short- vs. medium-term horizons. In the short run, our results suggest that inequality is positively related to regional economic growth. This response changes as we move to a medium-term horizon, which suggests that as inequality persists over longer periods of time, it has a negative and significant impact on regional growth trajectories. Panel vector autoregressive models are also used to further explore the direction of causality of the growth-inequality relationship.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".