The Impact of Housing Prices and GDP Growth on Income Inequality in Canada: A National Study (1990–2022) with Unemployment as a Control Variable
Bibliographic record
Abstract
This study examines the relationships among housing prices, GDP growth, and unemployment and their effects on income inequality in Canada from 1990 to 2022. Employing multiple regression analysis, the research reveals that rising housing prices are significantly associated with lower income inequality, contradicting the common assumption that higher housing costs exacerbate socio-economic disparities. In contrast, GDP growth appears to have little measurable effect on income distribution, challenging the Kuznets Curve Hypothesis, which posits that economic growth ultimately leads to reduced inequality. Unemployment shows minimal relevance as a mediating variable between housing prices, GDP growth, and inequality, suggesting that its role in shaping income disparities is limited. These findings emphasize the importance of government intervention and robust social policies to mitigate labor market shocks and their distributional consequences. The study contributes to the literature by challenging conventional economic thought and highlighting the critical influence of housing markets and redistributive policies on income inequality. Although homeownership can serve as a short-run equalizer for middle-class households, persistent long-term affordability issues remain. Moreover, the results suggest that economic growth, when measured solely by GDP, is insufficient for addressing inequality without the support of progressive fiscal and social measures. From a policy perspective, the findings underscore the need for enhanced regulatory oversight of the housing sector, implementation of progressive taxation, and enforcement of strong labor standards to promote a fair distribution of economic growth. Future research should explore regional variations, long-term implications of rising housing expenses, and the causal mechanisms driving income inequality in Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".