Rising Education, Declining Homeownership? HumanCapital and Housing Market Disparities in Canada
Bibliographic record
Abstract
This study investigates the relationship between educational attainment and homeownership in Canada, assessing whether gains in human capital have translated into higher ownership rates amid rising housing costs. Using microdata from the 2001 and 2021 Canadian Census Public Use Microdata Files (PUMFs), the analysis combines a pseudo-panel framework with survey-weighted regression models, Oaxaca–Blinder decompositions, and a control function approach to account for unobserved earnings- related factors. The results show that higher education is consistently associated with a greater probability of homeownership, with the relationship strengthening over time. Counterfactual predictions suggest that, under 2001 structural conditions (that is, applying coefficients from 2001 regressions to 2021 covariates), 2021 homeownership rates would have been lower than observed, indicating an increased effect of returns to education in the housing market. Decomposition results reveal that most of the education-based gap in homeownership is structural rather than compositional, implying that differences in the returns to characteristics drive disparities. These findings underscore the need for housing affordability policies that complement human capital development to ensure education-driven income gains are not undermined by structural market constraints for Canadians.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".