Decomposing Canada's Growing Housing Affordability Problem: Do City Differences Matter?
Bibliographic record
Abstract
This article examines the role of eight factors that affect the prevalence and incidence of housing affordability problems: geography, demography, migration/immigration/ ethnicity, income recipients, income source, employment and education. It develops bivariate probit models that use the 1991 and 1996 Canadian census public use microdata to predict the joint probability that a household spends more than half of its income on housing and that its income is below the poverty line. The conclusions show that city and regional differences are negligible after the effects of the factors common to all the cities have been accounted for. Changing employment levels and sources of household income are the most important factors explaining the prevalence and growth of housing poverty. While single parents have the highest incidence, the growth of the problem is mostly in the young non-family households. Migration, immigration and ethnicity play a role that is independent of the other factors. Education has almost no effect. The changes are in the underlying structures and in the variable profiles that differ remarkably across the factors. There were minor adjustments in the demographic and occupational profiles that would tend to reduce the problem but these are unlikely to stem its growth in the foreseeable future.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".