Canadian versus US Mortgage Markets: A Comparative Study from an Austrian Perspective
Bibliographic record
Abstract
Abstract Why did the United States experience a housing and mortgage market boom and bust in the 2000s, while analogous Canadian markets were relatively stable? Both US and Canadian markets are replete with government interventions. In this paper, I account for the US and Canada’s different experiences by arguing that government interventions are not created equal. Some government interventions prevent market participants from pursuing actions that ex ante are reckoned beneficial. Alternatively, other interventions lead to the pursuit of actions that turn out to be costly ex post. It is the latter type that we expect to manifest in crises. The US case is one where government interventions in the mortgage markets led to actions that appeared ex ante beneficial but were revealed to be costly ex post. Alternatively, Canada’s mortgage market was and remains essentially a regulated oligopoly. Regulatory capture makes for a sclerotic market that likely imposes costs on Canadian borrowers in the forms of limited financing options and higher interest rates. However, this sclerosis also lends itself to stability. This market structure made the Canadian mortgage market relatively insusceptible to a bubble.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".