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Record W2274719183 · doi:10.34989/swp-2015-40

Credit Conditions and Consumption, House Prices and Debt: What Makes Canada Different?

2021· preprint· en· W2274719183 on OpenAlexaffabout
John Muellbauer, Pierre St‐Amant, David Williams

Bibliographic record

VenueEconstor (Econstor) · 2021
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsBank of Canada
Fundersnot available
KeywordsCollateralConsumption (sociology)DebtEconomicsMonetary economicsHouse priceMonetary policyHousehold debtBusinessFinance

Abstract

fetched live from OpenAlex

There is widespread agreement that, in the United States, higher house prices raise consumption via collateral or possibly wealth effects. The presence of similar channels in Canada would have important implications for monetary policy transmission. We trace the impact of shifts in non-price household credit conditions through joint estimation of a system of error-correction equations for Canadian aggregate consumption, house prices and mortgage debt. We find strong evidence that, after controlling for income and household portfolios, easier credit conditions raise house prices, debt and consumption. However, unlike in the United States, housing collateral effects on consumption are absent. Given credit conditions, rising house prices increase the mortgage down-payment requirement and reduce consumption, although there is evidence for some attenuation of this effect over the 2000s. We also find that high and rising levels of both house prices and debt since the late-1990s can be mostly explained by movements in incomes, housing supply, mortgage interest rates and credit conditions, suggesting that the outlook for house prices and debt could depend mainly on the future paths of these variables.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.021
GPT teacher head0.208
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2021
Admission routes2
Has abstractyes

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