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Record W3121673396 · doi:10.34989/swp-2008-19

Inflation, Nominal Portfolios, and Wealth Redistribution in Canada

2021· preprint· en· W3121673396 on OpenAlexaffabout
Césaire Meh, Yaz Terajima

Bibliographic record

VenueRePEc: Research Papers in Economics · 2021
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsBank of Canada
Fundersnot available
KeywordsEconomicsInflation (cosmology)Monetary economicsDebtRedistribution (election)Monetary policyRedistribution of income and wealthReal interest rateWelfareRelative priceMacroeconomicsMarket economyUnemployment

Abstract

fetched live from OpenAlex

There is currently a policy debate on potential refinements to monetary policy regimes in countries with low and stable inflation such as the U.S. and Canada. For example, in Canada, a systematic review of the current inflation targeting framework is underway. An issue that has generally received relatively less attention in this debate is the redistributional effects of inflation. This omission is likely to be important since the welfare costs of inflation depend not only on aggregate effects but also on redistributional consequences. The goal of this paper is to contribute to this policy debate by assessing the redistributional effects of inflation in Canada that arise through the revaluation of nominal assets and liabilities.We find that the redistributional effects of inflation are sizeable even for low and moderate inflation episodes. The main winners are young middle-class households with substantial amounts of mortgage debt. Besides young households, inflation also represents a windfall gain for the government because of its long-term debt. Old households, rich households, and the middle-aged middle-class lose from inflation, largely due to their sizeable holdings of bonds and non-indexed defined benefit pension assets.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.259
Teacher spread0.243 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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Same venueRePEc: Research Papers in EconomicsSame topicFinancial Literacy, Pension, Retirement AnalysisFrench-language works237,207