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Record W2118791774 · doi:10.1017/s147474720500209x

The foreign property rule: a cost–benefit analysis

2005· article· en· W2118791774 on OpenAlexaffabout
David F. Burgess, Joel Fried

Bibliographic record

VenueJournal of Pensions Economics and Finance · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsWestern University
Fundersnot available
KeywordsLiberian dollarEconomicsDiversification (marketing strategy)PortfolioEquity (law)Monetary economicsVolatility (finance)Financial economicsBusinessFinance

Abstract

fetched live from OpenAlex

The foreign property rule (FPR) requires that no more than 30% of the assets held in tax-deferred retirement savings accounts be foreign property. The FPR is supposed to increase the value of the dollar and reduce its volatility and decrease the cost of capital and promote investment in Canada as well as decrease the extent of inequality inherent in these plans. On the basis of evidence from the easing of this regulation from 20% to 30% over the period 2001/02 we find that it accomplishes none of these objectives. There was no measurable impact on the exchange rate predicted from the Bank of Canada's forecasting equation; the capital outflow from the change amounted to no more than two days trade in the foreign exchange market over the period 2000/01; Canada's equity markets did significantly better internationally when the FPR was eased than in the prior two-year period. Finally, closer inspection reveals that the rule exacerbates income inequality by imposing the largest costs on lower middle-income groups. We estimate that the increase in the FPR from 20% to 30% increased Canadians expected income by between 500 million and one billion dollars annually by permitting greater portfolio diversification. The complete removal of the FPR would increase income by an estimated additional 1.5 billion to 3 billion dollars annually.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.742
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.207
Teacher spread0.195 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations6
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Pensions Economics and FinanceSame topicFinancial Literacy, Pension, Retirement AnalysisFrench-language works237,207