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Record W2613785627 · doi:10.1111/caje.12270

Calculating the real return on a sovereign wealth fund

2017· article· en· W2613785627 on OpenAlexvenueno aff
Andreas Benedictow, Pål Boug

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicState Capitalism and Financial Governance
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsSovereign wealth fundInvestment (military)Purchasing powerNews aggregatorWeightingRate of returnEconometricsMonetary economicsMacroeconomicsFinanceForeign direct investmentComputer science

Abstract

fetched live from OpenAlex

Abstract We present a new methodology for calculating the real return on sovereign wealth funds (SWF) that share the investment objective of maximizing international purchasing power in terms of goods and services. Specifically, we modify the traditional approach for deflating the nominal return along three dimensions: the aggregator formula, the measure of international prices and the weighting scheme. We argue that a geometric average of price levels is an appropriate aggregator formula for capturing the deflationary effects of imports increasingly originating from low‐cost countries, and that import prices paid by the SWF owner and weights reflecting the owner's import pattern are consistent with the investment objective. Our proposed approach, using the Norwegian Government Pension Fund Global as an illustration, raises the estimated average annual real rate of return over the sample period of 1998–2012 from 3.1% to 4.9%.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.997
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.001
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 head0.140
GPT teacher head0.203
Teacher spread0.062 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations9
Published2017
Admission routes1
Has abstractyes

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