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Policy Forum: The Case for an Annual Net Wealth Tax

2020· article· en· W3096362170 on OpenAlexvenueaboutno aff
Andrew Jackson, Toby Sanger

Bibliographic record

VenueCanadian Tax Journal/Revue fiscale canadienne · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsNational wealthPublic economicsIndirect taxDouble taxationTax reformTax deferralCapital (architecture)InequalityRevenueDistribution (mathematics)Gross incomeTax revenueDirect taxState income taxFinance

Abstract

fetched live from OpenAlex

The public and policy makers are showing a renewed interest in wealth taxes as a possible response to the problem of wealth inequality. This article describes the recent trend of rising wealth inequality, and the factors contributing to it, in developed countries, including Canada. The authors argue that an annual wealth tax could and should counter the trend, and that such a tax is both justifiable on economic grounds and technically feasible. They assert that the taxation of capital income does not adequately address the largely tax-free accumulation of large fortunes, and that an annual wealth tax is preferable to property taxes, which exclude the taxation of financial assets and are levied on gross rather than net wealth. The article responds in detail to major criticisms of an annual tax on wealth, arguing that such a tax is needed to achieve a more equal distribution of wealth while raising additional revenues.

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)0.022
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.937
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.055
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.007
Scholarly communication0.0120.015
Open science0.0020.004
Research integrity0.0170.015
Insufficient payload (model declined to judge)0.0160.002

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 head0.039
GPT teacher head0.218
Teacher spread0.180 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2020
Admission routes2
Has abstractyes

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