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Record W4407962878 · doi:10.5771/9780815720003

Tax Reform and the Cost of Capital

2010· book· en· W4407962878 on OpenAlexaboutno aff

Bibliographic record

VenueRowman & Littlefield Publishers eBooks · 2010
Typebook
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsCapital (architecture)EconomicsMonetary economicsBusinessHistoryAncient history

Abstract

fetched live from OpenAlex

The tax reform movement that swept the U.S., Great Britain, and most other industrialized nations during the last decade has focused attention on international comparisons of the cost of capital. More recently, international comparability has become a critical issue of tax harmonization. This is a vital concern in the European Community, as well as between Canada and the United States. This volume provides international comparisons of the cost of different types of capital for nine major industrialized countries—Australia, Canada, France, Germany, Italy, Japan, Sweden, the United Kingdom, and the United States—for the period 1980-1990. In the early 1980s the introduction of tax incentives for saving and investment gradually shifted the tax base from income toward consumption. By 1990 most of these special tax provisions had been reduced or repealed in order to lower tax rates and equalize the tax treatment of different forms of capital income. Income was firmly reestablished as the most appropriate basis for taxation. Separate chapters for each of the nine countries, written by leading experts in public economics, provide detailed accounts of tax policy changes over the decade. Each chapter contains a quantitative description of these tax policies and summarizes this information in the form of effective tax rates. The book thus serves as an indispensable reference for comparing capital income taxation in industrialized countries during a period of rapid policy change.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.366
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.018
GPT teacher head0.186
Teacher spread0.169 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations0
Published2010
Admission routes1
Has abstractyes

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