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Regional Tax Competition in Canada, the United States and Russia: Assessment of Regulatory Experience

2022· article· en· W4313210124 on OpenAlexaboutno aff
Michael Alexeev, Andrey Korytin

Bibliographic record

VenueFinancial Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Government Finance and Decentralization
Canadian institutionsnot available
Fundersnot available
KeywordsTax competitionLegislationValue-added taxPublic economicsRevenueBusinessCompetition (biology)Ad valorem taxTax reformTax revenueEconomic policyEconomicsInternational economicsInternational tradePolitical scienceAccounting

Abstract

fetched live from OpenAlex

Recent changes in the Russian tax legislation aimed at weakening the tax powers of the federal subjects in relation to profit tax open the question of a reasonable degree of limitation of the fiscal autonomy of Russian regions, as well as the regulation of regional tax competition. This article discusses the peculiarities of regulation of domestic tax competition in Canada and the United States on the basis of an analysis of the provisions of the tax legislation related to the fiscal powers of regional jurisdictions (provinces, states). The article focuses on approaches to limiting harmful tax practices that affect the economy or budget revenues of other regions. Empirical studies confirm the existence of negative effects of domestic tax competition, as well as the relevance of measures to restrict it. The considered countries make efforts to limit domestic tax competition both at the level of interregional agreements and at the level of central authorities. However, practice shows that it is difficult to ensure actual implementation of agreements at the regional level, so regulation by the central government is of key importance. Based on this result, recommendations are given for taking measures against harmful tax competition between the regions of Russia.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.380
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.014
GPT teacher head0.265
Teacher spread0.252 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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