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Record W4319727931 · doi:10.32782/2224-6282/181-10

CORPORATE INCOME TAX GAP: FACTORS AND ASSESSMENT

2022· article· en· W4319727931 on OpenAlexaboutno aff
Olha Kuvaldina

Bibliographic record

VenueEconomic scope · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessPublic economicsValue-added taxTax reformIndirect taxTax avoidanceState income taxAd valorem taxDouble taxationCorporate taxTax creditAccountingEconomics

Abstract

fetched live from OpenAlex

Control over compliance with tax legislation is mainly entrusted to the governments of countries, which can use various instruments to monitor payment of main budget-forming taxes, including corporate income tax. There are many uncertainties and threats to corporate tax revenue, given recent technological advances and current abilities of international companies to do business around the world.Therefore, it is within countries that there are the greatest opportunities for mobilizing tax resources by increasing the level of compliance with tax legislation and eliminating or reducing the tax gap. Based on the results of the analysis of tax gaps in the income tax, it is possible to develop new relevant directions for improving tax policy and tax administration within the country. The article examines the prerequisites and factors that lead to the emergence of a tax gap, substantiates the importance of control and assessment of tax gaps. The meaning and difference between gross and net tax gap is specified. The main approaches to assessing tax gaps are considered - bottom-up approach and top-down approach. The main approaches to assessing tax gaps are considered - bottom-up approach and top-down approach. The differences, features of application and shortcomings of these methods, necessary conditions and elements for applying the chosen approach and sources of information, such as requests, information about taxpayers, data comparison, data of tax audits, are analyzed. The two main goals of the tax gap assessment were considered, namely, the improvement of tax policy, which is related to the "top-down" method, and the improvement of administration of the corporate income tax. At the same time, the use of the results of the assessment of the tax gap depends on the approach used to calculate the corporate income tax. International experience in assessing the size and structure of the tax gap from the corporate income tax is summarized based on the examples of such countries as Great Britain, Canada and the USA, which conduct tax gap analysis to improve fiscal legislation and reduce the level of the tax gap.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score0.997

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.046
GPT teacher head0.242
Teacher spread0.196 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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