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Record W3092434420 · doi:10.5430/rwe.v11n6p108

Optimal Tax Behaviour and Corporate Survival: The Nigeria Experience

2020· article· en· W3092434420 on OpenAlexvenueno aff
Joseph Ugochukwu Madugba, Egbide Ben-Caleb, Uche T. Agburuga, E. Obadiaru David, Wilson U. Ani, Jane O. Ben-caleb

Bibliographic record

VenueResearch in World Economy · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
Fundersnot available
KeywordsDescriptive statisticsNormality testNormalityTest (biology)OutlierTaxpayerAccountingBusinessActuarial scienceEconomicsEconometricsStatistical hypothesis testingStatisticsMathematicsBiology

Abstract

fetched live from OpenAlex

Every entity operates with the entity concept, which endues management to strategize for survival. This study examined optimal tax behaviour and corporate survival with a focus in Nigeria. Ex-post-facto was adopted and data computed from annual accounts of 52 out of 198 quoted companies were used. Descriptive Statistics, test of normality, outliers, and multi-collinearity tests were carried out to establish the normality of the data. Both fixed and random effects of the generalized least square multiple regressions were conducted and the outcome of the test showed that ETR is a positive but insignificant determinant of EPS while EATS were found to be a positive and significant determinants of EPS of companies in Nigeria. The study concluded that quoted companies are yet to effectively and efficiently explore loopholes in tax laws. The study recommended that companies in Nigeria should urgently explore these loopholes and improve their performance and experts with professional skills should be engaged as not infringe tax laws.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.169
GPT teacher head0.323
Teacher spread0.154 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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