MétaCan
Menu
Back to cohort
Record W3173280700 · doi:10.5430/ijfr.v12n5p24

Tax Awareness, Taxpayers’ Perceptions and Attitudes and Tax Evasion in Informal Sector of Ekiti State, Nigeria

2021· article· en· W3173280700 on OpenAlexvenueno aff
Oluyinka Isaiah Ogungbade, Adekunle Enitan, Adeleke Clement Adekoya

Bibliographic record

VenueInternational Journal of Financial Research · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsInformal sectorTax evasionTax revenueRevenueGovernment (linguistics)NigeriansPerceptionEvasion (ethics)BusinessGovernment revenueOil boomState (computer science)Indirect taxTax avoidancePublic economicsTax creditEconomicsTax reformPsychologyEconomic growthAccountingPolitical scienceMacroeconomics

Abstract

fetched live from OpenAlex

Nigeria has been desperately seeking tax revenues since 2016 due to a persistent fall in oil price and production. However, many Nigerians are not tax compliant since there was little or no emphasis on tax revenue, particularly in the informal sector during the oil boom. This paper examined the effect of Tax awareness and Taxpayers' perception of government spending on Tax evasion in the informal sector of Ekiti state. Also, the moderating effect of Taxpayers' attitude was examined. A structured questionnaire was used to collect data from 150 respondents, but only 108 returned the completed questionnaire, and only 100 respondents’ data were useful. The findings show that Taxpayers' awareness considerably reduced tax evasion, but the effect of Taxpayers' perception lacks analytical support. The findings also reveal that taxpayers' attitude has a significant moderating effect on the relationship between taxpayers' awareness and tax evasion. However, the moderating effect of Taxpayers' attitude on the relationship between Taxpayers' perception about government spending and Tax evasion was not statistically significant.

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)0.001
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 score0.006
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.091
GPT teacher head0.360
Teacher spread0.268 · 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

Citations12
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Financial ResearchSame topicTaxation and Compliance StudiesFrench-language works237,207