Corruption and Tax Noncompliance Variables: An Empirical Investigation From Yemen
Bibliographic record
Abstract
Tax revenue is an important source of income for various governments around the world. However, challenges, as a result of corruption and tax noncompliance behaviour among the taxpayers, are hindering the adequate generation of such revenues for the government. The objective of this study is to investigate the effect of corruption and other tax noncompliance variables on tax revenue generation in Yemen. The study used survey research design via a questionnaire to collect data from 264 individual taxpayers in the Hadhramout Governorate. The collected data was analyzed using SPSS to perform reliability test, descriptive statistics, multicollinearity test, and regression analysis. The findings of the study show that corruption and tax rate are positively related to tax noncompliance; income level is negatively related to tax noncompliance; whereas penalty rate and education level are positive but not related to tax noncompliance. The implication of the study is that the government and the tax authority should update and institute new tax laws and policies that could minimize corruption among their officials and create more awareness among the taxpayers on the importance of paying tax to the government, so as to increase their compliance behaviour.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".