Impact of Capital Flight on Tax Revenue in Nigeria: A Co-integration Approach
Bibliographic record
Abstract
This study presents an empirical analysis of the impact of capital flight on tax revenue in Nigeria. We made use of secondary data collected from the Central Bank of Nigeria Statistical Bulletin of various issues, Federal Inland Revenue Services and National Bureau of Statistics. The empirical measurement covers the sample period between 1980 and 2015. An Ordinary Least Square, Augmented Dickey-Fuller unit root test, Error Correction Mechanism and Co-integration test was adopted in the study. The results revealed that the Gross Domestic Product has a significant effect in the positive direction, while capital flight and inflation rate have a significant effect in the negative direction. The study recommended that the Federal Inland Revenue System, the department saddled with the responsibility of tax collection, should review the tax system and policies with the aim of plugging loopholes in the existing tax system thereby preventing organizations from evading and avoiding taxes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".