Tax Evasion and Its Consequences on an Emerging Economy: Nigeria as a Focus
Bibliographic record
Abstract
There is a universal understanding that tax evasion is a product of underground economy, however, the two economic scenarios have dissimilar influences on an economy. The effects of tax evasion and underground economy have been examined in this study, in order to establish a statistical evidence of the varying consequences of the two economic circumstances. The study employs secondary form of data ranging from 1991 to 2018 while using ordinary least squares multiple regression technique for the analysis. The findings reveal that tax evasion has a robust significant negative influence on economic growth while underground economy is having a strong and positive significant impact on economic growth of Nigeria. The two independent variables are found to be having very strong relationship with economic growth and are also determining 98.7% of the variation in the Nominal Gross Domestic Product. In the light of these findings, it is obvious that informal economy and tax evasion have become an integral part of Nigeria’s economy doing both harm and good substantially. Therefore, the study recommends more effort on the part of the government to encourage good leadership, formulate policies that will enhance voluntary tax compliance and help the informal sector businesses that are legitimate to become officially registered with little or no cost.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".