Taxes and Foreign Direct Investment (FDI) in Nigeria
Bibliographic record
Abstract
This study examines the relationship between the different forms of taxes collected and foreign direct investment in Nigeria. The study adopted the ex-post facto research design and covers a period of thirtyfour years from 1982 – 2015. Secondary data were analysed using the Autoregressive Distributed Lag (ARDL) regression technique. The study found that there is a negative and significant relationship between taxes collected in the form of National Information Development Fund and Education Tax and Foreign Direct Investment. Also, that there exists a positive and significant relationship between Value Added Tax, Companies Income Tax and Foreign Direct Investment, while Petroleum Profits Taxes and Custom and Excise Duties do not influence Foreign Direct Investments in Nigeria. Based on the findings, the study recommends that there is need for government to come up with more friendly economic policies such as tax incentives and macroeconomic adjustments that will enhance continuous increase and growth of the nation's GDP and by implication, attracts FDI into Nigeria.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".