Bibliographic record
Abstract
Today, countries, especially the developing ones rebase their Gross Domestic Product (GDP) to determine their economic strength. Nigeria as an acclaimed giant in Africa cannot but continuously examine variables which may impact the economy. It is in this light that this study was intended to investigate the Determinants of Tax Revenue Effort in Nigeria. To achieve this, secondary data, as time series data, covering a period of 1980 to 2015, were used and sourced from the Central Bank of Nigeria Statistical Bulletin, Annual Abstract from the Office of the National Bureau of Statistics and the Federal Inland Revenue Service, both in Nigeria. The dependent variable of Tax Revenue Effort (TTAXeff) was regressed on macro independent variables of Agricultural Sector Productivity(AGRICSP), Manufacturing Sector Productivity (MANSP), Tourism Sector Productivity(TOURSP), Telecommunication Sector Productivity(TELCOMSP), Capital Flight(CAPFR), Trade Openness (TOPEN) and Human Capital Development(HCD). The study adopted a longitudinal research design and used the Autoregressive Distributed Lag (ARDL) technique to evaluate the models. The findings revealed that Agricultural Sector Productivity, Tourism Sector Productivity, Trade Openness and Human Capital Development had significant and positive effects on Tax Revenue Effort in Nigeria. The Manufacturing Sector Productivity, Telecommunication Sector Productivity and Capital Flight had significant but negative effects on Tax Revenue Effort in Nigeria. There is however the need to consistently ensure better performance of tax efforts in the country through strict and meticulous enforcement of tax rules and tax administrations procedures in the country.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".