Empirical Analysis of the Impact of Fiscal Policy on Economic Growth of Nigeria
Bibliographic record
Abstract
This study provides empirical analysis of the impact of fiscal policy on economic growth in Nigeria. Time series data from 1986 to 2010 relevant to the study were collected from the Central Bank of Nigeria statistical bulletin, Volume 22 and the National Bureau of Statistics. The ordinary least square method of multivariate regression was utilized in analyzing the log-linearized Model. The Augmented Dickey-Fuller unit root test was employed to establish the stationarity of the variables while the General-to-Specific approach to Autoregressive Distributed Lag (ARDL) model was used for testing for the existence of long-run and short-run equilibrium conditions. The findings were that, there is evidence of long run equilibrium relationship between fiscal policy and economic growth in Nigeria during the period studied. The adjusted R2 value of 0.6850 showed that about 68.5% of the total variation in the real GDP is explained by the independent variables included in the model. Specific fiscal policy variables that have significant and positive impact on economic growth in Nigeria are government recurrent and capital expenditures. Non-oil taxes and government total debts have no significant impact on real GDP. Only capital expenditure has short run equilibrium relationship with economic growth. It is therefore recommended that government should establish a strong fiscal responsibility and transparency system in the fiscal institutions; and tax reforms should be such that would encourage increase in investment and fight corruption. Government debts should be channelled towards provision of critical infrastructure so as to provide the enabling investment environment, while fiscal policy should be complemented with the use of effective monetary policy.
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 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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".