A Vector Error Correction Modeling of Security Spending and Economic Growth in Nigeria
Bibliographic record
Abstract
The study has been on the impact of security expenditure on the level of economic growth in Nigeria. Using data covering the period between 1980 and 2010, the ECM result shows that the expenditure on defence has a negative impact on the level of economic growth. An indication of flawed expenditure budgeting and implementation in the defence sector. Expenditure on internal security played important role in generating the desired level of economic growth in Nigeria. The low elasticity indicates that the significance was below expectations. The result of the variance decomposition shows that the shocks to expenditure on defense did not significantly explain the changes in the level of economic growth in Nigeria. Expenditure on internal security however played a role in influencing the level of economic growth in Nigeria. The result of the Johansen cointegration test shows a long run relationship among the variables and the error correction result shows a satisfactory speed of adjustment. It is thus recommended that government should reassess the content of her defense expenditure and make it more transparent and growth oriented.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".