Foreign Aid, Corruption, Economic Growth Rate and Development Index in Nigeria: The ARDL Approach
Bibliographic record
Abstract
Foreign aid when properly utilized is expected to grow the economy of the receiving nation. Over the years Nigeria has benefitted from foreign aid inflows in a bid to stabilize its economy and build its infrastructure. This study desires to look into how the various foreign aid components (humanitarian aids, project aids and programme aids) have impacted the Nigerian economic growth rate and human development index giving the prevailing corruption index in the country as a moderating variable. Ex-post facto research design was adopted and data obtained from the Central Bank of Nigeria (CBN) Statistical Bulletin from 1990 to 2019. The study adopted autoregressive distributive lag (ARDL) techniques. It was revealed that as a result of the corruption perception index, there was a significant negative effect of foreign aid on the growth rate of Nigeria economy in the long run, while having a significant positive impact on human development index as well. In short run, foreign aids had a significant positive effect on the growth rate of the Nigerian economy, but an insignificant negative effect on human development index. However, government is encouraged to ensure that foreign aid is effectively channeled into agriculture, health, education and other productive areas.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".