Effect of Foreign Aids on Economic Growth in Nigeria
Bibliographic record
Abstract
This study investigated the impact of foreign aids on economic growth in Nigeria using time series data spanned from 1990 to 2017. The research considered the secondary data that were gathered from CBN statistical bulletin 2017 and World Bank Data Indictors. Ordinary Least Square techniques was adopted in the study and used Augmented Dickey-Fuller Unit Root Test, co integration test, granger causality test, ECM to estimates data employed. The findings revealed that all the variables employed were stationary at first difference and integrated at the same order1(I), the co-integration test shows that variables are co-integrated at one co-integrating equation which means that there is a long run relationship. The Error Correction Model established that the error that caused disequilibrium in the short run is being corrected in the long-run at a speed of adjustment at 6%. The findings revealed real gross domestic product responds inversely to changes in official development assistance and foreign direct investment. Based on these findings the study concluded that foreign aids have a significant impact on economic growth in Nigeria. Different diagnostic tests are applied in order to confirm the major assumption of multiple regression analysis like multicollinearity, heteroskedasticity and autocorrelation. Therefore, the study recommends among others that government needs to formulate strong and effective education and healthcare policies to facilitate and attract investment in the sectors and improve their efficiency in the long-run that will influence productivity.
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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 | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".