Foreign Capital Flows and Growth of the Nigeria Economy: An Empirical Review
Bibliographic record
Abstract
The volume of investment capital flows between foreign nationals and developing nations has necessitated that a study be conducted to assess the impact of this foreign capital flows on the economic growth of these developing nations. This paper therefore aims at empirically determining the extent to which foreign capital flows have impacted on the growth performance of the Nigeria economy from 1982–2012. Data were drawn from the publications of the Nigerian Bureau of Statistics (NBS), the Central Bank of Nigeria (CBN) and the World Bank report. The multiple regression analysis method was adopted for the test of the hypotheses. The SPSS statistical software (version 17.0) was used for the data analysis. From the results of the analysis, it was discovered that Foreign Capital Inflows had a positive and significant effect on economic growth as proxied by the GDP, which is an indication that foreign capital inflows exerted considerable influence as a key fiscal policy instrument of economic growth over the stated period. Also the Foreign Capital Outflow in the same vein had a positive and significant effect on the GDP, which is another indication that it exerted considerable influence as a key fiscal policy instrument of economic growth over the stated period. Furthermore, the Openness of the economy, which was another explanatory variables used to ascertain the growth performance of the economy, had a positive and significant effect on the GDP. On the other hand, the Human Capital Development had a negative and insignificant effect on the GDP. The implication is that it did not exert much influence on economic growth over the stated period. Finally, the inflation rate had a positive sign with GDP. It was however; statistically insignificant which points to the severity of the inflationary pressure brought to bear on the economy over the stated period. The paper concludes that policy on foreign capital flows should be vigorously pursued and enhanced to provide a buffer to the nations dwindling internally generated revenue (IGR) amidst astronomically growing population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".