Foreign Direct Investment and Economic Growth in Ghana
Bibliographic record
Abstract
The relationship between Foreign Direct Investment (FDI) and Economic Growth has been a topical issue for several decades. Policymakers in a large number of countries are engaged in creating all kinds of incentives to attract FDI, because it is assumed to positively affect economic growth. This paper investigates the effect of FDI on economic growth in Ghana. The paper, test for the presence of the long run linear relationship between FDI inflows and Economic Growth (GDP) for Ghana. The study employs various econometrics tools such as Dickey Fuller (DF) and Augmented Dickey Fuller (ADF) tests, Vector Auto Regression (VAR) and Johansen Co-integration test on time series data from the first quarter of 2001 to the fourth quarter of 2010. The results reveal that a long run relationship exists between the variables, and that FDI is positively related to economic growth in Ghana. Ghana should therefore continue to reform its economic and foreign policy to attract more investors which can help boost its economy. Keywords: Foreign Direct Investment, Economic Growth, Vector Auto Regression (VAR), Co- Integration, and Unit Roots
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".