Evidence on the Co-Integration of the Determinants of Foreign Direct Investment in Ghana
Bibliographic record
Abstract
The mining industry has traditionally been a major recipient of foreign direct investment in sub-Saharan Africa and has commonly been an important foreign exchange earner for the region. The purpose of this study is to empirically determine the factors that have influence FDI flows in Ghana from 1983 to 2012, using co-integration analysis. The major empirical and methodological contribution of this study is the use of co-integration approach to determine FDI inflows to the mining sector in Ghana. The results of the study registered exchange rate, inflation and openness of trade to be significant in the long run. Natural resources were designated to have a negative long-run relationship between FDI inflows. GDP was used as a proxy for market size and economic liberalization were also registered to be insignificant. In the short run all the variables were found to be insignificant except natural resources which contributed negatively and significant to the mining sector. One economic task facing Ghana, therefore, is how to articulate the necessary policies that can attract the right kind of FDI in the mining sector.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".