The Effect of Investment Promotion on Foreign Direct Investment Inflow into Ghana
Bibliographic record
Abstract
The paper investigated the effect of investment promotion (IP) on foreign direct investment flow (FDI) into Ghana. Cointegration among the variables was established using auto regressive distributed lag (ARDL) models in the presence of a mix of I (0) and I (1) variables. The control variables, inflation and trade openness were statistically significant in the short run. Whilst inflation exerted a negative effect on FDI inflow; trade openness positively induced FDI inflow. GDP per capita and exchange rate did not statistically significantly influence FDI inflow in the short run. In respect of the key variable GIPC, in the short run, there was a positive but statistically insignificant effect on FDI inflow. The estimated long-run ARDL model showed that macroeconomic variables such as inflation, GDP and trade openness determined FDI inflow into Ghana. Notwithstanding the positive relationship between establishment of IP agency (GIPC) and FDI inflow, this relationship was statistically insignificant. Greater efforts at macroeconomic management specifically, promotion of external trade, increasing GDP and reducing inflation holds more promise to attracting FDI into Ghana. GIPC should be maintained to provide a supportive role when the foreign investors arrive in Ghana.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".