Foreign Direct Investment (FDI) Inflows into Ghana: Should the Focus Be on Infrastructure or Natural Resources? Short- Run and Long -Run Analyses
Bibliographic record
Abstract
This paper conducts a time series analysis of annual data set from 1980-2010, to study the potential determinants of FDI inflows to Ghana. The paper used modern econometric methodology which includes unit root testing, and co-integration analysis. Both the long-run and short run determinants of FDI were analysed using the Vector Error Correction Model (VECM). The VECM also enabled the researchers predict the speedy with which the short- run and long- run disequilibrium is corrected. The robustness of the estimated coefficients was investigated and found to be robust. The research reveals that infrastructural development and political stability have long-run positive and significant impact on the level of FDI inflows in Ghana. The study further established FDI targeting Ghana to be predominantly resource seeking for now. The short-run estimate for natural resources is positive and significant. However, Ghana cannot continue to rely on its natural resources to attract FDI as the long-run relationship is negative. Factors associated with market and efficiency seeking FDI such as market size, and value of the cedi were either found to be insignificant or unstable coefficients on inflows. Political instability is found to significantly deter inflows implying that strengthening of democratic institutions can bring in economic dividends by serving as a driver of FDI. The state of infrastructure is found to be below the required level necessary and sufficient to serve as a driver of inflows hence the negative short term effect. The policy implication of this finding is that for Ghana to fully realize its potential as far as foreign direct investment inflows is concerned it needs to embark on massive investments in infrastructure.
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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 | 0.000 |
| Research integrity | 0.000 | 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".