Does Foreign Direct Investment Affect Employment in Guinea: Empirical Assessment
Bibliographic record
Abstract
This study shed light on the extent to which foreign direct investment contribute to employment in Guinea. FDI per GDP net inflows and unemployment rate are adopted as key indicators whereas inflation, trade openness, credit to private sector are control variables. The empirical evidence is computed through ARDL method and the subsequent findings are established: first, foreign investment negatively and insignificantly affects unemployment in the short run. This result may be linked to the fact that a huge portion of FDI in Guinea is resource seeking type which itself does not generate enough jobs in the affiliate firms. Moreover, the interactions between such kind of investment and local suppliers are very limited, mitigating its effect on employment in the supplier’s side. Second, the short term coefficients for inflation and credit to private sector are positive and insignificant, contradicting a popular macroeconomic theory known as Phillips curve. Overall, government should promote investments that can have transformative effect on domestic economy through linkages and spillovers. Furthermore, special emphasis must be put on human capital (education and healthcare) so that Guinean youth could be more competitive and capable to seize job opportunities offered both by foreign multinationals and local firms.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".