FDI and Economic Growth in the Central African Economic and Monetary Community (CEMAC) Countries: An Analysis of Seven Economic Indicators
Bibliographic record
Abstract
Foreign Direct Investment (FDI) can help create jobs, reduce unemployment, improve world-class technology transfer, and grow countries’ economies. During the past 10 years, FDI net inflows to the Central African Economic and Monetary Community (CEMAC) has highly fluctuated and remained below to the total amount reached in 2010. The focus of this research was to statistically analyze the mean difference for FDI net inflows, GDP per capita, natural resource rents, inflation rate, corruption index, trade openness index, rule of law index, and political stability index received in each CEMACs country. Paired t-test methodology was used to conduct the analysis. Data were collected from the World Bank Group database from 2007 to 2017. This research revealed that FDI net inflows decreased by an average of two billion dollars in CEMAC when conducting a mean-to-mean analysis from the recession period to the recovery period. The findings showed that FDI net inflows inversely affected the GDP per capita in Congo and Gabon. FDI net inflows may have contributed to the improvement of the GDP per capita in countries such as Cameroon, Chad, Central Africa Republic, and Equatorial Guinea. Researcher recommendation for continued study is a qualitative research using the same variables through the same periods in addition to year 2018. Improvement of economic policies, regulations and laws, as well as the digitalization of public funds management are also recommended to boost economic development and growth in the CEMAC region.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".