FDI and Economic Growth in Côte d’Ivoire: An Empirical Analysis Based on the Service Sector
Bibliographic record
Abstract
This paper analyzes the contribution of FDI to service sector growth in Côte d’Ivoire over the period 1980-2021. The data are extracted from World Development Indicators (World Bank). The econometric test based on the ARDL cointegration approach is used. The results show that FDI has a negative and significant effect on growth in the service sector. These results could be explained by the insufficiency of inward FDI in Côte d’Ivoire in recent years, compared to other sub-Saharan African countries, by the sectoral allocation of inward FDI, which does not take into account the growth sector of the economy, and finally, by the unbalanced geographical distribution of FDI, most of which is heavily concentrated in Abidjan. Our results highlight the importance of boosting investment in the service sector, in order to stimulate activities in this sector in Côte d’Ivoire. Besides aiming at creating a solid national economy and improving living standards, we suggest the mobilization of sufficient domestic savings and raising the level of education, developing infrastructure in sufficient quantity and quality and increasing internal and external trade flows, and finally, preserving the population’s purchasing power. We have also proposed measures to improve FDI to promote economic growth in Côte d’Ivoire’s service 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".