<scp>FDI</scp> and Financial Market Development in Africa
Bibliographic record
Abstract
Abstract The literature on the relationship between foreign direct investment (FDI), financial market development (FMD) and economic growth focuses mainly on two aspects: the relationship between FDI and economic growth, and the role played by FMD in that linkage. The literature is almost silent on the relationship and the direction of causality between FDI and FMD. Although it has been established that FDI contributes more to growth in countries with a more developed financial market, it is not clear how FDI and FMD interact with each other. The aim of this paper is to fill this gap in the African context. Particularly, in Africa, where stock markets experience low liquidity and less transparency, FDI can be an impetus for financial market reforms and serve as a mechanism to improve the transparency and the depth of the financial markets. Also, well‐functioning financial markets can help channel foreign investments more efficiently into productive sectors, and therefore create more value for investors, hence making the countries more attractive to FDI. In short, both FDI and FMD will impact each other simultaneously, which is confirmed by our findings. We document a bidirectional causality between FDI and FMD. Furthermore, the multivariate regression results of the system of simultaneous equations also confirm the positive relationship between FDI and FMD in Africa. We also find that FDI contributes to economic growth in Africa after controlling for endogeneity between FDI, FMD and economic growth.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".