Financial sector development and intra-African trade
Bibliographic record
Abstract
• Relationship between Financial Sector Development (FSD) and intra-African trade. • Positive impact of financial development and cross-border banking flows on intra-African trade. • Financial institution indicators have more impact than financial market indicators. • The relationship between financial development and trade is heterogeneous across African regions. • Financial development affects trade indirectly through the services and industrial sectors. This paper examines the nature of the relationship between Financial Sector Development (FSD) and intra-African trade. Using a sample of African countries with available data from 1998 to 2021 , and robust estimation techniques that address endogeneity and omitted variables biases, we find a positive significant impact of the composite financial development indicator and cross-border banking flows on intra-African trade. Further analysis reveals that the effects of the financial institution sub-indicators are more pronounced than those of the financial market sub-indicators. The effects are also heterogeneous across the different African Regional Economic Communities (RECs). Finally, our results show that financial sector development affects intra-African trade indirectly through its impact on the services and industrial sectors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".