Islamic Development Bank, Foreign Aid and Economic Growth in Africa: A Simultaneous Equations Model Approach
Bibliographic record
Abstract
This study is an empirical investigation on the role of the Islamic Development Bank (IDB) Group through its foreign aid activities in contributing to the economic growth of African countries, especially the African Muslim Countries (AMCs). The AMCs, which is serving as the sample countries for this study constitute more than two-third of the IDB member countries from Africa. Therefore, this study provides empirical evidences from AMCs like Algeria, Burkina Faso, Egypt, Senegal, Niger, Morocco and Tunisia among others, on the impact of its development assistance (DA) on the economic growth of these countries using balanced panel data of six years average from 1987-2010. In order to accomplish the objectives of this paper, Simultaneous Equations Model (SEM) was adopted and Seemingly Unrelated Regressions Estimate (SURE) method was utilized for its estimation. In view of this, the findings from this study revealed that the DA of IDB has positive impact on the economic growth of AMCs through investment as the major transmission mechanism. Moreover, the impacts of the DA on human capital were more than that of investment and growth. This paper hereby recommends that the IDB should give more attention to these important transmission mechanisms, since they are among the expected gains of foreign aid to LDCs as theoretically advanced in the literature and empirically established. Evidently, this study is perhaps the first of its kind to empirically investigate the impact of the foreign aid activities of IDB in Africa, especially in AMCs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".