The Heterogeneous Impact of Sectoral Foreign Aid Inflows on Sectoral Growth: SUR Evidence from Selected Sub-Saharan African and MENA Countries
Bibliographic record
Abstract
A great deal of the foreign aid–growth literature finds that the net effect of aggregate aid on total growth appears to be insignificant. This study argues that this aid–growth nexus can be better explained by testing the variation responses for each of growth sectors to their corresponding allocated aid inflows. It aims to investigate the heterogeneous effects of sectorally allocated aid inflows on their corresponding growth sectors (industry, agriculture and services) using data from 37 Sub-Saharan African and MENA-recipient developing nations from 1996 to 2017. We constructed two measures; one is the (SAASG) Sectoral-Allocated-Aid-for Sectoral-Growth, which was used as a major measure in the first two econometric specifications, and another one was the revised Clemens early-impact aid categories measure, which was used as the secondary measure in the third specification. The seemingly unrelated regression framework (SUR) was employed as the basic estimation approach, while the GMM approach was used to check robustness. The empirical findings revealed clear systematic impacts associated with aid distributed to each sector of growth, which may explain why the net effect of overall aid on total growth appears to be insignificant. The findings show that allocated aid inflows have a strong positive impact on agricultural growth, helping boost overall growth, whereas aid allocated to the service and industrial growth sectors tends to minimize the net benefits of total aid on growth due to financial and institutional reasons. The success of the planned scaling-up of aid to recipient countries depends on the financial system, institutional quality policies, and the ability to design a way to maintain incentives in the MENA and SSA regions’ selected recipient countries to overcome structural bottlenecks of sectoral growth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".