Can foreign aid contribute to sustained growth? A comparison of selected African and Asian countries
Bibliographic record
Abstract
Purpose Identifying if aid flows have contributed to economic growth or growth divergence between a sample of Asian and African countries is the purpose of this paper. Using data over the period of 1980–2015, the paper attempts to establish whether aid, in any of its forms, has played a role in economic growth in these countries. Design/methodology/approach A comprehensive literature analysis over the past 70 years sets the scene for the paper. A panel data fixed-effects model is applied for each sample (Africa and Asia) between 1980 and 2015. Both theoretical predictions and empirical studies are used to derive the independent variables selected for modelling. Findings The findings strongly suggest that aid flows in both the Asian and African samples have no relation at all to either long-run growth paths or growth divergence. However, there is a suggestion in the case of the Africa sample that governance decline may well be the primary source of growth divergence. Research limitations/implications This result cannot be generalised because it only focuses on six countries but as demonstrated in the paper, other possible samples (from both regions) actually make no difference to the results. It could also be argued (given the comprehensive literature analysis presented here) that it is not essential to have a theoretical relationship between aid and growth because aid is given to different countries with very different characteristics, needs, governance and policy environments. Practical implications Donor countries must play a more supervisory role to ensure aid flows are directed to the right channels in recipient countries. Aid should be given to countries which have a certain degree of macroeconomic stability and “good” policy to ensure effectiveness. They also need to pay attention to the sectoral distribution of aid as do recipient countries to better allocate aid flows to productive sectors that contribute to both short- and long-term growth. Social implications These are not given much emphasis in this paper. Originality/value Most aid–growth studies are based on a large number of countries from different regions with different characteristics or on a single country case. This paper compares between two samples of countries sharing the same characteristics to overcome the heterogeneity problem. This paper is based on a more protracted time series from 1980 to 2015 to capture more accurately the impact of foreign aid on economic 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".