Does Political Instability in Developing Countries Attract More Foreign Aid?
Bibliographic record
Abstract
While foreign aid has many determinants, an important factor influencing aid allocation is the political risk prevailing in the aid receiving country. This paper uses panel approach to investigate empirically how different political instabilities in the aid receiving country influence aid allocation by donors. The paper specifies and estimates models using fixed effect and random effect approach that explain the allocation of net per capita ODA among 50 developing countries over the period 1990-2012. Out of the total eight risk indices used, five exerts a significant impact on aid allocation of which four are indicators of governance while the fifth is an indicator of internal conflict. Based on the models, there is a negative relationship between corruption and aid flow indicating donors’ intolerance for malfeasance. However, the significantly positive association between aid flow and other three governance indicators- government stability, law and order and bureaucratic quality is questionable. While addressing the concept of governance in the development agenda reflects donors’ increasing concern for aid effectiveness, the rise in aid inflow with the worsening of government stability, law and order and bureaucratic quality leads to one critical question- Are donors aiding bad governance? Based on the positive significance of poor governance and the insignificance of the socioeconomic condition on aid flow, the paper argues that donors are motivated by self-interest rather than altruistic nature.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".