Development Aid in the Presence of Corruption: Differential Games among Donors
Bibliographic record
Abstract
In this paper, we complement the work of Kemp and Shimomura (2002) by considering the case of many donors playing a dynamic non-cooperative game of foreign aid. We consider two models. Model 1 deals with the case where donor countries continually feel the warm glow of from the act of giving. Model 2 postulates that donors will stop giving aid when a target level of development is reached. One of the main results of Model 1 is that there are multiple equilibria that can be Pareto ranked. Another interesting result is that an increase in the level of corruption in the recipient country will reduce the aid level of the low aid equilibrium, but increase that of the high aid equilibrium. In Model 2, the equilibrium strategies are non-linear functions of the level of development. The flow of aid falls at a faster and faster rate as the target is approached. An increase in corruption will increase the flow of aid in this model. On présente deux modèles d'aide internationale dans lesquels deux pays avancés s'engagent dans un jeu dynamique. Dans le premier modèle, les aides apportent aux donateurs des gains moraux. On montre qu'une hausse de la corruption du pays sous-développé peut augmenter les aides. Il y a une multiplicité d'équilibres de Nash, qui peuvent être ordonnés sous le critère de Pareto. Dans le deuxième modèle, les pays donateurs cessent de donner aussitôt que le niveau du développement atteint un but fixé. On montre que l'équilibre de ce modèle implique que le flux d'aide devient de plus en plus faible au fur et à mesure que le niveau de développement s'approche du but fixé. Les pays avancés donnent plus si le taux de corruption augmente.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".