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Record W1969912361 · doi:10.1093/wber/lhw019

Donor Competition for Aid Impact, and Aid Fragmentation

2012· article· en· W1969912361 on OpenAlex
Kurt Annen, Luc Moers

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe World Bank Economic Review · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsInefficiencyEconomicsFragmentation (computing)MicroeconomicsNash equilibriumMaximizationAid effectivenessCompetition (biology)Public economicsDeveloping countryComputer scienceEconomic growth

Abstract

fetched live from OpenAlex

This paper shows that donors that maximize relative aid impact spread their budgets across many recipient countries in a unique Nash equilibrium, explaining aid fragmentation. This equilibrium may be inefficient even without fixed costs, and the inefficiency increases in the equality of donors budgets. The paper presents empirical evidence consistent with theoretical results. These imply that, short of ending donors maximization of relative aid impact, agreements to better coordinate aid allocations are not implementable. Moreover, since policies to increase donor competition in terms of aid effectiveness risk reinforcing relativeness, they may well backfire, as any such reinforcement increases aid fragmentation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.956
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.032
GPT teacher head0.352
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it