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Record W3123308185 · doi:10.1093/wber/lhaa017

Better Policies from Policy-Selective Aid?

2020· article· en· W3123308185 on OpenAlex
Kurt Annen, Stephen Knack

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 · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsIncentiveEconomicsPublic economicsAid effectivenessInternational economicsDeveloping countryEconomic growthMicroeconomics

Abstract

fetched live from OpenAlex

Abstract The increased policy selectivity of aid allocations observed in recent years provides aid-recipient countries with an incentive to improve policies. The paper estimates that a change in the World Bank’s Country Policy and Institutional Assessment policy index from 1.5 to 2 for a recipient is associated with an increase of about 13 percent in aid. The analysis also finds a modest but statistically significant positive relationship between the global level of policy-selective aid and policy, suggesting that policy-selective aid improves policies in aid-recipient countries. This effect is properly identified, as the level of policy-selective aid in the global aid budget is exogenous to a recipient country’s policy choice. Furthermore, the paper provides a game-theoretic model that establishes the link between the policy selectivity of the global budget and better recipient-country policies in equilibrium.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.609
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0030.003

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 head0.034
GPT teacher head0.320
Teacher spread0.286 · 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