MétaCan
Menu
Back to cohort
Record W3084181118 · doi:10.5539/jas.v12n10p28

The Role of Development Aid in Agriculture in the Common Market for Eastern and Southern Africa: A Panel Vector Autoregression Analysis

2020· article· en· W3084181118 on OpenAlexvenueno aff
Grace Gondwe, Josue Mbonigaba

Bibliographic record

VenueJournal of Agricultural Science · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureGovernment (linguistics)Aid effectivenessEconomicsProductivityDutch diseaseDevelopment aidVector autoregressionPanel dataResource (disambiguation)BusinessInternational economicsInternational tradeDevelopment economicsEconomic growthDeveloping countryMacroeconomicsMonetary economicsExchange rateGeography

Abstract

fetched live from OpenAlex

This paper assessed the impact of foreign aid on agricultural productivity and growth in the Common Market for Eastern and Southern Africa (COMESA), using panel vector autoregressive methods. The results show a significant unidirectional causality from agricultural growth to foreign aid and thus confirming the theoretical dispositions of the developmental role of foreign aid. However, instead of complementing domestic resources in this regard, the results showed that foreign aid in the sector substitutes government financing, which effectively reduces its effectiveness. A mismatch in government resources and aid allocations to a sub-sector erodes the synergy that should typically exist between donor aid and government expenditure in a sector. A policy shift towards Result-Based (Aid on Delivery) approaches in aid disbursements will be critical to eliminating fungible resources. Misalignment of aid allocations that are inconsistent with the relative importance of subsectors in the sectoral development goals further undermines the potency of aid. A better understanding of the contribution of the various sub-sectors to the overall growth of the agriculture sector will be crucial for equitable resource allocation and enhanced aid effectiveness. Moreover, the higher impact of domestic resources compared to foreign aid calls for policies to increase domestic resource mobilization and a broader focus on reducing aid dependency.

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.

How this classification was reachedexpand

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.511
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.256
Teacher spread0.235 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Agricultural ScienceSame topicInternational Development and AidFrench-language works237,207