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Record W4384132964 · doi:10.1002/jid.3816

Impacts of agricultural capital subsidies for women in Burkina Faso: Lessons from a Computable General Equilibrium model

2023· article· en· W4384132964 on OpenAlexfundno aff
Patrice Rélouendé Zidouemba, Romuald S. Kinda, Pouirkèta Rita Nikiema

Bibliographic record

VenueJournal of International Development · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsnot available
FundersDepartment for International DevelopmentDepartment for International Development, UK GovernmentInternational Development Research CentreGovernment of Canada
KeywordsSubsidySocial accounting matrixComputable general equilibriumEconomicsEndowmentAgricultureAgricultural productivityEconomic inequalityInequalityUnemploymentLabour economicsDemographic economicsEconomic growthMacroeconomicsGeographyMarket economyPolitical science

Abstract

fetched live from OpenAlex

Abstract Sub‐Saharan African countries have a strong involvement of women in the agricultural sector. However, women have limited access to productive resources. A better endowment of productive resources for women is seen as a crucial option for achieving noteworthy results in terms of agricultural production, income, and economic growth and for reducing income inequalities between men and women. This study aims to analyse the potential impacts of a subsidy policy on women's agricultural capital in Burkina Faso. It makes use of a recursive dynamic computable general equilibrium model and a gendered social accounting matrix. The results indicate that the subsidy policy contributes to increasing women's income more than men's income, helping to reduce income inequality between men and women. Moreover, unemployment for women decreases significantly. Finally, the policy is conducive to economic growth regardless of the funding source.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.570
Threshold uncertainty score0.328

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)0.0000.000

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 head0.033
GPT teacher head0.305
Teacher spread0.272 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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