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Record W2034872593 · doi:10.5539/sar.v1n2p141

Simulating the Impact of Exogenous Food Price Shock on Agriculture and the Poor in Nigeria: Results from a Computable General Equilibrium Model

2012· article· en· W2034872593 on OpenAlexvenueno aff
Nkang Nkang, B. T. Omonona, Suleiman Yusuf, O. Timothy Oni

Bibliographic record

VenueSustainable Agriculture Research · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsnot available
FundersWilliam and Flora Hewlett Foundation
KeywordsComputable general equilibriumSocial accounting matrixShock (circulatory)EconomicsPovertyAgricultureFood pricesWelfareGeneral equilibrium theoryFood securityFood policyAgricultural economicsMacroeconomicsEconomic growthGeographyMarket economy

Abstract

fetched live from OpenAlex

<p>Motivated by the recent global economic crisis, this paper simulated the impact of a rise in the price of imported food on agriculture and household poverty in Nigeria using a computable general equilibrium (CGE) model and the Foster, Greer and Thorbecke (FGT) class of decomposable poverty measures on the 2006 social accounting matrix (SAM) of Nigeria and the updated 2004 Nigeria Living Standards Survey (NLSS) data. Results show that a rise in import price of food increased domestic output of food, but reduced the domestic supply of other agricultural commodities as well as food and other agricultural composites. Furthermore, a rise in the import price of food increased poverty nationally and among all household groups, with rural-north households being the least affected by the shock, while their rural-south counterparts were the most affected. A major policy implication drawn from this paper is that high import prices in import competing sectors like agriculture tend to favour the sector but exacerbate poverty in households. Thus, efforts geared at addressing the impact of this shock should strive to balance welfare and efficiency issues.</p>

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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.049
GPT teacher head0.360
Teacher spread0.310 · 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 designQualitative
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

Citations6
Published2012
Admission routes1
Has abstractyes

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