MétaCan
Menu
Back to cohort
Record W1508779177 · doi:10.1111/cjag.12049

Policy or Markets? An Analysis of Price Incentives and Disincentives for Rice and Cotton in Selected African Countries

2014· article· en· W1508779177 on OpenAlexvenueno aff
Cameron Short, Jesús Barreiro‐Hurlé, Jean Balié

Bibliographic record

VenueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Innovations and Practices
Canadian institutionsnot available
FundersBill and Melinda Gates Foundation
KeywordsNonmarket forcesIncentiveBusinessAgricultureDeveloping countryCompetition (biology)Market failureAgricultural economicsEconomicsAgricultural policyInternational economicsMarket economyEconomic growthFactor market

Abstract

fetched live from OpenAlex

African governments have intervened extensively in markets through regulations and other price, trade, or marketing policies to provide price incentives to farmers. Using data from the Monitoring and Analyzing Food and Agricultural Policies program, this paper reports nominal rates of protection (NRPs) for rice and cotton at wholesale and farm level in selected African countries between 2005 and 2010. Rice is an import that has received high levels of border protection by the governments concerned while cotton is a key export crop which has been the focus of direct and indirect public intervention. For both commodities, we provide evidence for both market and nonmarket failures. In the case of rice, these prevent border protection from reaching farmers while raising consumer prices. Cotton ginning and marketing is concentrated in a small number of private sector companies in most countries studied. The farm level NRPs provide evidence of market failure in these countries that may be mitigated by policies that set indicative prices and encourage competition. The NRPs indicate nonmarket failure in the two countries that maintain parastatal monopsonies for cotton.

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 score0.828
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.199
Teacher spread0.181 · 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

Citations12
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomieSame topicAgricultural Innovations and PracticesFrench-language works237,207