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Record W2911600148 · doi:10.1515/jafio-2019-0054

Is Commodity Storage an Option for Enhancing Food Security in Developing Countries?

2019· article· en· W2911600148 on OpenAlexaff
G. Cornelis van Kooten, Andrew Schmitz, P. Lynn Kennedy

Bibliographic record

VenueJournal of Agricultural & Food Industrial Organization · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic theories and models
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCommodityEconomicsGovernment (linguistics)Subsistence agricultureFood securityMicroeconomicsAgricultural economicsMonetary economicsBusinessMarket economyAgriculture

Abstract

fetched live from OpenAlex

Abstract We revisit the underlying economics of commodity storage and its relation to food security by first clarifying the standard model used to analyze the economic efficiency and distributional effects of commodity storage programs. We find that producers prefer stabilization, although their incomes are more variable, while consumers are indifferent. However, numerical simulations indicate that physical stocks will build up inexorably over a sustained period or the government will need to raise prices continuously over a prolonged period. For the least developed countries facing fluctuating world prices, government should guarantee the price received by producers because, with price uncertainty, farmers could experience losses even under a ‘good’ weather outcome; this would guarantee the producer price, benefitting farmers, while allowing the consumer price to vary with the world price benefits consumers as they prefer price instability. In some cases, however, the government may wish to impose a price ceiling so that households living at or near subsistence can afford to buy grain – an argument based on the grounds of food security. Numerical simulations indicate that such a mixed-price policy increases the wellbeing of both consumers and producers. Physical storage is not a necessity.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.553

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 communication0.0000.001
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 head0.040
GPT teacher head0.220
Teacher spread0.180 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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