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Record W2275510807 · doi:10.1111/cjag.12106

Food Price Bubbles and Government Intervention: Is China Different?

2016· article· en· W2275510807 on OpenAlexvenueno aff
Jian Li, Chongguang Li, Jean‐Paul Chavas

Bibliographic record

VenueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
FundersNational Social Science Fund of ChinaNational Natural Science Foundation of China
KeywordsEconomicsChinaHumanitiesEconomyWelfare economicsPolitical scienceArt

Abstract

fetched live from OpenAlex

The last decade has witnessed different price trajectories in the international and Chinese agricultural commodity markets. This paper compares and contrasts these dynamic patterns between markets from the perspective of price bubbles. A newly developed right‐tailed unit root testing procedure is applied to detect price bubbles in the Chicago Board of Trade (CBOT) and Chinese agricultural futures market during the period 2005–14. Results show that Chinese markets experienced less prominent speculative bubbles than the international markets for its high self‐sufficiency commodities (wheat and corn), but not for low self‐sufficiency commodities (soybean). The difference in price behavior is attributed to differences in market intelligence, and to Chinese agricultural policies related to trade as well as domestic government policies. Besides, it discusses challenges to the sustainability of the stable price trajectory in Chinese markets. Au cours de la dernière décennie, les prix des produits agricoles sur les marchés chinois et international ont suivi des trajectoires variées. Dans le présent article, nous comparons les divers marchés sur le plan des bulles de prix des produits agricoles. Nous avons utilisé un nouveau test de racine unitaire qui exploite la queue de droite de la distribution de la statistique pour déceler les bulles de prix des produits agricoles sur le Chicago Board of Trade (CBOT) et sur les marchés à terme chinois au cours de la période 2005–14. Les résultats de notre étude montrent que les marchés chinois ont connu des bulles spéculatives moins prononcées que les marchés internationaux dans le cas des produits agricoles pour lesquels l'autosuffisance de la Chine est très élevée (le blé et le maïs), par rapport aux produits pour lesquels l'autosuffisance est faible (le soja). La différence sur le plan du comportement des prix est attribuable aux différences sur le plan de l'information commerciale ainsi qu'aux politiques agricoles chinoises en matière de commerce et à la politique intérieure du pays. Nous avons également examiné les défis par rapport à la durabilité d'une trajectoire de prix stables sur les marchés chinois.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.056
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.150
Teacher spread0.134 · 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 source (direct Gemma or distilled Codex), 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

Citations34
Published2016
Admission routes1
Has abstractyes

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