Was China's Inflation in 2004 Led by an Agricultural Price Rise?
Bibliographic record
Abstract
The escalation of agricultural prices starting from the end of 2003 raised concern about a new round of inflation in China. This paper assesses the impacts of China's 2003 agricultural output decline on agricultural prices and inflation using a general equilibrium model calibrated to actual data. The results indicate that the 5% decline in agricultural output can only explain 50% of observed changes in agricultural prices, 40% of observed changes in the consumer price index, and 20% of observed changes in the commodity price index. This suggests that China's 2003 agricultural output decline was not sufficient alone to produce the observed agricultural price increases and inflationary pressure in 2004. This position is counter to the conventional view that agricultural prices led to the 2004 inflation. L'escalade des prix agricoles qui a commencé vers la fin de 2003 a suscité des inquiétudes au sujet d'une nouvelle poussée inflationniste en Chine. Le présent article évalue l'impact de la diminution du rendement agricole de la Chine en 2003 sur les prix agricoles et l'inflation, à l'aide d'un modèle d'équilibre général calibréà partir de données réelles. D'après les résultats, la diminution de 5% du rendement agricole ne peut expliquer que 50% des changements observés dans les prix agricoles, que 40% des changements observés dans l'indice des prix à la consommation et que 20% des changements observés dans l'indice des prix des produits de base. Ces résultats autorisent à penser que la diminution du rendement agricole n'a pu à elle seule susciter la hausse observée des prix agricoles et la pression inflationniste en 2004. Cette position est contraire à l'explication classique selon laquelle les prix agricoles ont contribuéà l'inflation observée en 2004
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".