Bibliographic record
Abstract
Climate has historically played a critical role in the development of nations, primarily due to its intrinsic linkage with agricultural production and prices. This study examines one such relationship between the better known and most talked about climate anomaly, El Niño Southern Oscillation (ENSO), and the international prices of wheat, one of the most produced and consumed grain cereals in the world. The ENSO–price relationship, moreover, is assumed to be characterized by nonlinear dynamics, because of the known asymmetric nature of ENSO cycles, as well as that of wheat prices. This study applies a vector smooth transition autoregressive (VSTAR) modeling framework to monthly spot prices of wheat from the United States, the European Union, Australia, Canada, and Argentina, as well as the sea surface temperature anomalies from the Nino3.4 region, which serves as a proxy for the ENSO variable. Results show that, overall, wheat prices tend to increase after La Niña events, and decrease after El Niño events. The regime-dependent dynamics are apparent with more amplified price responses after La Niña shocks, and with more persistent price responses during the La Niña conditions. This is consistent with the economics of storage, wherein shocks related to expected supply and demand are known to have a more pronounced effect in a low-inventory regime. Findings of this study have strong implications for development economics, as they point to an additional channel of adversity due to the ENSO-related climate shocks. Moreover, the ENSO-induced price fluctuations are likely to affect the dynamics of international food and cash programs during extreme episodes of this climate anomaly.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".