Effect of Grain Corridor Agreement on Grain Prices
Bibliographic record
Abstract
This study investigates the impact of the Grain Corridor Agreement (GCA), particularly in the aftermath of the Russia–Ukraine conflict, on the prices of major grains (wheat, maize, and barley), pivotal for global sustenance. By delineating three significant shocks: the initiation of the conflict, the enforcement of the GCA, and Russia's subsequent withdrawal from it, we employ an Integrated GARCH (IGARCH) model to investigate the impact of the Russia–Ukraine conflict on grain prices. Our empirical findings reveal that all grain prices surged at the onset of the conflict, with barley experiencing the most pronounced increase. Additionally, volatility escalated across all grain prices during the conflict's inception, albeit subsiding upon the implementation of the GCA. Price volatility spiked initially but decreased with the GCA's enforcement. The evidence suggests that the conflict is driving up world grain prices and causing global vulnerability, and that conciliatory policies such as the GCA offer a short-term solution. However, long-term strategies should focus on reducing external dependence by reviewing agricultural policies and promoting domestic production. Moreover, policymakers are advised to consider both domestic and global market vulnerabilities when designing sound policies.Highlights International grain prices (wheat, maize and barley) spiked during the onset of the ongoing Russia–Ukraine conflict.The conflict triggered an international response to resume safe maritime humanitarian transportation of agricultural grains via GCA.We develop an empirical framework to assess the impact of the Russia–Ukraine conflict on grain prices.Empirical findings indicate that Russia–Ukraine conflict increased all grain prices.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".