Does the financialization of agricultural commodities impact food security? An empirical investigation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The possible effect of the financialization of agricultural commodities on food security has become an evolving worry in recent years. This study seeks to empirically investigate this complicated problem and influence policy choices to ensure a more stable and secure food system by analyzing the role of financialization in global food markets. The study uses the panel data regression model, moderating effects model, and panel data regression with threshold variable to analyze financialization due to three agricultural commodities: wheat, maize, and soybean. For wheat, maize, and soybean futures traded on the Chicago Board of Trade, we utilize data related to annual trading volume, annual open interest contracts, and a ratio of annual trading volume to annual open interest contracts. The sample covers five developed countries - the United States, Australia, Canada, France, and Germany, and seven developing countries- China, Russia, India, Indonesia, Brazil, Vietnam, and Thailand. Annual panel data are constructed for the 2000–2021 period. The Human Development Index (HDI) is the threshold variable to differentiate the impact across these countries. The findings reveal that the financialization of agricultural commodities has negatively impacted food security globally, with wheat and soybean having a greater negative impact than corn. Also, there is a more considerable impact on developing countries compared to developed countries. The study finds that monetary policy can potentially reduce the impact of agricultural financialization on food security. The findings of this paper act as a guide to assist policymakers in ensuring that the world's food supply stays secure and available.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it