Whether Factors Affecting the Price of US Coffee C Futures Are Influenced by the COVID-19 Social Environment
Bibliographic record
Abstract
During COVID-19, the social environment influenced factors, including monetary policy, precipitation, temperature, exports, imports, tariffs, inflation, and household incomes, resulting in the fluctuation of coffee pricing. This paper selects US Coffee C futures as the benchmark of global coffee pricing, with the US as the representative of the exporting country and Brazil as the representative of the importing country. Based on the data collected from Brazil and US from 2015 to 2022, the research found the following: (1) Before the COVID-19 pandemic, the relationship between the US & EU tariffs and coffee pricing showed a positive trend due to the increase of substitutions' price. Monetary policy, delegated by the exchange rate of BRL against USD, indicates a negative direction toward coffee pricing. This is because it changes the value of coffee beans. Also, the import of coffee beans from Brazil to the US is slightly positively related to coffee pricing since the demand for coffee beans in the US exceeds Brazil's supply. (2) Combined with what is mentioned in (1), the following three factors most affect the COVID-19 social environment. The EU tariffs are becoming insignificant due to general and preferential rates converging. Because the monetary policy has lost much of its ability to influence local markets, it becomes unimportant to global coffee pricing. Meanwhile, international import from the US and export from Brazil becomes relevant to coffee pricing because of the transportation and Brazilian production problems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".