Are Russian Wheat Exporters Able to Price Discriminate? Empirical Evidence from the Last Decade
Bibliographic record
Abstract
Abstract Significant changes have taken place in the world wheat market in the last decade. Russia, a former net wheat importer, has become a leading exporter with a world market share of 11.2% in 2009. This increasing importance and the discussion about the establishment of a grain‐OPEC consisting of Ukraine, Kazakhstan and Russia has raised the issue of pricing behaviour of Russian wheat exporters. Although there are several studies on the pricing behaviour of Canadian and US wheat exporters, there is none so far for Russian wheat exporters. This study provides a quantitative analysis of the pricing behaviour of Russian wheat exporters, explicitly taking account of the export tax imposed between 2007 and 2008. We employ a pricing‐to‐market (PTM) model on quarterly Russian wheat‐export data, covering the period from 2002 to 2010 and 25 export destinations. Our findings indicate that (i) Russian wheat exporters exercised PTM in only a few importing countries over the whole time period, and (ii) PTM behaviour was more pronounced in the aftermath of the export tax period (i.e. 2008–2010) than before.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".