The Accession of Kazakhstan, Russia and Ukraine to the WTO: What will it Mean for the World Trade in Wheat?
Bibliographic record
Abstract
International trade in wheat accounts for approximately one third of world grain trade and is expected to double by 2050.The KRU (Kazakhstan, Russia and Ukraine) countries account for approximately a quarter of world wheat exports and are collectively considered one of the key wheat exporting regions. The Ukraine became a member of the WTO only in 2008. Russia became an official member of the WTO in 2012 and Kazakhstan is expected to follow Russia and reach an accession deal with WTO members shortly. As a result of WTO accession, all three countries will be entitled to “most favoured nation” (MFN tariffs), and hence, gain improved access to a number of important markets that have been largely inaccessible due to very high tariffs that could be charged on imports from non-WTO countries. World wheat trade liberalization, reflecting the move to the MFN tariff as a result of accession, was simulated using the global simulation model (GSIM). The KRU region’s increased market accessibility as a result of successful accession to the WTO has the potential to foster important re-alignments in world wheat trade flows, prices and changes in welfare among major wheat trading countries. The simulation results suggest that the change to MFN tariffs leads to KRU countries trading more with now freer markets such as Turkey, the EU and China. Major traditional wheat exporters such as Australia, Canada, the EU, and the US do not seem to be negatively impacted to any important degree. Their relative market access conditions, however, erode in Turkish, Middle Eastern, and North African markets with their exports being diverted and broadly distributed among other countries and regions at marginally reduced prices. Trade liberalization is not uniform across regions and, hence, leads to different net welfare changes across countries. Those welfare changes, however, appear to be modest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".