Welfare Implications of Animal Disease-Related Trade Restrictions:Case of BSE-Related Export Bans on Cattle and Beef
Bibliographic record
Abstract
Trade restrictions are sometimes necessary to ensure food safety and animal and plant health protection. Between 2003 and 2006, Canada faced a series of trade restrictions related to BSE. Some refer to the events as a crisis for the Canadian cattle and beef sector, and some estimates placed the loss for Canada s high as $5 billion. This paper examines the impacts of BSE related trade bans on cattle and beef on economic welfare and trade flows. While our analysis is global, the discussion focuses somewhat on Canada. The analysis was performed using GTAP and the GTAP data aggregated to 15 sectors and 10 regions. Four policy experiments simulated actual trade bans placed on Canadian and the US products soon after the BSE crisis were simulated. The results suggest a Canadian welfare loss of between $70 and $700 million depending on the extent of the export ban. The US, Canada and Japan were the most adversely affected. Australia and New Zealand acquired welfare gains. Not surprisingly (given the huge volume of bilateral trade) Canada's welfare is very sensitive to access to the US market, especially for cattle trade. Price and trade impacts were visible on grain and other agricultural markets as well.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".