Bibliographic record
Abstract
This dissertation includes three essays on U.S. beef market. Each essay has looked at this market from a different point of view. The first essay investigates the price adjustment along the different levels of this market. The second essay discusses the impact of food safety incidents on export levels in this market. The third essay considers the environmental loading of U.S. beef market. A summary of each article is as follows. The first essay (chapter 2) analyzes price adjustment of the U.S. beef sector with a focus on the Great Recession. To this purpose, the Vector Error Correction Model (VECM) and historical decomposition graphs are applied to monthly data. The results indicate that retail prices have lower speeds of adjustment than wholesale prices. Also, the magnitude of price adjustment in the presence of the Great Recession, as an exogenous shock, is different for each level of the U.S. beef market. It is concluded that, with respect to both the speed and magnitude of the price adjustment, the U.S. beef sector has an asymmetric price adjustment, pointing to the inefficiency of the U.S. beef supply chain. These results have welfare implications for U.S. beef consumers and producers. The primary objective of the second essay (chapter 3) is to quantify the impact of consumer awareness about beef safety on U.S. beef exports. To do that, an index is used to reflect consumer’s awareness about beef safety based on the publicized reports in the media. Quarterly panel data is applied to the top importing countries, Japan, South Korea, Mexico, and Canada for the period 2000-2016. Applying the gravity model, results show that a 0.8% reduction in U.S. beef exports arose from the foodborne-disease news. In addition, using impulse response functions derived from panel vector autoregressive (Panel VAR) estimation, results show that the negative impact of a shock in food safety news intensified after three quarters, and then diminished slowly over time. In order to regain consumers’ confidence and to compensate for the economic loss arising from a foodborne outbreak, bilateral cooperation among trade partners seems necessary. Investing in any scheme that minimizes the impact of food safety events, such as disease eradication programs, traceability systems, quality labeling, and third-party certification that conveys the safety message to consumers is suggested. The third essay (chapter 4) has two purposes. First, it quantifies the environmental loading of U.S. beef sector by calculating emission levels over the period 1970-2014. Beef cattle is one of the most emission-intensive sectors, which is responsible for 35% to 54% of total GHGs from livestock. Following International Panel on Climate Change (IPCC) guideline, this study identifies three sources of emissions, including enteric fermentation, manure management, and manure left on pastures. Second, it provides an understanding of consumption-environmental connection related to the beef industry using time series techniques. Finally, it is suggested that providing information to the public regarding livestock and climate change relationship would be beneficial. This knowledge might help to avoid the catastrophic consequences of climate change in the future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".