Three Essays on Agricultural and Food Trade Shocks and Regional Integration
Bibliographic record
Abstract
This research investigates the impact of disruptions and regional integration on agricultural and food trade, relying on a unique international and intranational (domestic) agri-food trade dataset and structural gravity. In the first chapter, we investigate the impact of animal diseases on trade of animal-based products. We found that animal disease outbreaks decrease exports by 4% on average per year, amounting to annualized losses of 96 billion of 2019 USD. Trade quantities decline by 8% (51 million tons) on average per year. Impacts are mostly concentrated on consumer products (mainly pork), and low-income and lower-medium-income exporting countries. Our results suggest that animal diseases affect more domestic markets than foreign ones, and that dependent importers are the most sensitive to animal disease outbreaks abroad. Lastly, participation in the same RTA is found to mitigate animal diseases' trade impact, showing another potential channel through which regional integration could affect members' trade. In the second chapter, we explore the effect of the North American Free Trade Agreement (NAFTA) on agricultural and food trade flows. We consider the entire official lifetime of the agreement, including its 14-years phase-in period, which allow us to offer a comprehensive evaluation of one of the biggest trade agreements on agri-food trade. NAFTA is found to increase members' trade on average by 54%, corresponding to 11.9 billion of 2020 USD, annually. Trade involving Mexico, and especially Canada-Mexico, has increased substantially showing that trade agreements between developed and developing countries could be beneficial to both members. NAFTA's impact is found to be heterogeneous by products with cereals experiencing the biggest increase. Trade of products incompletely liberalized by NAFTA such as dairy, poultry, and eggs, did not increase as much as the trade of liberalized products. We do not find evidence of trade diversion, suggesting that NAFTA's agri-food trade gains did not come at the expense of trading with other partners. Lastly, NAFTA appears to be more trade enhancing (about four times more) than other agreements of Canada, Mexico and the U.S. (e.g. Canada-E.U., or Mexico-Brazil, or U.S.-Korea.) In the third chapter, we question whether trade agreements alleviate the impact of shocks on trade. More specifically, we investigate if RTAs mitigate the impact of exchange rate (ER) volatility on agri-food trade. We found that RTAs amplify the effect of ER volatility on agri-food trade. The trade impact of ER volatility on RTA members is found to be positive, suggesting that members' agri-food trade benefits from ER volatility, contrary to non-members' trade. This could result from larger profits from arbitrage due to reduced trade costs between RTA members. Our results display a strong heterogeneity according to sectors, exporters and importers' income, and level of integration of RTA. Only Partial Scope Agreements, the lowest regional integration level, amplify the effect of ER volatility on members' agri-food trade.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".