Agricultural Trade Potential of the United States with South Asian Countries: A Stochastic Frontier Gravity Model Approach
Bibliographic record
Abstract
The United States' agricultural export sector faces significant risk due to its reliance on a concentrated market, with 60.6% of its $107.1 billion exports in 2021 going to just five countries: China, Mexico, Canada, Japan, and South Korea. This dependency was highlighted when its major trading partners imposed retaliatory tariffs on the U.S. agricultural products, and as a result, United States’s losses exceeded $27 billion between 2018 and 2019, with China accounting for 95% of these losses. This situation illustrates the need to diversify the United States’ agricultural export markets with the developing and emerging economies to mitigate the risk stemming from concentrated market reliance. This study employed a stochastic frontier gravity model and analyzed the panel data from 2000 to 2021 to determine the drivers and export potential of U.S. agricultural exports with the South Asian countries. The study found that the GDP per capita, freedom to trade, and institutional quality of the South Asian countries positively influenced U.S. agricultural exports, while geographic distance, average tariff rates, globalization index, and landlocked status negatively impacts it. The result of this study reveals that the U.S. has the highest level of export potential mainly with India, Pakistan, and Bangladesh among the South Asian countries, by analyzing the gap between potential and actual export value. Meanwhile, employing Lafay (LFI) index, it is revealed that the U.S. has a high level of comparative advantage in exporting cotton, corn, soybeans, and tree nuts in the South Asian countries due to their local demands and dependency on imports. In contrast, U.S. rice showed a comparative disadvantage. Finally, the findings of this study stress the necessity of strategic policy initiatives, trade facilitation programs and logistic partnership to boost U.S. agricultural exports in South Asia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".