A Comparative Analysis of The Economic Effects of U.S. Reciprocal Tariffs on India, China, and Canada
Bibliographic record
Abstract
is research examines the impact of the reciprocal tariffs introduced by the protectionist administration between 2020 and 2024 on the economies of India, China, and Canada. These tariffs were part of a broader shift in U.S. trade policy aimed at reducing trade deficits and encouraging fairer trade practices. However, the retaliatory nature of these measures led to trade tensions with several key U.S. partners. By focusing on India, China, and Canada—countries with different economic structures and levels of trade dependence on the U.S.—this study highlights how each nation was affected in unique ways. China, as the largest target of the tariffs, experienced a significant decline in exports to the U.S, disruptions in its manufacturing and technology sectors, and broader economic slowdowns, particularly in global supply chains. Canada, though a close economically of the U.S., faced tariffs on steel and aluminum, which negatively impacted its industrial output and led to retaliatory tariffs on American goods. This briefly strained U.S.-Canada trade relations and pushed Canada to strengthen trade ties with other global partners. In India’s case, although the economic impact was smaller, the country responded with its own tariffs on U.S. products and saw shifts in its trade policy stance, aiming to reduce dependence on any single trading partner. The research uses a mix of economic data—including GDP growth, trade volumes, and sector-level performance—alongside policy analysis to understand how each country adapted to the changing trade environment. The findings show that while the tariffs were meant to protect U.S. industries, they caused unintended economic disruptions and forced other countries to rethink their trade strategies. The study concludes that unilateral tariff measures, especially when used against major trading partners, can have far-reaching global consequences. It emphasizes the need for more cooperative and balanced trade policies to ensure economic stability and long-term growth for all involved nations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".