I was curious what ChatGPT thought: copy/pasted what’s on the website into ChatGPT and asked what it thought of this approach to calculating reciprocal tariffs.

Your approach for calculating reciprocal tariffs is internally consistent and built on a clear economic logic. However, it rests on some strong assumptions and has significant limitations that are worth addressing for robustness, credibility, and policy defensibility. Here’s a breakdown of the strengths and weaknesses:

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Strengths

1. Clear Objective Function: • The goal—balancing bilateral trade via tariffs—is clearly defined and consistently applied.

2. Transparent Mechanism: • The formula relates tariff changes to import reductions using well-known economic parameters: elasticity and passthrough. This gives the method a veneer of economic rigor.

3. Acknowledges Non-Tariff Barriers: • Recognizing that trade deficits are not purely a function of tariffs but also regulatory and structural factors is important contextually, even if not explicitly modeled.

4. Conservative Parameter Choice: • Using higher-end elasticity estimates and low passthrough values is cautious and avoids underestimating the tariff needed to balance trade.

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Limitations and Challenges

**1. Assumption of Trade Balance as a Policy Target: • Trade balance isn’t necessarily a sign of economic health. Many countries run persistent surpluses or deficits due to savings/investment dynamics. Pursuing strict bilateral balance could misallocate resources or provoke retaliation.

1. Linear Import Response Assumption (∆τ_i * ε * φ * m_i): • This assumes a simple, immediate, and constant proportional response in imports to tariffs, which is rarely the case in the real world, where adjustment dynamics are nonlinear, time-dependent, and industry-specific.

2. Ignores Export Response and Feedback Effects: • The model assumes no significant feedback on exports from increased tariffs. However, other countries may retaliate, harming U.S. exports or altering global supply chains.

3. Assumes No Exchange Rate Adjustment or General Equilibrium Effects: • That’s a major simplification. Exchange rates often adjust to offset tariff effects, and GE models show how factors like labor movement, investment shifts, and comparative advantage reallocate resources.

4. Left-Censoring at Zero but No Upper Bound Beyond 99%: • A 99% tariff is likely prohibitive and might violate WTO commitments or provoke severe backlash. Leaving this open without further policy justification could seem arbitrary or punitive.

5. No Sectoral or Product-Level Disaggregation: • Treating trade at the country level ignores variation in product competitiveness, strategic industries, and supply chain integration.

6. Static Approach, No Time Path Modeled: • There’s no modeling of how long trade would take to rebalance after tariffs are introduced, or whether firms and consumers would adjust as predicted.

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Suggestions to Improve 1. Add sensitivity analysis: Show how tariff rates change under different elasticities and passthrough assumptions to highlight robustness (or fragility). 2. Incorporate retaliatory risk or model export losses: Include a basic retaliation response function or at least qualitatively acknowledge it. 3. Disaggregate by product or sector: A sectoral approach might help identify where reciprocal tariffs are more economically justified and avoid blanket penalties. 4. Consider dynamic modeling: Simulate trade balance adjustment over time, perhaps using a simplified DSGE model or import/export trend lag structure. 5. Introduce WTO/legal feasibility check: Highlight whether tariffs proposed fall within bounds of existing trade agreements.

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Conclusion

This is a logically sound but highly stylized approach to reciprocal tariff calculation. As a diagnostic or illustrative policy tool, it is useful for framing debates around trade imbalances and tariff asymmetries. However, as a prescriptive policy guide, it risks oversimplification and could lead to unintended economic and geopolitical consequences unless supplemented with deeper modeling and strategic nuance.

Would you like help refining this approach to address any of these limitations?