Journal: Modern Economics & Management Forum DOI: 10.32629/memf.v7i4.5519
Abstract
The reciprocal tariff regime announced by the United States in April 2025 raised the average tariff level sharply and exposed firms and policy makers to a poorly mapped space of economic, security, and fiscal outcomes. This study develops an integrated decision-analytics framework that traces tariff effects through three strategic sectors — soybeans, automobiles, and semiconductors — and links them to fiscal revenue and macroeconomic restructuring. Using HTS-coded trade data for 2020–2024, we combine isolation-forest screening and t-SNE visualization with ridge regression and VARX scenario forecasting, a dynamic-panel System GMM design with value-at-risk (VaR/CVaR) measures, a nested CES demand system embedded in a bi-objective optimization solved by L-BFGS-B and Bayesian surrogate search, a Laffer-curve model augmented by a Stackelberg retaliation game and SVAR analysis, and a fixed-effects/VAR/Probit/DID battery for policy evaluation. Tariff exposures are strongly heterogeneous: a 1% tariff rise reduces soybean import shares by up to 26.2% (Brazil) but Japanese auto imports by only 2.4% and chip market shares by 2.2%. Chip tariffs trace an efficiency–security frontier in which high-end rates dominate security risk (r = 0.72). The estimated revenue-maximizing tariff falls from 3.86% to 2.78% once foreign retaliation is anticipated, far below the current burden. Counter-tariff policy triggers significant capital reshoring (fixed-asset investment +90.7%) without short-run output gains, implying that capacity revitalization lags capital relocation.
Keywords
CES demand model, difference-in-differences, Laffer curve, manufacturing reshoring, multi-objective optimization, Stackelberg game, System GMM, tariff policy, trade risk, value at risk.
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