Alberto Cavallo Harvard University Paola Llamas Northwestern University Franco Vazquez Universidad de San Andr´es
Tariffs have significant economic impacts that directly affect businesses, consumers, and policymakers. Understanding how tariffs influence retail prices is crucial, as price changes determine consumers’ purchasing power, shape business decisions, and inform government trade policy. Despite their importance, measuring the price effects of tariffs at the retail level remains a challenge. Official price statistics and traditional surveys typically provide data with low frequency and with significant delays, limiting their usefulness for timely policy analysis. Furthermore, such aggregate measures lack sufficient granularity, obscuring which specific product categories are most affected or how goods from particular countries respond differently to tariff adjustments. A more detailed and timely analysis is therefore essential to provide clarity and actionable insights into the short-run price effects of tariff changes.
To address these challenges, we conduct an empirical analysis that combines micro-level retail price data with detailed information on product origin and tariff classifications. We link daily prices from major U.S. retailers to country-of-origin data, obtained either by searching UPC codes online or by using GenAI models to find the product’s origin. We match these products to their corresponding tariff lines using publicly available data from the U.S. International Trade Commission, which reports effective tariff rates and their revisions by HS10 code and country. Using this integrated dataset, we construct custom price indices by product category, country of origin, and tariff exposure, and analyze their movements around the tariff implementation dates. This high-frequency, granular view reveals which goods were affected, how quickly prices adjusted, and whether the impact varied by country—insights not captured in conventional price statistics.
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