by hnburnsy | 2026-01-20 03:42:21+00:00
Problems with this study...

Short Time Horizon for Data Analysis: The study relies on data from January 2024 to November 2025, covering only about 7-8 months after the April 2025 tariff announcements. This captures primarily short-run effects, where pass-through to import prices is often high (near 100%), but longer-term adjustments could lead to greater absorption by foreign exporters. For instance, studies of the 2018-2019 trade war, such as Amiti, Redding, and Weinstein (NBER Working Paper 26610, 2019), found that initial pass-through was complete but declined over time in sectors like steel (falling to around 50% after a year). The Kiel analysis may overstate long-term incidence on U.S. buyers by not accounting for delayed responses like supply chain reorganization or price negotiations.

Incomplete Data Coverage: The dataset from Panjiva includes only ocean-freight shipments (over 25.6 million transactions valued at $4 trillion), excluding air and land imports. This omission could bias results, as air freight often involves higher-value goods with different pricing dynamics, and land imports (e.g., from Mexico or Canada) might respond differently to tariffs. Broader U.S. import data from sources like the Census Bureau show that ocean freight accounts for a significant but not comprehensive share of trade, potentially skewing estimates of overall pass-through.

Reliance on Unit Values as Price Proxies: Import prices are measured as unit values (value per kg), which can be confounded by changes in product quality, mix, or shipping costs rather than true price adjustments. The authors acknowledge this as a potential issue and use validations like Indian FOB export data, but critics of similar studies (e.g., in the 2018-2019 trade war analyses by the U.S. International Trade Commission) note that unit values often overestimate pass-through by not distinguishing between price changes and compositional shifts. This could inflate the estimated 96% pass-through rate.

Weak Statistical Significance in Key Estimates: The baseline regression coefficient (β = -0.039) indicating 96% pass-through is only statistically significant at the 10% level, which is below conventional thresholds (5% or 1%) in economic research. This suggests the result may not be robust to minor specification changes or data noise, raising questions about the reliability of the claim that exporters absorb less than 4% of the burden.

Aggregation Masks Heterogeneity: The study reports an aggregate pass-through estimate but does not break it down sufficiently by product, country, or sector. Research on prior tariffs (e.g., Fajgelbaum et al., NBER Working Paper 25638, 2019) shows significant variation: undifferentiated goods like commodities may see more exporter absorption, while differentiated consumer goods exhibit fuller pass-through. By pooling data, the analysis may overlook these differences, leading to overly generalized conclusions.

Focus on Border Prices Without Retail Pass-Through Analysis: The paper examines pass-through at the border (to importers) but extrapolates to consumers bearing "nearly all the cost" without direct evidence on retail prices. Studies like Cavallo et al. (NBER Working Paper 26396, 2019) and the Federal Reserve Bank of Boston (Working Paper 19-12, 2019) find that while border pass-through is near-complete, retail pass-through is partial (around 50-80%), with retailers absorbing some costs through reduced margins. This gap could mean the study overstates the ultimate burden on end consumers, ignoring supply chain buffers.