Did US Section 301 tariffs pass through to border prices? A BACI replication of Amiti-Redding-Weinstein (2019) extended through 2024

Amiti, Redding & Weinstein (2019, Journal of Economic Perspectives 33(4), 187–210) reported that the 2018 Section 301 tariffs on Chinese imports were passed through essentially one-for-one into US import prices at the border, with no evidence of foreign-exporter absorption. We re-run an annual event-study on CEPII BACI 202501 data from 2015 to 2024, treating 16 HS6 lines from USTR List 3 (83 FR 47974, 9/2018; 25% final rate from May 2019) as the treatment group, and 11 food and pharmaceutical HS6 lines (excluded from Section 301 Lists 1–4) as the control. Our replication does not recover the ARW headline at BACI’s annual-and-pooled resolution: β̂₂₀₁₉ on log unit value is small and, from 2020, negative, because the food and pharma control absorbed the 2020–2022 global commodity pulse faster than the List-3 basket did. The takeaway is not that ARW is wrong (their bilateral monthly CBP/BLS data are the right instrument); it is that BACI annual unit values are a blunt tool for border-price pass-through, and this page documents exactly how blunt.

Methodology

Unit value per HS6 line i and year t is defined as UVit = import_valueit / import_qtyit, with BACI values in thousands of US dollars (multiplied by 1,000 before the division) and import quantities in metric tons. The event-study equation is

ln UV_{i,t}  =  α_i  +  γ_t  +  Σ_{t ≠ 2017} β_t · Treated_i · 1{year=t}  +  ε_{i,t}

HS6 fixed effects absorb product-level time-invariant differences in value-per-ton (a specialty filter part has a higher baseline than a commodity rubber gasket); year fixed effects absorb dollar-global input-cost shocks. The β_tcoefficient is identified off the differential between List-3 HS6 price growth and the food-plus-pharma control after 2017. Because there is one observation per HS6×year and the treatment is binary at the HS6 level, β_t is numerically equivalent to the mean of Δln UV_i,t = ln UV_i,t− ln UV_i,2017 across treated HS6, minus the same average across control HS6. Standard errors cluster at the HS6 level (Abadie, Athey, Imbens & Wooldridge 2023, QJE); the two-sample-mean SE formula used here (√(s²_T/n_T + s²_C/n_C)) is the collapsed-panel analogue, following Roth, Sant’Anna, Bilinski & Poe (2023, JoE).

Two caveats matter for interpretation. First, BACI at HS6 is at the reporter–world level, not bilateral. We measure the price of US imports from all origins on List-3 HS6, not just from China. Because China was the dominant pre-war supplier on these lines, the US-from-world UV movement tracks the US-from-China UV movement closely, but the two are not identical: as other origins (Vietnam, Mexico) entered List-3 HS6 post-2018, the pooled US unit value includes their supply curves too. ARW (2019) and Fajgelbaum, Goldberg, Kennedy & Khandelwal (2020, QJE 135(1), 1–55) used bilateral US CBP micro-data that resolves this cleanly; we do not. Second, BACI is annual; ARW and Cavallo, Gopinath, Neiman & Tang (2021, Journal of Monetary Economics 119, 1–18) used monthly data, which matters for the exact timing of the tariff step.

Figure 1: parallel-trends check and DiD visual

First we plot the mean log-unit-value change relative to 2017 for the two groups from 2015 to 2024. If parallel-trends held, the two lines would track each other through 2015–2017 and diverge only after 2017. In BACI annual data they do not: the treated and control groups differ by double digits in log unit value even in 2015 and 2016. That is a pre-trend problem, and it defines what BACI annual HS6–world unit values can and cannot identify.

Figure 2: event-study β̂_t with 95% confidence interval

The year-by-year treatment effect β̂_t, normalised to zero in 2017 (the omitted pre-period reference). Values above zero mean treated HS6 unit values rose faster than control; values near zero mean no differential.

Figure 3: heterogeneity by HS chapter (2019 coefficient)

Treatment intensity should differ by chapter: HS94 furniture faced the 10%-then-25% List-3 rate with limited substitutability (bulky, shipping-cost-sensitive), whereas HS39 plastics and HS84 machinery parts had deeper third-country supply options. Chapter-level β̂₂₀₁₉ anchors each chapter’s treated mean against the same (pooled) food-plus-pharma control mean for 2019.

Figure 4: ARW (2019) headline pass-through vs this workbench replication

Amiti-Redding-Weinstein (2019, JEP 33(4), Table 2 and Figure 4) report roughly 100% pass-through of Section 301 tariff changes to US border prices, using monthly BLS import-price-index micro-data paired to the USTR tariff-code list. Their sample ends in October 2018 for the first paper and extends to early 2019 in their NBER WP revision (Amiti, Redding & Weinstein 2020, NBER WP 26610). Our BACI-annual replication cannot match their bilateral monthly resolution, but the sign and cumulative magnitude should be comparable by 2019.

Figure 5: did Section 301 move volumes even if BACI cannot price pass-through?

Fajgelbaum, Goldberg, Kennedy & Khandelwal (2020, QJE 135(1), 1–55) document a large extensive-margin response: US import volumes on List-3 HS codes fell sharply, with China-origin volumes absorbing the bulk of the drop. At BACI annual HS6, we can see the aggregate (pooled-origin) volume response; the partner split is not resolvable here. Figure 5 plots log-volume change vs 2017 for treated and control groups.

Figure 6: Section 201 / 232 decomposition on three salient HS6 baskets

Section 301 tariffs on Chinese-origin List 1-4 goods were not the only border-tax shock of 2018. Three narrower actions had sharper product-level bite and more tractable pre/post comparisons at the HS6 level: the Section 201 safeguard on washing machines (Presidential Proclamation 9694, 23 Jan 2018; rates 20-50% ad valorem; Flaaen, Hortacsu & Tintelnot 2020, American Economic Review 110(7): 2103-2127); the Section 232 national-security tariff on steel (Presidential Proclamation 9705, 8 March 2018; 25% MFN with country exclusions); and the Section 201 safeguard on solar cells and modules (Presidential Proclamation 9693, 23 Jan 2018; 30% declining to 15%; Houde & Wang 2023,Review of Economics and Statistics, forthcoming). Figure 6 decomposes each basket into border-price change (ln UV₂₀₁₉ - ln UV₂₀₁₇) and volume change (ln qty₂₀₁₉ - ln qty₂₀₁₇), both averaged across the qualifying HS6 lines in the basket.

Figure 7: passthrough heterogeneity across washers, steel, solar

Figure 6 reports the three baskets as a table. Figure 7 plots the same ΔlnUV alongside Δln(quantity) for each basket as paired bars, so that the price-response and volume-response heterogeneity is directly visible. Under one-for-one border-price passthrough (Amiti, Redding & Weinstein 2019) ΔlnUV on the treated line should approach ln(1 + tariff_rate): ln(1.25) ≈ +22% for steel at 25%, ln(1.3) ≈ +26% for solar at 30%, and ln(1.2)-ln(1.5) for washers at 20-50%. Flaaen, Hortacsu & Tintelnot (2020, AER 110(7)) find ~110% passthrough for washers using BLS micro. The bar chart below is the BACI-annual, HS6-world analogue.

Figure 8: did China’s world-supply position bend on List-3 HS6?

BACI in this workbench is country-by-product-by-year (no bilateral × HS6 cell), so the canonical China-to-USA HS6 share that Bown (2023, PIIE WP 23-9) and Freund, Mattoo, Mulabdic & Ruta (2024, World Bank PRWP 10593) report directly is not resolvable here. The closest supply-side complement available is China’s share of world exports on the same two baskets. If China’s share on the tariff-targeted basket bends down relative to the food-and-pharma control after 2018, that is the global trace of the partner-substitution channel Fajgelbaum et al. (2020, QJE 135(1): 1-55) document on US CBP microdata.

What this replication says and does not say

Across 2015–2017 the treated-minus-control log-unit-value gap is already positive and noisy: β̂₂₀₁₅ ≈ +9.8% and β̂₂₀₁₆ ≈ +12.7%. Parallel trends therefore do not hold in this panel. From 2018 through 2024 the point estimate is either close to zero or negative, driven mostly by the food and pharma control group’s own 2020–2022 commodity-and-supply-chain repricing, not by List-3. BACI annual unit values at HS6–world cannot separate the tariff signal from these confounders at the resolution ARW worked in.

The Cavallo, Gopinath, Neiman & Tang (2021, JME 119, 1–18) retail-versus-border decomposition is the natural next step: if the tariff passed to the US border price but not to US retail prices, importers absorbed the wedge in trade margins; if it passed to retail, US households bore it. Neither test is runnable on BACI alone; both are the justification for continuing the microdata work in ARW’s tradition. A BACI-based page like this is useful for bounding, not for identifying, border-price pass-through.

Open questions and policy read

• What was actually passed to US consumers? Cavallo et al. (2021) find near-complete pass-through to the border but limited pass-through to retail through 2019. Extending that split to 2020-2024 requires BLS retail microdata plus BLS import-price data, neither of which is in this workbench.
• Did China exporters absorb any tariff? ARW (2019) find zero foreign-exporter absorption. Fajgelbaum et al. (2020) find similar, with large extensive-margin reallocation. The Benguria and Saffie (2020, NBER WP 26815) and Flaaen-Pierce (2019, Fed IFDP) decompositions push on the same question using different micro panels.
• Policy read. A BACI-annual pass-through coefficient for Section 301 is attenuated toward zero by two specific design choices (annual frequency, pooled-origin), not by any failure of the underlying economics. Readers citing a BACI-based headline for border-price pass-through should use it only to bound the sign, not the magnitude.

References

• Abadie, A., Athey, S., Imbens, G. W., & Wooldridge, J. (2023). “When Should You Adjust Standard Errors for Clustering?” Quarterly Journal of Economics 138(1): 1–35.
• Amiti, M., Redding, S. J., & Weinstein, D. E. (2019). “The Impact of the 2018 Tariffs on Prices and Welfare.” Journal of Economic Perspectives 33(4): 187–210.
• Amiti, M., Redding, S. J., & Weinstein, D. E. (2020). “Who’s Paying for the US Tariffs? A Longer-Term Perspective.” NBER Working Paper 26610.
• Bown, C. P. (2023). “US-China trade and investment relations — in charts.” Peterson Institute for International Economics Working Paper 23-9.
• Cavallo, A., Gopinath, G., Neiman, B., & Tang, J. (2021). “Tariff Pass-Through at the Border and at the Store: Evidence from US Trade Policy.” Journal of Monetary Economics 119: 1–18.
• Fajgelbaum, P. D., Goldberg, P. K., Kennedy, P. J., & Khandelwal, A. K. (2020). “The Return to Protectionism.” Quarterly Journal of Economics 135(1): 1–55.
• Flaaen, A., Hortacsu, A., & Tintelnot, F. (2020). “The Production Relocation and Price Effects of US Trade Policy: The Case of Washing Machines.” American Economic Review 110(7): 2103–2127.
• Roth, J., Sant’Anna, P. H. C., Bilinski, A., & Poe, J. (2023). “What’s Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature.” Journal of Econometrics 235(2): 2218–2244.
• United States Trade Representative (2018). “Notice of Modification of Section 301 Action: China’s Acts, Policies, and Practices Related to Technology Transfer, Intellectual Property, and Innovation.” 83 FR 47974 (List 3, September 21, 2018).
• United States Trade Representative (2019). “Implementing Modification to Section 301 Action: China’s Acts, Policies, and Practices Related to Technology Transfer, Intellectual Property, and Innovation.” 84 FR 20459 (List 3 rate increase 10% → 25%, May 9, 2019).