What happens if USA sets a 25% tariff on China’s HS 854231?

A scenario-driven tariff simulator: the direct partial-equilibrium effect is computed under a CES-Armington import demand with substitution elasticity sigma = 4 (Saito 2004 IMF WP 04/36 on HS2 aggregates; Broda & Weinstein 2006 QJE report a lower median near 3; policy simulations routinely use 3 to 5). Upstream pass-through is decomposed using OECD TiVA foreign value-added intensity (Koopman, Wang & Wei 2014), and downstream consumer impact is allocated across destination activities by TiVA FVA intensity weighted by export share. Amiti, Redding & Weinstein (2019 JEP) document near-complete pass-through of the 2018 US tariffs onto domestic prices, which justifies treating the USA importer and consumer as the primary incidence bearers in the diagrams below.

Direct bilateral impact

With the world-price line horizontal under the small-country assumption, the tariff raises the in-USA price from P0 = 1 to P1 = (1 + t1) = 1.25. Imports slide down the CES demand curve from Q0 to Q1, and the shaded regions partition the welfare change into revenue (a transfer to the treasury), consumer surplus loss (paid by the importer), and the deadweight Harberger triangle (Harberger 1964 AER P&P) which no one collects.

Upstream pass-through via TiVA

A tariff on CHN’s HS 854231 is not just a bill paid by the final importer: part of that price is cost pass-back to whichever economies supply inputs embedded in CHN’s production. Leontief (1936 REStat) gives the accounting identity that gross output decomposes into direct and indirect value added contributions via the (I − A)⁻¹ inverse; Koopman, Wang & Wei (2014 AER) operationalise that for gross exports. We approximate the partner decomposition by combining CHN’s economy-wide foreign value-added share in gross exports (OECD TiVA EXGR_FVA, activity aggregate _T) = 15.8% (OECD TiVA 2020) with CHN’s bilateral sourcing mix from BACI.

Downstream burden across USA activities

On the destination side, the question is which end-use sectors actually bear the tariff bill. We weight USA activities by their foreign-value-added intensity (TiVA EXGR_FVA by activity, 2020) times their share of USA’s total exports, and allocate the imported-intermediate tariff bill across activities by that intensity. Activities with high FVA content in exports are the ones whose cost base rises most when an intermediate like HS 854231 is taxed: the textbook Grossman & Rossi-Hansberg (2008 AER) “trading tasks” incidence channel.

Multi-sector welfare preview

Caliendo & Parro (2015, Review of Economic Studies 82(1): 1–44) generalise Eaton-Kortum to multiple sectors with input-output linkages and show that the welfare consequences of a tariff depend on the sector substitution elasticity sigma_s, the base expenditure share on the tariffed origin (1 − lambda_ii), and the I-O loop through domestic industries. Ossa (2014, AER 104(12): 4104–4146) uses the same apparatus to quantify trade-war payoffs. Arkolakis, Costinot & Rodríguez-Clare (2012, QJE 127(1): 51–80) prove that in a broad class of trade models the gain from trade is dW/W = (1 − lambda_ii)^{1/(1−sigma)}, so high-sigma, small-domestic-share sectors deliver the largest swings. As a preview of the sectoral heterogeneity, the bars below rank HS6 lines inside chapter 85 by their Broda–Weinstein-style sigma (trade_elasticity.parquet) and colour by their USA MFN. A full Caliendo–Parro solve requires an ICIO cube that is scheduled but not yet ingested in the workbench.

Substitution menu: who else exports HS 854231?

Amiti, Redding & Weinstein (2019, JEP 33(4): 187–210) document that the 2018 US tariff schedule did not just compress imports of Chinese goods, it re-sorted them across third countries: Vietnam, Mexico, Korea and the EU absorbed a substantial share of the diverted flow. The destination’s import elasticity with respect to the tariffed origin therefore depends on how concentrated the global supply of this HS6 is. The bars rank the world’s top exporters of HS 854231 in 2024 (excluding China); the more weight in the right tail, the cheaper the substitution menu the destination importer faces under the 25% scenario.

A 10-pp uniform hike: revenue, deadweight, and terms-of-trade

A canonical textbook question is how a tariff’s welfare cost decomposes into a transfer to the treasury (revenue), a triangle nobody collects (deadweight), and any terms-of-trade improvement the tariff buys by pushing world prices down. Under the small-country assumption pass-through is complete and the ToT channel is zero (Amiti, Redding & Weinstein 2019 JEP find near-complete pass-through of the 2018 US tariffs to US retail prices). Cavallo, Gopinath, Neiman & Tang (2021, Review of Economics and Statistics) decompose the same tariffs into border pass-through (~1.0) and retail pass-through (0.93 at an 18-month horizon), leaving 7% of the tariff borne by foreign producers as a ToT gain. We apply a +10 percentage-point uniform hike on top of the current schedule and attribute PT = 0.93 to the consumer, 1 - PT = 0.07 to the foreign producer. The rectangles+triangle sum to the consumer’s ex-ante marginal outlay; colours match the Harberger (1964) diagram in Figure 1.

Sigma sensitivity: how much does the elasticity choice matter?

Every headline number above is built on sigma = 4. The policy literature uses a range: Broda & Weinstein (2006, QJE) report a median HS10 sigma near 3; Saito (2004, IMF WP 04/36) around 4 on HS2 aggregates; CGE practitioners commonly use sigma in [3, 5]; higher values (6–8) emerge from highly disaggregated estimates on differentiated goods. At this scenario’s bilateral flow of $3.65B and a tariff change from 0.0% to 25.0%, we recompute the import-volume contraction, the Harberger deadweight triangle, and tariff revenue under sigma ∈ {2, 4, 6, 8} to show the elasticity-choice footprint. DWL scales roughly linearly in sigma; the volume contraction scales more steeply because sigma enters as an exponent. Revenue is non-monotone in sigma: higher sigma shrinks Q1 faster, which shrinks the revenue rectangle even though t₁ is held fixed.

Retaliation: symmetric tariff response and the DWL doubling result

Ossa (2014, AER 104(12): 4104–4146) and Caliendo & Parro (2015) show that trade wars are not one-sided: when the destination imposes a tariff, the origin optimally retaliates, and the welfare loss doubles because the Harberger triangle is now incurred on both sides of the border. Fajgelbaum, Goldberg, Kennedy & Khandelwal (2020, QJE 135(1): 1–55) document that US agricultural exports were targeted almost dollar-for-dollar in the 2018–19 retaliation by China, EU, and Mexico. Under a small-country PE assumption on both legs, a symmetric counter-tariff of25% imposed by China on USA’s reciprocal bilateral flow generates a mirror Harberger triangle and revenue rectangle. The global welfare loss is the sum of both DWLs; neither side captures the other’s revenue. We bring in the reciprocal bilateral flow (USA exports to China of the same HS6) and compute the retaliation decomposition.

How to use this page

• Edit the URL to change scenario: ?origin=CHN&destination=USA&hs=854231&rate=25. ISO3 codes are uppercased server-side; HS6 must be six digits.
• For live slider interaction on the same PE math see /tariff-lab. That page is a client component; this one is server-rendered so the URL alone reproduces any scenario shown in a report or screenshot.
• The upstream decomposition here uses aggregate TiVA FVA; a full ICIO-inverse decomposition (Koopman-Wang-Wei 2014) requires a partner-resolved I-O cube that is scheduled but not yet ingested.

References

• Amiti, M., Redding, S. J., & Weinstein, D. E. (2019). “The Impact of the 2018 Trade War on US Prices and Welfare.” Journal of Economic Perspectives 33(4): 187–210.
• Arkolakis, C., Costinot, A. & Rodríguez-Clare, A. (2012). “New Trade Models, Same Old Gains?” Quarterly Journal of Economics 127(1): 51–80.
• Armington, P. S. (1969). “A Theory of Demand for Products Distinguished by Place of Production.” IMF Staff Papers 16(1): 159–178.
• Broda, C., & Weinstein, D. E. (2006). “Globalization and the Gains from Variety.” Quarterly Journal of Economics 121(2): 541–585.
• Caliendo, L., & Parro, F. (2015). “Estimates of the Trade and Welfare Effects of NAFTA.” Review of Economic Studies 82(1): 1–44.
• Grossman, G. M., & Rossi-Hansberg, E. (2008). “Trading Tasks: A Simple Theory of Offshoring.” American Economic Review 98(5): 1978–1997.
• Harberger, A. C. (1964). “The Measurement of Waste.” American Economic Review, Papers & Proceedings 54(2): 58–76.
• Koopman, R., Wang, Z., & Wei, S.-J. (2014). “Tracing Value-Added and Double Counting in Gross Exports.” American Economic Review 104(2): 459–494.
• Leontief, W. W. (1936). “Quantitative Input and Output Relations in the Economic System of the United States.” Review of Economics and Statistics 18(3): 105–125.
• Ossa, R. (2014). “Trade Wars and Trade Talks with Data.” American Economic Review 104(12): 4104–4146.
• Saito, M. (2004). “Armington Elasticities in Intermediate Inputs Trade: A Problem in Using Multilateral Trade Data.” IMF Working Paper 04/36.