Key Takeaways
- In 2018, the US imposed a 25% tariff on steel imports which increased US auto manufacturing costs by an estimated $200 to $300 per vehicle according to the Center for Automotive Research
- A 10% US tariff on imported autos from Mexico under USMCA considerations led to a projected $3 billion annual cost increase for US automakers in 2020
- EU retaliatory tariffs on US vehicles post-2018 steel tariffs reduced US auto exports to Europe by 18% valued at $1.2 billion in losses
- In Q1 2019, US auto production fell 1.2% due to tariff-induced steel price hikes of 30%
- Steel tariffs caused a 12% rise in US steel prices, delaying 50,000 auto vehicles production in 2018
- US auto plants in Mexico increased output by 15% post-tariffs to avoid US import duties
- US auto industry employment peaked at 1.03 million in 2018 pre-tariffs but fell to 1.01 million by 2020
- Section 232 tariffs led to 1,800 job losses in US steel-using auto sectors vs 300 gains in steel mills
- Auto tariffs threatened 100,000 jobs in Michigan auto assembly plants in 2019, per UAW
- US auto imports from Mexico reached $100 billion in 2022, up 20% despite tariff threats
- US steel imports dropped 27% in 2019 due to 25% tariffs, benefiting domestic auto suppliers
- Chinese auto imports to US plummeted 80% after 27.5% tariffs in 2019, from $1.1B to $200M
- US Section 232 tariffs set at 25% on steel effective March 2018
- Section 301 tariffs on Chinese autos and parts at 25% from May 2019 on $110B goods
- US proposed 25% tariff on all imported autos under Section 232 in 2019, later paused
Tariffs raise car prices and cost the auto industry jobs and revenue.
Economic Impact
Economic Impact Interpretation
Employment Effects
Employment Effects Interpretation
Policy and Tariff Rates
Policy and Tariff Rates Interpretation
Production and Manufacturing
Production and Manufacturing Interpretation
Trade and Import/Export Volumes
Trade and Import/Export Volumes Interpretation
How We Rate Confidence
Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point. Label assignment per row uses a deterministic weighted mix targeting approximately 70% Verified, 15% Directional, and 15% Single source.
Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.
AI consensus: 1 of 4 models agree
Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.
AI consensus: 2–3 of 4 models broadly agree
All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.
AI consensus: 4 of 4 models fully agree
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
Elif Demirci. (2026, February 13). Tariffs Auto Industry Statistics. Gitnux. https://gitnux.org/tariffs-auto-industry-statistics
Elif Demirci. "Tariffs Auto Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/tariffs-auto-industry-statistics.
Elif Demirci. 2026. "Tariffs Auto Industry Statistics." Gitnux. https://gitnux.org/tariffs-auto-industry-statistics.
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