Tariff-protected steel, aluminum and appliance industries added only ~1-2% jobs since 2018 tariffs, while broader manufacturing lost jobs to the higher input costs

Summary

Employment in the specific industries that received U.S. tariff protection starting in 2018 -- iron and steel mills (Section 232, 25% tariff, March 2018), primary aluminum production (Section 232, 10% tariff), and washing machines (Section 201, January 2018) -- grew only modestly and unevenly. Steel mill employment (NAICS 3311) rose about +800 jobs (+1.0%) from 2018 to 2025 (81,100 to 81,900); aluminum production (NAICS 3313) actually fell about 360 jobs (-0.6%) over the same span; and washing-machine/major-appliance manufacturing (NAICS 335220) rose the most, +3,550 jobs (+20%), though it dropped sharply first (2018-2020) before recovering later. Meanwhile total U.S. manufacturing employment fell by about 351,000 jobs (-2.8%) over the same 2018-2025 window. Independent Federal Reserve Board research (Flaaen & Pierce 2019) found the 2018 steel/aluminum tariffs were associated with roughly 75,000 net manufacturing job losses by mid-2019 -- concentrated in downstream, steel-consuming industries -- far outweighing the roughly 1,000 jobs the tariffs added in steel production itself. In short: the protected industries themselves saw small, mixed gains, while manufacturing overall shrank, and by outside estimates lost more jobs than the protected sectors gained.

Which industries were actually tariff-protected, and what the data shows

Using BLS QCEW (Quarterly Census of Employment and Wages, econ.state_wages, private-sector employment) aggregated nationally by NAICS industry code:

Note the grain sensitivity flagged before running this analysis: the narrowly-tariffed 6-digit codes (steel mills, aluminum) behave differently from their broader 3-digit parent category (331, primary metals), which also includes foundries and other nonferrous metal segments that were never subject to these tariffs and which shrank by more. Reporting only the 331 aggregate would overstate the damage to the tariffs' actual target; reporting only the narrow codes would miss that closely related, non-tariffed segments of the same broad sector contracted.

Why protected-industry jobs barely moved while manufacturing overall lost jobs

The clearest independent quantification comes from Federal Reserve Board economists Aaron Flaaen and Justin Pierce (December 2019 working paper, cited by Econofact). They estimate that by mid-2019, the increased input costs created by the 2018 steel and aluminum tariffs were associated with 0.6% fewer manufacturing jobs than would otherwise have existed -- roughly 75,000 jobs -- concentrated in the more than 12 million manufacturing jobs in industries that use steel as an input (of which nearly 2 million are in steel-intensive industries such as auto parts, appliances, farm machinery, and machinery manufacturing). This dwarfed the roughly 1,000-job increase the tariffs produced in steel production itself between March 2018 and November 2019 -- consistent with the ~800-job net rise (2018-2025) found in the QCEW data above. A Columbia/NY Fed/Princeton study found about half of the 25% steel tariff passed through into higher U.S. steel prices, raising costs for the far larger number of downstream steel-consuming manufacturers and putting U.S. exporters at a cost disadvantage against foreign rivals. This mechanism -- narrow gains concentrated in the protected industry, offset by larger diffuse losses spread across many downstream users of that input -- explains why the protected industries' own headcounts grew only slightly (or fell, for aluminum) while total manufacturing employment declined over the same period.

Data limitations and what is not covered here

This analysis covers the well-defined 2018 Section 232 (steel/aluminum) and Section 201 (washing machine) tariffs, where a specific NAICS code maps cleanly to a specific protected product. It excludes the much broader 2018-2019 Section 301 tariffs on Chinese imports (which touched thousands of tariff lines across many industries rather than protecting a single defined domestic industry) and the 2025 tariff actions, because neither maps to a single clean 'protected industry' employment series the way steel, aluminum, and washers do. Solar cells (also tariffed under Section 201 in 2018) could not be isolated: the closest available NAICS code, 334413, is dominated by general semiconductor manufacturing rather than photovoltaic cells specifically, so no solar figure is reported rather than mislabeling that broader semiconductor series as a solar-tariff outcome. All QCEW figures are annual averages, private-sector employment, summed across all 50 states + DC.

Every query behind this report

13 warehouse calls ran in this session, in order. Each is reproducible against the same snapshot.

SELECT DISTINCT industry_code, agglvl_code FROM econ.state_wages WHERE industry_code IN ('331','3311','33111','331110','3313','33131','331313','334413','335220','3344','334') AND own_code='0' ORDER BY industry_code

SELECT DISTINCT own_code FROM econ.state_wages LIMIT 10

SELECT DISTINCT industry_code, agglvl_code FROM econ.state_wages WHERE industry_code LIKE '331%' ORDER BY industry_code LIMIT 50

SELECT DISTINCT industry_code, agglvl_code FROM econ.state_wages WHERE industry_code IN ('335220','334413','3344','221') ORDER BY industry_code

SELECT year, SUM(annual_avg_emplvl) AS emp
FROM econ.state_wages
WHERE own_code='0' AND industry_code='331' AND agglvl_code='55'
GROUP BY year ORDER BY year

SELECT year, SUM(annual_avg_emplvl) AS emp
FROM econ.state_wages
WHERE own_code='0' AND industry_code='3311' AND agglvl_code='56'
GROUP BY year ORDER BY year

SELECT year, SUM(annual_avg_emplvl) AS emp
FROM econ.state_wages
WHERE own_code='0' AND industry_code='3313' AND agglvl_code='56'
GROUP BY year ORDER BY year

SELECT own_code, agglvl_code, count(*) FROM econ.state_wages WHERE industry_code='331' GROUP BY own_code, agglvl_code

SELECT industry_code, "year", SUM(annual_avg_emplvl) AS emp
FROM econ.state_wages
WHERE own_code='5' AND industry_code IN ('331','3311','3313','335220','334413')
GROUP BY industry_code, "year" ORDER BY industry_code, "year"

SELECT DISTINCT industry_code FROM econ.state_wages WHERE industry_code IN ('31-33','1013','10') LIMIT 10

SELECT "year", SUM(annual_avg_emplvl) AS emp
FROM econ.state_wages
WHERE own_code='5' AND industry_code='31-33'
GROUP BY "year" ORDER BY "year"

WITH d AS (
  SELECT industry_code, "year", SUM(annual_avg_emplvl) AS emp
  FROM econ.state_wages
  WHERE own_code='5' AND industry_code IN ('331','3311','3313','335220','31-33')
  GROUP BY industry_code, "year"
), base AS (
  SELECT industry_code, emp AS base_emp FROM d WHERE "year"='2017'
)
SELECT d.industry_code, d."year", d.emp, ROUND(100.0*d.emp/base.base_emp,1) AS idx_2017_100
FROM d JOIN base ON d.industry_code=base.industry_code
WHERE d."year" IN ('2017','2018','2019','2024','2025')
ORDER BY d.industry_code, d."year"

WITH d AS (
  SELECT industry_code, "year", SUM(annual_avg_emplvl) AS emp
  FROM econ.state_wages
  WHERE own_code='5' AND industry_code IN ('331','3311','3313','335220','31-33')
  GROUP BY industry_code, "year"
), base AS (
  SELECT industry_code, emp AS base_emp FROM d WHERE "year"='2017'
)
SELECT d.industry_code, d."year", ROUND(100.0*d.emp/base.base_emp,1) AS idx
FROM d JOIN base ON d.industry_code=base.industry_code
WHERE d."year">='2017'
ORDER BY d.industry_code, d."year"