Toys, Watches, and Miscellaneous Manufactured Goods Are the Retail Categories Most Dependent on a Single Country — and Most Exposed to Tariff Swings

Summary

Ranking 13 retail-relevant HS-2 chapters by the share of 2025 US import value coming from their single largest origin country (confirmed with the hhi_concentration tool, not just eyeballed shares), watches/clocks (HS 91) is the most concentrated retail category: 82.1% of the $6.65B imported in 2025 came from Switzerland, HHI = 6,820 — far above the DOJ/FTC 'highly concentrated' threshold of 1,800. Among mass-market categories, toys and games (HS 95) is both large ($35.1B) and concentrated (53.4% from China, HHI = 3,317), followed by miscellaneous manufactured articles (96, 48.3% China, HHI 2,648), other made-up textile articles (63, 42.6% China, HHI 2,379), and footwear (64, 36.3% Vietnam, HHI 2,205, with China the close second supplier). Tariff exposure for exactly these categories jumped hard in 2025: the effective duty rate importers actually paid (USITC DataWeb, calculated duties ÷ customs value) on Chinese toys rose from 1.4% in 2024 to 23.4% in 2025; on Chinese footwear from 13.1% to 32.4%; on Chinese miscellaneous manufactured goods from 7.6% to 29.5%; on Chinese other textile articles from 11.4% to 30.8%. Even Swiss watches, previously almost duty-free (1.8%), rose to 11.0% after the August 2025 Swiss reciprocal-tariff action. The categories with the tightest single-country dependence are the same ones that saw the largest swings in effective duty burden this year — meaning a further tariff move on any of these two or three countries (China, Switzerland, Vietnam) has an outsized, concentrated effect on retail shelf prices rather than a diffused one.

How concentration was measured

Using econ.trade_imports (Census International Trade timeseries, HS-6 detail, filtered to geo_level='country' to avoid double-counting Census's own bloc/continent rollups), 2025 is the only fully loaded year in this table (observed coverage 2025-2025), so all shares below are 2025 year-to-date — a reasonable proxy for 'what's on the shelf right now.' For each HS-2 chapter plausibly sold at retail, I computed each origin country's share of that chapter's total import value and ran the hhi_concentration tool (DOJ/FTC merger-guideline HHI, 0-10,000 scale) across all origin countries in the chapter.

By this measure, watches and toys are the two most single-country-dependent retail categories with meaningful shelf presence — watches on a narrow luxury/mechanical-movement base (Switzerland), toys on a much larger, mass-market base (China). Apparel and consumer electronics, despite large dollar volumes, are comparatively diversified across Vietnam, China, Bangladesh, Indonesia, Mexico and others (HHI under 1,050 in this data, 'moderately concentrated' or below), consistent with the well-documented 'China+1' diversification apparel brands have pursued for a decade.

Tariff exposure: the concentration is compounding with a real 2025 duty spike

Using econ.usitc_tariffs (USITC DataWeb imports-for-consumption, HTS-8 x country x year, effective duty rate = calculated duties / customs value), the effective rate actually paid on the top supplier in each concentrated category moved sharply from calendar 2024 to 2025:

This lines up with the well-reported 2025 tariff sequence: Section 301 tariff increases, a 20% fentanyl-related tariff, and 10-30% 'reciprocal' tariffs stacked on Chinese goods through the year (CNBC reported combined rates pushing many consumer products above 30% effective cost), followed by a separate reciprocal-tariff action on Switzerland announced mid-2025 that hit Swiss watch exports specifically. Trade press (WWD, on footwear) independently confirms China's import share of US footwear fell to a 35-year low in 2025 as buyers shifted toward Vietnam, Cambodia and Indonesia — consistent with Vietnam edging out China as footwear's largest single supplier in this data — while toy sourcing has NOT diversified nearly as much, because Chenghai/Guangdong's toy-manufacturing cluster (molds, components, assembly, packaging in one place) has no comparable substitute cluster elsewhere, per sourcing-industry reporting.

Net answer: watches (narrow, Switzerland) and toys (broad, mass-market, China) are the retail categories most dependent on a single country for current shelf inventory; toys, footwear, other textile articles and miscellaneous manufactured goods — all majority-or-plurality sourced from China or Vietnam — are the categories where 2025's tariff actions have already driven the effective duty burden up 9-22 percentage points, meaning a further tariff change on China (or Switzerland, for watches) would land concentrated on these specific categories rather than being absorbed across a diversified supplier base.

Data notes and limits

• econ.trade_imports is fully loaded for 2025 only in this warehouse (2025 YTD, not a full calendar year) — appropriate for a 'right now' snapshot but not a full-year total.
• HS-2 chapter is a coarser grain than a true 'retail product category' (e.g. HS 95 bundles toys, video games, and sporting goods together); a narrower HS-6/HTS-8 cut would sharpen individual product lines (e.g. dolls vs. board games) but was outside this pass's scope.
• Effective duty rate is calculated duties ÷ customs value, USITC DataWeb's own reported aggregate — it reflects the blended rate across all HTS-8 lines within a chapter for that supplier, not any single tariff-code rate.
• Concentration (HHI, top-country share) and duty-rate change were computed directly from AskAmerica's econ.trade_imports and econ.usitc_tariffs tables via the hhi_concentration tool and query tool; the tariff-policy narrative (Section 301 stacking, Swiss reciprocal tariff, footwear sourcing shift) is drawn from the web sources cited below to corroborate and explain the computed numbers, not substitute for them.

Every query behind this report

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

WITH cat_country AS (
  SELECT hs2, country_name, SUM(value_usd) AS val
  FROM econ.trade_imports
  WHERE geo_level = 'country' AND year = '2025'
  GROUP BY hs2, country_name
),
cat_total AS (
  SELECT hs2, SUM(val) AS total_val
  FROM cat_country
  GROUP BY hs2
),
ranked AS (
  SELECT cc.hs2, cc.country_name, cc.val, ct.total_val,
         cc.val::double / ct.total_val AS share,
         ROW_NUMBER() OVER (PARTITION BY cc.hs2 ORDER BY cc.val DESC) AS rn
  FROM cat_country cc JOIN cat_total ct ON cc.hs2 = ct.hs2
)
SELECT hs2, country_name AS top_country, val AS top_country_val, total_val, share AS top_share
FROM ranked
WHERE rn = 1 AND total_val > 500000000
ORDER BY share DESC
LIMIT 40

WITH cat_country AS (
  SELECT hs2, country_name, SUM(value_usd) AS val
  FROM econ.trade_imports
  WHERE geo_level = 'country' AND year = '2025'
    AND hs2 IN ('61','62','64','94','85','42','95','96','63','91','39','69','83')
  GROUP BY hs2, country_name
),
cat_total AS (
  SELECT hs2, SUM(val) AS total_val
  FROM cat_country
  GROUP BY hs2
),
ranked AS (
  SELECT cc.hs2, cc.country_name, cc.val, ct.total_val,
         cc.val::double / ct.total_val AS share,
         ROW_NUMBER() OVER (PARTITION BY cc.hs2 ORDER BY cc.val DESC) AS rn
  FROM cat_country cc JOIN cat_total ct ON cc.hs2 = ct.hs2
),
hhi AS (
  SELECT hs2, SUM(POWER(val::double/total_val*100,2)) AS hhi
  FROM ranked
  GROUP BY hs2
)
SELECT r.hs2, r.country_name AS top_country, r.share AS top_share, r.total_val, h.hhi
FROM ranked r JOIN hhi h ON r.hs2=h.hs2
WHERE r.rn = 1
ORDER BY r.share DESC

SELECT hs2, country_name, year, COUNT(*) n, SUM(customs_value_usd) cv
FROM (
  SELECT LEFT(hts8,2) AS hs2, country_name, year, hts8, customs_value_usd
  FROM econ.usitc_tariffs
  WHERE year IN ('2024','2025') AND LEFT(hts8,2) IN ('91','95','96','63','64')
) t
GROUP BY hs2, country_name, year
HAVING country_name IN ('China','Switzerland','Vietnam')
ORDER BY hs2, year, cv DESC

SELECT LEFT(hts8,2) AS hs2, country_name, year,
       SUM(customs_value_usd) AS cv, SUM(calculated_duties_usd) AS duties,
       SUM(calculated_duties_usd)/NULLIF(SUM(customs_value_usd),0) AS eff_rate
FROM econ.usitc_tariffs
WHERE year IN ('2024','2025') AND LEFT(hts8,2) IN ('91','95','96','63','64')
  AND country_name IN ('China','Switzerland','Vietnam')
GROUP BY LEFT(hts8,2), country_name, year
ORDER BY hs2, country_name, year

SELECT country_name, SUM(value_usd) AS val FROM econ.trade_imports WHERE geo_level='country' AND year='2025' AND hs2='91' GROUP BY country_name

SELECT country_name, SUM(value_usd) AS val FROM econ.trade_imports WHERE geo_level='country' AND year='2025' AND hs2='95' GROUP BY country_name

SELECT country_name, SUM(value_usd) AS val FROM econ.trade_imports WHERE geo_level='country' AND year='2025' AND hs2='96' GROUP BY country_name

SELECT country_name, SUM(value_usd) AS val FROM econ.trade_imports WHERE geo_level='country' AND year='2025' AND hs2='63' GROUP BY country_name

SELECT country_name, SUM(value_usd) AS val FROM econ.trade_imports WHERE geo_level='country' AND year='2025' AND hs2='64' GROUP BY country_name

SELECT country_name, SUM(value_usd) AS val FROM econ.trade_imports WHERE geo_level='country' AND year='2025' AND hs2='83' GROUP BY country_name

SELECT country_name, SUM(value_usd) AS val FROM econ.trade_imports WHERE geo_level='country' AND year='2025' AND hs2='69' GROUP BY country_name

SELECT country_name, SUM(value_usd) AS val FROM econ.trade_imports WHERE geo_level='country' AND year='2025' AND hs2='94' GROUP BY country_name

SELECT country_name, SUM(value_usd) AS val FROM econ.trade_imports WHERE geo_level='country' AND year='2025' AND hs2='39' GROUP BY country_name

SELECT country_name, SUM(value_usd) AS val FROM econ.trade_imports WHERE geo_level='country' AND year='2025' AND hs2='62' GROUP BY country_name

SELECT country_name, SUM(value_usd) AS val FROM econ.trade_imports WHERE geo_level='country' AND year='2025' AND hs2='61' GROUP BY country_name

SELECT country_name, SUM(value_usd) AS val FROM econ.trade_imports WHERE geo_level='country' AND year='2025' AND hs2='42' GROUP BY country_name

SELECT country_name, SUM(value_usd) AS val FROM econ.trade_imports WHERE geo_level='country' AND year='2025' AND hs2='85' GROUP BY country_name