USITC DataWeb: U.S. Footwear Import, Export and Tariff Statistics

The United States' official count of every shoe that crosses its border, kept at the statistical line. USITC DataWeb - U.S. Footwear Import/Export and Tariff data is the U.S. International Trade Commission's query view of official U.S. merchandise trade statistics, built on Census Bureau trade data, scoped here to Chapter 64: every harmonized footwear line across headings 6401 through 6405 - waterproof molded boots, rubber and plastic footwear, leather uppers, textile sports styles, and the remainder category - roughly 400+ active HTS 10-digit statistical reporting numbers.

Each line carries money and matter side by side. The money: customs value, calculated duty, landed duty-paid value, FAS value, CIF value. The matter: first and second units of quantity, which for most footwear lines means pairs - the one unit the entire industry argues about and this dataset simply settles.

Around the lines sit dimensions most trade products treat loosely. Six defined trade flows (imports for consumption, general imports, total exports, domestic exports, foreign exports, balance) crossed with partner country and customs district of entry or export, at annual or monthly grain.

Get a sample of this dataset and the rows below arrive live, keyed and typed - not screenshots.

Two panels per line: the trade query result and the tariff reference behind the same footwear line. Values shown are the dictionary's documented example values laid out in delivered column order, not a certified extract - your sample pulls live Chapter 64 lines:

trade row - one Chapter 64 line, one year, one country
hts_number        6403996090
description       <commodity text as published on the line>
customs_value_usd 1269699
first_unit_qty    76759            (pairs on most footwear lines)
second_unit_qty   -                (sq meters where the schedule demands one)
country           Vietnam
district          <customs district of entry>
year              2024

 tariff reference - the same line at HTS8 grain
 hts8              6403.99.60
 mfn_text_rate     duty expressed in words
 mfn_ad_val_rate   duty expressed as a percentage
 mfn_spec_rate     duty expressed per unit
 col1_special_text preference-program rate text
 wto_binding_code  B=Bound / U=Unbound

Read both panels together and the analytical move writes itself: join the pairs column to the duty column at the same 8-digit line and you have a landed-cost curve by country, by month, going back to 1989. No survey, no estimate - reported values from the official record, at a grain fine enough to isolate one style of dress shoe from another.

Eight core fields carry most of the weight, verified against the source card - nothing inferred. The dictionary below shows the trade-side fields first, then the tariff-reference fields that key to the same lines at HTS8. Anything beyond the core folds under additional fields on request rather than pretending to a completeness we have not verified line by line.

Geography - U.S. trade with every partner country, with drilldown to the customs district of entry or export. There is no state-level cut; if a supplier map has to stop somewhere official, it stops at district.

Temporal - 1989 to the present, sliceable as full years or individual months. Electronic records simply do not reach further back, which makes 1989 the hard left edge of American digital trade history rather than a vendor's convenience cutoff.

Granularity - year or month x HTS aggregation level (2, 4, 6, 8 or 10 digits) x country x district, each cell holding one or two quantity measures beside the selected value measures. Scale check: sweeping all of Chapter 64 for one full year, broken out by every partner country, fits comfortably inside a single pull - the whole footwear universe runs roughly 400+ active statistical lines deep, not millions.

API, files, or your warehouse. Daily, weekly, or hourly.

• Sourcing and procurement teams track share shifts at the statistical line - Vietnam versus China versus Indonesia within leather-uppers or sports footwear - and watch pairs volume move quarters before revenue does.
• Pricing and trade-compliance analysts model landed cost properly: duty columns joined to customs values at the same HTS8 line, instead of a single blended rate smeared across the category.
• Investors and quant researchers read import volume and unit values as a demand signal for brands and retailers, cut finely enough to separate athletic footwear from dress shoes inside one aggregate.
• Market researchers size categories in pairs rather than dollars, which is how the industry counts itself, then reconcile against value for price-mix stories.
• Journalists and academics cite the official record outright - these are the government's own numbers, which end arguments about whose import figure is right.
• Consultants and customs advisors scan preference-program rate columns across lines to flag where a client's origin strategy changes the duty math.

Competitive-intel and product teams get the cleanest external read on rival sourcing footprints available anywhere - country-of-origin shifts at the statistical line, monthly. Data scientists get a rare combination: official provenance, decade-deep history, and a natural-experiment surface (duty schedules changing under stable volumes) that trains models on something firmer than estimated proxies. Developers and builders get flat, wide tables that load once and join forever. Journalists and academics get citable federal statistics with a paper trail behind every figure. Market researchers and consultants get the denominator under every footwear market-share claim made in the trade press.

Cards worth reading next:

• Footwear data hub - the full pooled view of the industry, from resale marketplaces to image corpora.
• vs Observatory of Economic Complexity (OEC) - Footwear HS Profile - worldwide bilateral flows in bulk versus official U.S. figures at HTS-10 with pairs, duties and districts; both score 9 of 10, and the comparison page settles when each wins.
• StockX sneaker resale marketplace data - street-price context to lay over the official import record for the sneaker half of the market.
• Persona pages - what competitive-intel, data-science and developer teams each do with this slice, industry by industry.