Footwear Data: Customs Lines, Pair Counts, Shoe Images and Resale Prints · Head-to-head
Observatory of Economic Complexity (OEC) - Footwear HS Profile vs USITC DataWeb - U.S. Footwear Import/Export and Tariff Data
Which footwear data: customs lines, pair counts, shoe images and resale prints data fits your job: Observatory of Economic Complexity - Footwear HS Profile, or USITC DataWeb - U.S. Footwear Import/Export and Tariff Data. API, files, or your warehouse. Daily, weekly, or hourly.
Observatory of Economic Complexity (OEC) - Footwear HS Profile
USITC DataWeb - U.S. Footwear Import/Export and Tariff Data
Where the fields line up
No shared field names. These two answer different questions.
| Field | Observatory of Economic Complexity - Footwear HS Profile | USITC DataWeb - U.S. Footwear Import/Export and Tariff Data |
|---|---|---|
Year | Calendar year of the trade flow. | not in this set |
HS4 ID | Numeric identifier for an HS4 heading in the revision-17 product tree; the leading digits encode HS chapter 64 footwear and the trailing pair indexes the heading. | not in this set |
HS4 | Heading-level product name within chapter 64 - waterproof, rubber, leather, textile and the remaining footwear groups. | not in this set |
Trade Value | Nominal monetary value of traded goods in current USD for the selected product/country/year slice. | not in this set |
Exporter Country / Importer Country | Reporter and partner country drilldowns; roughly 226 economies appear in the 2024 file, covering both directions of every flow. | not in this set |
HTS Number / HTS10 & DESCRIPTION | not in this set | documented |
Customs Value (CONS_CUSTOMS_VALUE) | not in this set | documented |
First Unit of Quantity (CONS_FIR_UNIT_QUANT) | not in this set | documented |
Second Unit of Quantity | not in this set | documented |
Calculated Duty | not in this set | documented |
Landed Duty-Paid Value | not in this set | documented |
FAS Value | not in this set | documented |
Coverage, side by side
| Observatory of Economic Complexity - Footwear HS Profile | USITC DataWeb - U.S. Footwear Import/Export and Tariff Data | |
|---|---|---|
| Geographic | Worldwide; roughly 226 reporter economies with bilateral partner detail | US trade with all partner countries, plus customs district of entry/export; no state-level cut |
| Temporal | 1995-2024 under revision-92 coding; 2018-2024 under r17; 2022-2024 under r22 | 1989 to present, annual or monthly periods |
What each contains
They tie on 2 attributes. Pick by fit, not by loyalty.
| Observatory of Economic Complexity - Footwear HS Profile | USITC DataWeb - U.S. Footwear Import/Export and Tariff Data | |
|---|---|---|
| Publisher | Observatory of Economic Complexity, built by Datawheel; figures from CEPII's BACI database | U.S. International Trade Commission, built on Census Bureau trade data |
| Industry slice | Footwear | Footwear (plus 10 tagged secondary industries) |
| Subject lens | World bilateral trade in HS 64 footwear: treemaps, rankings, net trade, economic complexity analytics | Official US import/export statistics and tariff reference for all of chapter 64 |
| Geographic coverage | Worldwide; roughly 226 reporter economies with bilateral partner detail | US trade with all partner countries, plus customs district of entry/export; no state-level cut |
| Temporal coverage | 1995-2024 under revision-92 coding; 2018-2024 under r17; 2022-2024 under r22 | 1989 to present, annual or monthly periods |
| Detail level | Year x product (HS2/HS4/HS6) x exporter x importer | Period x HTS 2/4/6/8/10-digit line x country x district |
| Formats | JSON, CSV, Parquet, TSV | JSON, XLSX, HTML tables |
| Delivery cadence | Daily, weekly, or hourly - your call | Daily, weekly, or hourly - your call |
| Distinctive surface | Corridor-level unit values and complexity/growth-potential analytics | Pairs-based quantities joined to duty columns at the same tariff line |
| Best for | Global sourcing maps, origin-share studies and market-sizing denominators | US landed-cost modeling, supplier tracking and citation-grade trade statistics |
What each does better
the Observatory of Economic Complexity - Footwear HS Profile
Global reach, both directions of every corridor. Roughly 226 reporter economies appear with bilateral partner detail, so China-to-Germany and Germany-from-China resolve as the same flow seen twice, and no market is off-limits: the 2024 ledger puts Vietnam at $32.8 billion of exports and Germany at $13.9 billion of imports alongside the US figures. Any question that names two countries starts here; see bilateral trade flows.
Material economics inside the chapter. The six HS4 headings split 2024 trade into leather footwear at $59.1 billion, textile at $45.9 billion, rubber at $42.8 billion and waterproof at just $1.48 billion - proof that material choice, not branding, decides where value concentrates inside footwear. Drill further to any of the 25 HS6 lines, down to waterproof boots with metal toe-caps.
Analytic layers nobody else ships. Beyond raw flows the profile computes net trade, bilateral relatedness ("growth potential"), economic complexity rankings and diversification frontiers, so a first-pass strategy deck can be built from the same source as the underlying table. Depth follows the Harmonized System revision queried: revision-92 coding reaches back to 1995, with r17 covering 2018-2024 for modern line naming - see HS code 64 for how the coding works.
the USITC DataWeb - U.S. Footwear Import/Export and Tariff Data
Resolution. Chapter 64 goes under a microscope. Where the global profile stops at HS6, USITC runs to the HTS 10-digit statistical reporting number - roughly 400+ active footwear lines - fine enough to separate one style of dress shoe from another inside heading 6403. Classification is not limited to HTS either: queries re-key to SIC, SITC or NAICS when the question is industry-side rather than tariff-side.
Quantities in the units the industry actually counts. The first statistical reporting quantity is pairs on most footwear lines, with a second measure (square meters) on the material categories that require one. Volume analysis needs no weight-to-pair conversion, and pairs move quarters before revenue does - which is why sourcing teams read this record weekly.
Duties joined to the same lines. Calculated duty sits beside customs value, with landed duty-paid, FAS and CIF values selectable, and a companion tariff reference carrying MFN rates in words, as percentages and as specific amounts per HTS8 line, plus preference-program rate columns - GSP, AGOA, CBI, CBTPA, USMCA-era and FTA partners. See customs district for the geographic finer point: drilldown to the district of entry or export, the last official stop before state-level would begin - and it has none.
Trade-flow completeness on the US leg. Six flows - imports for consumption, general imports, total exports, domestic exports, foreign exports, balance - with export lines keyed to Schedule B codes. It is also the most broadly tagged record in the slice, carrying secondary-industry tags for aluminum, forest products, leisure products and seven more verticals that share its tariff machinery.
Where they're equivalent
More than their different shapes suggest. Both carry 9/10 rubric scores with fully verified field dictionaries, a bar not every record clears. Both scope cleanly to chapter 64 footwear rather than treating shoes as a footnote in general merchandise data. Both deliver tabular extracts with documented columns, and both observe time on calendar periods - annual grain on the OEC side, annual-or-monthly on USITC's - so a combined dataset aligns on years without gymnastics. And both print money in current USD without inflation adjustment, which means multi-decade work wants a deflator applied deliberately on either side.
The verdict
Verdict: sample both, pick by fit - tied on score, divided by question.
Take Observatory of Economic Complexity (OEC) - Footwear HS Profile if your question names a border or a market. Sourcing-strategy maps of who makes what where, origin-share studies, unit-value drift as an upmarket signal, category sizing with defensible denominators ($162 billion, 0.71 percent of world trade), long-run trend work stitched across HS revisions. Accept annual grain and kilogram-based unit values - it will not tell you what a specific import lot paid in duty.
Take USITC DataWeb - U.S. Footwear Import/Export and Tariff Data if your question lands on the United States specifically. Accept a single-country frame - it cannot rank Vietnam against Indonesia globally.
If the project spans both - most sourcing engagements eventually do - that is the both-of-them case, and one sample request settles it: get a sample of either dataset.
Sample both, pick by fit. See Observatory of Economic Complexity - Footwear HS Profile · See USITC DataWeb - U.S. Footwear Import/Export and Tariff Data
Or take both in one feed
Yes - they stack because they occupy different layers of the same system. A defensible workflow: let the OEC profile set the world stage - which corridors grew, where leather footwear concentrates, how exporter shares shifted since 1995 - then drop to USITC for the American leg at statistical-line grain: which districts took the volume, what the schedule charged, whether pairs moved before dollars did. The supply-chain-mapping playbook runs exactly this loop, and the market-sizing playbook leans on the OEC half for denominators.
Align three things before joining. First, grain: aggregate USITC months into years, or accept that only annual cells have a partner on both sides. Second, classification: HS6 lines map onto HTS prefixes within headings 6401-6405, but the 10-digit statistical suffixes exist only on the US side, so build the bridge at six digits deliberately. Third, mirror effects: exporter-reported and importer-reported values of the same flow rarely match exactly, so decide whose side of the border each number comes from before summing anything.
Handled that way the pair yields something neither delivers alone: a world map of footwear production shifts with the US customs record bolted onto it, delivered daily, weekly, or hourly - your call. Or take both in one feed.
API, files, or your warehouse. Daily, weekly, or hourly.
Fair questions
Is the Observatory of Economic Complexity (OEC) - Footwear HS Profile better than the USITC DataWeb - U.S. Footwear Import/Export and Tariff Data?
Better at different jobs, tied at 9/10 on Datadory's rubric. The OEC profile wins whenever the question crosses a border: $162 billion of 2024 world footwear trade, roughly 226 reporter economies read bilaterally, corridors decomposable to HS6 since 1995. USITC wins whenever the question is the United States: 400+ HTS 10-digit lines with customs values, duties and pairs-based quantities by country and district from 1989.
Do the two datasets cover the same fields anywhere?
Three concepts overlap: an observation period, a dollar value in current USD, and a partner-country dimension. Everything else diverges - the OEC side adds exporter/importer pairs, kilogram quantities and unit values; USITC adds HTS numbers to ten digits, duty measures, second quantities and a companion tariff reference with MFN and preference-program rates per HTS8 line. There is no shared key between them.
Which footwear archive reaches further back?
Depends on the coding you need. OEC's revision-92 window runs 1995-2024 for long-run trend work, with r17 (2018-2024) carrying modern line naming. USITC's electronic record starts at 1989 - nothing earlier exists digitally - but slices to individual months. For pre-1989 footwear history, neither replaces printed sources.
Can I get quantities in pairs from both?
No - this is the sharpest operational difference. USITC reports pairs on most Chapter 64 lines (square meters where the schedule requires a second measure), which is how the footwear industry counts itself.
Which should a sourcing analyst sample first?
Add OEC when the lens widens beyond the US: Vietnam's $32.8 billion against Italy's $12.7 billion in 2024 exports, or corridor growth since 1995, only exist in the bilateral cube. Sample both and let returned rows settle the fit.
Can Datadory deliver both datasets together?
Yes - alone or merged onto one calendar, delivered daily, weekly, or hourly, your call. Each arrives normalized to its verified field dictionary (six core fields on the OEC side, eight on USITC's) with sample rows attached for validation, and the pair joins on year once HS6-level mapping and mirror-side choices are agreed. Or take both in one feed.