One platform for worldwide customs records. Search shipments by country, HS code, product, importer or exporter and see the whole movement of goods, not a summary of it.
Official import and export totals as filed by each national statistics authority with the UN. Open any market for its partners, its top HS chapters and its multi-year trend.
| # | Market | Imports | Exports | Year |
|---|---|---|---|---|
| 01 | China | $2.59T | $3.58T | 2024 |
| 02 | United States | $3.36T | $2.06T | 2024 |
| 03 | Germany | $1.38T | $1.63T | 2024 |
| 04 | Japan | $742.7B | $707.4B | 2024 |
| 05 | France | $762.0B | $646.0B | 2024 |
| 06 | Hong Kong | $697.9B | $639.4B | 2024 |
| 07 | United Kingdom | $813.3B | $520.2B | 2024 |
| 08 | Netherlands | $621.1B | $699.3B | 2024 |
| 09 | South Korea | $631.7B | $683.1B | 2024 |
| 10 | Italy | $621.6B | $673.9B | 2024 |
| 11 | Mexico | $636.8B | $619.7B | 2024 |
| 12 | India | $697.7B | $434.4B | 2024 |
| 13 | Canada | $560.0B | $569.3B | 2024 |
| 14 | United Arab Emirates | $470.5B | $570.2B | 2023 |
| 15 | Singapore | $457.5B | $504.8B | 2024 |
| 16 | Taiwan | $394.0B | $474.6B | 2024 |
| 17 | Spain | $451.3B | $403.7B | 2024 |
| 18 | Switzerland | $366.9B | $441.5B | 2024 |
| 19 | Poland | $379.5B | $380.3B | 2024 |
| 20 | Belgium | $356.5B | $364.1B | 2024 |
Source: UN Comtrade, official statistics reported by national authorities. Figures are country-level aggregates, not shipment records.
Import and export records for Asia's major trading economies, refreshed on the customs release cycle.
Learn more ›Import and export records for Europe's major trading economies, refreshed on the customs release cycle.
Learn more ›Import and export records for North America's major trading economies, refreshed on the customs release cycle.
Learn more ›Import and export records for South America's major trading economies, refreshed on the customs release cycle.
Learn more ›Import and export records for Africa's major trading economies, refreshed on the customs release cycle.
Learn more ›Import and export records for Oceania's major trading economies, refreshed on the customs release cycle.
Learn more ›Size real demand before you invest — who imports your product in a target country, in what volume, and how that has moved year on year.
Check that a prospective buyer or supplier actually ships what they claim, at the scale they claim.
Track named shippers to see new suppliers, new markets and changing volumes as they happen.
Compare declared unit values across origins to see where you are being undercut and by how much.
See which ports and transport modes dominate a lane before you book.
Work back from real shipment descriptions to the classification customs actually accepts.
| Data type | Detail level | Typical refresh |
|---|---|---|
| Shipment-level customs data | Importer, exporter, HS code, value, quantity, ports | Monthly |
| Bill of lading data | Consignee, shipper, container, vessel, port pair | Monthly |
| Statistical trade data | Country-product totals by period | Monthly to quarterly |
| Company profiles | Trade history, partners, products, volumes | Continuous |
No single source covers world trade uniformly, and any provider claiming otherwise is describing a marketing position rather than a dataset. What exists is a patchwork: some administrations publish shipment-level records naming both counterparties, some publish one side, some publish statistical aggregates only, and a few publish very little. A global view is therefore assembled from sources of different depth, and knowing which depth applies to your market matters more than the headline country count.
| Coverage tier | What you get | Typical use |
|---|---|---|
| Shipment-level, both parties named | Importer, exporter, HS code, quantity, value, ports | Buyer and supplier discovery, competitor tracking |
| Shipment-level, one party named | Usually the domestic party plus full product detail | Demand sizing, price benchmarking, partial counterparty work |
| Manifest only | Shipper, consignee, vessel, container, port pair | Counterparty identification and lane analysis |
| Statistical aggregates | Country totals by product and partner | Market structure, trend, and cross-country comparison |
Three properties of aggregate trade statistics catch people out repeatedly. Values are declared on different bases — exports free on board, imports including freight and insurance — so a country’s reported exports to a partner never equal that partner’s reported imports from it. Entrepôt trade attributes goods to the last shipping country rather than to the true origin, which inflates hub economies and deflates the countries behind them. And classification differences mean two administrations can code identical goods differently, which shows up as a discrepancy that is really a definitional artefact.
None of these make the data unusable; they make it data that needs handling. The practical rules are short: normalise the basis before comparing two countries, treat hub economies with suspicion when you are looking for origin, and compare at the tightest common classification level rather than at chapter level.
Start from the HS chapter, then the markets that import it in volume, then the named importers inside them.
Learn more ›Start from the export side of the same chapters and filter on consistency rather than on volume.
Learn more ›Start from the country pages for reported totals, then decide whether imports are the whole market.
Learn more ›Start from declared unit values on a tight tariff line in a recent complete period.
Learn more ›Start from the industry hub, which sums the chapters the industry actually spans.
Learn more ›Start from the location pages, where the clearance point narrows the geography.
Learn more ›What matters is the depth of coverage in the specific markets you trade with, and whether counterparty names are published there. A provider that will tell you exactly which tier your markets sit in is worth more than one quoting a larger total.
Trade data rewards a short, boring discipline far more than it rewards technique. Four steps cover most of it, and skipping any one of them is where the confident wrong conclusions come from. Fix the classification first, because every filter, every duty figure and every price comparison downstream is keyed to the tariff line and inherits any error in it. Then read at least three years, so that seasonality and trend can be told apart rather than conflated. Then exclude the most recent one or two periods, which are still filling in as late filings arrive. Then separate value from volume, because a value movement can be price, quantity or a change of mix inside the line, and those point in different directions.
| Step | What it prevents | Cost of skipping it |
|---|---|---|
| Confirm the tariff line | Filtering the wrong product | Every downstream figure is wrong by an unknown amount |
| Read three years | Mistaking a season for a trend | Strategy built on a cyclical high or low |
| Drop the incomplete tail | Reading reporting lag as decline | Writing off buyers who never stopped buying |
| Separate value from volume | Reading price as demand | Investing against a movement that was not demand |
| Check the counterparty | Acting on an unverified name | Credit or capacity committed to a company with no history |
| Record the period and source | Unrepeatable analysis | Figures nobody can reconcile three months later |
The material here is one layer of a set that is meant to be used together. The country pages establish the shape of a market from official reported figures. The HS chapter pages take a single classification down to product level. The industry hubs group the chapters that make up a real industry and sum them, because almost no industry is one chapter. The India location pages read the national record as places, using the clearance point as a geographic signal. And the trade role pages take one job at a time — building an importer list, checking an exporter, reading a lane — and set out the signals that matter for it.
Reported totals, partner markets and chapter breakdown for 99 markets.
Learn more ›All 98 chapters, each with the markets that trade it.
Learn more ›Thirty-eight industries, each summed across the chapters it spans.
Learn more ›Seventy-nine trading places, their gateways and their clusters.
Learn more ›The same record read as a buyer list, a supplier check or a lane analysis.
Learn more ›Classification, documentation, pricing, sourcing and compliance in practice.
Learn more ›Customs records cover goods that physically crossed a border and were declared to an authority. They do not cover services. They do not cover domestic trade, so a business selling mainly inside its own market will look far smaller here than it is. They carry no margin, no contract terms, no payment behaviour and no intent. Coverage of counterparty names varies by jurisdiction and is not universal, data arrives on a lag, and published periods are revised as corrections come in.
None of that reduces what the record is good for, and stating it plainly is what makes the rest credible. Used within its limits, customs data is one of the very few commercial sources where the underlying event actually happened, was documented at the time, and was documented under legal obligation rather than for promotional purposes. That is a rare property, and it is worth not overselling.
Before relying on any trade dataset — ours or anyone else's — ask which markets are covered at which depth, what the lag is in each, how company names were matched, and what happens to a historical series across an HS revision. The answers tell you more than any headline figure.
Coverage is a patchwork rather than a uniform layer. Some administrations publish shipment-level records naming both counterparties, some publish one side, some publish only statistical aggregates. What matters is the depth available in the markets you actually trade.
Export values are normally declared free on board and import values include freight and insurance, so the same flow is reported at two different values. Timing across a year boundary and entrepôt attribution add further gaps.
After normalising the value basis and comparing at the same classification depth, yes. Raw comparison of declared values across countries is one of the more common analytical errors.
It refreshes on each authority's own release cycle — typically monthly, with a thirty to sixty day lag. The most recent periods are always incomplete and should be excluded from trend work.
The country pages carry official aggregates reported to UN Comtrade, which establish market structure. The shipment-level records are a separate dataset filed with national customs authorities, and they are what a buyer or supplier list is built from.