TradeWeave provides public trade analytics and research. Its BACI HS96 core covers 1996–2024; country and product coverage varies by dataset and HS revision. Protected APIs require an eligible account.

What data sources does TradeWeave use?

TradeWeave combines data from BACI/CEPII (bilateral trade flows), WITS/TRAINS (tariff rates), World Bank WDI (macroeconomic indicators), ESCAP-World Bank (bilateral trade costs), FRED and BLS (freight indices, exchange rates), and FAOSTAT (agricultural trade).

How can I analyze a country's trade profile?

Visit the Country Profiles page and select a country or territory to see their export composition, top trade partners, revealed comparative advantage (RCA), Economic Complexity Index (ECI), trade diversification, and trends over the available years.

What is the Economic Complexity Index (ECI)?

The Economic Complexity Index (ECI), developed by Hidalgo and Hausmann, measures the productive knowledge embedded in a country's export basket. Countries that export a diverse set of sophisticated products score higher. TradeWeave reports ECI ranks for eligible economies from 1996 to 2024.

Can I simulate the impact of tariff changes?

Yes. TradeWeave's Tariff Simulator lets you model how tariff changes affect import volumes using real product-level demand elasticities and MFN tariff rates for over 1,200 HS4 products.

How far back does the trade data go?

The primary BACI dataset covers 1996-2024 with HS6 product detail. TradeWeave also includes historical trade data spanning over 200 years (1800-present) from academic compilations.

MERCATOR · METHODOLOGY

Ranking suppliers after an incumbent loses import share

A conditional-logit model trained on supplier-share changes with baseline years 2015–2020 in BACI HS92 data. A separate backtest evaluates selected Russia-incumbent episodes from 2021 to 2022.

The question

Mercator ranks which retained alternative supplier is most likely to record the largest year-over-year change in import share. Training and evaluation include suppliers observed in both years. They exclude complete incumbent exits, new entrants and suppliers that disappear. The results describe this selected sample, rather than all possible responses to a supplier cutoff.

Held-out episodes: Russia, 2021–2022

Loading model results…

Associations in the training sample

Standardized conditional-logit coefficients describe associations with the modeled winner probability. They are not causal effects or elasticities of trade quantities.

Higher world-export capacity and prior import share, and shorter distance, are associated with higher modeled winner probabilities in the training sample.
The governance coefficient is negative in the training sample, conditional on the other included predictors. It does not identify a Russia 2022-specific effect or establish that governance caused trade diversion.
RCA and FTA access have smaller standardized coefficients than capacity, prior share and distance in this specification. Their economic importance is not established by this comparison alone.

Candidate concentration index

The 2022 application summarizes concentration in the model’s candidate probabilities as 1 / Σp². Lower values mean the probabilities are concentrated among fewer candidates. This is not a count of verified viable suppliers or an estimate of replacement capacity. The full candidate set includes domestic candidates; the explorer displays its stored foreign subset.

Limitations

Training and evaluation condition on suppliers surviving into the following year. The backtest does not validate complete cutoff responses or entry by new suppliers.
The 2022 application uses a world-export-capacity candidate set, which differs from the retained bilateral suppliers used for training and evaluation.
Probabilities are not calibrated estimates of displaced trade quantities; no causal decomposition is identified.
The original episode-level inputs are unavailable locally, so the reported backtest and coefficient uncertainty cannot currently be independently re-estimated.

Loading chart...

Guided journeyTrace a supply-chain chokepoint riskstep 6 of 6All journeys

Related analyses

Supply Chain

platform overview

Vulnerability

concentration and vulnerability

Shock Cascade

supplier disruption impact

Competition

alternative supplier overlap

Sanctions Diversion

re-routing around suppliers

Risk Board

global trade-risk ranking

The question

Associations in the training sample

Standardized conditional-logit coefficients describe associations with the modeled winner probability. They are not causal effects or elasticities of trade quantities.

Higher world-export capacity and prior import share, and shorter distance, are associated with higher modeled winner probabilities in the training sample.

The governance coefficient is negative in the training sample, conditional on the other included predictors. It does not identify a Russia 2022-specific effect or establish that governance caused trade diversion.

RCA and FTA access have smaller standardized coefficients than capacity, prior share and distance in this specification. Their economic importance is not established by this comparison alone.

Candidate concentration index

Limitations

Training and evaluation condition on suppliers surviving into the following year. The backtest does not validate complete cutoff responses or entry by new suppliers.

The 2022 application uses a world-export-capacity candidate set, which differs from the retained bilateral suppliers used for training and evaluation.

Probabilities are not calibrated estimates of displaced trade quantities; no causal decomposition is identified.

The original episode-level inputs are unavailable locally, so the reported backtest and coefficient uncertainty cannot currently be independently re-estimated.