[{"id":45,"title":"International trade","reports":[],"subFolders":[{"id":1,"title":"Trade in merchandise","reports":[{"id":2650,"version":10011,"title":"Merchandise: Trade - Annual","lastUpdatedDate":"2026-10-08T12:58:00","category":"International trade in goods and services","keywords":"Export,import,value,share,merchandise,trade","notes":"

Description:
\r\nThis table shows the value of total merchandise exports and imports, expressed in millions of United States dollars, the percentages of the world total and their growth rates, year-to-year.

Sources:
\r\n- UN DESA Statistics Division, International Trade Statistics Yearbook
\r\n- UN DESA Statistics Division, Monthly Bulletin of Statistics
\r\n- UN DESA Statistics Division, UN Comtrade database
\r\n- WTO, Statistics Database
\r\n- WTO estimates
\r\n- IMF, International Financial Statistics
\r\n- IMF, Direction of Trade Statistics
\r\n- IMF, Balance of Payments Statistics
\r\n- Eurostat, Comext
\r\n- World Bank, World Development Indicators
\r\n- OECD, OECD.Stat
\r\n- Economist Intelligence Unit, Country Data
\r\n- AfDB, African Development Bank
\r\n- ASEAN, Association of South-East Asian Nations
\r\n- BEAC, Bank of Central African States
\r\n- BCEAO, Central Bank of West African States
\r\n- CIS, Commonwealth of Independent States Statcommittee - Online database
\r\n- SESRIC, Statistical, Economic and Social Research and Training Centre of Islamic Countries
\r\n- ECCB, Eastern Caribbean Central Bank
\r\n- AFRISTAT, Economic and Statistical Observatory for Sub-Saharan Africa
\r\n- PRISM, Secretariat of Pacific Community database
\r\n- UNECE, Statistical Database, Labor Force & Wages
\r\n- UN ESCWA, Trade and industry statistics
\r\n- National statistical offices and national central banks
\r\n- UNCTAD secretariat calculations.

Data from 2014 onwards are jointly produced with WTO, except for Andorra, Anguilla, Bonaire, Sint Eustatius and Saba, Saint Helena, State of Palestine, Tokelau, Turks and Caicos Islands, and Wallis and Futuna Islands.
\r\n 

Notes:
\r\nInternational merchandise trade measures the value of goods which add or subtract from the stock of material resources of an economy by entering or leaving its territory (United Nations, 2011).
\r\nUnless otherwise stated in country notes, the value of exports is recorded as the free-on-board value (FOB), whereas the value of imports includes the cross-border insurance and freight costs from the exporting to the importing economy (cost, insurance and freight or CIF value).
\r\nThe trade flows shown in this table are on a general trade system basis for all countries except those listed in the notes in dimension "flow", which employ the special trade system. The general trade system is used when the statistical territory of a compiling country coincides with its economic territory. Consequently, imports include all goods entering the economic territory of a compiling country and exports include all goods leaving the economic territory of the compiling country. The special trade system is used when the statistical territory comprises only a particular part of the economic territory within which “goods may be disposed of without customs restriction”. In such a case, imports include all goods entering the free circulation area of the compiling country, which means cleared through customs for home use, and exports include all goods leaving the free circulation area of a compiling country.
\r\nTotals of international merchandise trade presented in this table are not strictly comparable with those in the UNCTADstat merchandise trade matrix. Discrepancies arise due to the use of differing complementary sources.

References:
\r\nUnited Nations (2011). International Merchandise Trade Statistics. Concepts and Definitions. Department of Economic and Social Affairs, Statistics Division, Statistical Papers, Series M, No. 52. 

","name":"US.TradeMerchTotal","order":3},{"id":2649,"version":10011,"title":"Merchandise: Trade balance - Annual","lastUpdatedDate":"2026-10-08T12:58:00","category":"International trade in goods and services","keywords":"Export, import, value, share, balance, merchandise, trade","notes":"

Description:
\r\nThis table shows the trade balance, the difference between exports FOB and imports CIF (or imports FOB when applicable), expressed in millions of United States dollars and as a percentage of imports.
\r\n
\r\nSource:
\r\nUNCTAD secretariat calculations, based on the UNCTADstat table "Merchandise: Total trade and share".
\r\n 
\r\nNotes:
\r\nThe trade balances of groups of economies are the sums of the trade balances of the component individual economies.
\r\nA positive sign of the trade balance indicates a trade surplus, a negative sign a trade deficit.

","name":"US.TradeMerchBalance","order":4},{"id":2612,"version":10019,"title":"Merchandise: Trade volume - Growth rates - Quarterly (analytical)","lastUpdatedDate":"2026-10-08T12:58:00","category":"International trade in goods and services","keywords":"International, merchandise, trade, trend, volume, growth, indices","notes":"

Description:
\r\nThis table presents quarterly statistics on world total merchandise export and import volume indices by individual economy, geographical region and economic grouping. The data presented are the result of the common work of World Trade Organization (WTO) and UNCTAD, and are published simultaneously by both organizations. (WTO short-term merchandise trade statistics). Differences in the results between UNCTAD and WTO may be due to the differences in unit values indices and groups’ composition.
\r\nThis table is marked as analytical rather than official statistics because it contains estimates for some economies not validated by statistical offices.

Sources:


\r\nNotes:
\r\nReported merchandise trade volume data is collected directly from websites of the national statistical authorities of the United States; Canada; Argentina; Brazil; Chile; Mexico; the European Union (EU); Norway; Switzerland; United Kingdom; Turkey; Israel; Australia; Japan; China; China, Hong Kong SAR; Korea, Republic of; Singapore; China, Taiwan Province of; Malaysia and Thailand. Quarterly volume indices are either retrieved directly or derived from monthly data, or through the deflation of merchandise trade values with the corresponding price or unit value indices.

Estimated data for individual economies are included in geographical regions or economic groupings calculation, but not always shown separately.

Estimation of non-reported volume index

When trade volume indices are not reported by national sources, estimates are made for missing economies and regions by deflating the value index (sourced from WTO's quarterly trade values using an estimated deflator). The deflator estimate consists of a weighted average of quarterly prices (see below) using as weights the value of products traded in the previous year by the entity (country or country group) (see UNCTAD Trade Matrix).

The quarterly export/import price index of an economy (or a group of economies) j is calculated using chain method:

Weighted average of relative price ratios (WPR), i.e. weighted quarter-on-quarter aggregated growth rates:

    WPR(f,j,q)= \"WPR\"
\r\n    Price Index05q1=100 (f,j,q) =100*\"ProductSymbol\" t=05q2 to q WPR(f,j,q)
\r\n    =100 * WPR(f,2,j) * WPR(f,3,j) *…*WRP(f,q-1,j)*WPR(f,q,j)
\r\n 
\r\nwhere
\r\ni: product group,
\r\nj: economy, or group of economies,
\r\nf: flow,
\r\nq: quarter (05q1, 05q2, 05q3, 05q4, 06q1….)
\r\np(i,q): the price of product group i, for quarter q
\r\nweight(j,i, f,w(q)): the share of imports/exports of product group i in total  trade values of an economy j at year - 1.
\r\nw(q): Weight varies every year, i.e. taken from the previous year.
\r\n 
\r\nNote: Price Index includes gold.
\r\n
\r\nCalculation of volume index of a non-reporting economy /group of economies (VIND)                                                                             
\r\n      Volume index_j (f,y,q,j) = Value index_j (f,y,q,j)/ Price index_j (f,y,q,j)
\r\n
\r\nCalculation of regional aggregations
\r\nThe aggregated quarterly volume index of a region r is calculated using chain method:
\r\nWeighted average of relative Volume indices ratios (WVIND), i.e. weighted quarter-on-quarter aggregated growth rates:
\r\n 
\r\n     WVIND (f,q,r) = \"WVIND\"
\r\n    Group volume index05q1=100 (f,q,r) =100*\"ProductSymbol\" t=05q2 to q WVIND (f,q,r)
\r\n    =100 * WVIND (f,2,r) * WVIND (f,3,r) *…*WVIND(f,q-1,r)* WVIND (f,q,r)
\r\n 
\r\nwhere
\r\nf: flow,
\r\nj: country j in a region,
\r\nq: quarter (05q1, 05q2, 05q3, 05q4, 06q1….),
\r\nr: region,
\r\nweight(j, f,q-1): the share of exports/imports of economy j in a region, r, at q-1.
\r\nNRP economies are treated as one member of that group.

Notes: Hong Kong's reported volume index is not included in the aggregation. Instead, the total trade values for domestic trade are included in summations for value index. 
 
For countries resulting from a territorial split, the trade structure observed before the split is attributed to the main successor state for calculating weights used in unit value indices. Economies without trade values in the base quarter (2005Q1) are not assigned individual volume indices, but their observed trade values are included in regional value indices and, where applicable, their product-level trade structures are incorporated into the weighting scheme for the non-reporting countries aggregate, with coverage aligned to periods for which trade values are observed.
 
UNCTAD and WTO may use different definitions for certain country groups (“developed” and “developing” economies, for example). Consequently, some aggregates might be different, although the individual-economy underlying data are the same.
 
Estimation of quarterly price indices for volume non-reporting countries
 
Selection and estimation of International price indices at 3-digit SITC level.
 
Assumption: Whether it is import or export price index, or an average of export and import price index, an unique price index is assigned to a product group at 3-digit SITC rev.3 level. Same set of price index is used in the calculation of aggregated exports and imports price indices, but different weights by trade flow are applied.
 
Method: 
Price indices are compiled using international and national prices or price indices that are reliable, frequently updated, and readily available. Conversion from original classifications to SITC are made using correspondence tables. When 3-digit prices/indices are missing, available higher level (2-digit) prices/indices are used instead. Price indices based on non-US$ currencies are converted to US$ base using IFS monthly exchange rates. Quarterly price indices are derived after necessary calculations, since the above-mentioned data may be available on monthly or quarterly basis.
 
 
Sources:
 
1. FAO, Commodity price database, International Commodity Price
2. IMF, IFS Exchange rates
3. IMF, Primary Commodity Prices
4. World Bank, Commodity Markets, Historical Data (XLS) 
5. Japan Customs, Ministry of Finance
6. Germany, Statistisches Bundesamt Destatis
7. US BLS Import and Export price indexes, MXP Databases
8. US DA (Department of agriculture)
9. US Federal Reserve Bank of St.Louis
10. CBS statistics Netherlands, import prices by product
 
Discontinued series are extended using growth rates of similar products.  When appropriate and reliable data sources are unavailable, UNCTAD estimates are used instead. UNCTAD proxies are based on growth rates of similar products.
 
UNCTAD estimates based on proxies are used for around 50 products including: 
 
017 Meat, edible meat offal, prepared, preserved, n.e.s.
024 Cheese and curd
047 Other cereal meals and flour
244 Cork, natural, raw & waste (incl. blocks, sheets)
266 Synthetic fibres suitable for spinning
267 Other man-made fibres suitable for spinning
278 Other crude minerals
283 Copper ores and concentrates; copper mattes, cemen
288 Non-ferrous base metal waste and scrap, n.e.s.
289 Ores & concentrates of precious metals; waste, scrap
322 Briquettes, lignites and peat
325 Coke & semi-cokes of coal, lign., peat; retort carbon
345 Coal gas, water gas & similar gases (excludinghydrocar.)
431 Animal or veg. oils & fats, processed, n.e.s.; mixt.
511 Hydrocarbons, n.e.s., & halogenated, nitr. derivative
513 Carboxylic acids, anhydrides, halides, per.; derivati.
514 Nitrogen-function compounds
515 Organo-inorganic, heterocycl. compounds, nucl. acids
522 Inorganic chemical elements, oxides & halogen salts
582 Plates, sheets, films, foil & strip, of plastics
583 Monofilaments, of plastics, cross-section > 1mm
593 Explosives and pyrotechnic products
613 Furskins, tanned or dressed, excluding those of 8483
629 Articles of rubber, n.e.s.
652 Cotton fabrics, woven  (not including narrow or special fabrics)
653 Fabrics, woven, of man-made fabrics
655 Knitted or crocheted fabrics, n.e.s.
657 Special yarn, special textile fabrics & related
662 Clay construction, refracto. construction materials
697 Household equipment of base metal, n.e.s.
733 Mach.-tools for working metal, excluding removing mate.
737 Metalworking machinery (excluding machine-tools) & parts
745 Other non-electr. machinery, tools & mechan. appar.
749 Non-electric parts & accessor. of machinery, n.e.s.
751 Office machines
762 Radio-broadcast receivers, whether or not combined
778 Electrical machinery & apparatus, n.e.s.
783 Road motor vehicles, n.e.s.
843 Men's or boy's clothing, of textile, knitted, croche.
845 Articles of apparel, of textile fabrics, n.e.s.
846 Clothing accessories, of textile fabrics
848 Articles of apparel, clothing access., excluding textile
881 Photographic apparatus & equipment, n.e.s.
882 Cinematographic & photographic supplies
883 Cinematograph films, exposed & developed
884 Optical goods, n.e.s.
885 Watches & clocks
893 Articles, n.e.s., of plastics
 
","name":"US.MerchVolumeQuarterly","order":5},{"id":2676,"version":10009,"title":"Merchandise: Trade matrix - Annual (analytical)","lastUpdatedDate":"2026-10-30T10:00:00","category":"International trade in goods and services","keywords":"International trade, structure, partner, economy, product, group","notes":"
Description: 
This table presents merchandise exports and imports of economies, with various economy groupings defined by development status as well as geographical and economic criteria. The data are detailed by trading partner and product based on the SITC commodity classification, Revision 3, expressed in thousands of current United States dollars. They are also summarized by group of trading partner, and by product group with the option to select the level of detail desired (1, 2 or 3 digit).
 
Sources:
UNCTAD secretariat calculations, based on UN DESA Statistics Division, UN COMTRADE; UNCTAD, UNCTADstat Merchandise Trade Indices and Total Merchandise Trade; IMF, Direction of Trade Statistics (DOTS). Some data prior to 2004 are sourced from an estimate file provided by the United Nations Statistics Division. 
 
Notes:
The principal data source for the trade matrix is UN COMTRADE, which provides detailed raw trade data by partner and product. COMTRADE totals are typically quality controlled using the UNCTAD total merchandise trade series (which gives the total trade of all countries in the world from 1948 onwards). 
 
The total trade annual series, also called the reference series, is used to validate Comtrade data, with the reference series being taken when the difference exceeds a threshold of 11%. 
Following this, partner breakdowns are deduced from the structure of the trade series by partner, together with the direction of trade statistics series “DOTS” from the International Monetary Fund, IMF, as well as the mirror data available in Comtrade. A partner redistribution is carried out when the apparent world value is 20% or more greater than the sum of the identified partners. 
 
In case of missing COMTRADE data, the figures are estimated from top to bottom: first the overall totals taken from the reference series, then the partner breakdowns using both Comtrade mirror flows and DOTS (direct or mirror flows), and finally their product breakdown using Comtrade mirrors or historical flows. 
 
Another step concerns special cases requiring specific corrections carried out manually. This is particularly the case for American Samoa (1995-1999), Andorra (2005), Cayman Islands (2013 and 2015), Cook Islands (exports 2007 and imports 2008), Curaçao, Guam (1995-1999), South Africa Customs Union (SACU) countries (1995-1999), Palestine (1998-1999), and Vanuatu and Wallis and Futuna (from 2017 onwards).
 
These corrected or estimated data are clearly identified by footnotes so that they can be differentiated from the original data.
 
To the extent possible, attempts were made to distribute the reported figures for unknown partners or products to actual partners or products. As a consequence, this matrix does not include unknown partners (such as codes 536,837,838 and 839) or products (SITC, Rev.3 codes 931 and 911). In addition, the sum of individual partners and the sum of individual products for a reporter may not add-up to the (total) trade to the world and the (total) trade of all products, respectively.
 
Total trade figures in Merchandise Trade Matrix Table may differ from those reported in the 'Total trade and share, annual' table due to data discrepancies among their principal sources.
 
","name":"US.TradeMatrix","order":6},{"id":2674,"version":10009,"title":"Merchandise: Intra-trade and extra-trade of country groups by product, annual (analytical)","lastUpdatedDate":"2026-10-30T10:00:00","category":"International trade in goods and services","keywords":"Intra-trade, extra-trade, value, share, group, region","notes":"

Description:
\r\nThis table shows intra- and extra-trade of groups of economies by product. It presents their respective exports and imports within the group, with the region to which they belong and with the world, in millions of United States dollars and as percentages.
\r\nIn addition to the total trade of each group of economies, the table also presents the trade of its group members:
\r\n- with the other members of the same group (intra-trade),
\r\n- with non-member trading partners located in the same region (Africa, America, Asia, Europe or Oceania) (rest of the region),
\r\n- with non-member trading partners in the world (rest of the world).
\r\n
\r\nTrade partners in the rest of the region are also part of rest of the world.
\r\n
\r\nProducts are shown by SITC Revision 3, 1 to 3 digit levels and by selected broad product groups.
\r\n
\r\nSource:
\r\nUNCTAD secretariat calculations, based on UNCTAD, UNCTADstat Merchandise Trade Matrix
\r\n
\r\nNotes:
\r\nThe re-imports, which are imports of domestic goods previously recorded as exports, are included in the calculation.

","name":"US.IntraTrade","order":7},{"id":2671,"version":10016,"title":"Merchandise: Trade - Core indices - Annual (analytical)","lastUpdatedDate":"2026-11-09T09:48:00","category":"International trade in goods and services","keywords":"Trade, indices, value, volume, unit value, terms of trade, purchasing power of exports","notes":"

Warning:
\r\nSubsequent revision in data sources of UNCTAD Commodity Price index dataset, data may differ for some countries.
\r\n
\r\nDescription:
\r\nThe dataset contains trade indices, calculated by UNCTAD based on the UNCTADstat Trade Matrix available at (https://unctadstat.unctad.org/datacentre/dataviewer/US.TradeMatrix)  and the UNCTAD/WTO Merchandise total trade annual dataset: (https://unctadstat.unctad.org/datacentre/dataviewer/US.TradeMerchTotal).

The dataset includes export and import value indices, export and import unit value indices, export and import volume indices and derived indices of terms of trade and of purchasing power of exports provided in a reference year (currently 2015).
\r\n
\r\nNotes:
\r\nThe value indices show the current value of exports (FOB) or imports (CIF) for an economy, after conversion to United States dollars and with the reference year =100. Thus, it is simply derived from monetary values from the total trade series or from the trade matrix and exchange rates. For reported unit value indices, total trade series values are used while for non-reported unit value indices, aggregated values from trade matrix are used.

Reported unit value indices (UVIs) are collected directly from websites of international organizations or from the national statistical authorities of Algeria, Angola, Argentina, Australia, Bolivia (Plurinational State of), Brazil, Belarus, Canada, Sri Lanka, Chile, China, Taiwan Province of China, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Haiti, Honduras, China, Hong Kong SAR, Iceland, India, Indonesia, Iraq, Israel, Japan, Jordan, Korea, Republic of, Malaysia, Mauritius, Mexico, Moldova, Republic of, Morocco, New Zealand, Nicaragua, Norway, Pakistan, Panama, Paraguay, Peru, Philippines, Russian Federation, Saudi Arabia, Singapore, Viet Nam, South Africa, Switzerland, Liechtenstein, Thailand, Tunisia, Türkiye, Ukraine, United States of America, Uruguay, Venezuela (Bolivarian Rep. of), United Kingdom and EU countries.

Yearly indices are either retrieved directly or derived from quarterly data.

Estimation of non-reported UVIs:

A set of average price indices at the SITC Revision 3, 3-digit level are constructed using sources described below in the section “Sources” (obtaining over 255 different prices on a quarterly basis). Annual indices are derived from quarterly data.

The export/import UVI of an economy (or a group of economies) j is calculated using the chain method as follows:

Weighted Average of Relative Price Ratios (WPR):

The WPR formula calculates the weighted average of relative price ratios, representing year-on-year aggregated growth rates for a specific flow f in a given economy or group of economies j for the year y.

 

Unit Value Index (2015=100) (UVI):

The UVI formula calculates the unit value index for a particular flow f in a specific economy or group of economies j for the year y. It is calculated as the product of the weighted average relative price ratios from year 2006 to the current year y.

Where

i: Product group,

j: Economy or group of economies,

f: Flow (export or import),

y: Year (2005, 2006, 2007, 2008, 2009, ...),

p(i,y): the price of product group i, for year y,

weight(j,i,f,w(y-1)): The share of imports/exports of product group i in total trade values of an economy j at year y-1,

w(y): Weight, varying every year.

The derived unit value indices are then rescaled to 2015=100.

The prices used for the calculation of non-reported UVIs exclude SITC products 896, 911, 931, for which prices were not available.

Estimation of volume indices:

Volume indices (for exports or imports) measure the changes in the value of exports/imports after considering the variations in the prices of foreign trade.

The volume indices of an economy are derived by deflating the export or import value index by the corresponding unit value index. These volume indices are derived from monetary values and representative prices rather than directly from physical quantities reported in trade statistics, as the quality of physical quantity data in trade statistics is typically inferior to that of value statistics.  Therefore, they can be thought of as trade quantities corrected for price and currency exchanges.

where

VOL(f,y,j): volume index of economy j, flow f and year y

VAL(f,y,j): value index of economy j, flow f and year y

UVI(f,y,j): unit value index of economy j, flow f and year y

Calculation of regional aggregations:

The aggregated volume index of a region r is calculated using the chain method:

Weighted average of relative Volume indices ratios (WVOL), i.e. weighted year-on-year aggregated growth rates:

Group volume index

where

f: flow,

j: country j in a region,

y: year (2005, 2006, 2007, 2008, 2009….),

r: region,

weight(j,f,y-1): the share of exports/imports of economy j in a region r, at year y-1.

The derived volume indices are then rescaled to 2015=100.

In some instances, these volume, unit value indices and terms of trade indices may differ from the same estimates published in national sources, due to differences between the assumed international prices and actual prices paid. Some commodity codes also cover a wide range of products of differing prices. Furthermore, some products have very regional price structures, such as natural gas (whether it is traded through pipeline or tankers). The main aim of this dataset is to provide tentative estimates for countries on a reasonably transparent and comparable basis.

The terms of trade or "net barter" terms of trade is the ratio of the export unit value index to the import unit value index. As a simple example, if a country imports product X and exports product Y, and the price of X increases and that of Y decreases, then their terms of trade worsen over time, as for the same amount of exports it can purchase less imports.

Where

TOT(y,j): terms of trade of economy or group of economies j and year y

UVIexports(y,j): exports unit value of economy or group of economies j and year y

UVIimports(y,j): imports unit value of economy or group of economies j and year y

 

The purchasing power index of exports, or “income terms of trade” is the value index of exports deflated by the import unit value index. This indicator is an alternative or complement to the net barter terms of trade. If a country increases its exports significantly, this can sometimes negatively affect the price of those exports, and thus while its net barter terms of trade worsen, increased value from exports improves its income terms of trade vis-à-vis its imports, which is what this indicator measures.

where

PPIexports(y,j): purchasing power index of exports of economy or group of economies j and year y

VALexports(y,j): exports value index of economy or group of economies j and year y

UVIimports(y,j): imports unit value index of economy or group of economies j and year y

Due to insufficient information, the UNCTAD secretariat does not estimate indices for years before 2005.


\r\nSources:
\r\nUNCTAD secretariat estimates, based on:

","name":"US.TermsOfTrade","order":8},{"id":2668,"version":10008,"title":"Merchandise: Standard product concentration and diversification indices - Annual (analytical)","lastUpdatedDate":"2026-10-30T07:07:00","category":"International trade in goods and services","keywords":"Concentration index, diversification index, number of commodities","notes":"
Description: 
This table presents indices measuring the concentration and diversification of exports and imports by product group. The product concentration index shows to which degree exports and imports of individual economies or of groups of economies are concentrated on a few products rather than being distributed in a more homogeneous manner among several products. The diversification index indicates to what extent the structure of exports or imports by product of a given economy or group of economies differs from the world pattern. 
 
For detailed methodology on the calculation of the indices, see https://unctadstat.unctad.org/EN/IndicatorsExplained/statie2019d1_en.pdf for exports and https://unctadstat.unctad.org/EN/IndicatorsExplained/statie2019d2_en.pdf for imports.
 
 
Source:
UNCTAD secretariat calculations, based on UNCTAD, UNCTADStat Merchandise Trade Matrix.
","name":"US.ConcentDiversIndices","order":9},{"id":2667,"version":10007,"title":"Merchandise: Market concentration and structural change indices - Annual (analytical)","lastUpdatedDate":"2026-10-30T07:07:00","category":"International trade","keywords":"Indices, imports, exports, concentration, structural, trade","notes":"
Description: 
This table provides market concentration and structural change indices for exports and imports by product group. The first indicator shows whether a product market is concentrated in a few economies or more homogeneously distributed among several economies. The structural change index shows how the distribution of the world market share across exporters or importers for a product differs from the benchmark year (1995). 
 
For detailed methodology on the calculation of the indices, see https://unctadstat.unctad.org/EN/IndicatorsExplained/statie2018d1_en.pdf for exports and https://unctadstat.unctad.org/EN/IndicatorsExplained/statie2018d2_en.pdf for imports.
 
Source:
UNCTAD secretariat calculations, based on UNCTAD, UNCTADStat Merchandise Trade Matrix
","name":"US.ConcentStructIndices","order":10},{"id":2003,"version":10005,"title":"Merchandise: Theil concentration indices - Annual (analytical)","lastUpdatedDate":"2025-12-03T09:00:00","category":"International merchandise trade","keywords":"Theil indices, concentration indices, product concentration, market concentration, LDCs","notes":"
Description:
\r\nThis table presents Theil indices measuring both export/import concentration in the basket of exported/imported products (excluding services) and in the set of export/import markets of an individual economy or a group of economies. They are derived from all bilateral export/import flows of all individual products (at the SITC, Rev.3, 3-dig level) from the UNCTAD Merchandise Trade Matrix and are further decomposed additively into two sub-indices; one to measure product concentration and another to measure market (per product) concentration, that is, the two components add up to the overall Theil export/import concentration index. 
 
The Product concentration Theil index of a given economy or a group of economies equals the Theil index of product-level exports/imports to all partners.
\r\nThe Market concentration Theil index, aggregated by products, equals the weighted sum of Theil indices measuring the market concentration for each exported/imported product, with shares of each product in total exports/imports used as weights. It is an aggregate measure for market concentration of individual products, rather than a measure of the market concentration of total exports. 
\r\n 
The Overall Theil index will equal 0, its minimum value, in case an economy or a group of economies exports/imports the same amount of each product to each market (maximum of product and market diversity) and will equal ln(n*m), its maximum value, in case an economy or a group of economies exports/imports a single product to a single market (minimum of product and market diversity), where n is a number of all available (potential) products, and m is a number of all available (potential) markets. Higher values for all three indices represent a higher degree of concentration and a lower export/import diversification. 
\r\n 
As a general note, it is important to consider different indicators of trade concentration, most often used being the ones used in the income-distribution literature: Herfindal-Hirschman (HH), Theil, and Gini Coefficient, - to ensure that results are not dependent on the particular method of calculation. However, in the context of least developed countries (LDCs) identification, the use of Theil index may be more appropriate due to its decomposition property. For more details, see Bruckner (2023).

\r\n 
Calculation:
\r\n 
Overall Theil index (T):
\"\"
\r\n 
The share of product k in total exports/imports of an economy (Xk): 
\r\n\"\"
 
 
The share of market j in the exports/imports of product k (Xj,k):
\"\"
 
Where:
\r\n 
n: total number of all available (potential) exported/imported products.
m: total number of all available (potential) markets these products are exported to /imported from. (Included are individual economies and most other areas present in the United Nations Standard Country or Area Codes for Statistical Use). 
Xkj : value of exports/imports of product k to market j
xk : share of product k in total exports/imports, as weight
xj,k : share of market j in the exports/imports of product k
μ:  average exports/imports value (across products and markets)
\r\n 
Subscripts for time and for the exporting/importing economy are omitted.
 
Condition: If  Xk is zero (hence, the natural logarithm of zero is undefined), lim(x→0)⁡(x × ln ⁡x ) = 0.     
\r\n              
 
Overall Theil index (T) can be decomposed additively as: ( T = TP + TM ). 
\r\n 
Where:
TP : Product concentration Theil index
\r\n\"\"
 
TM  : Market (per product) concentration Theil index 
\"\"
 
However, the two sub-indices TP  and TM  are not derived from each other but calculated separately.

\r\n 
Source: 
UNCTAD secretariat calculations, based on UNCTAD, UNCTADStat Merchandise Trade Matrix.
\r\n 
 
Notes: 
Number of products is based on the SITC, Rev.3, 3-dig level. The maximum number of products is 261. This number stays constant for all years. The products include unallocated products: Postal packages not classified according to kind (code 911) and Special transactions and commodities not classified (code 931), which are not shown separately in Merchandise Trade Matrix Table.
\r\n 
The maximum number of all available markets is 244. This number stays constant for all years. The markets include unallocated partners: Bunkers, ship stores (code 837), Free zones (code 838), Neutral zone (code 536), and Confidential information and differences (code 839), which are not shown separately in Merchandise Trade Matrix Table. The markets which are never present in Merchandise Trade Matrix are excluded:  Åland Islands (code 248), Kosovo (code 412), Liechtenstein (code 438), Monaco (code 492), Puerto Rico (code 630), Saint Martin (French part) (code 663), Svalbard and Jan Mayen Islands (code 744), Guernsey (code 831), Jersey (code 832), Isle of Man (code 833), United States Virgin Islands (code 850).
\r\n 
The results per group are derived by aggregating individual reporters but keeping markets as separate.
 
 
References:
\r\nBruckner (2023). Measuring export concentration for identifying least developed countries. Committee for Development Policy Background Paper No. 59. New York: United Nations.
","name":"US.MerchTheilIndices","order":11},{"id":2675,"version":10009,"title":"Merchandise: Revealed Comparative Advantage index - Annual (analytical)","lastUpdatedDate":"2026-10-30T10:00:00","category":"Trade indicators","keywords":"Revealed comparative advantage index","notes":"

Revealed comparative advantage (RCA) is based on Ricardian trade theory, which posits that patterns of trade among countries are governed by their relative differences in productivity. Although such productivity differences are difficult to observe, an RCA metric can be readily calculated using trade data to "reveal" such differences. While the metric can be used to provide a general indication and first approximation of a country's competitive export strengths, it should be noted that applied national measures which affect competitiveness such as tariffs, non-tariff measures, subsidies and others are not taken into account in the RCA metric.

Trade data used are based on the three-digit level of the SITC commodity classification, Revision 3. Data are available here.

Do not miss our RCA radar chart which gives a full picture of any country's RCAs from 1995!

 

The RCA metric

Country A is said to have a revealed comparative advantage in a given product i when its ratio of exports of product i to its total exports of all goods (products) exceeds the same ratio for the world as a whole:

\"RCA

Where

When a country has a revealed comparative advantage for a given product (RCA >1), it is inferred to be a competitive producer and exporter of that product relative to a country producing and exporting that good at or below the world average. A country with a revealed comparative advantage in product i is considered to have an export strength in that product. The higher the value of a country’s RCA for product i, the higher its export strength in product i.

","name":"US.RCA","order":12},{"id":2640,"version":10004,"title":"Merchandise: Trade price index – Quarterly (analytical)","lastUpdatedDate":"2026-10-26T10:50:00","category":"International trade in goods and services","keywords":"Merchandises, Trade, Indices, Prices","notes":"

Description:

This table presents the quarterly UNCTAD Trade Price Indices for 258 product groups, providing a consistent reference for monitoring price movements in international merchandise trade. It is particularly useful for supporting countries with limited data availability.

The series is quarterly, with a base period 2005 Q1 = 100, covering 2005 Q1 to the latest available quarter.

UNCTAD developed the trade price indices to calculate unit value indices for international merchandise trade. The dataset has been used internally since 2014, notably in the jointly published UNCTAD-WTO quarterly trade volume indices, and in other UNCTAD outputs, including terms of trade.

The indices are compiled at the 3-digit level of SITC Revision 3. Out of 261 groups, 258 are covered. Groups 896, 911, and 931 are excluded due to insufficient or unreliable price information.

A single price index is assigned to each product group and period and used for both export and import flows. Depending on data availability, it may be based on export prices, import prices, or an average of the two. This ensures a consistent and comparable series across all periods.

Compilation relies on international and national sources selected for reliability and frequency of updates. Data are converted to SITC, Rev.3, 3-dig using correspondence tables. When 3-digit data are missing, higher-level indices are used. Non-US$ series are converted using IMF exchange rates. Monthly data are aggregated to quarterly frequency where necessary. Where no suitable source exists, estimates are produced using price movements of related products; this applies to about 50 product groups.

Source:
\r\nUNCTAD secretariat calculations.
\r\nTo ensure data consistency and frequent update, data are collected from international and national sources below.

 
\r\nNote:

The following SITC, groups are excluded due to insufficient or unreliable price information:

896      Works of art, collectors’ pieces and antiques

911      Postal packages not classified according to kind

931      Special transactions and commodities not classified according to kind

","name":"US.TradePriceIndices_Q","order":13},{"id":2587,"version":10004,"title":"Merchandise: Import tariff rates - Annual","lastUpdatedDate":"2026-09-28T11:02:00","category":"International trade in goods and services","keywords":"Tariff, duty, rate","notes":"
Description:
This table presents MFN (Most Favoured Nation) and effectively applied import tariff rates for major categories of non-agricultural and non-fuel products by individual country (as market economy) and economic grouping (as origin), expressed in Simple averages and Weighted averages of rates. 
 
Warning:
The data in the current table are not comparable to those in its previous release since the data sources and the method of calculations are different. The current dataset is based on UNCTAD and International Trade Center (ITC) data. Furthermore, this dataset includes Ad-Valorem Equivalents duties. See Notes for more detail. The series starts in 2019.
 
Source:
UNCTAD calculations based on data collected by ITC and UNCTAD
 
Notes:
Average tariff of a market country for an origin group (except for world) is calculated by taking those products (at HS 6-digit level) that are imported by the market country from each country included in the origin group. i.e., tariff rates for those products that are not traded are not included in the calculation. For world as origin, however, all products are taken into consideration regardless of trade.
Product categories are defined in terms of SITC Revision 3 and all corresponding Harmonized System (HS) 6-digit codes have been aggregated for each category.  
 
The method used in this dataset differs from the one used in the discontinued table.
This new dataset is built on a higher number of tariff lines compared to the older dataset. 
In the discontinued dataset, only Ad-valorem duties were used, i.e. the duties are calculated as percentage of the value of the imported goods. The only exception was for Switzerland for which Ad-Valorem Equivalents were used.
In contrast to the above, in this dataset, both Ad-Valorem duties and Ad-Valorem Equivalents duties are used for all countries whenever specific duty exists. 
A specific duty is a fixed amount charged per unit of the imported good such as (per kilogram, per item) regardless of its market value. 
Ad-valorem equivalents are calculated as percentage of value of the imported goods for which a specific duty is normally applied. It is an ad-valorem proxy of specific duty. 
 
","name":"US.Tariff","order":14},{"id":2678,"version":10009,"title":"GSTP: Merchandise trade matrix - Annual (analytical)","lastUpdatedDate":"2026-10-30T10:00:00","category":"International trade","keywords":"International trade, structure, partner, economy, product, group, GSTP, Global System of Trade Preferences","notes":"

Description: 

This table presents merchandise exports and imports of the Global System of Trade Preferences (GSTP) participants. The data are detailed by trading partner and product based on the SITC commodity classification, Revision 3, expressed in thousands of current United States dollars. They are also summarized by group of trading partner, and by product group with the option to select the level of detail desired (1, 2 or 3 digit).

The table shows bilateral trade between countries or territories. Accordingly, trade between MERCOSUR member States is included in the value of intra-GSTP trade as recorded in this table.

 

Sources:

UNCTAD secretariat calculations, based on UN DESA Statistics Division, UN COMTRADE; UNCTAD, UNCTADstat Merchandise Trade Indices and Total Merchandise Trade; IMF, Direction of Trade Statistics (DOTS). Some data prior to 2004 are sourced from an estimate file provided by the United Nations Statistics Division. 

 

Notes: 

The Global System of Trade Preferences among Developing Countries (GSTP), which entered into force in 1989, is the only inter-regional South-South trade agreement and the only trade agreement managed by UNCTAD. It comprises 42 members.

The GSTP has been notified under the WTO Enabling Clause as a legal exception to the Most Favoured Nation (MFN) principle. It allows negotiations on tariffs and para-tariffs, non-tariff barriers, direct purchasing agreements and sectoral agreements.

The third round of GSTP negotiations, known as the São Paulo Round (SPR), was signed in 2010 by eight members (with MERCOSUR counted as one member). Members agreed to a minimum 20 per cent tariff reduction on their MFN tariff rates, covering a wide range of products (at least 70 per cent of HS dutiable tariffs lines).

If GSTP participants wish to analyse trade flows and MFN applied tariffs based on specific HS codes, they can access directly UN COMTRADE and the ITC Trade Map.

 

Disclaimer:

Membership of each group (GSTP participants, GSTP São Paulo Round signatories, GSTP São Paulo Round ratifying participants) reflects the state as of 15 July 2026. 

 

","name":"US.GSTP_TradeMatrix","order":16}],"subFolders":[],"isParentFolder":false},{"id":5,"title":"Trade in services","reports":[{"id":2672,"version":10010,"title":"Services: Trade by main category - Quarterly","lastUpdatedDate":"2026-10-30T16:04:00","category":"International trade in goods and services","keywords":"Services, exports, imports, seasonal, adjustment, trade, transport, travel, quarterly, growth, BPM6","notes":"

Description:

This table provides the latest available figures on quarterly services exports and imports in millions of United States dollars, for individual economies and selected regional groups. Seasonally adjusted values and growth rates are also included.

Data are available for total services and main service categories: goods-related services, transport, travel, and other services.

For individual economies, other services are further split in construction, insurance and pension, financial services, intellectual property charges, telecommunication, computer, and information services, other business services, and personal, cultural, and recreational services. The seasonal adjustment for other services’ sub-categories is not presented. Commercial services, which cover all categories except government goods and services, are presented as a memo item.

The quarterly trade in services statistics presented follow the IMF Balance of Payments and International Investment Position Manual, Sixth Edition (BPM6) classification.

The statistics presented are the result of the joint work of UNCTAD and World Trade Organization (WTO).


\r\nTerms of Use:

Using any data and metadata from this table must include a citation “WTO-UNCTAD Trade in Services Data Set”.

All materials provided on the UNCTADstat website are copyrighted under the Creative Commons Attribution 3.0 IGO license. The data and metadata may be copied freely, duplicated and further distributed provided that United Nations Trade and Development Data Hub is cited as the source.

Sources:

Notes:

Statistics disseminated in national currency are converted into United States dollars using the period average exchange rates published by the IMF.

When the latest quarters are not available in Eurostat or IMF, they are estimated by using growth rates obtained from the data available in national sources.

Quarterly growth estimates for the world and regional groups are based on the statistics for some 150 economies, representing at least 85% of total international services trade.

The seasonally adjusted figures are UNCTAD secretariat’s estimates compiled for a selection of main services items, using the X-11-arima seasonal adjustment method. Each series was adjusted based on its values in current U$ dollars. Consequently, the seasonally adjusted total services do not represent the exact sum of relevant seasonally adjusted sub-categories. In the same way, the seasonally adjusted series for groups of economies do not represent the sum of the seasonally adjusted series at the level of individual economies.

The quarterly statistics included may not add up to the annual figures from other tables published by UNCTAD and WTO.

The statistics presented correspond to the concepts and definitions of the IMF Balance of Payments and International Investment Position Manual, Sixth Edition (BPM6). Figures reported according to the fifth edition of the Manual (BPM5) have been adjusted to the BPM6 definitions, provided that such adjustments were possible.

In BPM6 (paragraph 10.8) services are defined as follows:

"Services are the result of a production activity that changes the conditions of the consuming units or facilitates the exchange of products or financial assets. Services are not generally separate items over which ownership rights can be established and cannot generally be separated from their production. However, […] some knowledge-capturing products, such as computer software and other intellectual property products, may be traded separately from their production, like goods. In the balance of payments goods and services account, the valuation of goods includes transport within the exporting economy as well as wholesale and retail services indistinguishably in the price of the goods.

Furthermore, the value of some service items includes the values of some goods, in the cases of travel, construction, and government goods and services n.i.e. Some services, particularly manufacturing services, repairs, and freight transport, also relate to goods."

For details, please refer to Balance of Payments and International Investment Position Manual, Sixth Edition (BPM6):

(https://www.imf.org/external/pubs/ft/bop/2007/pdf/bpm6.pdf),

and to the Manual on Statistics of International Trade in Services (MSITS, 2010):

(http://unstats.un.org/unsd/tradeserv/tfsits/manual.htm)

","name":"US.TotAndComServicesQuarterly","order":19},{"id":2544,"version":10005,"title":"Services: Trade by category - Annual","lastUpdatedDate":"2026-07-23T11:34:00","category":"International trade in goods and services","keywords":"services, exports, imports, trade, transport, travel, sector, category","notes":"

Description:
\r\nThis table presents annual statistics on international trade in services of individual economies by some 100 service categories for trade-partner “World total”. It further contains aggregated figures for various groups of economies for selected principal service categories. The data presented are the result of the common work of UNCTAD and the World Trade Organization (WTO). Most country aggregates have been constructed by combining figures from official statistics and UNCTAD-WTO estimates. As a rule, the country aggregates are not flagged as estimates.

This table includes only trade-partner “World total” statistics, if available.  

Another UNCTADstat table - “ Services: Bilateral trade by category - Annual “ - covers individual economies’ figures for trade in services by bilateral trade-partner, when such figures are available. The figures presented in the two tables are consistent and updated at the same time. 
\r\n
\r\nThe data are shown in five different measures:
\r\n- millions of United States dollars
\r\n- percentages of the world total
\r\n- annual percentage changes (growth rates)
\r\n- shares of each service category in total services
\r\n- shares in current GDP (gross domestic product) (selected main service categories)
\r\n
\r\nSources:
\r\nUNCTAD–WTO Trade in Services Data Set, based on:
\r\n-    Eurostat, online database
\r\n-    OECD, OECD.Stat
\r\n-    IMF, Balance of Payments Statistics
\r\n-    Other international and national sources
\r\n-    UNCTAD - WTO estimates.
\r\n
\r\nTerms of use:
\r\nUsing any data and metadata from this table must include a citation “WTO-UNCTAD Trade in Services Data Set”.

All materials provided on the UNCTADstat website are copyrighted under the Creative Commons Attribution 3.0 IGO license. The data and metadata may be copied freely, duplicated and further distributed provided that United Nations Trade and Development Data Hub is cited as the source
\r\n
\r\nNotes:
\r\nThe statistics presented correspond to the concepts and definitions of the IMF Balance of Payments and International Investment Position Manual, Sixth Edition (BPM6, 2009). Figures reported according to the fifth edition of the Manual (BPM5) have been adjusted to the BPM6 definitions, provided that such adjustment was possible. When possible, the values missing in principal international sources are estimated by using growth rates derived from the data available in national or other international sources.

UNCTAD and WTO may use different definitions for certain groups of economies (“developed” and “developing” economies, for example). Consequently, data for some aggregates will be different from those published by WTO, although the individual-economy data are the same.

BPM6 (paragraph 10.8) explains services and its distinction from goods as follows:
\r\n"Services are the result of a production activity that changes the conditions of the consuming units, or facilitates the exchange of products or financial assets. Services are not generally separate items over which ownership rights can be established and cannot generally be separated from their production. However, […] some knowledge-capturing products, such as computer software and other intellectual property products, may be traded separately from their production, like goods. In the balance of payments goods and services account, the valuation of goods includes transport within the exporting economy as well as wholesale and retail services indistinguishably in the price of the goods. Furthermore, the value of some service items includes the values of some goods, in the cases of travel, construction, and government goods and services n.i.e. Some services, particularly manufacturing services, repairs, and freight transport, also relate to goods."

For summary definitions of the main services categories, refer to the item notes attached to the category- labels. The full list of the categories presented can be displayed by clicking on the "CATEGORY" label in the online table view.

For detailed explanations, please refer to Balance of Payments and International Investment Position Manual, Sixth Edition (BPM6, 2009):
\r\n (https://www.imf.org/external/pubs/ft/bop/2007/pdf/bpm6.pdf ).
\r\nand to the Manual on Statistics of International Trade in Services (MSITS, 2010).
\r\n (http://unstats.un.org/unsd/tradeserv/tfsits/manual.htm)

","name":"US.TradeServCatTotal","order":20},{"id":2545,"version":10005,"title":"Services: Bilateral trade by category - Annual","lastUpdatedDate":"2026-07-23T11:34:00","category":"International trade in goods and services","keywords":"services, exports, imports, trade, bilateral, partner, transport, travel, sector, category","notes":"

Description: 
\r\nThis table presents annual statistics on international trade in services of individual economies by trading partner and by some 100 service categories. The data are the result of the common work of UNCTAD and the World Trade Organization (WTO).  

This table includes only those economies for which bilateral partner data are available. 
Another UNCTADstat table - “Services: Trade by category - Annual“ - covers all individual economies and various groups of economies. The figures presented in the two tables are consistent and updated at the same time.
 
The figures are shown in four different measures:
- millions of United States dollars
- percentages of the world total
- annual percentage changes (growth rates)
- shares of each service category in total services.
\r\n
\r\nSources:
\r\nUNCTAD–WTO Trade in Services Data Set, based on:
\r\n-    Eurostat, online database
-    OECD, OECD.Stat
-    IMF, Balance of Payments Statistics
-    Other international and national sources
-    UNCTAD - WTO estimates
\r\n
\r\nTerms of Use:
\r\nUsing any data and metadata from this table must include a citation “WTO-UNCTAD Trade in Services Data Set”.

All materials provided on the UNCTADstat website are copyrighted under the Creative Commons Attribution 3.0 IGO license. The data and metadata may be copied freely, duplicated and further distributed provided that United Nations Trade and Development Data Hub is cited as the source.
\r\n
\r\nNotes:
\r\nThe statistics presented correspond to the concepts and definitions of the IMF Balance of Payments and International Investment Position Manual, Sixth Edition (BPM6, 2009). Figures reported according to the fifth edition of the Manual (BPM5) have been adjusted to the BPM6 definitions, provided that such adjustment was possible. When possible, the values missing in principal international sources are estimated by using growth rates derived from the data available in national or other international sources.

Breakdowns by trading partner are published with as much detail as available in the source data. In some instances, the principal source may not report the detail by individual partner-economy, but by a group of partner-economies. In these cases, in UNCTADstat, data are also presented for groups of partner-economies. These figures do not always represent complete aggregations.

BPM6 (paragraph 10.8) explains services and its distinction from goods as follows:
\r\n"Services are the result of a production activity that changes the conditions of the consuming units, or facilitates the exchange of products or financial assets. Services are not generally separate items over which ownership rights can be established and cannot generally be separated from their production. However, […] some knowledge-capturing products, such as computer software and other intellectual property products, may be traded separately from their production, like goods. In the balance of payments goods and services account, the valuation of goods includes transport within the exporting economy as well as wholesale and retail services indistinguishably in the price of the goods. Furthermore, the value of some service items includes the values of some goods, in the cases of travel, construction, and government goods and services n.i.e. Some services, particularly manufacturing services, repairs, and freight transport, also relate to goods."

For summary definitions of the main services categories, refer to the item notes attached to the category- labels. The full list of the categories presented can be displayed by clicking on the "CATEGORY" label in the online table view.

For detailed explanations, please refer to Balance of Payments and International Investment Position Manual, Sixth Edition (BPM6, 2009):
\r\n (https://www.imf.org/external/pubs/ft/bop/2007/pdf/bpm6.pdf ).
\r\nand to the Manual on Statistics of International Trade in Services (MSITS, 2010).
\r\n (http://unstats.un.org/unsd/tradeserv/tfsits/manual.htm)

 

","name":"US.TradeServCatByPartner","order":21}],"subFolders":[],"isParentFolder":false},{"id":6,"title":"Trade in creative goods and services","reports":[{"id":2222,"version":10002,"title":"Creative goods: Bilateral trade by product group - Annual (analytical)","lastUpdatedDate":"2026-04-02T15:01:00","category":"Creative economy","keywords":"Creative goods, trade, exports, imports","notes":"

Description:
\r\nThis table presents exports and imports of creative goods by individual country, geographical region and economic groups, expressed in millions of dollars and broken down by trading partner. The data are also presented as percentage of world (both as economy and trading partner), product totals, and region as well as share of exports and imports of creative goods as percentage of total merchandise export and import flows by individual country, country group, geographical region and economic groups. Creative goods are defined by UNCTAD as explained in UNCTAD, 2024, Advancing the measurement of the creative economy: A revised framework for creative industries and trade.
\r\n
\r\nSource:
\r\nUNCTAD secretariat calculations, based on UN DESA Statistics Division, UN COMTRADE.
\r\n
\r\nNotes:
\r\nThe trade data presented in this table are based on different versions of the Harmonized System product classification (HS1992, HS1996, HS2002, HS2007, HS2012, HS2017 and HS2022). The version applied differs over time and across countries, depending on the coding system used by the reporting countries for the respective years. The correspondence tables used for the aggregation of Harmonized-System product groups to the product groups of the creative economy are available on the UNCTADstat Classifications website.

Exports by destination may differ considerably in some cases from data on imports as reported by trade partners for a variety of reasons, among which the following are of particular importance:

The data in this table should be used with good knowledge of the coverage and the limitations of UN COMTRADE, the main data source. The most important limitations are outlined under the following link: https://uncomtrade.org/docs/read-me-first-disclaimer/. In particular, care should be taken when looking at regional totals over time, as data availability will be different for each year.
\r\n
\r\nIf economies did not report any data to UN Comtrade, the corresponding cells are filled by dashes (“-“). If non-reporting economies account for a significant proportion of total merchandise trade of a group of economies, the value of that group has been estimated, based on interpolation or extrapolation of trends of the missing reporting economies or imputation of shares in proportion to total merchandise trade. If the merchandise trade share of non-reporting economies exceeds a certain limit, the value of the corresponding group of economies has been left missing.
\r\n
\r\nReferences:
\r\nUNCTAD, 2024, Advancing the measurement of the creative economy: A revised framework for creative industries and trade.

UNCTADStat, 2024, Classifications – Creative products, https://unctadstat.unctad.org/EN/Classifications.html.

","name":"US.CreativeGoodsValue","order":23},{"id":2220,"version":10002,"title":"Creative goods: Bilateral trade by product group - Growth rates - Annual (analytical)","lastUpdatedDate":"2026-04-02T15:01:00","category":"Creative economy","keywords":"Creative goods, trade, exports, imports, growth rates","notes":"

Description:
\r\nThis table presents annual average growth of creative goods exports and imports for individual countries, geographical regions and economic groups.  
\r\n 
\r\nSource:
\r\nUNCTAD secretariat calculations, based on UN DESA Statistics Division, UN COMTRADE
\r\n 
\r\nNotes:
\r\nThe trade data presented in this table are based on different versions of the Harmonized System product classification (HS1992, HS1996, HS2002, HS2007, HS2012, HS2017 and HS2022). The version applied differs over time and across countries, depending on the coding system used by the reporting countries for the respective years.  The correspondence tables used for the aggregation of Harmonized-System product groups to the product groups of the creative economy are available under the UNCTADstat Documentation tab (see: http://unctadstat.unctad.org/EN/Classifications.html).

Exports by destination may differ considerably in some cases from data on imports as reported by trade partners for a variety of reasons, among which the following are of particular importance:

The data in this table should be used with good knowledge of the coverage and the limitations of UN COMTRADE, the main data source. The most important limitations are outlined under the following link: https://uncomtrade.org/docs/read-me-first-disclaimer/. In particular, care should be taken when looking at regional totals over time, as data availability will be different for each year.
\r\n
\r\nIf economies did not report any data to UN Comtrade, the corresponding cells are filled by dashes (“-“). If non-reporting economies account for a significant proportion of total merchandise trade of a group of economies, the value of that group has been estimated, based on interpolation or extrapolation of trends of the missing reporting economies or imputation of shares in proportion to total merchandise trade. If the merchandise trade share of non-reporting economies exceeds a certain limit, the value of the corresponding group of economies has been left missing.

","name":"US.CreativeGoodsGR","order":24},{"id":2073,"version":10003,"title":"Creative services: Trade - Annual (analytical)","lastUpdatedDate":"2026-04-02T15:05:00","category":"Creative economy","keywords":"Creative, services, trade, international, economy","notes":"

Description:
\r\nThis table presents estimates of creative services exports and imports by individual economy. The data set is experimental: only 32 per cent of the values included represent official statistics. The remaining values are extrapolations or interpolations using growths or shares based on:

 a) related data available for an economy, and 
 b) ratios and growths from selected benchmark economies. 
For each economy, the estimates are fitted to the boundaries of the official trade in services statistics. 

\r\nMeasures presented:

-    Millions of United States dollars
\r\n-    Percentages in total trade in services
\r\n-    Annual growth rates
\r\n
\r\nSources:
\r\nUNCTAD estimates, based on the UNCTAD-WTO annual trade-in-services data set
\r\n
\r\nNotes:
\r\nThe following Extended Balance of Payments Services classification (EBOPS 2010) items are included in creative services:

\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
EBOPS Item Code  Item Name
SH2Licenses for the use of outcomes of research and development
SH3Licenses to reproduce or distribute computer software
SH4Licenses to reproduce or distribute audio-visual and related products
SI21Computer services, Software
SI3Information services
SJ1Research and development (R&D)
SJ22Advertising, market research, and public opinion polling services
SJ311Architectural services
SK1Audiovisual and related services
SK23 Other personal, cultural, and recreational services: heritage and recreational services 

Many economies do not report detailed statistics on international trade in service for the categories listed. It is further recognized that these categories contain transactions that may not qualify as creative services. In the lack of more disaggregated information, the items were selected recognizing that they include a significant creative component.
\r\n 
\r\nThe values may not correspond to other trade-in-services statistics published by UNCTAD, owing to a different timing of updates.
\r\n
\r\nMore information is available in the UNCTAD Creative Economy Outlook.
\r\n
\r\nThe statistics presented correspond to the concepts and definitions of the Sixth edition of the IMF Balance of Payments Manual (BPM6, 2009). For detailed description of the service categories covered, refer to the Manual on Statistics of International Trade in Services (MSITS, 2010).
\r\n(http://unstats.un.org/unsd/tradeserv/tfsits/manual.htm)

","name":"US.CreativeServ_Indiv_Tot","order":26},{"id":2072,"version":10003,"title":"Creative services: exports of selected groups of economies - Annual (analytical)","lastUpdatedDate":"2026-04-02T15:05:00","category":"Creative economy","keywords":"Creative, services, trade, international, economy","notes":"

Description:
\r\nThis table presents estimates of creative services exports of selected groups of economies. Creative services are split into six sub-categories. The data set is experimental: only 32 per cent of the values included represent official statistics. The remaining values are extrapolations and estimates using growths or shares based on:
\r\na) related data available for an economy, and 
\r\nb) ratios or growth rates from selected benchmark economies.
\r\nThe estimates are fitted to the boundaries of the official trade in services statistics. 
\r\n
\r\nMeasures presented:
\r\n-    Millions of United States dollars
\r\n-    Percentages in total trade in services
\r\n-    Annual growth rates


\r\nSources:
\r\nUNCTAD estimates, based on the UNCTAD-WTO annual trade-in-services data set
\r\n
\r\nNotes:
\r\nThe below listed Extended Balance of Payments Services classification (EBOPS 2010) categories are included in creative services, some being regrouped as follows:

\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
EBOPS
\r\nCode		EBOPS Services categoryRegroup CodeCreative services category
SJ22
\r\n
\r\nSJ311		Advertising, market research, and public opinion polling services
\r\n
\r\nArchitectural services		SAMAAdvertising, market research, and architectural services
SH4
\r\n
\r\n
\r\nSK1		Licenses to reproduce and/or distribute audio-visual and related products
\r\n
\r\nAudiovisual and related services		SAUVAudio-visual services
SH2
\r\n
\r\nSJ1		Licenses for the use of outcomes of research and development
\r\n
\r\nResearch and development (R&D)		SRNDResearch and development services
SH3
\r\n
\r\nSI21		Licenses to reproduce and/or distribute computer software
\r\n
\r\nComputer services, Software		SSFTSoftware services
SK23Other personal, cultural, recreation serv., Heritage, and recreational serv.SCRHCultural, recreational, and heritage services
SI3Information servicesSINFInformation services

Many economies do not report detailed statistics on international trade in service for the categories listed. It is further recognized that these categories contain transactions that may not qualify as creative services. In the lack of more disaggregated information, the items were selected recognizing they could include a significant creative component.
\r\n
\r\nThe values may not correspond to other trade-in-services statistics published by UNCTAD, owing to a different timing of updates.
\r\n
\r\nMore information is available in the UNCTAD Creative Economy Outlook.
\r\n
\r\nThe statistics presented correspond to the concepts and definitions of the Sixth edition of the IMF Balance of Payments Manual (BPM6, 2009). For detailed description of the service categories covered, refer to the Manual on Statistics of International Trade in Services (MSITS, 2010).
\r\n(http://unstats.un.org/unsd/tradeserv/tfsits/manual.htm)

","name":"US.CreativeServ_Group_E","order":27}],"subFolders":[],"isParentFolder":false},{"id":54,"title":"Trade in critical minerals","reports":[{"id":2502,"version":10004,"title":"Critical minerals: Bilateral trade by product group - Annual (analytical)","lastUpdatedDate":"2026-07-14T09:51:00","category":"International trade","keywords":"Trade, bilateral trade, exports, imports, merchandise trade, critical minerals, energy transition minerals, minerals, commodities, HS, products","notes":"

Description:

This table presents the annual bilateral trade values of exports and imports of critical minerals by reporting economy, partner economy, trade flow and product group. The table contains the following indicators:

Trade values are compiled from merchandise trade statistics reported to the United Nations Commodity Trade Statistics Database (UN Comtrade) and aggregated according to the UNCTAD critical minerals classification.

Growth rates and shares are derived from the trade values presented in the table.

To support analysis of energy-transition supply chains, critical minerals are grouped according to their relevance to energy-transition technologies. They are organized into three mutually exclusive product groups:

  1. Critical minerals required for energy transition;
  2. Critical minerals relevant for energy transition;
  3. Other critical minerals.
    4. \r\n

The grouping reflects the varying importance of critical minerals for low-carbon energy technologies and related industrial applications. Minerals classified as “required” are essential inputs for key energy-transition technologies, while “relevant” minerals play a supporting role. The remaining critical minerals are classified as “other critical minerals”.

The aggregate “Critical minerals total” is calculated as the sum of the three product groups.  Since the groups are mutually exclusive, the aggregate represents the total trade value of all critical minerals included in the UNCTAD classification.

For reporting gaps where an economy does not provide merchandise trade statistics for a given year, trade values are estimated using corresponding mirror statistics reported by trading partners. No further statistical processing, such as outlier treatment or data smoothing, is applied.

Notes:

Given the absence of an internationally agreed classification of critical minerals, the data are based on a UNCTAD classification developed for analytical purposes. The classification was established through a review and consolidation of critical mineral lists published by national governments and international organizations to reflect the priorities of both developed and developing economies. It identifies 60 critical minerals and maps them to Harmonized System (HS) commodity codes representing minerals, ores, concentrates, metals and related products associated with these minerals. The classification supports the analysis of trade patterns related to industrialization, sustainable development, digital transformation and the energy transition.

Data are available according to the HS version applicable to the reporting period. Product group aggregates are constructed using HS-version-specific correspondence tables maintained by UNCTAD.

Bituminous coal (HS 2701.12) is included in the UNCTAD critical minerals classification because this HS code covers coking coal, which remains an essential input for steel production. Steel is a foundational material for energy transition technologies and infrastructure, including wind turbines, electric vehicles, transmission networks and other industrial applications. Unlike thermal coal used primarily for electricity generation, coking coal currently has limited commercially viable alternatives at scale in primary steelmaking. For many developing economies, coking coal therefore remains an important input for industrial development and structural transformation, including in the context of the transition to low carbon economies.

Users should note that HS code 2701.12 includes both coking coal and thermal coal. Its inclusion in the UNCTAD critical minerals classification is intended to serve as a proxy for coking coal based on end use in steel production rather than on geological classification. Accordingly, coal is included for analytical purposes related to industrial and energy transition supply chains and should not be interpreted as a mineral in the strict geological sense.

Correspondence tables linking HS commodity codes and descriptions to critical mineral product groups, including concordances across HS versions, are available on the Data Hub Classifications website.

Source:

UNCTAD calculation based on the United Nations Commodity Trade Statistics Database (UN Comtrade).

References :

UNCTAD. (2025). Trade in critical minerals shapes energy transition, digital transformation and industrial development worldwide. SDG Pulse In Focus. Available at https://sdgpulse.unctad.org/critical-minerals/index.html (accessed 8 July 2026).

","name":"US.CriticalMinerals_TradeByPart","order":29}],"subFolders":[],"isParentFolder":false},{"id":31,"title":"Food trade by processing level","reports":[{"id":1676,"version":10003,"title":"Food categories: Bilateral trade by processing level - Annual (analytical)","lastUpdatedDate":"2025-03-20T12:39:00","category":"International trade","keywords":"Food processing, food category, trade","notes":"

Description:

This table shows international trade of food by food category and detailed by level of processing. These trade flows are measured in value, expressed in US dollars, year-on-year growth rate, share of total merchandise and share of total food in per cent. 

Food categories are presented broken down into 19 food categories, as per the following table: 

Table 1. Aggregated food categories

\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
CodeLabel
1Vegetables, meals and flours thereof
2Fruits
3Cereals and grains, flours thereof
4Meat, insects (for human consumption)
5Poultry
6Fish
7Shellfish, crustacea, other aquatic spp.
8Dairy and dairy products
9Eggs
10Nuts
11Oil seeds and oleaginous fruits, flours thereof
12Animal fats and oils
13Vegetable fats and oils (assumes nuts, seeds and plant origin)
14Non-alcoholic beverages
15Alcoholic beverages
16Sugars, jams and confectionery
17Cocoa and cocoa preparations
18Edible preparations and formulations, beyond scope of HS chapters of 1 - 17
19Other, Not Elsewhere Classifiable in the above categories
\r\n*also allowing for future evolution of market or HS in response to technology

Each of these 19 categories is presented broken down into 7 food processing categories, as per the following table: 

Table 2. Classification of processed food

\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
CodeLabel
ARaw: unprocessed*, fresh, chilled, frozen
BMinimally processed: cooked, steamed, or dried; crude oils
CProcessed: added salt/pickled/brined; added sugar**; smoked
DProcessed: fermentation*** / smoking (neither salted nor brined)
EComposite foods; Preparations/refined oils
FIngredients: spices et al; products provisionally preserved but unsuitable for immediate consumption in that form; ingredients for brewing
GPrecursors - seeds, trees etc. for sowing, fertilized eggs, livestock, bees

Notes: * Non-alcoholic beverages: will include non-SSBs (unsweetened water).

** Non-alcoholic beverages: will include SSBs; where salted and smoked, salted is identified as processing.
\r\n*** Alcoholic beverages.

The breakdown is only shown for categories that are available and existent, e.g., a combination of D-1 (processed by fermentation and for vegetables) does not exist, hence this or such categories are not shown. But the D-4 combination (processed by fermentation of meat) does exist and data for this category are shown.

The table shows trade flows considering commodities being traded. More specifically, it comprises exports and imports, where the figures on international merchandise trade measure the value of goods which add or subtract from the stock of material resources of an economy by entering (imports) or leaving (exports) its territory (United Nations, 2011). The value of exports is mostly recorded as the free-on-board (FOB) value, whereas the value of imports includes cost, insurance and freight (CIF). Services trade is excluded from analysis.
\r\nRe-exports, defined as exports of foreign goods which were previously recorded as imports, and re-imports, defined as re-imports of goods imported in the same state as previously exported, are not considered by the present study. Although some countries report these flows separately in their international trade statistics, yet not all of them do so consistently.

Trade flows are observed by reporting and partner economy, but also provided for several pre-defined groups of economies as available at UNCTADStat (UNCTAD, 2023c).

Source:

UNCTAD calculation based on UNSD Comtrade database.

Notes:

Using the trade matrix of processing by food categories, users are advised to exert usual caution when using and interpreting the data. First, it needs to be noted that the use of data in this trade matrix should be used with good knowledge of the coverage and the limitations of UN Comtrade, the main data source. The most important limitations are outlined at their website (United Nations, 2023b).

Second, the trade matrix reveals separately imports and exports flows, without any attempt to provide a balanced trade matrix. This also entails no modifications, edits, or imputations are made to the missing values, or reported to try and account for different valuation, i.e., FOB vs CIF. Numbers reported are as in the original data source.

Third, for analysis and interpretation purposes, it is worth mentioning that trade flows do not reveal domestic consumption. That does not only mean that the absolute value of traded food is less than the absolute value of consumed food. There is also likely to be relative differences between levels of processing in the proportion of food that is internationally traded. Processed and packaged food is less perishable and therefore more suitable for trade. Indeed, low values of traded unprocessed food could be seen as something positive as it could indicate a lot of sustainable and healthy food consumed as farm to table. Any interpretation of what trade of specific processed level of food groups therefore needs to be complemented with subject-expert knowledge and additional data series, e.g., on national production and consumption.

Furthermore, trade flows are measured in value, not volume. This may impact direct comparison of values of different processing groups, as such values may incorporate for example packaging values (indeed, processed food products also include more packaging than raw commodities). Processed, packaged and branded food costs more per calorie than raw commodities. A ton of tomatoes imported has a lower import value than the export value of the same tomatoes processed and exported, e.g., as ketchup. These considerations need to be accounted for in any interpretation of results of the analysis and in informing policies, to provide a reliable and robust evidence base (usually needs to be complemented with additional subject expertise).

Additionally, further considerations on more specific items, including economies and groups of economies considered, or coverage of different food items in specific processing and/or food categories, need to be accounted for when processing analysis. Users are requested to consult this technical paper and/or technical documentation at UNCTADstat (UNCTAD, 2023a, 2023b, 2023c).

Given the above challenges, this table is marked as analytical rather than official statistics. 

 

UNCTAD (2023a). Trade in processed food. UNCTAD data explorer. Available at https://unctadstat.unctad.org/EN/ProcessedFood.html (accessed 15 December 2023).

UNCTAD (2023b). UNCTADstat: Classifications. Available at https://unctadstat.unctad.org/EN/Classifications.html (accessed 1 October 2023).

UNCTAD (2023c). UNCTADstat. Available at https://unctadstat.unctad.org/EN/Index.html (accessed 1 January 2023).

United Nations (2023a). UN Comtrade database. Available at https://comtrade.un.org/ (accessed 10 November 2023).

United Nations (2023b). UN Comtrade disclaimer (read me first). Available at http://comtrade.un.org/db/help/uReadMeFirst.aspx (accessed 14 December 2023).

","name":"US.TradeFoodCatByProc","order":31},{"id":1674,"version":10003,"title":"Food categories: Revealed comparative advantage index by processing level - Annual (analytical)","lastUpdatedDate":"2025-03-20T12:39:00","category":"International trade in goods","keywords":"Food processing, food category, trade, revealed comparative advantage","notes":"

Description:

This table shows international trade of food by food category and detailed by level of processing. These trade flows are measured as a revealed comparative advantage (RCA) indicator. 

Food categories are presented broken down into 19 food categories, as per the following table:


\r\nTable 1. Aggregated food categories

\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
CodeLabel
1Vegetables, meals and flours thereof
2Fruits
3Cereals and grains, flours thereof
4Meat, insects (for human consumption)
5Poultry
6Fish
7Shellfish, crustacea, other aquatic spp.
8Dairy and dairy products
9Eggs
10Nuts
11Oil seeds and oleaginous fruits, flours thereof
12Animal fats and oils
13Vegetable fats and oils (assumes nuts, seeds and plant origin)
14Non-alcoholic beverages
15Alcoholic beverages
16Sugars, jams and confectionery
17Cocoa and cocoa preparations
18Edible preparations and formulations, beyond scope of HS chapters of 1 - 17
19Other, Not Elsewhere Classifiable in the above categories
\r\n*also allowing for future evolution of market or HS in response to technology

Each of these 19 categories is presented broken down into 7 food processing categories, as per the following table: 

Table 2. Classification of processed food

\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
CodeLabel
ARaw: unprocessed*, fresh, chilled, frozen
BMinimally processed: cooked, steamed, or dried; crude oils
CProcessed: added salt/pickled/brined; added sugar**; smoked
DProcessed: fermentation*** / smoking (neither salted nor brined)
EComposite foods; Preparations/refined oils
FIngredients: spices et al; products provisionally preserved but unsuitable for immediate consumption in that form; ingredients for brewing
GPrecursors - seeds, trees etc. for sowing, fertilized eggs, livestock, bees

Notes: * Non-alcoholic beverages: will include non-SSBs (unsweetened water).
\r\n** Non-alcoholic beverages: will include SSBs; where salted and smoked, salted is identified as processing.
\r\n*** Alcoholic beverages.


\r\nThe breakdown is only shown for categories that are available and existent, e.g., a combination of D-1 (processed by fermentation and for vegetables) does not exist, hence this or such categories are not shown. But the D-4 combination (processed by fermentation of meat) does exist and data for this category are shown.

Revealed comparative advantage (RCA) is an indicator observing an economy’s comparative advantage in producing and exporting certain commodity. It is based on Ricardian trade theory, which posits that patterns of trade among economies are governed by their relative differences in productivity. Although such productivity differences are difficult to observe, an RCA metric can be calculated using trade data to "reveal" such differences. While the metric can be used to provide a general indication and first approximation of an economy’s competitive export strengths, it should be noted that applied national measures which affect competitiveness such as tariffs, non-tariff measures, subsidies and others are not taken into account in the RCA metric. This section builds on the work presented at UNCTAD (2023a).
 
Economy A is said to have a revealed comparative advantage in a given product i when its ratio of exports of product i to its total exports of all goods (products) exceeds the same ratio for the world as a whole. RCA is calculated based on the following formula:

 

\"RCA

Where

When an economy has a revealed comparative advantage for a given product (with values of RCA >1), it is inferred to be a competitive producer and exporter of that product relative to an economy producing and exporting that good at or below the world average. An economy with a revealed comparative advantage in product i is considered to have an export strength in that product. The higher the value of an economy’s RCA for product i, the higher its export strength in product i. (UNCTAD, 2023a)
Trade matrix provides calculated values of RCA indicator for each of the individual economy (yet not groups of economies) and for each of the processing food categories

 

Source:

UNCTAD calculation based on UNSD Comtrade database.

 

Notes:

Using the trade matrix of processing by food categories, users are advised to exert usual caution when using and interpreting the data. First, it needs to be noted that the use of data in this trade matrix should be used with good knowledge of the coverage and the limitations of UN Comtrade, the main data source. The most important limitations are outlined at their website (United Nations, 2023b).

Second, the trade matrix reveals separately imports and exports flows, without any attempt to provide a balanced trade matrix. This also entails no modifications, edits, or imputations are made to the missing values, or reported to try and account for different valuation, i.e., FOB vs CIF. Numbers reported are as in the original data source.

Third, for analysis and interpretation purposes, it is worth mentioning that trade flows do not reveal domestic consumption. That does not only mean that the absolute value of traded food is less than the absolute value of consumed food. There is also likely to be relative differences between levels of processing in the proportion of food that is internationally traded. Processed and packaged food is less perishable and therefore more suitable for trade. Indeed, low values of traded unprocessed food could be seen as something positive as it could indicate a lot of sustainable and healthy food consumed as farm to table. Any interpretation of what trade of specific processed level of food groups therefore needs to be complemented with subject-expert knowledge and additional data series, e.g., on national production and consumption.

Furthermore, trade flows are measured in value, not volume. This may impact direct comparison of values of different processing groups, as such values may incorporate for example packaging values (indeed, processed food products also include more packaging than raw commodities). Processed, packaged and branded food costs more per calorie than raw commodities. A ton of tomatoes imported has a lower import value than the export value of the same tomatoes processed and exported, e.g., as ketchup. These considerations need to be accounted for in any interpretation of results of the analysis and in informing policies, to provide a reliable and robust evidence base (usually needs to be complemented with additional subject expertise).

Additionally, further considerations on more specific items, including economies and groups of economies considered, or coverage of different food items in specific processing and/or food categories, need to be accounted for when processing analysis. Users are requested to consult this technical paper and/or technical documentation at UNCTADstat (UNCTAD, 2023b, 2023c, 2023d).

Given the above challenges, this table is marked as analytical rather than official statistics. 

 

UNCTAD (2023a). Revealed Comparative Advantage. Available at https://unctadstat.unctad.org/EN/RcaRadar.html (accessed 15 December 2023).
\r\n
\r\nUNCTAD (2023b). Trade in processed food. UNCTAD data explorer. Available at https://unctadstat.unctad.org/EN/ProcessedFood.html (accessed 15 December 2023).

UNCTAD (2023c). UNCTADstat: Classifications. Available at https://unctadstat.unctad.org/EN/Classifications.html (accessed 1 October 2023).

UNCTAD (2023d). UNCTADstat. Available at https://unctadstat.unctad.org/EN/Index.html (accessed 1 January 2023).

United Nations (2023a). UN Comtrade database. Available at https://comtrade.un.org/ (accessed 10 November 2023).

United Nations (2023b). UN Comtrade disclaimer (read me first). Available at http://comtrade.un.org/db/help/uReadMeFirst.aspx (accessed 14 December 2023).

","name":"US.TradeFoodProcCat_Cat_RCA","order":32},{"id":1677,"version":10003,"title":"Food processing: Bilateral trade by food category - Annual (analytical)","lastUpdatedDate":"2025-03-20T12:39:00","category":"International trade","keywords":"Food processing, food category, trade","notes":"

Description:

This table shows international trade of food by level of processing and detailed by food category. These trade flows are measured in value, expressed in US dollars, year-on-year growth rate, share of total merchandise, and share of total food in per cent. 

Food processing follows a classification of processed food with seven (7) categories, as follows:

Table 1. Classification of processed food

\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
CodeLabel
ARaw: unprocessed*, fresh, chilled, frozen
BMinimally processed: cooked, steamed, or dried; crude oils
CProcessed: added salt/pickled/brined; added sugar**; smoked
DProcessed: fermentation*** / smoking (neither salted nor brined)
EComposite foods; Preparations/refined oils
FIngredients: spices et al; products provisionally preserved but unsuitable for immediate consumption in that form; ingredients for brewing
GPrecursors - seeds, trees etc. for sowing, fertilized eggs, livestock, bees

Notes:
\r\n* Non-alcoholic beverages: will include non-SSBs (unsweetened water).
\r\n** Non-alcoholic beverages: will include SSBs; where salted and smoked, salted is identified as processing.
\r\n*** Alcoholic beverages.

 

Each of the seven categories is presented broken down into 19 food categories, as per the following table:

Table 2. Aggregated food categories

\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
CodeLabel
1Vegetables, meals and flours thereof
2Fruits
3Cereals and grains, flours thereof
4Meat, insects (for human consumption)
5Poultry
6Fish
7Shellfish, crustacea, other aquatic spp.
8Dairy and dairy products
9Eggs
10Nuts
11Oil seeds and oleaginous fruits, flours thereof
12Animal fats and oils
13Vegetable fats and oils (assumes nuts, seeds and plant origin)
14Non-alcoholic beverages
15Alcoholic beverages
16Sugars, jams and confectionery
17Cocoa and cocoa preparations
18Edible preparations and formulations, beyond scope of HS chapters of 1 - 17
19Other, Not Elsewhere Classifiable in the above categories
\r\n*also allowing for future evolution of market or HS in response to technology

Each of these 19 categories is presented broken down into 7 food processing categories, as per the following table: 

Table 2. Classification of processed food

\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
CodeLabel
ARaw: unprocessed*, fresh, chilled, frozen
BMinimally processed: cooked, steamed, or dried; crude oils
CProcessed: added salt/pickled/brined; added sugar**; smoked
DProcessed: fermentation*** / smoking (neither salted nor brined)
EComposite foods; Preparations/refined oils
FIngredients: spices et al; products provisionally preserved but unsuitable for immediate consumption in that form; ingredients for brewing
GPrecursors - seeds, trees etc. for sowing, fertilized eggs, livestock, bees

 

The breakdown is only shown for categories that are available and existent, e.g., a combination of D-1 (processed by fermentation and for vegetables) does not exist, hence this or such categories are not shown. But the D-4 combination (processed by fermentation of meat) does exist and data for this category are shown.

The table shows trade flows considering commodities being traded. More specifically, it comprises exports and imports, where the figures on international merchandise trade measure the value of goods which add or subtract from the stock of material resources of an economy by entering (imports) or leaving (exports) its territory (United Nations, 2011). The value of exports is mostly recorded as the free-on-board (FOB) value, whereas the value of imports includes cost, insurance and freight (CIF). Services trade is excluded from analysis.
\r\nRe-exports, defined as exports of foreign goods which were previously recorded as imports, and re-imports, defined as re-imports of goods imported in the same state as previously exported, are not considered by the present study. Although some countries report these flows separately in their international trade statistics, yet not all of them do so consistently.

Trade flows are observed by reporting and partner economy, but also provided for several pre-defined groups of economies as available at UNCTADStat (UNCTAD, 2023c).

 

Source:

UNCTAD calculation based on UNSD Comtrade database.

 

Notes:

Using the trade matrix of processing by food categories, users are advised to exert usual caution when using and interpreting the data. First, it needs to be noted that the use of data in this trade matrix should be used with good knowledge of the coverage and the limitations of UN Comtrade, the main data source. The most important limitations are outlined at their website (United Nations, 2023b).

Second, the trade matrix reveals separately imports and exports flows, without any attempt to provide a balanced trade matrix. This also entails no modifications, edits, or imputations are made to the missing values, or reported to try and account for different valuation, i.e., FOB vs CIF. Numbers reported are as in the original data source.

Third, for analysis and interpretation purposes, it is worth mentioning that trade flows do not reveal domestic consumption. That does not only mean that the absolute value of traded food is less than the absolute value of consumed food. There is also likely to be relative differences between levels of processing in the proportion of food that is internationally traded. Processed and packaged food is less perishable and therefore more suitable for trade. Indeed, low values of traded unprocessed food could be seen as something positive as it could indicate a lot of sustainable and healthy food consumed as farm to table. Any interpretation of what trade of specific processed level of food groups therefore needs to be complemented with subject-expert knowledge and additional data series, e.g., on national production and consumption.

Furthermore, trade flows are measured in value, not volume. This may impact direct comparison of values of different processing groups, as such values may incorporate for example packaging values (indeed, processed food products also include more packaging than raw commodities). Processed, packaged and branded food costs more per calorie than raw commodities. A ton of tomatoes imported has a lower import value than the export value of the same tomatoes processed and exported, e.g., as ketchup. These considerations need to be accounted for in any interpretation of results of the analysis and in informing policies, to provide a reliable and robust evidence base (usually needs to be complemented with additional subject expertise).

Additionally, further considerations on more specific items, including economies and groups of economies considered, or coverage of different food items in specific processing and/or food categories, need to be accounted for when processing analysis. Users are requested to consult this technical paper and/or technical documentation at UNCTADstat (UNCTAD, 2023a, 2023b, 2023c).

Given the above challenges, this table is marked as analytical rather than official statistics.

 

UNCTAD (2023a). Trade in processed food. UNCTAD data explorer. Available at https://unctadstat.unctad.org/EN/ProcessedFood.html (accessed 15 December 2023).

UNCTAD (2023b). UNCTADstat: Classifications. Available at https://unctadstat.unctad.org/EN/Classifications.html (accessed 1 October 2023).

UNCTAD (2023c). UNCTADstat. Available at https://unctadstat.unctad.org/EN/Index.html (accessed 1 January 2023).

United Nations (2023a). UN Comtrade database. Available at https://comtrade.un.org/ (accessed 10 November 2023).

United Nations (2023b). UN Comtrade disclaimer (read me first). Available at http://comtrade.un.org/db/help/uReadMeFirst.aspx (accessed 14 December 2023).

","name":"US.TradeFoodProcByCat","order":33},{"id":1675,"version":10003,"title":"Food processing: Revealed comparative advantage index by food category - Annual (analytical)","lastUpdatedDate":"2025-03-20T12:39:00","category":"International trade in goods","keywords":"Food processing, food category, trade, revealed comparative advantage","notes":"

Description:

This table shows international trade of food by level of processing and detailed by food category. These trade flows are measured as a revealed comparative advantage (RCA) indicator.

Food processing follows a classification of processed food with seven (7) categories, as follows:

Table 1. Classification of processed food

\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
CodeLabel
ARaw: unprocessed*, fresh, chilled, frozen
BMinimally processed: cooked, steamed, or dried; crude oils
CProcessed: added salt/pickled/brined; added sugar**; smoked
DProcessed: fermentation*** / smoking (neither salted nor brined)
EComposite foods; Preparations/refined oils
FIngredients: spices et al; products provisionally preserved but unsuitable for immediate consumption in that form; ingredients for brewing
GPrecursors - seeds, trees etc. for sowing, fertilized eggs, livestock, bees

Notes: * Non-alcoholic beverages: will include non-SSBs (unsweetened water).
\r\n** Non-alcoholic beverages: will include SSBs; where salted and smoked, salted is identified as processing.
\r\n*** Alcoholic beverages.

 

Each of the seven categories is presented broken down into 19 food categories, as per the following table:

Table 2. Aggregated food categories

\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
CodeLabel
1Vegetables, meals and flours thereof
2Fruits
3Cereals and grains, flours thereof
4Meat, insects (for human consumption)
5Poultry
6Fish
7Shellfish, crustacea, other aquatic spp.
8Dairy and dairy products
9Eggs
10Nuts
11Oil seeds and oleaginous fruits, flours thereof
12Animal fats and oils
13Vegetable fats and oils (assumes nuts, seeds and plant origin)
14Non-alcoholic beverages
15Alcoholic beverages
16Sugars, jams and confectionery
17Cocoa and cocoa preparations
18Edible preparations and formulations, beyond scope of HS chapters of 1 - 17
19Other, Not Elsewhere Classifiable in the above categories
\r\n*also allowing for future evolution of market or HS in response to technology

The breakdown is only shown for categories that are available and existent, e.g., a combination of D-1 (processed by fermentation and for vegetables) does not exist, hence this or such categories are not shown. But the D-4 combination (processed by fermentation of meat) does exist and data for this category are shown.

Revealed comparative advantage (RCA) is an indicator observing an economy’s comparative advantage in producing and exporting certain commodity. It is based on Ricardian trade theory, which posits that patterns of trade among economies are governed by their relative differences in productivity. Although such productivity differences are difficult to observe, an RCA metric can be calculated using trade data to "reveal" such differences. While the metric can be used to provide a general indication and first approximation of an economy’s competitive export strengths, it should be noted that applied national measures which affect competitiveness such as tariffs, non-tariff measures, subsidies and others are not taken into account in the RCA metric. This section builds on the work presented at UNCTAD (2023a).
 
Economy A is said to have a revealed comparative advantage in a given product i when its ratio of exports of product i to its total exports of all goods (products) exceeds the same ratio for the world as a whole. RCA is calculated based on the following formula: 
\r\n 

\"RCA

Where

 
When an economy has a revealed comparative advantage for a given product (with values of RCA >1), it is inferred to be a competitive producer and exporter of that product relative to an economy producing and exporting that good at or below the world average. An economy with a revealed comparative advantage in product i is considered to have an export strength in that product. The higher the value of an economy’s RCA for product i, the higher its export strength in product i. (UNCTAD, 2023a)
Trade matrix provides calculated values of RCA indicator for each of the individual economy (yet not groups of economies) and for each of the processing food categories

 

Source:

UNCTAD calculation based on UNSD Comtrade database.

 

Notes:

Using the trade matrix of processing by food categories, users are advised to exert usual caution when using and interpreting the data. First, it needs to be noted that the use of data in this trade matrix should be used with good knowledge of the coverage and the limitations of UN Comtrade, the main data source. The most important limitations are outlined at their website (United Nations, 2023b).

Second, the trade matrix reveals separately imports and exports flows, without any attempt to provide a balanced trade matrix. This also entails no modifications, edits, or imputations are made to the missing values, or reported to try and account for different valuation, i.e., FOB vs CIF. Numbers reported are as in the original data source.

Third, for analysis and interpretation purposes, it is worth mentioning that trade flows do not reveal domestic consumption. That does not only mean that the absolute value of traded food is less than the absolute value of consumed food. There is also likely to be relative differences between levels of processing in the proportion of food that is internationally traded. Processed and packaged food is less perishable and therefore more suitable for trade. Indeed, low values of traded unprocessed food could be seen as something positive as it could indicate a lot of sustainable and healthy food consumed as farm to table. Any interpretation of what trade of specific processed level of food groups therefore needs to be complemented with subject-expert knowledge and additional data series, e.g., on national production and consumption.

Furthermore, trade flows are measured in value, not volume. This may impact direct comparison of values of different processing groups, as such values may incorporate for example packaging values (indeed, processed food products also include more packaging than raw commodities). Processed, packaged and branded food costs more per calorie than raw commodities. A ton of tomatoes imported has a lower import value than the export value of the same tomatoes processed and exported, e.g., as ketchup. These considerations need to be accounted for in any interpretation of results of the analysis and in informing policies, to provide a reliable and robust evidence base (usually needs to be complemented with additional subject expertise).

Additionally, further considerations on more specific items, including economies and groups of economies considered, or coverage of different food items in specific processing and/or food categories, need to be accounted for when processing analysis. Users are requested to consult this technical paper and/or technical documentation at UNCTADstat (UNCTAD, 2023b, 2023c, 2023d).

Given the above challenges, this table is marked as analytical rather than official statistics.

 

UNCTAD (2023a). Revealed Comparative Advantage. Available at https://unctadstat.unctad.org/EN/RcaRadar.html (accessed 15 December 2023).
\r\n
\r\nUNCTAD (2023b). Trade in processed food. UNCTAD data explorer. Available at https://unctadstat.unctad.org/EN/ProcessedFood.html (accessed 15 December 2023).

UNCTAD (2023c). UNCTADstat: Classifications. Available at https://unctadstat.unctad.org/EN/Classifications.html (accessed 1 October 2023).

UNCTAD (2023d). UNCTADstat. Available at https://unctadstat.unctad.org/EN/Index.html (accessed 1 January 2023).

United Nations (2023a). UN Comtrade database. Available at https://comtrade.un.org/ (accessed 10 November 2023).

United Nations (2023b). UN Comtrade disclaimer (read me first). Available at http://comtrade.un.org/db/help/uReadMeFirst.aspx (accessed 14 December 2023).

","name":"US.TradeFoodProcCat_Proc_RCA","order":34}],"subFolders":[],"isParentFolder":false}],"isParentFolder":true},{"id":34,"title":"Economy, investment and finance","reports":[],"subFolders":[{"id":12,"title":"National accounts","reports":[{"id":2471,"version":10003,"title":"Gross domestic product: Total and per capita - Annual","lastUpdatedDate":"2026-06-26T12:34:00","category":"Economic trends","keywords":"Total GDP, per capita, nominal, real","notes":"

Description:
\r\nThis table provides information on gross domestic product (GDP), total and per capita at current and constant (2020) prices and annual average growth rates of gross domestic product (GDP), total and per capita, in per cent.
\r\nThe total GDP is expressed in millions of US dollars, while GDP per capita is expressed in US dollars.
\r\nGrowth rates are based on GDP in constant 2020 United States dollars.
\r\n
\r\nSources:
\r\n- UN DESA Statistics Division, National Accounts Main Aggregates Database
\r\n- UNCTAD secretariat estimates
\r\n 
\r\nNotes:
\r\nGDP data at current prices in US dollars are converted from GDP in national currency using annual period-average exchange rates.
\r\n 
\r\nGDP data in constant prices in US dollars are converted from GDP in national currency using the annual period-average exchange rate of the base year for all years.
\r\n 
\r\nPer capita GDP is defined as GDP at current prices in US dollars divided by the total population.

","name":"US.GDPTotal","order":37},{"id":2174,"version":10004,"title":"Gross domestic product: By expenditure and by sector - Annual","lastUpdatedDate":"2026-03-06T15:28:00","category":"Economic trends","keywords":"GDP, breakdown, expenditure, economic activity","notes":"

Description:
\r\nThis table presents gross domestic product (GDP) by type of expenditure and gross value added (VA) by kind of economic activity, expressed in millions of United States dollars, and the share of each component in GDP or VA as a percentage.
\r\n
\r\nSources:
\r\n- UN DESA Statistics Division, National Accounts Main Aggregates Database
\r\n- UNCTAD secretariat estimates
\r\n
\r\nNotes:
\r\nThe share of each component of GDP or VA is derived on the basis of current price series in national currencies. Detailed information on each country is available at: http://unstats.un.org/unsd/snaama/downloads/Download-GDPcurrent-USD-countries.xls
\r\n
\r\n The breakdown of GDP by kind of economic activity is classified into three categories: agriculture, industry and services. It is based on classification ISIC Rev.3 where:
\r\n- Agriculture (ISIC A-B),
\r\n- Industry (ISIC C-F): Mining, Manufacturing, Utilities (ISIC C-E), Manufacturing (ISIC D), Construction ISIC F),
\r\n- Services (ISIC G-P): Wholesale, retail trade, restaurants and hotels (ISIC G-H), Transport, storage and communications (ISIC I), Other activities (ISIC J-P). 

","name":"US.GDPComponent","order":38},{"id":543,"version":10001,"title":"DISCONTINUED - Nominal GNI, total and per capita, annual","lastUpdatedDate":"2023-07-18T16:51:00","category":"Economic trends","keywords":"GNI, per capita, nominal","notes":"

Description:
\r\nThis table presents total and per capita, nominal gross national income (GNI), by individual economy and group of economies. GNI figures are expressed in millions of dollars, while GNI per capita is expressed in dollars.
\r\n
\r\nSource:
\r\nUNCTAD secretariat calculations, based on UN DESA Statistics Division, National Accounts Main Aggregates Database
\r\n
\r\nNotes:
\r\nGNI data in dollars at current prices are converted from data in national currency at current prices by using current exchange rates (annual period average).
\r\n
\r\nPer capita GNI is defined as GNI in current prices in dollars divided by the total population.

","name":"US.GNI","order":39}],"subFolders":[],"isParentFolder":false},{"id":11,"title":"Investment and balance of payments","reports":[{"id":2575,"version":10005,"title":"Balance of payments: Goods and services trade - Annual","lastUpdatedDate":"2026-09-01T06:44:00","category":"International trade in goods and services","keywords":"trade, goods, services","notes":"

Description:
\r\nThis table is a compilation of statistics of trade in goods and services as reported in the Balance of Payments. The conceptual framework used for the compilation is based on the IMF Balance of Payments and International Investment Position Manual, Sixth Edition (BPM6).
\r\n 
\r\nSources:
\r\nUNCTAD secretariat calculations based on:
\r\n- IMF, Balance of Payments Statistics
\r\n- UNCTAD, UNCTADstat Trade in Services
\r\n- UNCTAD, UNCTADstat Total Merchandise Trade
\r\n- EIU, Country Data
\r\n- Other international and national sources
\r\n 
\r\nNotes:

The statistics presented correspond to the concepts and definitions of the IMF Balance of Payments and International Investment Position Manual, Sixth Edition (BPM6), except those countries and territories who present their figures according to the fifth edition of the Manual (BPM5).

Exports and imports of goods and services are credits and debits of goods and services as reported in the current account of the balance of payments.

Goods include general merchandise on a BOP basis, goods under "merchanting" and nonmonetary gold. In order for a transaction to be recorded under "goods", a change of ownership from/to a resident of a local country to/from a non-resident in a foreign country has to take place.

BOP-based statistics of trade in goods (change of ownership principle between residents and non-residents) are usually not comparable to IMTS-based merchandise trade data (physical border crossing principle) due to differences in the concepts and definitions. In some countries, the differences may be quite significant.

Trade in services results from intangible actions such as transportation, travel, maintenance and repairs, business services, royalties or licensing.

Estimated individual countries figures are included in geographical regions or economic groupings calculation but not always shown separately.

","name":"US.GoodsAndServicesBpm6","order":41},{"id":2576,"version":10005,"title":"Balance of payments: Goods and services trade balance indicators - Annual","lastUpdatedDate":"2026-09-01T06:44:00","category":"International trade in goods and services","keywords":"Trade, indicators, balances","notes":"

Description:
\r\nThis table shows indicators of trade balances as the following: 
\r\n- Normalized trade balance,
\r\n- Trade balance as percentage of imports and,
\r\n- Trade balance as percentage of nominal gross domestic product (GDP).
\r\n 
\r\nThe indicators are calculated for trade in goods, trade in services and total trade in goods and services.
\r\n 
\r\nSource:
\r\nUNCTAD secretariat calculations based on UNCTAD, UNCTADstat Trade in Goods and Services
\r\n 
\r\nNotes:
\r\nTrade balance is defined as the difference between exports f.o.b. and imports f.o.b. expressed in millions of dollars.
\r\n 
\r\nNormalized trade balance of goods and services is defined as the trade balance (total exports less total imports) divided by the total trade (exports plus imports).
\r\n 
\r\nEstimated individual countries figures are included in geographical regions or economic groupings calculation but not always shown separately.

","name":"US.GoodsAndServBalanceBpm6","order":42},{"id":2574,"version":10005,"title":"Balance of payments: Goods and services trade openness indicators - Annual","lastUpdatedDate":"2026-09-01T06:44:00","category":"International trade in goods and services","keywords":"Trade, Indicators, Exports, Imports","notes":"

Description:
\r\nThis table shows exports, imports and sum/average of exports and imports as percentage of nominal gross domestic product (GDP). The indicators are calculated for trade in goods, trade in services and total trade in goods and services.
\r\n 
\r\nSource:
\r\nUNCTAD secretariat calculations based on UNCTAD, UNCTADstat Trade in Goods and Services
\r\n 
\r\nNotes:
\r\nAverage of imports and exports, which indicates roughly the size of international trade, is the sum of imports and exports divided by two.
\r\n 
\r\nEstimated figures for individual countries are included in the calculation for geographical regions or economic groupings but are not always shown separately.

","name":"US.GoodsAndServTradeOpennessBpm6","order":43},{"id":2620,"version":10005,"title":"Balance of payments: Current account balance - Annual","lastUpdatedDate":"2026-09-24T07:08:00","category":"Economic trends","keywords":"Balance of payments, current account balance","notes":"
Description: 
This table shows the current account balance, expressed in millions of United States dollars and as percentage of GDP. 
 
Sources:
UNCTAD secretariat calculations, based on:
- IMF, Balance of Payments Statistics
- OECD, OECD.Stat
- IMF, World Economic Outlook
- National sources 
 
Notes:
The current account forms part of the balance of payments and displays the flows of goods, services, primary income, and secondary income between residents and nonresidents of an economy. The current account balance measures, in general, the difference between current receipts and expenditure for internationally traded goods, services and income payments. At the same time, from a national perspective, the current account balance represents the gap between domestic saving and investment. 
 
","name":"US.CurrAccBalance","order":44},{"id":2659,"version":10005,"title":"Personal remittances: receipts and payments, annual","lastUpdatedDate":"2025-11-05T10:12:00","category":"External financial resources","keywords":"remittances, transfers, personal, compensation, employees, payments, receipts","notes":"

Description:
\r\nThis table presents time series on receipts and payments of personal remittances in millions of dollars. These data are also shown as percentage of exports (receipts) and imports (payments) of goods and services, and as percentage of GDP. 
\r\n
\r\nSources:
\r\n- World Bank, Migration and Remittances Data
\r\n- IMF, Balance of Payments Statistics
\r\n- EIU, online database
\r\n- UNCTADstat (series on GDP and total trade)
\r\n- UNCTAD secretariat calculations
\r\n
\r\nNotes:
\r\nPersonal remittances, as defined in this table, comply with the guidelines of the Balance of Payments and International Investment Position Manual, Sixth Edition (BPM6) (IMF 2009, Appendix 5). They are the sum of two items: (1.) compensation of employees, defined as the income of workers employed in an economy where they are not resident and of residents employed by non-resident employers; (2.) personal transfers, defined as current transfers in kind or in cash, between resident and non-resident households (ibid., A5.5-7).

The main source of personal remittances data is World Bank. In cases of missing data, data from IMF or EIU have been imputed.
","name":"US.Remittances","order":45},{"id":2660,"version":10015,"title":"Foreign direct investment: Inward and outward flows and stock, annual","lastUpdatedDate":"2026-10-21T14:08:00","category":"Foreign direct investment","keywords":"foreign, direct, investment, flow, stock, inward, outward, FDI","notes":"

Description:

This table contains information on foreign direct investment (FDI) inward and outward flows and stock, expressed in millions of dollars.

The table also presents the percentage share of each economy/group in the world.

Sources:

Notes:

Foreign direct investment (FDI) is an investment made by a resident enterprise in one economy (direct investor or parent enterprise) with the objective of establishing a lasting interest in an enterprise that is resident in another economy (direct investment enterprise or foreign affiliate). The lasting interest implies the existence of a long-term relationship between the direct investor and the direct investment enterprise and a significant degree of influence on the management of the enterprise. The ownership of 10% or more of the voting power of a direct investment enterprise by a direct investor is evidence of such a relationship.

FDI flows comprise mainly three components:

FDI flows are transactions recorded during the reference period (typically year or quarter). FDI stocks are the accumulated value held at the end of the reference period (typically year or quarter).

In 2014, many countries implemented the new guidelines for the compilation of FDI data based on the Sixth edition of the Balance of Payments and International Investment Position Manual (BPM6) and the Fourth edition of OECD Benchmark Definition of Foreign Direct Investment (BD4). One of the major changes introduced in BPM6 and BD4 is the presentation of FDI statistics on an asset/liability basis instead of the directional principle (as recommended by the previous editions of these guidelines).
\r\nOn an asset/liability basis, direct investment statistics are organized according to whether the investment relates to an asset or a liability for the reporting country.
\r\nUnder the directional principle, the direct investment statistics are organized according to the direction of the investment for the reporting country - either inward or outward.

The two presentations differ in their treatment of reverse investment (reverse investment is when an affiliate provides loans to its parent). Under the directional presentation, reverse investment is subtracted to derive the total outward or inward investment of the reporting economy. Therefore, FDI statistics on an asset/liability basis tends to be higher than those under the directional principle, but such is not always the case.

While the presentation on an asset/liability basis is appropriate for macroeconomic analysis (i.e. the impact on the balance of payments), the presentation on directional principle is more appropriate to assist policymakers and government officials to formulate investment policies. This is because the presentation of the FDI data on directional basis reflects the direction of influence by the foreign direct investor underlying the direct investment: inward or outward direct investment.

FDI data in this table are on directional principle, unless otherwise indicated.

For more details, please refer to the Methodological Note of the World Investment Report.

","name":"US.FdiFlowsStock","order":46}],"subFolders":[],"isParentFolder":false},{"id":14,"title":"Productive capacities","reports":[{"id":2005,"version":10007,"title":"Structural transformation: Productive Capacities Index - Annual (analytical)","lastUpdatedDate":"2025-10-31T14:19:00","category":"Multi theme statistics","keywords":"Productive capacities index, PCI, structural transformation, natural capital, human capital, energy, transport, ICT, institutions, structural change, private sector","notes":"

Description:

The PCI was developed as an official metric to measure and benchmark productive capacities, following requests from member States through the Nairobi Maafikiano (TD/519/Add.2, para. 76(k)) and ECOSOC (E/RES/2017/29, para. 6) resolutions. It builds on UNCTAD’s conceptual framework for measuring productive capacities, structured around three pillars: “productive resources, entrepreneurial capabilities, and production linkages, which together determine a country’s capacity to produce goods and services and enable it to grow and develop” (UNCTAD, 2006, 2025a).

The PCI is further disaggregated into eight categories: natural capital, human capital, energy, institutions, private sector, structural change, transport, and information and communication technologies. These categories translate the three overarching pillars into actionable policy domains and reflect UNCTAD’s research on the optimal configuration of productive capacity dimensions for policy relevance.

The Table presents nine PCI scores: one for each category and an overall score that aggregates the eight categories into a single composite indicator.

Sources:

UNCTAD calculations based on 43 input indicators from international sources. The use of international sources ensures coherence and comparability across countries. The data sources are the following:

Notes:

The PCI is calculated as a geometric average of the indices for the eight categories. Each category index is obtained from the principal components extracted from the underlying indicators, weighted by their capacity to explain the variance of the original data. The category indices are normalized into 1-100 intervals.

Cells where the score where either the majority or all of the underlying indicators have been filled through interpolation, forecasting or imputation are flagged. The imputation step involves a stochastic process that in some cases give different results from one run of the process to the other. Where the consequence of this stochasticity is significant the same cell notes indicate this as well. The cell notes identify cases where the range of the published value, due to randomness in the process, vary more than ±1 index point and when the value varies more than ±3 point.  This range corresponds to the range of 95% of results of 200 identical runs of the same analysis only varying in the random seed.

The table uses the following cell notes:

\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n

Imputation rate

Range of 95% of repeated runs

Note

< 50%

<= 2

(No note)

< 50%

> 2

Run-to-run variation exceeds ±1 index point

>= 50%

<= 2

Half or more of the indicators imputed. Value stable across runs (variation within ± 1 index point).

>= 50%

> 2

Half or more of the indicators imputed. Run-to-run variation exceeds ±1 index points.

>= 50%

> 6

Half or more of the indicators imputed. Run-to-run variation exceeds +/- 3 index points.

100%

<= 2

All indicators imputed. Value stable across runs (variation within ±1 index point).

100%

> 2

All indicators imputed. Run-to-run variation exceeds ±1 index points.

100%

> 6

All indicators imputed. Run-to-run variation exceeds ±3 index points.


\r\n

Values for groups of economies are the medians of group member values.

A detailed description of the 3rd generation methodology  is presented in UNCTAD (2025b). The main methodological change from the previous, 2nd generation  PCI update (UNCTAD, 2023) is the inclusion of an input indicator on maritime transport within the Transport category.

The PCI is overseen by the Statistical and Technical Advisory Group (STAG) on the Productive Capacities Index, a peer-review body of experts that ensures the methodological integrity of the PCI, aligns its indicators with international standards, and evaluates the inclusion of new dimensions and data sources. A summary report of the peer-review process for this update is also available online (UNCTAD, 2025c).

 

References:

UNCTAD (2006). The Least Developed Countries Report 2006: Developing Productive Capacities. United Nations publication. Sales No. E.06.II.D.9. New York and Geneva.

UNCTAD (2023). Productive Capacities Index: 2nd Generation  - Enhanced Statistical and Methodological Approach with Results (UNCTAD/ALDC/2023/2). Available at https://unctad.org/publication/productive-capacities-index-2nd-generation (accessed 28 March 2024).

UNCTAD (2025a). Statistical Guidelines for Measuring Productive Capacities. Forthcoming.

UNCTAD (2025b). Productive Capacities Index: 3rd Generation - Enhanced Statistical and Methodological Approach with Results. Forthcoming.

UNCTAD (2025c). Report of the Third Meeting of the Statistical and Technical Advisory Group (STAG) on the Productive Capacities Index (PCI).23 June 2025. Banco de Portugal, Lisbon. Available at https://unctad.org/system/files/non-official-document/aldc2025_pci_stag_23june2025_en.pdf

 

","name":"US.PCI","order":48}],"subFolders":[],"isParentFolder":false},{"id":15,"title":"Inflation and exchange rates","reports":[{"id":2553,"version":10004,"title":"Inflation: Consumer price index - Annual","lastUpdatedDate":"2026-08-28T08:04:00","category":"Economic trends","keywords":"Consumer Price Indices, CPI","notes":"

Description:
\r\nThis table provides information on consumer price indices (CPI) with base year 2020 and, in addition, annual growth rate indicator.
\r\n
\r\nSources:
\r\nUNCTAD calculations, based on:


\r\nNotes:
\r\nThe Consumer Price Indices (CPI) is an inflationary indicator that measures the weighted average of prices of a basket of consumer goods and services, purchases by a consumer. The CPI is calculated by taking price changes for each item in the predetermined basket of goods and services during a year. Changes in CPI are used to assess price changes associated with the cost of living.

","name":"US.Cpi_A","order":50},{"id":2362,"version":10017,"title":"Exchange rates: Currency cross rates - Annual","lastUpdatedDate":"2026-05-29T14:04:00","category":"Economic trends","keywords":"exchange, rate, currency","notes":"
Description:
This table shows exchange rates for currencies used in over 210 world economies presented in a cross rates layout where countries are presented in both rows and columns.
 
Sources:
UNCTAD calculations, based on:
- UN DESA Statistics Division, National Accounts Main Aggregates Database
- IMF, International Financial Statistics
- other international and national sources
 
Notes:
National currency per US dollars exchange rates are used to derive explicit exchange rates for each of the countries presented with regard to any other country.
 
","name":"US.ExchangeRateCrosstab","order":51}],"subFolders":[],"isParentFolder":false},{"id":3,"title":"Commodity prices","reports":[{"id":2596,"version":10021,"title":"Commodity prices: UNCTAD Commodity Price Index - Annual","lastUpdatedDate":"2026-02-10T13:02:00","category":"Commodities","keywords":"commodity, price, index","notes":"

Description:
\r\nThis table presents the annual UNCTAD Commodity Price Index and its sub-indices for different groups of main commodities exported by developing economies.
\r\n
\r\nNotes:
\r\nThe annual price is usually calculated as an average of the monthly quotations at a main market place of the respective commodity.

The price index is a fixed-base Laspeyres index, where the weights are proportional to the individual commodities' shares in total merchandise exports of developing economies in the base period. The base period comprises the years 2014, 2015 and 2016. For further details, see the methodological note:
 https://unctadstat.unctad.org/UnctadStatMetadata/Documentation/UNCTAD_CommodityPriceIndex_MethodologicalNote.pdf..


\r\nSources:
\r\nThe price quotations used for the compilation of the indices, and their sources, are indicated in the table below.

\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n

Product description

Data source

Beef, Australia/New Zealand, frozen, CIF US ports ($/kg)

World Bank - Commodity-markets

Salmon, fresh, fish-farm bred, export price, Norway ($/kg)

IMF - Primary Commodity Prices

Shrimps, EU import price of warmwater shrimp from Extra EU countries (trade weighted average ($/kg)

IMF - Primary Commodity Prices

Bananas (Central & South America), major brands, US import price, free on truck (f.o.t.) Gulf ports ($/kg)

World Bank - Commodity-markets

Coffee, other mild Arabicas, ex-dock US/Europe ($/kg)

World Bank - Commodity-markets

Coffee, Robustas, ex-dock US/Europe ($/kg)

World Bank - Commodity-markets

Tea, Kenya Mombasa/Nairobi, auction price ($/kg)

World Bank - Commodity-markets

Wheat, United States, n° 2 Hard Red Winter (ordinary), FOB Gulf ($/t)

World Bank - Commodity-markets

Maize, United States, n° 3 yellow, FOB Gulf ($/t)

World Bank - Commodity-markets

Rice, Thailand, white milled, 5% broken, export price, FOB ($/t)

World Bank - Commodity-markets

Soybeans, in bulk, United States, n° 2 yellow, CIF Rotterdam ($/t)

World Bank - Commodity-markets

Soybean meal, in bulk, 45/46% protein, Hamburg FOB ex-mill ($/t)

World Bank - Commodity-markets

Cotton, Cotlook A Index, Middling 1-3/32", CFR Far Eastern ($/kg)

World Bank - Commodity-markets

Soybean oil, U.S. Soya Oil, FOB U.S. Gulf ($/t)

World Bank - Commodity-markets

Groundnut oil, in bulk, any origin, CIF Rotterdam ($/t)

World Bank - Commodity-markets

Palm oil, in bulk, Malaysia/Indonesia, 5% FFA, CIF ($/t)

World Bank - Commodity-markets

Sunflower/Safflower Oil, crude, US export price from Gulf of Mexico ($/t)

IMF – Primary Commodity Prices

Coconut oil, in bulk, Philippines/Indonesia, CIF Rotterdam ($/t)

World Bank - Commodity-markets

Palm kernel oil, in bulk, Malaysia/Indonesia, CIF Rotterdam ($/t)

World Bank - Commodity-markets

Sugar, average of I.S.A. daily prices, FOB & Stowed, in bulk, Caribbean ports ($/kg)

World Bank - Commodity-markets

Cocoa beans, average daily prices New York/London ($/kg)

World Bank - Commodity-markets

Fish meal, in bulk, 64/65% protein, FCA, Bremen ($/t)

World Bank - Commodity-markets

Tobacco, unmanufactured, US import unit value  ($/t)

World Bank - Commodity-markets

Phosphate rock, North Africa FOBV ($/t)

World Bank - Commodity-markets

Iron ore, Australia to China, fines, 62% Fe, offshore export price, CIF ($/dry ton)

World Bank - Commodity-markets

Manganese 9Electro CIF NWE ($/t)

IMF - Primary Commodity Prices

Copper, grade A, electrolytic wire bars/cathodes, LME, cash (£/t)

World Bank - Commodity-markets

Nickel, LME, cash ($/t)

World Bank - Commodity-markets

Aluminium, primary, high grade, LME, cash ($/t)

World Bank - Commodity-markets

Lead, LME, cash settlement ($/t)

World Bank - Commodity-markets

Zinc, special high grade, LME, cash settlement ($/t)

World Bank - Commodity-markets

Tin, LME, cash ($/t)

World Bank - Commodity-markets

Silver, 99.9%, Handy & Harman, New York (¢/troy ounce)

World Bank - Commodity-markets

Coal, steam coal, Australia, average FOB Newcastle ($/t)

World Bank - Commodity-markets

Crude oil, UK Brent, light blend API 38°, spot price, FOB UK ports ($/barrel)

World Bank - Commodity-markets

Crude oil, Dubai, medium, Fateh API 32°, spot price, FOB Dubai ($/barrel)

World Bank - Commodity-markets

Natural gas, index, Europe, United States and Japan (2010=100)

World Bank - Commodity-markets

Rubber, RSS n°3, Singapore Commodity Exchange, 1st contract ($/kg)

World Bank - Commodity-markets

Cattle hides, US Chicago packer's heavy native steers, FOB shipping point (¢/lb.)

IMF - Primary Commodity Prices

Tropical logs, Sapele, high quality (loyal and marchand), FOB, Cameroon ($/m3)

World Bank - Commodity-markets

Sawnwood, Dark Red Meranti, Malaysia, CFR plus commission, UK ($/m3)

World Bank - Commodity-markets

Plywood, Africa & SE Asia, Lauan, 3-ply, 91cmx182cmx4mm, wholesale Tokyo (¢/sheet)

World Bank - Commodity-markets

Fine wool, 19 Micron, AWEX auction price, Australia (¢/kg)

IMF - Primary Commodity Prices

Jute, Bangladesh, BWD, FOB Mongolia ($/t)

FAO – Food Price Monitoring and Analysis

Gold, 99.5% fine, afternoon fixing London ($/troy ounce)

World Bank - Commodity-markets

Platinum, 99.9% refined, London afternoon fixing ($/troy ounce)

World Bank - Commodity-markets

 

","name":"US.UCPI_A","order":53},{"id":2642,"version":10022,"title":"Commodity prices: UNCTAD Commodity Price Index - Monthly","lastUpdatedDate":"2026-10-09T14:30:00","category":"Commodities prices","keywords":"commodity, price, index","notes":"
Description:
This table presents the monthly UNCTAD Commodity Price Index and its sub-indices for different groups of main commodities exported by developing economies. 
 
Notes:
The monthly price is usually calculated as an average of daily or weekly quotations at a main market place of the respective commodity.
 
The price index is a fixed-base Laspeyres index, where the weights are proportional to the individual commodities' shares in total merchandise exports of developing economies in the base period. The base period comprises the years 2014, 2015 and 2016. For further details, see the methodological note:
https://unctadstat.unctad.org/UnctadStatMetadata/Documentation/UNCTAD_CommodityPriceIndex_MethodologicalNote.pdf.
\r\n 
The index was updated in 2025 to only consider prices from publicly available sources that were machine-readable.
 
 
Sources:
The price quotations used for the compilation of the indices, and their sources, are indicated in the table below.

\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n

Product description

Data source

Beef, Australia/New Zealand, frozen, CIF US ports ($/kg)

World Bank - Commodity-markets

Salmon, fresh, fish-farm bred, export price, Norway ($/kg)

IMF - Primary Commodity Prices

Shrimps, EU import price of warmwater shrimp from Extra EU countries (trade weighted average ($/kg)

IMF - Primary Commodity Prices

Bananas (Central & South America), major brands, US import price, free on truck (f.o.t.) Gulf ports ($/kg)

World Bank - Commodity-markets

Coffee, other mild Arabicas, ex-dock US/Europe ($/kg)

World Bank - Commodity-markets

Coffee, Robustas, ex-dock US/Europe ($/kg)

World Bank - Commodity-markets

Tea, Kenya Mombasa/Nairobi, auction price ($/kg)

World Bank - Commodity-markets

Wheat, United States, n° 2 Hard Red Winter (ordinary), FOB Gulf ($/t)

World Bank - Commodity-markets

Maize, United States, n° 3 yellow, FOB Gulf ($/t)

World Bank - Commodity-markets

Rice, Thailand, white milled, 5% broken, export price, FOB ($/t)

World Bank - Commodity-markets

Soybeans, in bulk, United States, n° 2 yellow, CIF Rotterdam ($/t)

World Bank - Commodity-markets

Soybean meal, in bulk, 45/46% protein, Hamburg FOB ex-mill ($/t)

World Bank - Commodity-markets

Cotton, Cotlook A Index, Middling 1-3/32", CFR Far Eastern ($/kg)

World Bank - Commodity-markets

Soybean oil, U.S. Soya Oil, FOB U.S. Gulf ($/t)

World Bank - Commodity-markets

Groundnut oil, in bulk, any origin, CIF Rotterdam ($/t)

World Bank - Commodity-markets

Palm oil, in bulk, Malaysia/Indonesia, 5% FFA, CIF ($/t)

World Bank - Commodity-markets

Sunflower/Safflower Oil, crude, US export price from Gulf of Mexico ($/t)

IMF – Primary Commodity Markets

Coconut oil, in bulk, Philippines/Indonesia, CIF Rotterdam ($/t)

World Bank - Commodity-markets

Palm kernel oil, in bulk, Malaysia/Indonesia, CIF Rotterdam ($/t)

World Bank - Commodity-markets

Sugar, average of I.S.A. daily prices, FOB & Stowed, in bulk, Caribbean ports ($/kg)

World Bank - Commodity-markets

Cocoa beans, average daily prices New York/London ($/kg)

World Bank - Commodity-markets

Fish meal, in bulk, 64/65% protein, FCA, Bremen ($/t)

World Bank - Commodity-markets

Tobacco, unmanufactured, US import unit value  ($/t)

World Bank - Commodity-markets

Phosphate rock, North Africa FOBV ($/t)

World Bank - Commodity-markets

Iron ore, Australia to China, fines, 62% Fe, offshore export price, CIF ($/dry ton)

World Bank - Commodity-markets

Manganese 9Electro CIF NWE ($/t)

IMF - Primary Commodity Prices

Copper, grade A, electrolytic wire bars/cathodes, LME, cash (£/t)

World Bank - Commodity-markets

Nickel, LME, cash ($/t)

World Bank - Commodity-markets

Aluminium, primary, high grade, LME, cash ($/t)

World Bank - Commodity-markets

Lead, LME, cash settlement ($/t)

World Bank - Commodity-markets

Zinc, special high grade, LME, cash settlement ($/t)

World Bank - Commodity-markets

Tin, LME, cash ($/t)

World Bank - Commodity-markets

Silver, 99.9%, Handy & Harman, New York (¢/troy ounce)

World Bank - Commodity-markets

Coal, steam coal, Australia, average FOB Newcastle ($/t)

World Bank - Commodity-markets

Crude oil, UK Brent, light blend API 38°, spot price, FOB UK ports ($/barrel)

World Bank - Commodity-markets

Crude oil, Dubai, medium, Fateh API 32°, spot price, FOB Dubai ($/barrel)

World Bank - Commodity-markets

Natural gas, index, Europe, United States and Japan (2010=100)

World Bank - Commodity-markets

Rubber, RSS n°3, Singapore Commodity Exchange, 1st contract ($/kg)

World Bank - Commodity-markets

Cattle hides, US Chicago packer's heavy native steers, FOB shipping point (¢/lb.)

IMF - Primary Commodity Prices

Tropical logs, Sapele, high quality (loyal and marchand), FOB, Cameroon ($/m3)

World Bank - Commodity-markets

Sawnwood, Dark Red Meranti, Malaysia, CFR plus commission, UK ($/m3)

World Bank - Commodity-markets

Plywood, Africa & SE Asia, Lauan, 3-ply, 91cmx182cmx4mm, wholesale Tokyo (¢/sheet)

World Bank - Commodity-markets

Fine wool, 19 Micron, AWEX auction price, Australia (¢/kg)

IMF - Primary Commodity Prices

Jute, Bangladesh, BWD, FOB Mongolia ($/t)

FAO – Food Price Monitoring and Analysis

Gold, 99.5% fine, afternoon fixing London ($/troy ounce)

World Bank - Commodity-markets

Platinum, 99.9% refined, London afternoon fixing ($/troy ounce)

World Bank - Commodity-markets

 

","name":"US.UCPI_M","order":54},{"id":1662,"version":10011,"title":"DISCONTINUED - UNCTAD Commodity Price Index, 1995-2024, annual (2015=100)","lastUpdatedDate":"2025-03-12T13:35:00","category":"Commodities","keywords":"commodities, price, index","notes":"

Description:
\r\nThis table presents the annual UNCTAD Commodity Price Index and its sub-indices for different groups of main commodities exported by developing economies.
\r\n
\r\nNotes:
\r\nThe annual price is usually calculated as an average of the monthly quotations at a main market place of the respective commodity.

The price index is a fixed-base Laspeyres index, where the weights are proportional to the individual commodities' shares in total merchandise exports of developing economies in the base period. The base period comprises the years 2014, 2015 and 2016. For further details, see the methodological note: https://unctad.org/en/PublicationsLibrary/statcpbmn1_en.pdf.

The product codes established by the UNCTAD secretariat for these files identify each price quotation of the different commodities presented. They are composed as follows:

- The first six digits correspond to the codes of the sub-headings of the Harmonized Commodity Description and Coding System (HS), Revision 2007, to which the commodity belongs,

- The last two digits have been added to differentiate the same commodities with different characteristics and different price quotations.
\r\n 

Sources:

The price quotations used for the compilation of the indices, and their sources, are indicated in the table below.

\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n

Commodity description

Code            

Source

All food

   Food

Bananas, average of US Northeast, Midwest, South and West prices ($/kg)

080300.03

Thomson Reuters Eikon Database

Beef, Australia/New Zealand, frozen, CIF US ports (East Coast), ex-dock ($/kg)

020100.01

World Bank, Commodity Markets, Washington D.C., United States

Maize, United States, n° 3 yellow, FOB Gulf ($/t)

100500.02

International Grains Council (IGC), London, United Kingdom

Pepper, Indonesia, white Muntok, FAQ, EXW Rotterdam ($/t)

090400.01

IEG Vu, Agribusiness intelligence Informa, London, United Kingdom

Rice, white milled, 5% broken, Thailand, export price indicative price based on weekly surveys of export transactions, government
\r\nstandard, FOB Bangkok ($/t)

100600.01

World Bank, Commodity Markets, Washington D.C., United States

Salmon, fresh, fish-farm bred, export price, Norway ($/kg)

030212.01

Statistics Norway

Shrimps, brown no. 1, shell-on, headless, Mexico ($/kg)

030613.01

World Bank, Commodity Markets, Washington D.C., United States

Sugar, average of I.S.A. daily prices, FOB & Stowed, in bulk, Caribbean ports (¢/lb.)

170100.01

International Sugar Organization (ISO), London, United Kingdom

Wheat, United States, n° 2 Hard Red Winter (ordinary), FOB Gulf ($/t)

100100.01

International Grains Council (IGC), London, United Kingdom

   Tropical beverages

Cocoa beans, average daily prices New York/London (¢/lb.)

180100.01

International Cocoa Organization (ICCO), Abidjan, Côte d’Ivoire

Coffee, Brazilian and other natural Arabicas, ex-dock USA (¢/lb.)
\r\nFor the weights used in the calculation of group and composite prices, please refer to the rules of statistics on ICO’s statistics page.

090100.02

International Coffee Organization (ICO), London, United Kingdom

Coffee, Colombian mild Arabicas, ex-dock US (¢/lb.)
\r\nFor the weights used in the calculation of group and composite prices, please refer to the rules of statistics on ICO’s statistics page.

090100.01

International Coffee Organization (ICO), London, United Kingdom

Coffee, other mild Arabicas, ex-dock US (¢/lb.)
\r\nFor the weights used in the calculation of group and composite prices, please refer to the rules of statistics on ICO’s statistics page.

090100.03

International Coffee Organization (ICO), London, United Kingdom

Coffee, Robustas, ex-dock US (¢/lb.)
\r\nFor the weights used in the calculation of group and composite prices, please refer to the rules of statistics on ICO’s statistics page.

090100.05

International Coffee Organization (ICO), London, United Kingdom

Tea, Kenya Mombasa/Nairobi, auction price ($/kg)

090200.01

World Bank, Commodity Markets, Washington D.C., United States

   Vegetable oilseeds and oils

Coconut oil, in bulk, Philippines/Indonesia, CIF Rotterdam ($/t)

151311.01

World Bank, Commodity Markets, Washington D.C., United States

Copra, in bulk, Philippines/Indonesia, CIF N.W. European ports ($/t)

120300.01

World Bank, Commodity Markets, Washington D.C., United States

Groundnut oil, in bulk, any origin, CIF Rotterdam ($/t)

150810.01

World Bank, Commodity Markets, Washington D.C., United States

Palm kernel oil, in bulk, Malaysia/Indonesia, CIF Rotterdam ($/t)

151321.01

World Bank, Commodity Markets, Washington D.C., United States

Palm oil, in bulk, Malaysia/Indonesia, 5% FFA, CIF ($/t)

151110.01

World Bank, Commodity Markets, Washington D.C., United States

Soybean meal, in bulk, 45/46% protein, Hamburg FOB ex-mill ($/t)

120810.01

World Bank, Commodity Markets, Washington D.C., United States

Soybean oil, in bulk, Netherlands, FOB ex-mill ($/t)

150710.01

World Bank, Commodity Markets, Washington D.C., United States

Soybeans, in bulk, United States, n° 2 yellow, CIF Rotterdam ($/t)

120100.01

World Bank, Commodity Markets, Washington D.C., United States

Sunflower oil, in bulk, European Union, FOB N.W. European ports ($/t)

151211.01

World Bank, Commodity Markets, Washington D.C., United States

Agricultural raw materials

Cattle hides, heavy native steer, plant value, FOB ($/piece)

410100.02

Harland M. Braun & Co., Los Angeles, California, United States

Cattle hides, US Chicago packer's heavy native steers, FOB shipping point (¢/lb.). Series discontinued in June 2017.

410100.01

International Monetary Fund (IMF), Primary Commodity Prices, Washington D.C., United States

Cotton, Cotlook A Index, Middling 1-1/8", CFR Far Eastern (¢/lb.)

140400.03

World Bank, Commodity Markets, Washington D.C., United States

Fine wool, 19 Micron, Australia ($/t). Series discontinued in June 2017.

510100.01

International Monetary Fund (IMF), Primary Commodity Prices, Washington D.C., United States

Fine wool, 19 Micron, AWEX auction price, Australia ($/t)

510100.03

Australian Wool Innovation (AWI)

Fish meal, in bulk, 64/65% protein, FCA, Bremen ($/t)

230120.01

World Bank, Commodity Markets, Washington D.C., United States

Jute, Bangladesh, BWD, FOB Mongla ($/t)

530300.01

Food and Agriculture Organization (FAO),Trade and Markets Division, Rome, Italy

Plywood, Africa & SE Asia, Lauan, 3-ply, 91cmx182cmx4mm, wholesale Tokyo (¢/sheet)

441200.01

World Bank, Commodity Markets, Washington D.C., United States

Rubber, TSR 20, New York CIF ($/t)

400100.01

International Rubber Study Group (IRSG), Singapore

Sawnwood, Dark Red Meranti, Malaysia, CFR plus commission, UK ($/m3)

440700.01

World Bank, Commodity Markets, Washington D.C., United States

Tobacco, unmanufactured, US import unit value ($/t)

240100.01

UNCTAD calculations based on United States Department of Agriculture (USDA) figures.

Tropical logs, Sapele, high quality (loyal and marchand), FOB, Cameroon ($/m3)

440300.01

World Bank, Commodity Markets, Washington D.C., United States

All minerals, ores and metals

  Minerals and non-precious metals

 

 

Aluminium, primary, high grade, LME, cash ($/t)

260600.01

Metal Bulletin Limited, London, United Kingdom

Copper, grade A, electrolytic wire bars/cathodes, LME, cash (£/t)

260300.01

Metal Bulletin Limited, London, United Kingdom

Iron ore, Brazilian to Europe, Vale Itabira SSF, 64.5% Fe content, FOB (¢/Fe unit). Series discontinued in 2009 due to the end of
\r\nannually negotiated prices.

260100.04

The TEX Report, Tokyo, Japan

Iron ore, China import, fines, 62% Fe,spot ($/dry tonne)

260100.01

Metal Bulletin Limited, London, United Kingdom
\r\n 

Lead, LME, cash settlement ($/t)

260700.02

Metal Bulletin Limited, London, United Kingdom

Manganese ore, 44% Mn, CIF Tianjin ($/t)

260200.01

Metal Bulletin Limited, London, United Kingdom

Nickel, LME, cash ($/t)

260400.01

Metal Bulletin Limited, London, United Kingdom

Phosphate rock, Khouribga, 70% BPL, contract, FAS Casablanca ($/t)

251000.01

World Bank, Commodity Markets, Washington D.C., United States

Tin, LME, cash ($/t)

260900.01

Metal Bulletin Limited, London, United Kingdom

Zinc, special high grade, LME, cash settlement ($/t)

260800.01

Metal Bulletin Limited, London, United Kingdom

  Precious metals

Gold, 99.5% fine, afternoon fixing London ($/troy ounce)

710800.01

Metal Bulletin Limited, London, United Kingdom

Platinum ($/.9995 fine ounces)

711000.01

Metal Bulletin Limited, London, United Kingdom

Silver, 99.9%, Handy & Harman, New York (¢/troy ounce)

261600.01

Metal Bulletin Limited, London, United Kingdom

Fuels

 

 

Coal, HBA, Indonesia, FOB ($/t)

270100.01

Thomson Reuters Eikon Database

Coal, steam coal, Australia, average FOB Newcastle (prior to May 2010)($/t)

270100.02

Thomson Reuters Eikon Database

Crude oil UK Brent, light blend API 38°, spot price, FOB UK ports ($/barrel)

270900.01

World Bank, Commodity Markets, Washington D.C., United States

Crude oil, Dubai, medium, Fateh API 32°, spot price, FOB Dubai ($/barrel)

270900.02

World Bank, Commodity Markets, Washington D.C., United States

Natural gas, index, Europe, United States and Japan (2010=100)

271100.01

World Bank, Commodity Markets, Washington D.C., United States

 

","name":"US.CommodityPriceIndices_A","order":55},{"id":1900,"version":10012,"title":"DISCONTINUED - UNCTAD Commodity Price Index, January 1995 - June 2025, monthly (2015=100)","lastUpdatedDate":"2025-08-11T14:32:00","category":"Commodities","keywords":"commodity, price, index","notes":"

Description:
\r\nThis table presents the monthly UNCTAD Commodity Price Index and its sub-indices for different groups of main commodities exported by developing economies. 
\r\n
\r\nNotes:
\r\nThe monthly price is usually calculated as an average of daily or weekly quotations at a main market place of the respective commodity.

The price index is a fixed-base Laspeyres index, where the weights are proportional to the individual commodities' shares in total merchandise exports of developing economies in the base period. The base period comprises the years 2014, 2015 and 2016. For further details, see the methodological note: https://unctad.org/en/PublicationsLibrary/statcpbmn1_en.pdf.

The product codes established by the UNCTAD secretariat for these files identify each price quotation of the different commodities presented. They are composed as follows:

- The first six digits correspond to the codes of the sub-headings of the Harmonized Commodity Description and Coding System (HS), Revision 2007, to which the commodity belongs,

- The last two digits have been added to differentiate the same commodities with different characteristics and different price quotations.
\r\n 

Sources:

The price quotations used for the compilation of the indices, and their sources, are indicated in the table below.

\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n

Commodity description

Code            

Source

All food

   Food

Bananas, average of US Northeast, Midwest, South and West prices ($/kg)

080300.03

Thomson Reuters Eikon Database

Beef, Australia/New Zealand, frozen, CIF US ports (East Coast), ex-dock ($/kg)

020100.01

World Bank, Commodity Markets, Washington D.C., United States

Maize, United States, n° 3 yellow, FOB Gulf ($/t)

100500.02

International Grains Council (IGC), London, United Kingdom

Pepper, Indonesia, white Muntok, FAQ, EXW Rotterdam ($/t)

090400.01

IEG Vu, Agribusiness intelligence Informa, London, United Kingdom

Rice, white milled, 5% broken, Thailand, export price indicative price based on weekly surveys of export transactions, government
\r\nstandard, FOB Bangkok ($/t)

100600.01

World Bank, Commodity Markets, Washington D.C., United States

Salmon, fresh, fish-farm bred, export price, Norway ($/kg)

030212.01

Statistics Norway

Shrimps, brown no. 1, shell-on, headless, Mexico ($/kg)

030613.01

World Bank, Commodity Markets, Washington D.C., United States

Sugar, average of I.S.A. daily prices, FOB & Stowed, in bulk, Caribbean ports (¢/lb.)

170100.01

International Sugar Organization (ISO), London, United Kingdom

Wheat, United States, n° 2 Hard Red Winter (ordinary), FOB Gulf ($/t)

100100.01

International Grains Council (IGC), London, United Kingdom

   Tropical beverages

Cocoa beans, average daily prices New York/London (¢/lb.)

180100.01

International Cocoa Organization (ICCO), Abidjan, Côte d’Ivoire

Coffee, Brazilian and other natural Arabicas, ex-dock USA (¢/lb.)
\r\nFor the weights used in the calculation of group and composite prices, please refer to the rules of statistics on ICO’s statistics page.

090100.02

International Coffee Organization (ICO), London, United Kingdom

Coffee, Colombian mild Arabicas, ex-dock US (¢/lb.)
\r\nFor the weights used in the calculation of group and composite prices, please refer to the rules of statistics on ICO’s statistics page.

090100.01

International Coffee Organization (ICO), London, United Kingdom

Coffee, other mild Arabicas, ex-dock US (¢/lb.)
\r\nFor the weights used in the calculation of group and composite prices, please refer to the rules of statistics on ICO’s statistics page.

090100.03

International Coffee Organization (ICO), London, United Kingdom

Coffee, Robustas, ex-dock US (¢/lb.)
\r\nFor the weights used in the calculation of group and composite prices, please refer to the rules of statistics on ICO’s statistics page.

090100.05

International Coffee Organization (ICO), London, United Kingdom

Tea, Kenya Mombasa/Nairobi, auction price ($/kg)

090200.01

World Bank, Commodity Markets, Washington D.C., United States

   Vegetable oilseeds and oils

Coconut oil, in bulk, Philippines/Indonesia, CIF Rotterdam ($/t)

151311.01

World Bank, Commodity Markets, Washington D.C., United States

Copra, in bulk, Philippines/Indonesia, CIF N.W. European ports ($/t)

120300.01

ISTA Mielke, Oilworld, Weekly Price Survey

Groundnut oil, in bulk, any origin, CIF Rotterdam ($/t)

150810.01

World Bank, Commodity Markets, Washington D.C., United States

Palm kernel oil, in bulk, Malaysia/Indonesia, CIF Rotterdam ($/t)

151321.01

World Bank, Commodity Markets, Washington D.C., United States

ISTA Mielke, Oilworld, Weekly Price Survey

Palm oil, in bulk, Malaysia/Indonesia, 5% FFA, CIF ($/t)

151110.01

World Bank, Commodity Markets, Washington D.C., United States

Soybean meal, in bulk, 45/46% protein, Hamburg FOB ex-mill ($/t)

120810.01

World Bank, Commodity Markets, Washington D.C., United States

Soybean oil, in bulk, Netherlands, FOB ex-mill ($/t)

150710.01

World Bank, Commodity Markets, Washington D.C., United States

Soybeans, in bulk, United States, n° 2 yellow, CIF Rotterdam ($/t)

120100.01

World Bank, Commodity Markets, Washington D.C., United States

Sunflower oil, in bulk, European Union, FOB N.W. European ports ($/t)

151211.01

World Bank, Commodity Markets, Washington D.C., United States

ISTA Mielke, Oilworld, Weekly Price Survey

Agricultural raw materials

Cattle hides, heavy native steer, plant value, FOB ($/piece)

410100.02

Harland M. Braun & Co., Los Angeles, California, United States

Cattle hides, US Chicago packer's heavy native steers, FOB shipping point (¢/lb.). Series discontinued in June 2017.

410100.01

International Monetary Fund (IMF), Primary Commodity Prices, Washington D.C., United States

Cotton, Cotlook A Index, Middling 1-1/8", CFR Far Eastern (¢/lb.)

140400.03

World Bank, Commodity Markets, Washington D.C., United States

Fine wool, 19 Micron, Australia ($/t). Series discontinued in June 2017.

510100.01

International Monetary Fund (IMF), Primary Commodity Prices, Washington D.C., United States

Fine wool, 19 Micron, AWEX auction price, Australia ($/t)

510100.03

Australian Wool Innovation (AWI)

Fish meal, in bulk, 64/65% protein, FCA, Bremen ($/t)

230120.01

World Bank, Commodity Markets, Washington D.C., United States

Jute, Bangladesh, BWD, FOB Mongla ($/t)

530300.01

Food and Agriculture Organization (FAO),Trade and Markets Division, Rome, Italy

Plywood, Africa & SE Asia, Lauan, 3-ply, 91cmx182cmx4mm, wholesale Tokyo (¢/sheet)

441200.01

World Bank, Commodity Markets, Washington D.C., United States

Rubber, TSR 20, New York CIF ($/t)

400100.01

International Rubber Study Group (IRSG), Singapore

Sawnwood, Dark Red Meranti, Malaysia, CFR plus commission, UK ($/m3)

440700.01

World Bank, Commodity Markets, Washington D.C., United States

Tobacco, unmanufactured, US import unit value ($/t)

240100.01

UNCTAD calculations based on United States Department of Agriculture (USDA) figures.

Tropical logs, Sapele, high quality (loyal and marchand), FOB, Cameroon ($/m3)

440300.01

World Bank, Commodity Markets, Washington D.C., United States

All minerals, ores and metals

  Minerals and non-precious metals

 

 

Aluminium, primary, high grade, LME, cash ($/t)

260600.01

Metal Bulletin Limited, London, United Kingdom

Copper, grade A, electrolytic wire bars/cathodes, LME, cash (£/t)

260300.01

Metal Bulletin Limited, London, United Kingdom

Iron ore, Brazilian to Europe, Vale Itabira SSF, 64.5% Fe content, FOB (¢/Fe unit). Series discontinued in 2009 due to the end of
\r\nannually negotiated prices.

260100.04

The TEX Report, Tokyo, Japan

Iron ore, China import, fines, 62% Fe,spot ($/dry tonne)

260100.01

Metal Bulletin Limited, London, United Kingdom
\r\n 

Lead, LME, cash settlement ($/t)

260700.02

Metal Bulletin Limited, London, United Kingdom

Manganese ore, 44% Mn, CIF Tianjin ($/t)

260200.01

Metal Bulletin Limited, London, United Kingdom

Nickel, LME, cash ($/t)

260400.01

Metal Bulletin Limited, London, United Kingdom

Phosphate rock, Khouribga, 70% BPL, contract, FAS Casablanca ($/t)

251000.01

World Bank, Commodity Markets, Washington D.C., United States

Tin, LME, cash ($/t)

260900.01

Metal Bulletin Limited, London, United Kingdom

Zinc, special high grade, LME, cash settlement ($/t)

260800.01

Metal Bulletin Limited, London, United Kingdom

  Precious metals

Gold, 99.5% fine, afternoon fixing London ($/troy ounce)

710800.01

Metal Bulletin Limited, London, United Kingdom

Platinum ($/.9995 fine ounces)

711000.01

Metal Bulletin Limited, London, United Kingdom

Silver, 99.9%, Handy & Harman, New York (¢/troy ounce)

261600.01

Metal Bulletin Limited, London, United Kingdom

Fuels

 

 

Coal, HBA, Indonesia, FOB ($/t)

270100.01

Thomson Reuters Eikon Database

Coal, steam coal, Australia, average FOB Newcastle (prior to May 2010)($/t)

270100.02

Thomson Reuters Eikon Database

Crude oil UK Brent, light blend API 38°, spot price, FOB UK ports ($/barrel)

270900.01

World Bank, Commodity Markets, Washington D.C., United States

Crude oil, Dubai, medium, Fateh API 32°, spot price, FOB Dubai ($/barrel)

270900.02

World Bank, Commodity Markets, Washington D.C., United States

Natural gas, index, Europe, United States and Japan (2010=100)

271100.01

World Bank, Commodity Markets, Washington D.C., United States

 

","name":"US.CommodityPriceIndices_M","order":56},{"id":1663,"version":10011,"title":"DISCONTINUED - Commodity prices, 1995-2024, annual","lastUpdatedDate":"2025-03-12T13:35:00","category":"Commodities","keywords":"commodities, price","notes":"

Description:
\r\nThis table shows annual prices of main commodities exported by developing economies. 

Note:
\r\nThe annual price is the average of the monthly quotations for each commodity.

The product codes established by the UNCTAD secretariat, for these files, identify each price quotation of the different commodities presented. They are composed as follows:
\r\n- The first six digits correspond to the codes of the sub-headings of the Harmonized Commodity Description and Coding System (HS), Revision 2007, to which the commodity belongs,
\r\n- The last two digits have been added to differentiate the same commodities with different characteristics and different price quotations.

Sources:

\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n

Commodity description

Code          

Source

Food

Bananas, Central and South America, FOT, US import price ($/kg). Discontinued.

080300.01

World Bank, Commodity Markets, Washington D.C., United States

Bananas, Central America, Main Brands (Average of East and West Coast prices) FOR ($/kg). Discontinued.

080300.02

Food and Agriculture Organization (FAO), GIEWS FPMA Tool, Rome, Italy

Beef, Australia/New Zealand, frozen, CIF US ports (East Coast), ex-dock ($/kg)

020100.01

World Bank, Commodity Markets, Washington D.C., United States

Maize, Argentina, Rosario, Up River, FOB ($/t)

100500.01

International Grains Council (IGC), London, United Kingdom

Maize, United States, n° 3 yellow, FOB Gulf ($/t)

100500.02

International Grains Council (IGC), London, United Kingdom

Rice, white milled, 5% broken, Thailand, indicative price based on weekly surveys of export transactions, government
\r\nstandard, FOB Bangkok ($/t)

100600.01

World Bank, Commodity Markets, Washington D.C., United States

Salmon, fresh, fish-farm bred, export price, Norway ($/kg)

030212.01

Statistics Norway

Shrimps, brown no. 1, shell-on, headless, Mexico ($/kg)

030613.01

World Bank, Commodity Markets, Washington D.C., United States

Soybean meal, in bulk, 45/46% protein, Hamburg FOB ex-mill ($/t)

120810.01

World Bank, Commodity Markets, Washington D.C., United States

Sugar, average of I.S.A. daily prices, FOB & Stowed, in bulk, Caribbean ports (¢/lb.)

170100.01

International Sugar Organization (ISO), London, United Kingdom

Wheat, Argentina, Trigo Pan, Up River, FOB ($/t)

100100.02

International Grains Council (IGC), London, United Kingdom

Wheat, United States, n° 2 Hard Red Winter (ordinary), FOB Gulf ($/t)

100100.01

International Grains Council (IGC), London, United Kingdom

Tropical beverages

Cocoa beans, average daily prices New York/London (¢/lb.)

180100.01

International Cocoa Organization (ICCO), Abidjan, Côte d’Ivoire

Coffee, Brazilian and other natural Arabicas, ex-dock USA (¢/lb.)
\r\nFor the weights used in the calculation of group and composite prices, please refer to the rules of statistics on ICO’s website.

090100.02

International Coffee Organization (ICO), London, United Kingdom

Coffee, Colombian mild Arabicas, ex-dock US (¢/lb.)
\r\nFor the weights used in the calculation of group and composite prices, please refer to the rules of statistics on ICO’s website.

090100.01

International Coffee Organization (ICO), London, United Kingdom

Coffee, other mild Arabicas, ex-dock European Union (¢/lb.)
\r\nFor the weights used in the calculation of group and composite prices, please refer to the rules of statistics on ICO’s website.

090100.04

International Coffee Organization (ICO), London, United Kingdom

Coffee, other mild Arabicas, ex-dock United States (¢/lb.)
\r\nFor the weights used in the calculation of group and composite prices, please refer to the rules of statistics on ICO’s website.

090100.03

International Coffee Organization (ICO), London, United Kingdom

Coffee, Robustas, ex-dock European Union (¢/lb.)
\r\nFor the weights used in the calculation of group and composite prices, please refer to the rules of statistics on ICO’s website.

090100.06

International Coffee Organization (ICO), London, United Kingdom

Coffee, Robustas, ex-dock United States (¢/lb.)
\r\nFor the weights used in the calculation of group and composite prices, please refer to the rules of statistics on ICO’s website.

090100.05

International Coffee Organization (ICO), London, United Kingdom

Tea, Kenya Mombasa/Nairobi, auction price ($/kg)

090200.01

World Bank, Commodity Markets, Washington D.C., United States

Vegetable oilseeds and oils

Coconut oil, in bulk, Philippines/Indonesia, CIF Rotterdam ($/t)

151311.01

World Bank, Commodity Markets, Washington D.C., United States

Copra, in bulk, Philippines/Indonesia, CIF N.W. European ports ($/t)

120300.01

World Bank, Commodity Markets, Washington D.C., United States

Groundnut oil, in bulk, any origin, CIF Rotterdam ($/t)

150810.01

World Bank, Commodity Markets, Washington D.C., United States

Palm kernel oil, in bulk, Malaysia/Indonesia, CIF Rotterdam ($/t)

151321.01

World Bank, Commodity Markets, Washington D.C., United States

Palm oil, in bulk, Malaysia/Indonesia, 5% FFA, CIF ($/t)

151110.01

World Bank, Commodity Markets, Washington D.C., United States

Soybean oil, in bulk, Netherlands, FOB ex-mill ($/t)

150710.01

World Bank, Commodity Markets, Washington D.C., United States

Soybeans, in bulk, United States, n° 2 yellow, CIF Rotterdam ($/t)

120100.01

World Bank, Commodity Markets, Washington D.C., United States

Sunflower oil, in bulk, European Union, FOB N.W. European ports ($/t)

151211.01

World Bank, Commodity Markets, Washington D.C., United States

Agricultural raw materials

Cattle hides, heavy native steer, plant value, FOB ($/piece)

410100.02

Harland M. Braun & Co., Los Angeles, California, United States

Cattle hides, US Chicago packer's heavy native steers, FOB shipping point (¢/lb.). Series discontinued in June 2017.

410100.01

International Monetary Fund (IMF), Primary Commodity Prices, Washington D.C., United States

Coarse wool, 23 Micron, AWEX auction price, Australia ($/t)

510100.02

Australian Wool Innovation Limited (AWI), Sydney, Australia

Fine wool, 19 Micron, Australia ($/t). Series discontinued in June 2017.

510100.01

International Monetary Fund (IMF), Primary Commodity Prices, Washington D.C., United States

Fine wool, 19 Micron, AWEX auction price, Australia ($/t)

510100.03

Australian Wool Innovation Limited (AWI), Sydney, Australia

Fish meal, in bulk, 64/65% protein, FCA, Bremen ($/t)

230120.01

World Bank, Commodity Markets, Washington D.C., United States

Jute, Bangladesh, BWD, FOB Mongla ($/t)

530300.01

Food and Agriculture Organization (FAO),Trade and Markets Division, Rome, Italy

Non-coniferous woods, UK import price index, dollar equivalent (2010=100)

440122.01

Office for National Statistics (ONS), Newport, South Wales, United Kingdom

Plywood, Africa & SE Asia, Lauan, 3-ply, 91cmx182cmx4mm, wholesale Tokyo (¢/sheet)

441200.01

World Bank, Commodity Markets, Washington D.C., United States

Rubber, RSS n°3, Singapore Commodity Exchange, 1st contract (¢/kg)

400100.02

World Bank, Commodity Markets, Washington D.C., United States

Rubber, TSR 20, New York CIF ($/t)

400100.01

International Rubber Study Group (IRSG), Singapore

Sawnwood, Dark Red Meranti, Malaysia, CFR plus commission, UK ($/m3)

440700.01

World Bank, Commodity Markets, Washington D.C., United States

Sisal, Tanzania/Kenya, n° 2 & 3 long, FOB ($/t)

530500.01

Food and Agriculture Organization (FAO),Trade and Markets Division, Rome, Italy

Sisal, Tanzania/Kenya, n° 3 & UG, FOB ($/t)

530500.02

Food and Agriculture Organization (FAO),Trade and Markets Division, Rome, Italy

Tobacco, unmanufactured, US import unit value ($/t)

240100.01

UNCTAD calculations based on United States Department of Agriculture (USDA) figures.

Tropical logs, Okoume (60% CI, 40% CE, 20% CS), West Africa, FOB ($/m3)

440300.02

International Tropical Timber Organization (ITTO), Yokohama, Japan

Tropical logs, Sapele, high quality (loyal and marchand), FOB, Cameroon ($/m3)

440300.01

World Bank, Commodity Markets, Washington D.C., United States

Minerals, ores and metals

Iron ore, Brazilian to Europe, Vale Itabira SSF, 64.5% Fe content, FOB (¢/Fe unit)
\r\nSeries discontinued in 2009 due to the end of annually negotiated prices.

260100.04

The TEX Report Limited, Tokyo, Japan

Phosphate rock, Khouribga, 70% BPL, contract, FAS Casablanca ($/t)

251000.01

World Bank, Commodity Markets, Washington D.C., United States

Zinc, Prime Western, delivered, North America (¢/lb.)

260800.02

United States Geological Survey (USGS)

Fuels

Crude oil, Dubai, medium, Fateh API 32°, spot price, FOB Dubai ($/barrel)

270900.02

World Bank, Commodity Markets, Washington D.C., United States

Crude oil, UK Brent, light blend API 38°, spot price, FOB UK ports ($/barrel)

270900.01

World Bank, Commodity Markets, Washington D.C., United States

Natural gas, index, Europe, United States and Japan (2010=100)

271100.01

World Bank, Commodity Markets, Washington D.C., United States

 

","name":"US.CommodityPrice_A","order":57},{"id":1899,"version":10012,"title":"DISCONTINUED - Commodity prices, January 1995 - June 2025, monthly","lastUpdatedDate":"2025-08-11T14:32:00","category":"Commodities","keywords":"commodity, price","notes":"

Description:
\r\nThis table shows monthly prices of main commodities exported by developing economies.

Notes:
\r\nThe monthly price is usually calculated as an average of daily or weekly quotations at a main market place of the respective commodity.

The product codes established by the UNCTAD secretariat for these files identify each price quotation of the different commodities presented. They are composed as follows:
\r\n- The first six digits correspond to the codes of the sub-headings of the Harmonized Commodity Description and Coding System (HS), Revision 2007, to which the commodity belongs,
\r\n- The last two digits have been added to differentiate the same commodities with different characteristics and different price quotations.

Sources:

\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n

Commodity description

Code          

Source

Food

Bananas, Central and South America, FOT, US import price ($/kg). Discontinued.

080300.01

World Bank, Commodity Markets, Washington D.C., United States

Bananas, Central America, Main Brands (Average of East and West Coast prices) FOR ($/kg). Discontinued.

080300.02

Food and Agriculture Organization (FAO), GIEWS FPMA Tool, Rome, Italy

Beef, Australia/New Zealand, frozen, CIF US ports (East Coast), ex-dock ($/kg)

020100.01

World Bank, Commodity Markets, Washington D.C., United States

Maize, Argentina, Rosario, Up River, FOB ($/t)

100500.01

International Grains Council (IGC), London, United Kingdom

Maize, United States, n° 3 yellow, FOB Gulf ($/t)

100500.02

International Grains Council (IGC), London, United Kingdom

Rice, white milled, 5% broken, Thailand, indicative price based on weekly surveys of export transactions, government
\r\nstandard, FOB Bangkok ($/t)

100600.01

World Bank, Commodity Markets, Washington D.C., United States

Salmon, fresh, fish-farm bred, export price, Norway ($/kg)

030212.01

Statistics Norway

Shrimps, brown no. 1, shell-on, headless, Mexico ($/kg)

030613.01

World Bank, Commodity Markets, Washington D.C., United States

Soybean meal, in bulk, 45/46% protein, Hamburg FOB ex-mill ($/t)

120810.01

World Bank, Commodity Markets, Washington D.C., United States

Sugar, average of I.S.A. daily prices, FOB & Stowed, in bulk, Caribbean ports (¢/lb.)

170100.01

International Sugar Organization (ISO), London, United Kingdom

Wheat, Argentina, Trigo Pan, Up River, FOB ($/t)

100100.02

International Grains Council (IGC), London, United Kingdom

Wheat, United States, n° 2 Hard Red Winter (ordinary), FOB Gulf ($/t)

100100.01

International Grains Council (IGC), London, United Kingdom

Tropical beverages

Cocoa beans, average daily prices New York/London (¢/lb.)

180100.01

International Cocoa Organization (ICCO), Abidjan, Côte d’Ivoire

Coffee, Brazilian and other natural Arabicas, ex-dock USA (¢/lb.)
\r\nFor the weights used in the calculation of group and composite prices, please refer to the rules of statistics on ICO’s website.

090100.02

International Coffee Organization (ICO), London, United Kingdom

Coffee, Colombian mild Arabicas, ex-dock US (¢/lb.)
\r\nFor the weights used in the calculation of group and composite prices, please refer to the rules of statistics on ICO’s website.

090100.01

International Coffee Organization (ICO), London, United Kingdom

Coffee, other mild Arabicas, ex-dock European Union (¢/lb.)
\r\nFor the weights used in the calculation of group and composite prices, please refer to the rules of statistics on ICO’s website.

090100.04

International Coffee Organization (ICO), London, United Kingdom

Coffee, other mild Arabicas, ex-dock United States (¢/lb.)
\r\nFor the weights used in the calculation of group and composite prices, please refer to the rules of statistics on ICO’s website.

090100.03

International Coffee Organization (ICO), London, United Kingdom

Coffee, Robustas, ex-dock European Union (¢/lb.)
\r\nFor the weights used in the calculation of group and composite prices, please refer to the rules of statistics on ICO’s website.

090100.06

International Coffee Organization (ICO), London, United Kingdom

Coffee, Robustas, ex-dock United States (¢/lb.)
\r\nFor the weights used in the calculation of group and composite prices, please refer to the rules of statistics on ICO’s website.

090100.05

International Coffee Organization (ICO), London, United Kingdom

Tea, Kenya Mombasa/Nairobi, auction price ($/kg)

090200.01

World Bank, Commodity Markets, Washington D.C., United States

Vegetable oilseeds and oils

Coconut oil, in bulk, Philippines/Indonesia, CIF Rotterdam ($/t)

151311.01

World Bank, Commodity Markets, Washington D.C., United States

Copra, in bulk, Philippines/Indonesia, CIF N.W. European ports ($/t)

120300.01

World Bank, Commodity Markets, Washington D.C., United States

Groundnut oil, in bulk, any origin, CIF Rotterdam ($/t)

150810.01

World Bank, Commodity Markets, Washington D.C., United States

Palm kernel oil, in bulk, Malaysia/Indonesia, CIF Rotterdam ($/t)

151321.01

World Bank, Commodity Markets, Washington D.C., United States

Palm oil, in bulk, Malaysia/Indonesia, 5% FFA, CIF ($/t)

151110.01

World Bank, Commodity Markets, Washington D.C., United States

Soybean oil, in bulk, Netherlands, FOB ex-mill ($/t)

150710.01

World Bank, Commodity Markets, Washington D.C., United States

Soybeans, in bulk, United States, n° 2 yellow, CIF Rotterdam ($/t)

120100.01

World Bank, Commodity Markets, Washington D.C., United States

Sunflower oil, in bulk, European Union, FOB N.W. European ports ($/t)

151211.01

World Bank, Commodity Markets, Washington D.C., United States

Agricultural raw materials

Cattle hides, heavy native steer, plant value, FOB ($/piece)

410100.02

Harland M. Braun & Co., Los Angeles, California, United States

Cattle hides, US Chicago packer's heavy native steers, FOB shipping point (¢/lb.). Series discontinued in June 2017.

410100.01

International Monetary Fund (IMF), Primary Commodity Prices, Washington D.C., United States

Cotton, Cotlook A Index, Middling 1-1/8", CFR Far Eastern (¢/lb.)

140400.03

World Bank, Commodity Markets, Washington D.C., United States

Coarse wool, 23 Micron, AWEX auction price, Australia ($/t)

510100.02

Australian Wool Innovation Limited (AWI), Sydney, Australia

Fine wool, 19 Micron, Australia ($/t). Series discontinued in June 2017.

510100.01

International Monetary Fund (IMF), Primary Commodity Prices, Washington D.C., United States

Fine wool, 19 Micron, AWEX auction price, Australia ($/t)

510100.03

Australian Wool Innovation Limited (AWI), Sydney, Australia

Fish meal, in bulk, 64/65% protein, FCA, Bremen ($/t)

230120.01

World Bank, Commodity Markets, Washington D.C., United States

Jute, Bangladesh, BWD, FOB Mongla ($/t)

530300.01

Food and Agriculture Organization (FAO),Trade and Markets Division, Rome, Italy

Non-coniferous woods, UK import price index, dollar equivalent (2010=100)

440122.01

Office for National Statistics (ONS), Newport, South Wales, United Kingdom

Plywood, Africa & SE Asia, Lauan, 3-ply, 91cmx182cmx4mm, wholesale Tokyo (¢/sheet)

441200.01

World Bank, Commodity Markets, Washington D.C., United States

Rubber, RSS n°3, Singapore Commodity Exchange, 1st contract (¢/kg)

400100.02

World Bank, Commodity Markets, Washington D.C., United States

Rubber, TSR 20, New York CIF ($/t)

400100.01

International Rubber Study Group (IRSG), Singapore

Sawnwood, Dark Red Meranti, Malaysia, CFR plus commission, UK ($/m3)

440700.01

World Bank, Commodity Markets, Washington D.C., United States

Sisal, Tanzania/Kenya, n° 2 & 3 long, FOB ($/t)

530500.01

Food and Agriculture Organization (FAO),Trade and Markets Division, Rome, Italy

Sisal, Tanzania/Kenya, n° 3 & UG, FOB ($/t)

530500.02

Food and Agriculture Organization (FAO),Trade and Markets Division, Rome, Italy

Tobacco, unmanufactured, US import unit value ($/t)

240100.01

UNCTAD calculations based on United States Department of Agriculture (USDA) figures.

Tropical logs, Okoume (60% CI, 40% CE, 20% CS), West Africa, FOB ($/m3)

440300.02

International Tropical Timber Organization (ITTO), Yokohama, Japan

Tropical logs, Sapele, high quality (loyal and marchand), FOB, Cameroon ($/m3)

440300.01

World Bank, Commodity Markets, Washington D.C., United States

Minerals, ores and metals

Iron ore, Brazilian to Europe, Vale Itabira SSF, 64.5% Fe content, FOB (¢/Fe unit)
\r\nSeries discontinued in 2009 due to the end of annually negotiated prices.

260100.04

The TEX Report Limited, Tokyo, Japan

Phosphate rock, Khouribga, 70% BPL, contract, FAS Casablanca ($/t)

251000.01

World Bank, Commodity Markets, Washington D.C., United States

Zinc, Prime Western, delivered, North America (¢/lb.)

260800.02

United States Geological Survey (USGS)

Fuels

Crude oil, Dubai, medium, Fateh API 32°, spot price, FOB Dubai ($/barrel)

270900.02

World Bank, Commodity Markets, Washington D.C., United States

Crude oil, UK Brent, light blend API 38°, spot price, FOB UK ports ($/barrel)

270900.01

World Bank, Commodity Markets, Washington D.C., United States

Natural gas, index, Europe, United States and Japan (2010=100)

271100.01

World Bank, Commodity Markets, Washington D.C., United States

 

","name":"US.CommodityPrice_M","order":58}],"subFolders":[],"isParentFolder":false},{"id":28,"title":"Public finance","reports":[{"id":590,"version":10001,"title":"Government expenditures: By function - Annual","lastUpdatedDate":"2023-07-18T16:51:00","category":"Public Finance","keywords":"Government, expenditures, COFOG","notes":"
Description:
This table presents expenditures of government according to COFOG classification, expressed in millions of United States dollars, and the share in GDP as a percentage.
 
Sources:
- IMF, Government Finance Statistics
- OECD, OECD.Stat
 
","name":"US.GovExpenditures","order":60}],"subFolders":[],"isParentFolder":false}],"isParentFolder":true},{"id":8,"title":"Maritime and other transport","reports":[],"subFolders":[{"id":38,"title":"Merchant fleet","reports":[{"id":2353,"version":10002,"title":"Merchant fleet: By flag of registration and ship type - Annual (analytical)","lastUpdatedDate":"2026-06-18T13:50:00","category":"Maritime transport","keywords":"merchant, fleet, maritime, transport, container, tanker, bulk, cargo, ship","notes":"
Description: 
This table shows statistics on international maritime transport. It includes data on the size of the world merchant fleet by flag of registration and by type of ship. Data are presented in thousands of dead-weight tons (DWT). In addition, the table presents countries’ or region’s respective shares in the world fleet as well as their shares of a given ship-type in the country’s/region’s total fleet. From 2011 onwards, the figures on the numbers of ships, as well as the data on ship-sizes in gross tonnage (GT) have become available. 
 
Source:
UNCTAD estimates based on data supplied by 
- Lloyds Register Fairplay (up to 2010) 
- Clarkson Research Services (from 2011 onwards) 
 
Notes:
This table supplements The UNCTAD Review of Maritime Transport. The Review analyses structural and cyclical changes affecting seaborne trade, ports and the various shipping markets, including liquid bulk, dry bulk and containerises trade.
Please see http://unctad.org/rmt.
 
The data refer to the beginning of an indicated year. 
 
The figures cover seagoing propelled merchant ships of 100 gross tons and above, excluding inland waterway vessels, fishing vessels (from 2011 onwards only), military vessels, yachts, and offshore fixed and mobile platforms and barges (with the exception of FPSO - floating production, storage and offloading vessels - and drillships).
 
For ships that do not have DWT recorded in the raw data, UNCTAD estimates DWT based on the GT and type of the ship.  For 2026, 20 926 ship of a total of 115 655 (18.1 per cent) lacked a DWT measure. These were mainly small ships. Taken together, ships without a dwt measure represented 0.34 per cent of the estimated total DWT of the world fleet. This proportion is relatively bigger for some individual economies and ship types.

 

","name":"US.MerchantFleet","order":63},{"id":2337,"version":10002,"title":"Merchant fleet: By beneficial ownership - Annual (analytical)","lastUpdatedDate":"2026-06-18T13:52:00","category":"Maritime transport","keywords":"beneficial, owner, flag, merchant, fleet, maritime, transport","notes":"
Description:  
The table provides information on the ownership of the merchant fleet for all countries. "Ownership” refers to “Beneficial Ownership Location”: it indicates the economy in which the company that has the main commercial responsibility for the vessel is located. The economy of beneficial ownership may be different from the country in which the vessel is registered.
 
Regarding “Flag of Registration” the table presents data for the 20 biggest registries for a given year. The registries with data presented thus vary from year to year. 
 
Data refer to the beginning of a year indicated and are presented in thousands of deadweight tons (DWT) and in number of ships. DWT is a weight measure of a vessel's carrying capacity. It includes cargo, fuel and stores. 
 
Source:
Clarkson Research Services (London)
 
Notes:
The vessels covered in this table include all propelled sea-going merchant vessels of 1000 gross tons (GT) and above, including offshore drill ships and Floating Production, Storage and Offloading units (FPSOs). Military vessels, yachts, waterway vessels, fishing vessels, and offshore fixed and mobile platforms and barges are excluded.
 
For ships that do not have DWT recorded in the raw data, UNCTAD estimates DWT based on the GT and type of the ship. For 2026, 1 013 ship of a total of 62 066 (1.6 per cent) lacked a DWT measure. These were mainly small ships. Taken together, ships without a dwt measure represented 0.15 per cent of the estimated total DWT of the world fleet. This proportion is relatively bigger for some individual economies and ship types.
 
For the purposes of this table, ships registered under the national flag are any ship where the registration and ownership are in the same country or territory of ownership. Ships in second registries of Brazil, China, Denmark, France, and Norway are considered to be under the national flag if they are owned in their respective country. For some economies in this table the ships owned are considered under national flag if they are registered somewhere in a broader set of economies. These sets of economies are United Kingdom, Isle of Man, Guernsey and Jersey; France (metropolitan), French Guiana, Guadeloupe, Martinique, Mayotte and Réunion; and United States of America, Puerto Rico and United States Virgin Islands. Likewise, Madeira and mainland Portugal are considered as one unit for the determination of national flag.
 
More detailed notes and discussions are provided in UNCTAD's Review of Maritime Transport (http://unctad.org/rmt).
","name":"US.FleetBeneficialOwners","order":64},{"id":2339,"version":10002,"title":"Merchant fleet: Share by flag of registration - Annual (analytical)","lastUpdatedDate":"2026-06-18T13:52:00","category":"Maritime transport","keywords":"beneficial, owner, flag, merchant, fleet, maritime, transport","notes":"

Description:
\r\nThis table shows data on each country's share of the value in US$ of the world merchant fleet by vessel registry. Data are presented in percentage share of the US dollar value of the total world fleet.

Source:
\r\nUNCTAD, Division on Technology and Logistics, based on data supplied by Clarkson Research Services (London)

Notes:
\r\nThe aggregate fleet values published by Clarksons Research are calculated from estimates of the value of each vessel based on type, size and age. Values are estimated for all oil/product tankers, bulk carriers, combined carriers, container ships and gas carriers with reference to matrices based on representative newbuilding, second-hand and demolition values provided by Clarksons Platou brokers. For other vessel types, values are estimated with reference to individual valuations, recently reported sales and residual values calculated from reported newbuilding prices. As coverage concerning specialized and non-cargo vessels may not be complete, figures might not accurately represent the total value of the world merchant fleet above 1000 gross tons. Desktop estimates are made on the basis of prompt charter-free delivery, as between a willing buyer and a willing seller for cash payment under normal commercial terms. For the purposes of this exercise, all vessels are assumed to be in good and seaworthy condition.

The data refer to the beginning of an indicated year.

The figures cover seagoing propelled merchant ships of 1000 gross tons and above, excluding inland waterway vessels, fishing vessels, military vessels, yachts, and offshore fixed and mobile platforms and barges (with the exception of FPSO - floating production, storage and offloading vessels - and drillships).

More information is provided in UNCTAD's Review of Maritime Transport (http://unctad.org/rmt). 

","name":"US.VesselValueByRegistration","order":65},{"id":2338,"version":10002,"title":"Merchant fleet: Share by beneficial ownership - Annual (analytical)","lastUpdatedDate":"2026-06-18T13:52:00","category":"Maritime transport","keywords":"beneficial, owner, flag, merchant, fleet, maritime, transport","notes":"

Description:  
\r\nThe table provides information on each country's share of the value in US$ of the world merchant fleet by country of beneficial ownership. Data are presented in percentage share of the US dollar value of the total world fleet.


\r\nSource:
\r\nUNCTAD, Division on Technology and Logistics, based on data supplied by Clarkson Research Services (London)
\r\n
\r\nNotes:
\r\nThe aggregate fleet values published by Clarksons Research are calculated from estimates of the value of each vessel based on type, size and age. Values are estimated for all oil/product tankers, bulk carriers, combined carriers, container ships and gas carriers with reference to matrices based on representative newbuilding, second-hand and demolition values provided by Clarksons Platou brokers. For other vessel types, values are estimated with reference to individual valuations, recently reported sales and residual values calculated from reported newbuilding prices. As coverage concerning specialized and non-cargo vessels may not be complete, figures might not accurately represent the total value of the world merchant fleet above 1000 gross tons. Desktop estimates are made on the basis of prompt charter-free delivery, as between a willing buyer and a willing seller for cash payment under normal commercial terms. For the purposes of this exercise, all vessels are assumed to be in good and seaworthy condition.
\r\n
\r\nThe data refer to the beginning of an indicated year.
\r\n
\r\nThe figures cover seagoing propelled merchant ships of 1000 gross tons and above, excluding inland waterway vessels, fishing vessels, military vessels, yachts, and offshore fixed and mobile platforms and barges (with the exception of FPSO - floating production, storage and offloading vessels - and drillships).
\r\n
\r\nMore detailed discussions are provided in UNCTAD's Review of Maritime Transport (http://unctad.org/rmt).  

","name":"US.VesselValueByOwnership","order":66}],"subFolders":[],"isParentFolder":false},{"id":43,"title":"Ship building and recycling","reports":[{"id":2439,"version":10002,"title":"Merchant fleet: Ship building - Annual","lastUpdatedDate":"2026-06-18T13:48:00","category":"Maritime transport","keywords":"ship,building","notes":"
Description: 
This table shows data on the merchant fleet built between 1 January and 31 December of a given year. 
 
Source:
UNCTAD, Division on Technology and Logistics, based on data supplied by Clarkson Research Services. 
 
Note:
Data are presented in gross tonnage (GT) and refer to ship tonnage deliveries by shipyards in the economy during the year indicated. The figures cover seagoing propelled merchant ships of 100 gross tons (GT) and above, excluding inland waterway vessels, fishing vessels, military vessels, yachts, and offshore fixed and mobile platforms and barges (with the exception of floating production, storage and offloading vessels [FSPO] and drillships).
 
The value 0 is reported for countries without shipbuilding observed for a given year. This is in order to distinguish countries where shipbuilding is assessed to be zero for some years, from countries without shipbuilding at any point of the time series.
","name":"US.ShipBuilding","order":68},{"id":2440,"version":10002,"title":"Merchant fleet: Ship recycling - Annual","lastUpdatedDate":"2026-06-18T13:48:00","category":"Maritime transport","keywords":"Merchant fleet, ship, scrapping, demolition","notes":"

Description:
\r\nThis table shows estimates of the merchant fleet recycled between 1 January and 31 December of a given year.

Source:
\r\nUNCTAD, Division on Technology and Logistics, estimates based on data supplied by Clarkson Research Services.

Note:
\r\nData are presented in gross tonnage (GT) and refer to the indicated year.
\r\nThe figures cover seagoing propelled merchant ships of 100 gross tons (GT) and above, excluding inland waterway vessels, fishing vessels, military vessels, yachts, and offshore fixed and mobile platforms and barges (with the exception of FPSOs and drillships).
\r\n
\r\nThe value 0 is reported for countries without ship recycling observed for a given year. This is in order to distinguish countries where ship recycling is assessed to be zero for some years, from countries without ship recycling at any point of the time series.

","name":"US.ShipScrapping","order":69}],"subFolders":[],"isParentFolder":false},{"id":39,"title":"Freight volumes by sea","reports":[{"id":2253,"version":10003,"title":"Freight: Seaborne trade by cargo type - Annual (analytical)","lastUpdatedDate":"2026-03-17T10:39:00","category":"Maritime transport","keywords":"seaborne, international, maritime, trade, cargo, volume","notes":"

Description:

This dataset features international maritime trade volume in metric tons, by type of cargo at individual economy and economy grouping levels. Aggregates of groups of economies can differ from the sum of individual economies because data for some economies are not presented individually but included in the aggregates.

Figures reflect the imports and exports carried onboard ships and loaded (exports) and unloaded (imports) at ports worldwide. Data on international maritime freight volumes exclude transhipments and domestic maritime freight.

In the case of multimodal transport, seaborne trade volumes are recorded if one of the transport modes is maritime. Maritime traffic is attributed to an origin or a destination economy (e.g., landlocked country), not to economies where cargo handling ports are located (e.g., transit coastal economies). In other words, the seaborne trade data includes maritime freight volumes that originate from and are destined to the reference economy’s territory but that are loaded and unloaded through ports located outside the reference economy’s territory. For example, maritime freight volumes assigned to landlocked countries are handled at ports located in relevant transit coastal economies. The seaborne trade volumes do not necessarily match the international trade data by mode of transport reported in Comtrade Plus, which in the case of exports, records the first mode of transport while for imports it records the last mode of transport.

The seaborne trade data generally reflect data recorded at goods’ arrival time at the country’s external border. Therefore, for loaded (export) cargo, the trade volume reflects cargo that enters the territory of a destination economy in the report year.

Key variables and concepts underpinning the dataset are set out below.

  1. Dimensions
    2. \r\n
\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n

Year

Description

Year in which the imported goods enter the territory of the economy of destination, usually approximated by the year of the lodgement of the customs declaration.

Economy

Description

The economy that has reported an import volume under its international merchandise trade statistics reporting submitted to the United Nations Statistics Division (UNSD) for UN Comtrade, or the economy of origin as reported by the importer economy.

Categories

Economies, as defined by the UNCTAD classification of economies

Cargo Type

Description

Maritime cargo categorized based on the type of vessels used, separately for loading (exporting) and discharging (importing)

Categories

  • Total goods loaded
  • Crude oil (tanker) loaded
  • Other tanker trade loaded (including refined petroleum products, liquefied natural gas (LNG), liquefied petroleum gas (LPG), and chemicals)
  • Dry cargo loaded (transported on bulkers, container ships, and general cargo ships)
  • Total goods discharged
  • Crude oil (tanker) discharged
  • Other tanker trade discharged (including refined petroleum products, liquefied natural gas (LNG), liquefied petroleum gas (LPG), and chemicals)
  • Dry cargo discharged (transported on bulkers, container ships, and general cargo ships)
  • Freight loaded and discharged (Sum of Total goods loaded and Total goods discharged. SDG Indicator 9.1.2 [IS_RDP_LULFRG])
     
    • \r\n
  1. Indicator
    2. \r\n
\r\n\r\n\r\n\r\n\r\n\r\n

Metric tons

Description

The weight of the internationally traded goods net of packaging.

Unit of measure

Metric tons in billions

Metric tons in millions

Metric tons in thousands (default)

Metric tons in hundreds

Metric tons

Cell footnotes

All figures are based on aggregated bilateral flows involving the economy in a given direction.

Theoretically, two separately reported time series are available for each bilateral flow:

-          The import data reported by economy A for imports from economy B

-          The export data reported by economy B for exports to economy A

In this compilation, import data are preferred by default, unless significantly longer time series are available from trading partners’ corresponding export data.

That means that if all economies would report for all years, then

-          Cargo discharged for an economy would be the sum of imports by sea from all trade partners as reported by the economy (direct data)

-          Cargo loaded for an economy would be the sum of imports by sea reported by trade partners for imports from the economy (mirror data)

 

In practice, in some cases, the value is the sum of a mix of import and export data. When the availability of the mirroring export data is significantly better than the import data for the same bilateral flow, the export data is used.

The time series used for a bilateral flow may have gaps. Those gaps are filled via trends of the time series and/or growth rates of the mirroring time series.

That means that in broad terms the underlying bilateral flows can be a mix of

-          direct reported, that is reported by the economy itself,

-          mirror reported, that is reported by the trade partner, or

-          estimated using available data from direct or mirror data

                    

The following cell notes are used.

 

For cargo discharged (imports):

-          No note: The direct import data are used for all bilateral flows and the economy has reported data for the year.

-          Majority reported direct data: The direct import data are used for most bilateral flows, the other bilateral flows are either reported mirror export data or estimated.

-          All reported mirror data. The mirroring export data is the time series used for all bilateral flows and all partners have reported.

-          Majority reported mirror data: The mirror export data are used for most bilateral flows, the other bilateral flows are either reported direct import data or estimated.

-          Majority estimated: The majority of the used time series have a data gap for this year and have been estimated. Some bilateral flows are based on reported data (direct or mirror data).

-          All estimated: All of the used time series have a data gap for this year and have been estimated.

 

For cargo loaded (exports):

-          All reported mirror data: The preferred data source, the mirror import data reported by trade partners, is available for all partners.

-          Majority reported mirror data: The mirror import data reported by trade partners is available for most partners. The other bilateral flows are either export data reported by the economy or estimated.

-          Majority reported direct data: The export data reported by the economy itself is used for most bilateral flows. The other bilateral flows are either reported mirror data or estimated.

-          Majority estimated: The majority of the used time series have a data gap for this year and have been estimated. Some bilateral flows are based on reported data (direct or mirror data).

 

No cell footnotes are provided for the sum freight loaded and discharged. Cell footnotes are also not provided for country groups.

Sources:

 

Notes:

In, 2025 this table was expanded to include country level data. New methodology has been developed for this purpose as described below.

The UNCTAD secretariat generates the maritime trade data by volume and in metric tons by using UN Comtrade. UNCTAD divides the UN Comtrade data on merchandise trade volume into maritime and non-maritime freight volumes and by cargo type, using baseline maritime transport shares and cargo type shares. The methodology applied to generate maritime freight volumes from UN Comtrade data was developed by the UNCTAD secretariat in collaboration with MDS Transmodal (https://www.mdst.co.uk) and UN Statistical Division.

Data are aggregated from data reported in metric tons on bilateral trade flows at the subheading (6-digit) level of the Harmonized System (HS) nomenclature. Commodities are inferred to be transported as a particular maritime cargo type (e.g. containerized carried in containers on containerships or oil carried as bulk in tankers) or a non-maritime cargo type (e.g. containerized cargo carried in containers on trucks or oil carried in tanks onboard trucks). This required the application of base rates of cargo types and base rates of how these cargo types are transported. These base rates are specific to product and bilateral trade flow but fixed over time. The baseline cargo type shares and maritime transport shares are based on MDST’s World Cargo Database. However, some maritime transport shares are adjusted using information from UNCTAD Trade-and-Transport Dataset.

Commodity and cargo type are different concepts. Commodity refers to goods as classified under HS codes while cargo types reflect the condition under which the freight is being transported (containerized (dry or reefer/refrigerated containers), bulk liquid (tanker ships or, tanks), gas carriers, car carriers, dry bulk (e.g. iron ore, coal, grain, etc.). Thus, maritime freight volumes of some commodities can be divided into various cargo types. For example, a share of frozen meats can be carried as dry bulk cargo while another share of this same commodity can be carried in containers on a container ship.

The maritime trade data generated by UNCTAD are primarily based on importer-side data (i.e., data on bilateral trade flows reported by the importing country) in Comtrade. Mirror data (i.e., data on bilateral trade flows reported by the exporting country) in Comtrade are used only when the importer-side data are missing or deemed unreliable, particularly when exporter-side data offer a substantially longer time series or display greater stability over time. Missing data in UN Comtrade are imputed by selecting the best method, based on a pre-determined criterion such as minimum mean squared error, for each trading pair and each commodity and from several options including interpolation/ extrapolation using linear trend and growth rates in mirror data. When mirror data are available, priority is given to the growth rates in mirror data. There are no other sources of missing values in the compilation process.

UNCTAD secretariat developed a methodology that automatically detects and corrects UN Comtrade merchandise trade volume records that appear to be outliers and unreliable. The detection and correction of unreliable values are conducted for each bilateral trade flow of each commodity. The detection and correction are based on time series information of unit values (i.e. trade value/volume). If an inferred unit value deviates from its time series trend by a pre-determined threshold, the corresponding trade volume data is considered an unreliable value. The methodology also considers the possibility that a reported trade volume figure might be correct while the reported trade value figure might be incorrect. The time series trend is calculated by combining several types of trends, such as linear trend, log-linear trend, and invert of inverted trend, according to a pre-determined rule, to ensure in-sample and out-of-sample validity. To guarantee that the time series trend is not significantly affected by unreliable values, a first-round check of these values is conducted before calculating the time series trend by comparing deviations of unit values from the corresponding world median (of unit values of the corresponding commodity across all trading pairs). When the number of observations is considered insufficient to calculate the time series trend, the algorithm checks for the presence of a significant deviation of unit values from the corresponding world median. In addition to the automated detection and correction of outliers, a manual adjustment process is applied to selected data points when highly reliable alternative sources are available, such as detailed trade statistics published on official national government websites.

The measure of volume does not include the impacts of changes in quality of commodities (i.e., technological improvements) over time. As a result, the seaborne trade data generally indicates a weaker long-term trend compared to merchandise trade volume indices that deflated trade values by corresponding unit value indices, published by UNCTAD and other organizations. Even though the calculation of unit value indices, unlike price indices, does not make a specific effort to adjust for changes in quality of commodities, changes in quality of commodities are implicitly accounted for when value indices are calculated based on data at a disaggregated level. As a result, trade volume indices include the impact of changes in quality. In addition, bulky commodities such as iron ore tend to have higher shares in total seaborne trade volume than in trade volume indices because trade volume indices use trade value in base year as a weight of each commodity. This also contributes to the weaker long-term trend in the seaborne trade volumes expressed in metric tons compared to merchandise trade volume indices in some countries.

UNCTAD secretariat checks for the consistency between the estimated international maritime freight volumes and other comparable datasets published by international organizations such as International Monetary Fund and World Trade Organization as well datasets published by national governments, and maritime specialised sources, including Clarksons Research Services.

For more information, please contact the Review of Maritime Transport team at: unctad-rmt@un.org.

","name":"US.SeaborneTrade","order":71},{"id":2263,"version":10003,"title":"Freight: Container port throughput - Annual (analytical)","lastUpdatedDate":"2026-04-15T15:06:00","category":"Maritime transport","keywords":"ship, maritime, transport, container, port, throughput, TEU","notes":"

Description:

This table presents the estimated total number of containers handled, per country, expressed in twenty-foot equivalent units (TEUs). A TEU represents the volume of a standard 20 feet long intermodal container used for loading, unloading, repositioning and transshipment. A 40-foot intermodal container is counted as two TEUs.

Sources:
\r\nUNCTAD secretariat based on data collected by various sources, including

The above-mentioned data collectors, in turn, source their data from a variety of port authority reports and other industry sources. The collected data may in some cases depend on the ultimate source consulted.

Notes:

Data published are country totals which may conceal the fact that some minor ports are not included. The data includes container port throughput for ports with time series that are complete since 2010 or that can be made complete by repeating an observation for a maximum of four years after the original observation. This means that country level time series are comparable over time but that the coverage of ports may differ between countries. UNCTAD continuously seeks to improve coverage in updates to this table. Ports included are noted in the country notes.

Repeated port level observations are treated as estimates and country level figures that are made up of more than 20 per cent estimates are noted as such. Observations, where more than 40 per cent of the country level container port throughput is estimated, are not published but included in the group level totals. Revisions of more than 20% from the previous version of this statistical table are also noted. Notes and cell suppressions are only applied for individual economies.

For any questions related to the country data and methodology, please contact the Review of Maritime Transport team at: unctad-rmt@un.org.

","name":"US.ContPortThroughput","order":72}],"subFolders":[],"isParentFolder":false},{"id":40,"title":"Port calls and performance","reports":[{"id":1064,"version":10002,"title":"Port calls: Performance indicators - Annual (analytical)","lastUpdatedDate":"2024-03-28T16:47:00","category":"Maritime transport","keywords":"ships, port, maritime, transport, port calls, ports, time in port, vessels, services, container","notes":"

Description: 

This table is part of a package of tables on the port call and performance statistics that provide an overview of ships’ characteristics and the time they spent in the country’s ports during a certain period. Together, the tables include statistics for up to 8 commercial markets, covering the number of port calls during the period, the median time the ships spend in the ports, as well as information about the average age and size, and the size of the largest ships that have called during the period.

This table displays eight out of nine measures. The number of port calls is displayed in a separate table. Furthermore, the period in this table is one calendar year. Statistics referring to port calls per semester are presented in additional separate tables.

The aggregated figures are derived from the fusion of automatic identification system (AIS) information with port mapping intelligence by MarineTraffic (http://marinetraffic.com), covering ships of 1000 GT.  Only arrivals have been considered to measure the total number of port calls. To produce any measurement, there must be at least 10 arrivals on a country level per commercial market made by at least 5 distinct vessels as segmented. Passenger ships and RO/RO ships are excluded from time at port calculations.

Sources:
\r\nUNCTAD, based on data provided by MarineTraffic (http://marinetraffic.com)
\r\n\"\"

Notes:

For the present analysis we work with the median time. The average time vessels spend in port is longer for practically all countries and markets, due to statistical outliers, i.e. ships that spend weeks or months in a port, for example for repairs. The statistical distribution of time spent in ports has a “long tail”. The global average time ships spent in port in 2018 was 41 hours, vis-à-vis 23.5 hours median time.

The table includes an up to 9 x 10 matrix of data for each year and country, i.e. up to 90 data points.

We have up to 8 different commercial markets for each country, plus the total, namely:

Furthermore, up to 9 different measures are available for each commercial market:

The last eight measures are displayed in this table. The number of port calls is displayed in a separate table.

","name":"US.PortCalls","order":74},{"id":1066,"version":10002,"title":"Port calls: Performance indicators - Semi-annual (analytical)","lastUpdatedDate":"2024-03-28T16:47:00","category":"Maritime transport","keywords":"ships, port, maritime, transport, port calls, ports, time in port, vessels, services, container","notes":"

Description: 

This table is part of a package of tables on the port call and performance statistics that provide an overview of ships’ characteristics and the time they spent in the country’s ports during a certain period. Together, the tables include statistics for up to 8 commercial markets, covering the number of port calls during the period, the median time the ships spend in the ports, as well as information about the average age and size, and the size of the largest ships that have called during the period.
 
This table displays eight out of nine measures. The number of port calls is displayed in a separate table. Furthermore, the period in this table is one semester. Statistics referring to port calls per calendar year are presented in additional separate tables.
 
The aggregated figures are derived from the fusion of automatic identification system (AIS) information with port mapping intelligence by MarineTraffic (http://marinetraffic.com), covering ships of 1000 GT.  Only arrivals have been considered to measure the total number of port calls. Passenger ships and RO/RO ships are excluded from time at port calculations.

Sources:
\r\nUNCTAD, based on data provided by MarineTraffic (http://marinetraffic.com)
\r\n\"\"

Notes:
\r\nThe accuracy of the data depends on the accuracy of available AIS data, this is especially true for economies and commercial markets where the number of port calls are low. Since caution is advised for numbers pertaining to countries and commercial markets with low numbers of port calls, Marine Traffic only produces measurements if there are at least 10 arrivals on a country level per commercial market made by at least 5 distinct vessels as segmented. In addition, for semestrial data, UNCTAD only publishes group level data and data for the twenty economies with the highest number of port calls on an annual basis 2018 – 2021.
\r\n
\r\nFor the present analysis we work with the median time. The average time vessels spend in port is longer for practically all countries and markets, due to statistical outliers, i.e. ships that spend weeks or months in a port, for example for repairs. The statistical distribution of time spent in ports has a “long tail”. The global average time ships spent in port in 2018 was 41 hours, vis-à-vis 23.5 hours median time.

The table includes an up to 9 x 10 matrix of data for each semester and country, i.e. up to 90 data points.

We have up to 8 different commercial markets for each country, plus the total, namely:

Furthermore, up to 9 different measures are available for each commercial market:

The last eight measures are displayed in this table. The number of port calls is displayed in a separate table.

","name":"US.PortCalls_S","order":75},{"id":1065,"version":10002,"title":"Port calls: Calls by commercial market - Annual (analytical)","lastUpdatedDate":"2024-03-28T16:25:00","category":"Maritime transport","keywords":"ships, port, maritime, transport, port calls, ports, time in port, vessels, services, container","notes":"

Description:
\r\nThis table is part of a package of tables on the port call and performance statistics that provide an overview of ships’ characteristics and the time they spent in the country’s ports during a certain period. Together, the tables include statistics for up to 8 commercial markets, covering the number of port calls during the period, the median time the ships spend in the ports, as well as information about the average age and size, and the size of the largest ships that have called during the period.
\r\n
\r\nThis table displays the number of port calls. Eight other measures are displayed in a separate table. Furthermore, the period in this table is one calendar year. Statistics referring to port calls per semester are presented in additional separate tables.
\r\n
\r\nThe aggregated figures are derived from the fusion of automatic identification system (AIS) information with port mapping intelligence by MarineTraffic (http://marinetraffic.com), covering ships of 1000 GT.  Only arrivals have been considered to measure the total number of port calls. To produce any measurement, there must be at least 10 arrivals on a country level per commercial market made by at least 5 distinct vessels as segmented. Missing data could therefore hide small numbers of arrivals that are then also excluded from aggregates. Passenger ships and RO/RO ships are excluded from time at port calculations.
\r\n
\r\nSources:
\r\nUNCTAD, based on data provided by MarineTraffic (http://marinetraffic.com)
\r\n\"\"


\r\nNotes:
\r\nThe accuracy of the data depends on the accuracy of available AIS data, this is especially true for economies and commercial markets where the number of port calls are low. Since caution is advised for numbers pertaining to countries and commercial markets with low numbers of port calls, Marine Traffic only produces measurements if there are at least 10 arrivals on a country level per commercial market made by at least 5 distinct vessels as segmented. In addition, UNCTAD only publishes group level data and data for the twenty economies with the highest number of port calls on an annual basis 2018 – 2022.
\r\n
\r\nFor the present analysis we work with the median time. The average time vessels spend in port is longer for practically all countries and markets, due to statistical outliers, i.e. ships that spend weeks or months in a port, for example for repairs. The statistical distribution of time spent in ports has a “long tail”. The global average time ships spent in port in 2022 was 45.5 hours, vis-à-vis 25.0 hours median time.  
\r\n
\r\nThe table includes an up to 9 x 10 matrix of data for each semester and country, i.e. up to 90 data points. We have up to 8 different commercial markets for each country, plus the total, namely:
\r\n•    All ships: All ships.
\r\n•    Liquid bulk carriers: Ships that carry wet bulk cargo such as crude or refined oil products, other liquid cargo except for liquefied gas.
\r\n•    Liquefied petroleum gas carriers: Ships that carry Liquefied Petroleum Gas.
\r\n•    Liquefied natural gas carriers: Ships that carry Liquefied Natural Gas.
\r\n•    Dry bulk carriers: Ships that carry dry bulk cargo such as iron ore, grains or coal and cargo that cannot be packaged or containerized.
\r\n•    Break bulk carriers: General cargo ships that carry dry cargo that is not containerized and not in bulk.
\r\n•    Roll-on/ roll-off ship: RO/RO, such as ferries or vehicle carriers.
\r\n•    Container ships: Ships that carry standardized sea-containers.
\r\n•    Passenger ships: Ships that carry passengers.
\r\n
\r\nThis table displays:
\r\n•    Number of port calls: port calls captured per country per semester per subject fleet (commercial market or all traffic)
\r\n
\r\nThe remaining eight measures are displayed in a separate table:
\r\n•    Median time in port (days): The median time vessels spent within port limits (in days).
\r\n•    Average age of vessels: The average age of the vessels that have called in the country’s ports during the semester. For the calculation of this average, each ship is counted as often as it has called in the country’s ports.
\r\n•    Average size (GT) of vessels: The average gross tonnage of the vessels that have called in the country’s ports during the semester. For the calculation of this average, each ship is counted as often as it has called in the country’s ports.
\r\n•    Maximum size (GT) of vessels: The gross tonnage (GT) of the vessel with the highest GT that has called in at least once at any of the country’s ports during the semester.
\r\n•    Average cargo carrying capacity (DWT) per vessel: The average deadweight tonnage of the vessels that have called in the country’s ports during the semester. For the calculation of this average, each ship is counted as often as it has called in the country’s ports.
\r\n•    Maximum cargo carrying capacity (DWT) of vessels: The dead-weight tonnage (DWT) of the vessel with the highest dwt that has called in at least once at any of the country’s ports during the semester.
\r\n•    Average container carrying capacity (TEU) per container ship: The average capacity to carry twenty-foot equivalent units (TEU) of the container ships. For the calculation of this average, each ship is counted as often as it has called in the country’s ports.
\r\n•    Maximum container carrying capacity (TEU) of container ships: The maximum capacity to carry twenty-foot equivalent units (TEU) of the largest container ship that has called at least once at any of the country’s ports during the period.

 

","name":"US.PortCallsArrivals","order":76},{"id":1067,"version":10002,"title":"Port calls: Calls by commercial market - Semi-annual (analytical)","lastUpdatedDate":"2024-03-28T16:25:00","category":"Maritime transport","keywords":"ships, port, maritime, transport, port calls, ports, time in port, vessels, services, container","notes":"

Description:
\r\nThis table is part of a package of tables on the port call and performance statistics that provide an overview of ships’ characteristics and the time they spent in the country’s ports during a certain period. Together, the tables include statistics for up to 8 commercial markets, covering the number of port calls during the period, the median time the ships spend in the ports, as well as information about the average age and size, and the size of the largest ships that have called during the period.
\r\n
\r\nThis table displays the number of port calls. Eight other measures are displayed in a separate table. Furthermore, the period in this table is one semester. Statistics referring to port calls per calendar year are presented in additional separate tables.
\r\n
\r\nThe aggregated figures are derived from the fusion of automatic identification system (AIS) information with port mapping intelligence by MarineTraffic (http://marinetraffic.com), covering ships of 1000 GT.  Only arrivals have been considered to measure the total number of port calls. Passenger ships and RO/RO ships are excluded from time at port calculations.
\r\n
\r\nSources:
\r\nUNCTAD, based on data provided by MarineTraffic (http://marinetraffic.com)
\r\n\"\"

Notes:
\r\nThe accuracy of the data depends on the accuracy of available AIS data, this is especially true for economies and commercial markets where the number of port calls are low. Since caution is advised for numbers pertaining to countries and commercial markets with low numbers of port calls, Marine Traffic only produces measurements if there are at least 10 arrivals on a country level per commercial market made by at least 5 distinct vessels as segmented. In addition, UNCTAD only publishes group level data and data for the twenty economies with the highest number of port calls on an annual basis 2018 – 2022.
\r\n
\r\nFor the present analysis we work with the median time. The average time vessels spend in port is longer for practically all countries and markets, due to statistical outliers, i.e. ships that spend weeks or months in a port, for example for repairs. The statistical distribution of time spent in ports has a “long tail”. The global average time ships spent in port in 2022 was 45.5 hours, vis-à-vis 25.0 hours median time.  
\r\n
\r\nThe table includes an up to 9 x 10 matrix of data for each semester and country, i.e. up to 90 data points. We have up to 8 different commercial markets for each country, plus the total, namely:
\r\n
\r\n•    All ships: All ships.
\r\n•    Liquid bulk carriers: Ships that carry wet bulk cargo such as crude or refined oil products, other liquid cargo except for liquefied gas.
\r\n•    Liquefied petroleum gas carriers: Ships that carry Liquefied Petroleum Gas.
\r\n•    Liquefied natural gas carriers: Ships that carry Liquefied Natural Gas.
\r\n•    Dry bulk carriers: Ships that carry dry bulk cargo such as iron ore, grains or coal and cargo that cannot be packaged or containerized.
\r\n•    Break bulk carriers: General cargo ships that carry dry cargo that is not containerized and not in bulk.
\r\n•    Roll-on/ roll-off ship: RO/RO, such as ferries or vehicle carriers.
\r\n•    Container ships: Ships that carry standardized sea-containers.
\r\n•    Passenger ships: Ships that carry passengers.
\r\n
\r\nThis table displays:
\r\n•    Number of port calls: port calls captured per country per semester per subject fleet (commercial market or all traffic)
\r\nThe remaining eight measures are displayed in a separate tale:
\r\n
\r\n•    Median time in port (days): The median time vessels spent within port limits (in days).
\r\n•    Average age of vessels: The average age of the vessels that have called in the country’s ports during the semester. For the calculation of this average, each ship is counted as often as it has called in the country’s ports.
\r\n•    Average size (GT) of vessels: The average gross tonnage of the vessels that have called in the country’s ports during the semester. For the calculation of this average, each ship is counted as often as it has called in the country’s ports.
\r\n•    Maximum size (GT) of vessels: The gross tonnage (GT) of the vessel with the highest GT that has called in at least once at any of the country’s ports during the semester.
\r\n•    Average cargo carrying capacity (DWT) per vessel: The average deadweight tonnage of the vessels that have called in the country’s ports during the semester. For the calculation of this average, each ship is counted as often as it has called in the country’s ports.
\r\n•    Maximum cargo carrying capacity (DWT) of vessels: The dead-weight tonnage (DWT) of the vessel with the highest dwt that has called in at least once at any of the country’s ports during the semester.
\r\n•    Average container carrying capacity (TEU) per container ship: The average capacity to carry twenty-foot equivalent units (TEU) of the container ships. For the calculation of this average, each ship is counted as often as it has called in the country’s ports.
\r\n•    Maximum container carrying capacity (TEU) of container ships: The maximum capacity to carry twenty-foot equivalent units (TEU) of the largest container ship that has called at least once at any of the country’s ports during the period.

","name":"US.PortCallsArrivals_S","order":77}],"subFolders":[],"isParentFolder":false},{"id":41,"title":"Maritime connectivity","reports":[{"id":2595,"version":10013,"title":"Liner shipping: Connectivity index - Monthly (analytical)","lastUpdatedDate":"2026-09-28T11:09:00","category":"Maritime transport","keywords":"maritime, transport, connectivity, ports, index","notes":"
Description: 
This table presents the Liner Shipping Connectivity Index (LSCI), which measures a country’s integration into global liner shipping networks. Access to global markets depends heavily on maritime connectivity, especially regular shipping services used to import and export manufactured goods. Higher LSCI values indicate better connectivity. The LSCI is generated for all countries that are serviced by regular containerized liner shipping services.
 
Methodology and components of the LSCI:
The LSCI is generated from the following six components:
  1. The number of scheduled ship calls per week (i.e., weekly average of annual schedule) in the country;
  2. Total scheduled annual deployed capacity, in Twenty-foot-Equivalent Units (TEU), offered in the country; 
  3. The number or regular liner shipping services from and to the country; 
  4. The number of liner shipping companies that provide services from and to the country; 
  5. The size, in TEU, of the largest ship deployed on services from and to the country; and
  6. The number of other countries that are connected to the country through direct liner shipping services (where a direct service is defined as a regular service between two countries – it may include other stops in between, but usually no transshipment, as this is not required in the transport of a container). 
    7. \r\n
The index is generated as follows: 
For each component, we divide the country’s value in that component by the average across countries for the component in February 2023 and then calculate the average across the six components for the country in that month. We then multiply this average by 100. The result is an LSCI that takes an average value of 100 in February 2023 and expresses all other values in relation to that average.
 
Data frequency and availability:
The monthly LSCI was introduced in 2025 to enhance the timeliness of maritime connectivity data. The time series of that index starts from 2006. However, from 2006 to 2022, only one month per quarter (i.e., February, May, August, and November) is available. 
 
For the time being, the latest monthly figures from the index are typically published in end-March, June, September and December (three monthly datapoints published simultaneously).
 
Sources:
UNCTAD, Division on Technology and Logistics, based on MDS Transmodal (https://www.mdst.co.uk).
The schedules, i.e. timetables, of containerized shipping services, made available by the shipping lines form the basis of the LSCI.

\r\n 
Notes:
 
UNCTAD first generated the LSCI in 2004 to measure how well a country is connected to the global containerized shipping network. Over 80 per cent of international trade by volume, and about 70 per cent by value, is seaborne. Roughly two-thirds of this seaborne trade by value is containerized and transported by regular liner shipping services. Being better connected through these international transport services helps a country to trade at lower costs, faster, and with more choices.
 
The original methodology to calculate the LSCI included 5 components. In 2016, the LSCI was revised with improved coverage and additional data from MDST, incorporating 6 components, as enumerated above. Each one of these six components can contribute to improved access to the global liner shipping network, helping shippers to better connect with overseas markets.
 
In 2024, the index was rescaled to better reflect the current characteristics of container ports, while retaining the same six components. Starting in 2024, the LSCI is set at 100, representing the average value of country connectivity in the first quarter (Q1) of 2023. This scaling was applied in March 2024 for the whole series (from Q1 2006). This is a change from the previous calculation methodology, where components and index were scaled to the maximum value in Q1 2006. For further description of the revision, its rationale and consequences see: https://unctad.org/news/new-context-calls-changing-how-we-measure-maritime-connectivity
 
The LSCI initially started as an annual index and later evolved into a quarterly index. The monthly LSCI was introduced in 2025. Starting in 2025, UNCTAD publishes two LSCI datasets: one quarterly and one monthly.
 
The LSCI is complemented by the Port Liner Shipping Connectivity Index (PLSCI) and the Liner Shipping Bilateral Connectivity Index also published on UNCTADstat.
 
Users interested in the earlier time series, covering 2006 until 2023, may contact rmt@unctad.org

 

","name":"US.LSCI_M","order":79},{"id":2594,"version":10013,"title":"Liner shipping: Connectivity index - Quarterly (analytical)","lastUpdatedDate":"2026-09-28T11:09:00","category":"Maritime transport","keywords":"maritime, transport, connectivity, ports, index","notes":"
Description: 
This table presents the Liner Shipping Connectivity Index (LSCI), which measures a country’s integration into global liner shipping networks. Access to global markets depends heavily on maritime connectivity, especially regular shipping services used to import and export manufactured goods. Higher LSCI values indicate better connectivity. The LSCI is generated for all countries that are serviced by regular containerized liner shipping services.
 
Methodology and Components of the LSCI:
The LSCI is generated from the following six components:
(1) The number of scheduled ship calls per week (i.e., weekly average of annual schedule) in the country;
(2) Total scheduled annual deployed capacity, in Twenty-foot-Equivalent Units (TEU), offered in the country; 
(3) The number or regular liner shipping services from and to the country; 
(4) The number of liner shipping companies that provide services from and to the country; 
(5) The size, in TEU, of the largest ship deployed on services from and to the country; and
(6) The number of other countries that are connected to the country through direct liner shipping services (where a direct service is defined as a regular service between two countries – it may include other stops in between, but usually no transshipment, as this is not required in the transport of a container). 
 
The index is generated as follows: 
For each component, we divide the country’s value by the average value for the component in Q1 2023 and then calculate the average of the six components for the country. The average across components for a given country and quarter is then multiplied by 100. The result is an average LSCI of 100 in Q1 2023. All other indices are in relation to this value. 
 
Data frequency and availability:
• For 2006–2022: Data is collected and available for February, May, August, and November. These are treated as quarterly observations; no values are estimated for other months.
• From 2023 onwards: Monthly data is available. Quarterly indices are calculated as the average of the three corresponding months for five of the six components. The sixth component, "maximum ship size," uses the highest monthly value in the quarter.

\r\nThis approach, where the only difference between the two periods is the number of observations per quarter, ensures methodological consistency, allowing meaningful long-term trend analysis across the entire dataset.
 
The latest quarterly figures from the index are typically published in end-March, June, September and December.
 
Sources:
UNCTAD, Division on Technology and Logistics, based on MDS Transmodal (https://www.mdst.co.uk).
The schedules, i.e. timetables, of containerized shipping services, made available by the shipping lines form the basis of the LSCI.

\r\n 
Notes:
 
UNCTAD first generated the LSCI in 2004 to measure how well a country is connected to the global containerized shipping network. Over 80 per cent of international trade by volume, and about 70 per cent by value, is seaborne. Roughly two-thirds of this seaborne trade by value is containerized and transported by regular liner shipping services. Being better connected through these international transport services helps a country to trade at lower costs, faster, and with more choices.
 
The original methodology to calculate the LSCI included 5 components. In 2016, the LSCI was revised with improved coverage and additional data from MDST, incorporating 6 components, as enumerated above. Each one of these six components can contribute to improved access to the global liner shipping network, helping shippers to better connect with overseas markets.
 
In 2024, the index was rescaled to better reflect the current characteristics of container ports, while retaining the same six components. Starting in 2024, the LSCI is set at 100, representing the average value of country connectivity in the first quarter (Q1) of 2023. This scaling was applied in March 2024 for the whole series (from Q1 2006). This is a change from the previous calculation methodology, where components and index were scaled to the maximum value in Q1 2006. For further description of the revision, its rationale and consequences see: https://unctad.org/news/new-context-calls-changing-how-we-measure-maritime-connectivity
 
The LSCI initially started as an annual index and later evolved into a quarterly index. The monthly LSCI was introduced in 2025. Starting in 2025, UNCTAD publishes two LSCI datasets: one quarterly and one monthly.
 
The LSCI is complemented by the Port Liner Shipping Connectivity Index (PLSCI) and the Liner Shipping Bilateral Connectivity Index also published on UNCTADstat.

\r\nUsers interested in the earlier time series, based on the 2016 methodology and covering 2006 until 2023, may contact rmt@unctad.org
","name":"US.LSCI","order":80},{"id":2474,"version":10004,"title":"Liner shipping: Bilateral connectivity index - Quarterly (analytical)","lastUpdatedDate":"2026-06-26T14:38:00","category":"Maritime transport","keywords":"Fleet, merchant, transport, maritime, ship, ports, connectivity, connections, index","notes":"

Description:
\r\nThe table presents the liner shipping bilateral connectivity index (LSBCI), which indicates a country pair's integration level into global liner shipping networks. The LSBCI is an extension of UNCTAD’s country-level Liner Shipping Connectivity Index (LSCI) and based on a proper bilateralization transformation. 

Transport connectivity is a crucial determinant of exports, including bilateral trade flows. UNCTAD's Liner Shipping Bilateral Connectivity Index is meant to reflect specifically the liner shipping connectivity between pairs of countries. 


\r\nSource:
\r\nUNCTAD, Division on Technology and Logistics, based on data from MDS Transmodal  (https://www.mdst.co.uk).
\r\n
\r\n
\r\nNotes:
\r\nThe current version of the LSBCI includes 5 components. For any pair of countries A and B represented in our sample, the LSBCI is based on: 

1) the number of transshipments required to get from country A to country B; 
2) the number of direct connections common to both country A and B; 
3) the number of common connections by country pair with one transshipment; 
4) the level of competition on services that connect country A to country B; 
5) the size of the largest ship on the weakest route connecting country A to country B. 
 
In order to establish a unit free index, all components are normalized using the standard formula:
 
Normalized_Value = (Raw - Min(Raw)) / (Max(Raw) - Min(Raw)).
 
Maximum and minimum values are taken from the whole sample. If any of these reference values change over time, then the whole series of LSBCI are recomputed explaining why some values possibly vary across the various editions of the LSBCI. For example, a significant change in reference values resulted in an adjustment of index values for the 2021 edition. This adjustment was bigger for higher index values.
 
The LSBCI is computed by taking the simple average of the five normalized components. As a consequence, the LSBCI can only take values between 0 (minimum) and 1 (maximum). As to the first component (i.e. the number of transshipments), we simply take its complement to unity that is 1-Normalized_Value to respect the correspondence between higher values and stronger connectivity.
 
In January 2026, first-quarter (Q1) values for all years were recalculated to ensure consistency across the time series. Only this harmonized set of Q1 values is published.
","name":"US.LSBCI","order":81},{"id":2584,"version":10008,"title":"Liner shipping: Port connectivity index - Quarterly (analytical)","lastUpdatedDate":"2026-09-28T11:09:00","category":"Maritime transport","keywords":"maritime, transport, connectivity, ports, index","notes":"
Description:
This table presents the Port Liner Shipping Connectivity Index (PLSCI), which measures a port's integration into global liner shipping networks. Access to global markets depends heavily on maritime connectivity, especially regular shipping services used to import and export manufactured goods. A higher value is associated with better connectivity.
 
Methodology and Components of the PLSCI:
The PLSCI is generated from the following six components: 
(1) The number of scheduled ship calls per week in the port. 
(2) Deployed annual capacity in Twenty-Foot-equivalent Units (TEU): total deployed capacity offered at the port. 
(3) The number of regular liner shipping services from and to the port.
(4) The number of liner shipping companies that provide services from and to the port. 
(5) The size in TEU (Twenty-Foot-equivalent Units) of the largest ship deployed on services from and to the port; and
(6) The number of other ports that are connected to the port through direct liner shipping services. (Note: a direct service is defined as a regular service between two countries; it may include other stops in between, but the transport of a container does not require transshipment). 
 
The index is generated as follows: 
For each component, we divide the port’s value by the average value for the component in Q1 2023 and then calculate the average of the six components for the port. The average across components for a given country and quarter is then multiplied by 100. The result is an average PLSCI of 100 in Q1 2023. All other indices are in relation to this value. 
 
Data frequency and availability
The latest quarterly figures from the index are typically published in mid-March, June, September and December.
The PLSCI is generated for about 900 ports with updated data each quarter. However, data is not available for all ports for every year.
 
Source:
UNCTAD, Division on Technology and Logistics, based on MDS Transmodal (https://www.mdst.co.uk).
The schedules, i.e. timetables, of containerized shipping services, made available by the shipping lines form the basis of the PLSCI.
 
Notes:
UNCTAD generated the Liner Shipping Connectivity Index (LSCI) in 2004 to measure how well a country is connected to the global containerized shipping network. Over 80 per cent of international trade by volume, and about 70 per cent by value, is seaborne. Roughly two-thirds of this seaborne trade by value is containerized and transported by regular liner shipping services. Being better connected through these international transport services helps a country to trade at lower costs, faster, and with more choices.
 
The original methodology to calculate the LSCI included 5 components. In 2016, the LSCI was revised with improved coverage and additional data from MDST, incorporating 6 components, as enumerated above. Each one of these six components can contribute to improved access to the global liner shipping network, helping shippers to better connect with overseas markets. In 2024, the index was rescaled to better reflect current characteristics of container ports while retaining the same six components.
 
The PLSCI was generated upon request of many users of the economy level LSCI who considered it useful to track the Liner Shipping Connectivity of individual ports. The Port LSCI (PLSCI) reflects a ports’ position in the global liner shipping network. Originally, the calculation methodology of the PLSCI entailed that components and index were scaled to the maximum value in Q1 2006.
 
The PLSCI was rescaled in 2024. This scaling was applied in March 2024 for the whole series (from Q1 2006). Starting in 2024, the PLSCI is an index set at 100 for the average value of port connectivity in the first quarter (Q1) of 2023. This means that a value of 100 no longer corresponds to a specific port but to the global average. A port with a score above 100 is better connected than the average global port in Q1 2023, and a port scoring below 100 is less connected. This change was introduced to improve comparability and neutrality of the index across time and ports. For a more detailed explanation of this revision, its rationale and consequences see: https://unctad.org/news/new-context-calls-changing-how-we-measure-maritime-connectivity.
 
The PLSCI complements the LSCI at the economy level (monthly and quarterly) and the Liner Shipping Bilateral Connectivity Index, also published on UNCTADstat.
 
Users interested in the earlier time series, based on the 2016 methodology and covering 2006 until 2023, may contact rmt@unctad.org
 

 

","name":"US.PLSCI","order":82}],"subFolders":[],"isParentFolder":false},{"id":42,"title":"Seafarer supply","reports":[{"id":981,"version":10002,"title":"Seafarer: Supply estimates by rank - Quinquennial (analytical)","lastUpdatedDate":"2023-07-18T11:00:00","category":"Maritime transport","keywords":"Seafarer, officers, ratings, nationality","notes":"

Description:

This table provides estimated supply of seafarers that operates the world merchant fleet, including their nationalities and rank.
\r\n
\r\nOfficers: refers to deck officers, engineer officers, telecommunications personnel holding certificates of competency issued by competent authorities.
\r\n
\r\nRatings: refers to seafarers other than officers.
\r\n
\r\nEstimations are done by the source and are based on one or more of the following:
\r\n-    figures reported by the country in 2021
\r\n-    figures reported by the country in 2015
\r\n-    figures reported by the country in 2010
\r\n-    ships under the country’s flag
\r\n-    industry experts.
\r\n
\r\n(See sources for more information.)
\r\n
\r\nSources:
\r\nBIMCO-ICS Manpower Report 2015
\r\n(http://www.ics-shipping.org/free-resources/manpower-report-2015)
\r\n
\r\nBIMCO-ICS Seafarer Workforce Report, 2021 edition
\r\n(https://www.ics-shipping.org/publication/seafarer-workforce-report-2021-edition)
\r\n
\r\nBIMCO and ICS also source some of the data from
\r\nSeafarer Statistics in the EU - Statistical review (2018 data STCW-IS)
\r\nby the European Maritime Safety Agency.
\r\n(http://www.emsa.europa.eu/publications/reports/item/3977-seafarer-statistics-in-the-eu-statistical-review-2018-data-stcw-is.html)
\r\n
\r\nNotes:
\r\nThe report on seafarers is published every five years by Baltic and International Maritime Council (BIMCO) (https://www.bimco.org/) and International Chamber of Shipping (ICS) (http://www.ics-shipping.org/). The next update is expected in 2026.

 

","name":"US.Seafarers","order":84}],"subFolders":[],"isParentFolder":false},{"id":9,"title":"Global transport of merchandise trade","reports":[{"id":1107,"version":10001,"title":"Trade and transport: By destination, mode and product group - Annual (analytical)","lastUpdatedDate":"2024-05-22T09:00:00","category":"International trade","keywords":"international, trade, transport, cost, merchandise, product, destination, origin","notes":"

Description:

This dataset records international trade, in values and volumes, alongside the transport expenditure and transport work incurred for its transport, by pair of trading partners, commodity and mode of transport, annually, from 2016 to 2021. It displays the trade and related transport from 237 economies of origin to 170 economies of destination. These data are broken down by commodity group at the level of headings (4-digit codes) of the Harmonized System classification (2017 revision) and by mode of transport, differentiating between air, sea, rail, road, and others.

The concepts behind the different variables in the dataset are provided below.

a) Dimensions

\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n

Product

Description

The traded commodity

Categories

Commodity groups as defined by headings (4-digit level) of the Harmonized System (HS) nomenclature, 2017 edition.

Origin

Description

The economie of origin as reported by the importer, more specifically the economie in which the traded goods have been produced or manufactured, in accordance with the Revised Kyoto Convention, Specific Annex K / Chapter 1/ E1 (UNSD, 2011),

Categories

Economies, as defined by the UNCTAD classification of economies

Destination

Description

The economie that has reported an import in its international merchandise trade statistics submitted to the United Nations Statistics Division (UNSD) for UN Comtrade

Categories

Economies, as defined by the UNCTAD classification of economies

Transport mode

Description

A mode of transport used for the shipment of the traded goods. Trade can be transported by more than one mode. Therefore, the sum of FOB value over the modes air (10), sea (21), railway (31), road (32) and other modes (99) may be greater than the FOB value and kilograms of the total trade. The same applies to the kilograms of that trade. To adjust for this difference, the item “multimodal adjustment” (80) records, with a negative sign, the FOB value (or kilograms) of the trade that is transloaded from one mode to another during its shipment from the origin to the economie of destination, times the number of modes involved additionally to the main mode. As a result, the sum of the FOB values (or kilograms) recorded for the five modes and the multimodal adjustment component equals the FOB value and kilograms of the total trade.

While a traded good can be transported by more than one mode, transport work is specific to the individual mode. Therefore, the transport work performed by air, sea, railway and road sums up to the overall transport work for the realization of a trade excluding the trade transported by other modes the transport work of which is not known.

The subtotal in category 01 records the sum over all ‘standard’ modes, comprising air (10), sea (21), railway (31), and road (32), and the “multimodal adjutment” component (80). The total in category 00 records the sum of that subtotal, other modes (99), and non-identified mode (0u).

Other modes (99) may include for example pipelines and cables, self-propelled good, postal consignments, mail or courier shipments.

The main source for the allocation of trade across different modes of transport is the information in UN Comtrade on the mode by which imported goods arrived at the border of the reporting economy. These data have been adjusted based on the global transport networks constructed for the different modes using geographic information systems.

Categories

00 - Total

  99 - Other modes

  0u - Non-identified mode

  01 - Subtotal: air, sea, rail, road

    10 - Air

    21 - Sea

    31 - Railway

    32 - Road

    80 - Multimodal adjustment

Year

Description

Year in which the imported goods enter the territory of the economy of destination, usually approximated by the year of the lodgement of the customs declaration (UNSD, 2011)

Categories

2016, 2017, 2018 …

 

b) Measures

\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n

FOB value (US$)

Description

The FOB-type value of internationally traded goods, as defined in “International Merchandise Trade. Concepts and Definitions”, article 4.6 (UNSD, 2011). It includes the transaction value of the goods and the value of services performed deliver the goods from their origin to the border of the exporting economy. It is usually derived by customs administrations from the invoice price and the terms of delivery indicated in the contract of sale, as well as from other supporting documents.

Kilograms

Description

The weight of the internationally traded goods net of packaging.

Unit value (US$/kg)

Description

The average value per kilogram

Formula

[FOB Value] / [kilograms]

Transport expenditure (US$)

Description

The expenditures invoiced for the services performed to deliver internationally traded goods from the border of the economy of origin (Origin) to the border of the importing economy (Destination), including the shipping the goods and the procurement of insurance against the risk of loss or damage during carriage. They have been calculated as the difference between the CIF value and the FOB value, as defined by IMTS Concepts and Definitions, article 4.6 (UNSD, 2011), in accordance with the definitions in Incoterms (International Chamber of Commerce, 2019). The CIF and the FOB values are usually derived by customs administrations from the invoice price and the terms of delivery indicated in the contract of sale, as well as from other supporting documents.

Unit of measure

United States dollars

Transport work in 1000 $-km

Description

The product of the distance over which traded goods are transported and their FOB value. Distance refers to the average minimum distance needed to transport goods from main city centres of the origin to main city centres of the economy of destination.

Transport work in ton-km

Description

The product of the distance over which traded goods are transported and their total weight in kilograms. Distance refers to the average minimum distance needed to transport goods from main city centres of the origin to main city centres of the destination country.

Ad-valorem freight rate

Description

The ratio of transport expenditures to the FOB value of the transported goods

Formula

[transport expenditure] / [FOB value]

Per-unit freight (US$/kg)

Description

Average transport expenditure per kilogram of the traded goods (QtyKg)

Formula

[transport expenditure] / [kilograms]

Transport costs intensity in US$ per 1000 $-km

Description

Average transport expenditure per 1000 kilometers as a ratio to their FOB value of the traded goods

Formula

( [transport expenditure] / [transport work in terms of value] ) * 1000

Transport costs intensity in US$ per ton-km

Description

Average transport expenditure per ton of the traded goods and per kilometer

Unit of measure

United States dollars / ton-kilometer

Formula

( [transport expenditure] / [transport work in terms of volume] ) * 1000

 

The symbol “..” in the measures FOB value, kilograms, and transport work should be interpreted as zero, as in two cases no trade was reported by the economy of destination for the specific commodity group. By contrast, if that symbol appears in the measures ad-valorem freight rate, freight rate per unit or transport cost intensity, it should be interpreted as “not applicable”.

Sources:

The dataset has been developed jointly by UNCTAD and the World Bank. It uses as main sources UN Comtrade (https://comtradeplus.un.org) and a transport network model based on geographic information systems developed by Equitable Maritime Consulting (Halim et al., 2018).

Notes:

The Trade-and-Transport dataset is a methodologically enhanced and extended version of the Global Transport Costs Dataset for International Trade (Hoffmeister, et al. 2022). International trade is reported from the import side. In cases of non-reporting of imports by any of the 170 economy of destination covered by the dataset in a specific year, these imports and their transport expenditure have been estimated.

Estimated values derived from information in UN Comtrade for the same observation are marked by the flag “(5) Estimated”. Estimated values derived from the values of other observations using Econometrics are marked by the flag “(7) Experimental”.

To avoid misinterpretation, some particularities in the variable definitions and their compilation require attention:

References:

Halim RA, Kirstein L, Merk O and Martinez L (2018). Decarbonization pathways for international maritime transport: A model-based policy impact assessment. Sustainability. 10(7):2243.

Hoffmeister O, Halim R, Lazarou N-J, Barnat N, Cristallo D, Englert D and Hoffmann J (2022). Developing a global transport costs dataset for international trade. UNCTAD Research Paper, 85.

UNSD (2011). International Merchandise Trade Statistics: Concepts and definitions 2010. ST/ESA/STAT/SER.M/52/Rev.3. United Nations publication. Sales No. E.10.XVII.13.

","name":"US.TransportCosts","order":86}],"subFolders":[],"isParentFolder":false}],"isParentFolder":true},{"id":35,"title":"Environment and related trade","reports":[],"subFolders":[{"id":26,"title":"Trade in biodiversity-based products","reports":[{"id":2517,"version":10003,"title":"Biotrade: Bilateral trade by product - Annual (analytical)","lastUpdatedDate":"2026-07-20T06:30:00","category":"Merchandise biotrade","keywords":"Export, import, balance, value, merchandise, trade, biotrade, biodiversity, growth, rate","notes":"
Description: 
This table shows the trade value of biodiversity-based products, expressed in thousands of United States dollars and its annual growth rate.
 
Source:
UN DESA Statistics Division, UN Comtrade
\r\n

Notes:
\r\nThe trade balance is expressed as the difference in trade value between exports and imports, and indicates whether an economy has a trade surplus or a trade deficit for the selected product(s) and period(s).

The trade balances of groups of economies are the sums of the trade balances of the component individual economies.

 

","name":"US.BiotradeMerch","order":89},{"id":2516,"version":10003,"title":"BioTrade: Bilateral trade as percentage of total trade - Annual (analytical)","lastUpdatedDate":"2026-07-20T06:30:00","category":"International trade in goods and services","keywords":"Export, import, value, share, merchandise, trade, biotrade, biodiversity","notes":"
Description: 
This table shows trade in biodiversity-based products expressed as a share of total trade.
 
Source:
UNCTAD calculations based on the UNCTADstat table “Merchandise biotrade - trade in thousands of United States dollars, annual”
 
Notes:
This measure indicates the size of the trade of biodiversity-based products of an economy relative to its total trade, including all products beyond the biodiversity realm. It is, simply put, the percentage biodiversity-based products represent of total exports and imports for an economy or group of economies.
","name":"US.BiotradeMerchShare","order":90},{"id":2513,"version":10003,"title":"BioTrade: Trade as percentage of Gross Domestic Product - Annual (analytical)","lastUpdatedDate":"2026-07-20T06:30:00","category":"International trade in goods and services","keywords":"Export, import, share, GDP, merchandise, trade, biotrade, biodiversity","notes":"
Description: 
 
This table shows the magnitude of the trade in biodiversity-based products of an economy expressed as a share of its Gross Domestic Product (GDP).
 
Source:

UNCTAD calculations based on the UNCTADstat tables “Merchandise biotrade - Total trade in thousands of United States dollars, annual” and “Gross domestic product: Total and per capita, growth rates, annual”

 
\r\nNotes:

The share of trade in an economy’s GDP is the sum of the economy’s imports and exports divided by its GDP, as expressed by the following formula:
\r\n
\r\n\"\"
\r\nwhere

Ij = index value for country j
\r\nxij = export value of biodiversity-based product i for country j
\r\nmij = import value of biodiversity-based product i for country j
\r\nn = number of biodiversity-based products traded by country j in a given year
\r\nGDPj = country j’s nominal Gross Domestic Product (GDP) for that same year.

","name":"US.BioTradeMerchGDPShare","order":91},{"id":2121,"version":10002,"title":"DISCONTINUED - BioTrade: Revealed Comparative Advantage index - Annual (analytical)","lastUpdatedDate":"2025-11-28T16:02:00","category":"Merchandise biotrade","keywords":"Export, import, merchandise, trade, Revealed Comparative Advantage, RCA, biotrade, biodiversity","notes":"
Description: 
Revealed comparative advantage (RCA) is based on Ricardian trade theory, which posits that patterns of trade among countries are governed by their relative differences in productivity. Although such productivity differences are difficult to observe, an RCA metric can be readily calculated using trade data to "reveal" such differences. While the metric can be used to provide a general indication and first approximation of a country's competitive export strengths, it should be noted that applied national measures which affect competitiveness such as tariffs, non-tariff measures, subsidies and others are not taken into account in the RCA metric.
 
This table shows the RCA of trade in biodiversity-based products.
 
Source:
UNCTAD calculations based on the UNCTADstat table “Merchandise biotrade - trade in thousands of United States dollars, annual”
 
Notes:

Country A is said to have a revealed comparative advantage in a given product i when its ratio of exports of product i to its total exports of all goods (products) exceeds the same ratio for the world as a whole:

\"RCA

Where

When a country has a revealed comparative advantage for a given product (RCA >1), it is inferred to be a competitive producer and exporter of that product relative to a country producing and exporting that good at or below the world average. A country with a revealed comparative advantage in product i is considered to have an export strength in that product. The higher the value of a country’s RCA for product i, the higher its export strength in product i.

","name":"US.BiotradeMerchRCA","order":92},{"id":2120,"version":10002,"title":"DISCONTINUED - BioTrade: Product concentration index - Annual (analytical)","lastUpdatedDate":"2025-11-28T16:02:00","category":"International trade in goods and services","keywords":"Export, import, merchandise, trade, product concentration, biotrade, biodiversity","notes":"
Description: 
This table shows the product concentration index of trade in biodiversity-based products.
 
Source:
UNCTAD calculations based on the UNCTADstat table “Merchandise biotrade - trade in thousands of United States dollars, annual”
 
Notes:
The product concentration index is a measure of the degree of concentration of the trade of an economy, meaning whether the composition of biodiversity-related exports and imports is concentrated around few products or more homogeneously distributed.  An index value closer to 1 indicates that the country’s exports or imports are highly concentrated around a few products. On the contrary, values closer to 0 reflect exports or imports more homogeneously distributed among a series of products. The product concentration index is defined as:
\"\"
 
Where
HHIj = index value for an economy or group of economies j
xij = value of exports or imports for economy j and product i
\r\n\"\"
n = number of biodiversity-based products traded by economy j in a given year
 
","name":"US.BioTradeMerchProdConcent","order":93},{"id":2124,"version":10002,"title":"DISCONTINUED - BioTrade: Market concentration and structural change indices - Annual (analytical)","lastUpdatedDate":"2025-11-28T16:02:00","category":"Environment and related trade","keywords":"Export, import, merchandise, trade, market concentration, structural change, biotrade, biodiversity","notes":"

Description:
\r\nThis table shows the market concentration and the structural change indices of trade in biodiversity-based products.

 

Source:
\r\nUNCTAD calculations based on the UNCTADstat table “Merchandise biotrade - trade in thousands of United States dollars, annual”

 

Notes:

Market concentration index

The market concentration index measures the degree of concentration of exports and imports of a product around few markets, or whether it is traded more homogeneously by a larger number of countries. A value close to 1 indicates that export or imports for the analyzed product are highly concentrated around few markets. On the contrary, a value closer to 0 reflects a more homogeneous distribution of market shares among economies. The market concentration index is defined as:
\r\n
\r\n\"\"
\r\nWhere
\r\nHHIi = index value for a specific biodiversity-based product i
\r\nxij = value of exports or imports for economy j and biodiversity-based product i
\r\n\"\"
\r\nn = number of individual markets (economies) in which biodiversity-based product i is traded

 

Structural change index

The structural change index is a measure of changes in the market structure of a product with respect to a base year. More specifically, it describes how the distribution of imports and exports in a specific product among markets (economies) has changed over time. A value close to 1 for a product indicates a significant change in the market shares among exporters (or importers) compared to a reference year. It is defined by the following formula:

\r\n\"\"
\r\n 
Where
Ii = index value for product i
Sij0 = share of trade of product i for economy j for the chosen reference year
Sij1 = share of trade of product i for economy j in the concerned year
n = number of individual markets (economies) in which product i is traded 
","name":"US.BioTradeMerchMarketIndices","order":94}],"subFolders":[],"isParentFolder":false},{"id":25,"title":"Trade in ocean goods and services","reports":[{"id":2426,"version":10001,"title":"Ocean goods: Bilateral trade by product group - Annual (analytical)","lastUpdatedDate":"2026-06-08T11:51:00","category":"International trade in goods and services","keywords":"Oceans, international, trade, growth, sectors, maritime, sea","notes":"

Description:
\r\nThis table presents annual statistics on exports and imports of ocean goods in current dollars by ocean economy sector.
\r\n
\r\nSources:

Notes:
I – Methodological constraint in the conversion of commodity groups 
The Harmonised System (HS) classification for merchandise trade by sector does not differentiate between goods obtain from ocean-based and land-based production activities. In many cases, the goods from several industries are grouped within one HS code. As a result, in creating a new database for ocean trade in goods by country and sector, UNCTAD had to determine coefficients which estimate for each country the percentage of each economic sector that is ocean-based. Once the coefficients above have been estimated for exports, the corresponding coefficients of imports have been obtained as their mirrors.
These coefficients are applied to UN Comtrade data on trade in goods.
 
 
II – Methodology for coefficients by country and sector
2.1. Country characteristics
Geographical and industrial characteristics of countries were used to estimate coefficients for ocean-economy activities.
 
The main geographical characteristic considered is each country’s proximity to the ocean, leading to the following categorization of countries classification:
- Landlocked 
- Coastal
- Large island: island country greater than 100,000km2 (the closest country below this threshold is nearly half of it: Sri Lanka with 65,000km2). 10 countries match this criterion: Indonesia, Madagascar, Papua New Guinea, Japan, Malaysia, Philippines, New Zealand, UK, Cuba, Iceland.
- Small island: island country smaller than 100,000 km2. 50 countries match this criterion. Note: Singapore and Hong Kong are classified as small islands, even if they do not strictly represent islands in the geographical sense, as they face similar characteristics, being city ports.
 
The main industrial characteristic considered is whether a country has a main shipbuilding nation. This influences the coefficients attributed for Sector E (ships, port equipment and parts thereof). A main shipbuilding nation is defined as a country of which the national shipbuilding industry has a global market share over one percent OR in which large domestic cargo handling equipment manufacturers can be found. The countries classified as main shipbuilding nations are: China, Japan, the Republic of Korea, Germany, Poland, Italy, Finland, Norway, India, Turkey, the United States of America, and Switzerland.
 
2.2. Sector characteristics
The Sustainable Ocean Economy sectors are divided into single-source sectors, regrouping activities that are only ocean-based, and dual-source sectors, which contain activities that are both land-based and ocean-based. 
 
Single-source sectors (A, B, C and E) have coefficients of 1 as they are only relevant to the ocean economy. 
 
Limitations: 
 
Dual-source sectors (D, E, F) have coefficients between 0 and 1 since they include both land-based and ocean-based activities. The coefficients estimating the share of the sector that is ocean-based are estimated using the available literature.
\r\n
\r\n
 
III – Methodology for estimating global and regional aggregates
For the calculation of aggregates of groups of economies, gaps in reporting have been filled with estimated by benchmarking exports and imports on the trends observed in corresponding broader trade segments defined by the following three dimensions: the reporting economy’s region, the imports or export market, and the ocean economy sector. For economies that did not report any data over the entire period of time, imports and exports in the latest years were estimated based on regional shares in total merchandise trade and then back-cast in the same way as above.
 
The export and import value reported for the same trade flow may be different, due to costs for insurance and freight, triangular trade, discrepancies in classification as well differences in the methods of measurement applied by statisticians in the importing and exporting countries.
 

 

","name":"US.OceanTrade","order":103},{"id":2609,"version":10012,"title":"Ocean services: Trade - Annual (analytical)","lastUpdatedDate":"2026-09-18T14:15:00","category":"International trade in goods and services","keywords":"Ocean, oceans, services, international, trade, growth, sectors, maritime, sea","notes":"

Description:
\r\nThis table presents annual statistics on exports and imports of ocean-related services in current dollars by ocean economy sector, by individual economy and selected groups of economies. 
\r\n
\r\nThe export and imports figures are not balanced at the world level owing to cross-country differences in compilation methods and data availability. Non-publishable estimates are included in aggregated data for groups of economies. 

Owing to a low coverage of detailed service categories for some economies, the country aggregates’ estimates lean towards a lower bound. The figures may differ compared with similar indicators published elsewhere in UNCTAD due to method used (described below). The timing of updates should be considered as well when comparing the relevant statistics. 
 
Sources:
-    UNCTADStat, Trade-in-services data set, jointly compiled by UNCTAD and WTO
\r\n-    For the Sustainable Ocean Economy (SOE) classification, UNCTAD (2021b) report “Towards a harmonized international trade classification for the development of sustainable ocean-based economies”
\r\n-    Other national and international sources, for coefficient estimates


\r\nNotes:
\r\nI – Overview of the ocean trade classification
\r\nThe Sustainable Ocean Economy sectors for trade in services are:
\r\nG- Marine and coastal tourism
\r\nH- Trade in fisheries services
\r\nI- Maritime transport and related services
\r\nJ- Port services, related infrastructure services and logistical services
\r\nK- Coastal and marine environmental services
\r\nL- Marine research and development and related licenses
\r\n
\r\nThe Sustainable Ocean Economy sectors for trade in goods are:
\r\nA-    Marine fisheries
\r\nB-    Aquaculture and hatcheries
\r\nC-    Seafood processing
\r\nD-    Sea minerals
\r\nE-    Ships, port equipment and parts thereof
\r\nF-    High-technology and other manufactures not elsewhere classified
\r\n
\r\nII – Methodological constraint: data aggregation 
\r\nHS* codes for trade in goods and EBOPS** codes for trade in services do not differentiate between ocean-based and land-based activities or products. In many cases, several industries are grouped within one code or reported at the aggregated level. As a result, in creating a new database for ocean trade in goods and services by country and sector, UNCTAD had to determine coefficients which estimate for each country the percentage of each economic sector that is ocean-based.

The coefficients are then applied to the dataset to isolate the share of trade that is ocean-related.

   * HS: Harmonized System
\r\n   ** EBOPS: Extended balance of payments services
\r\n
\r\nIII – Methodology for coefficients by country and sector
\r\nThe coefficients are estimated for exports. Import coefficients are mirrored on export coefficients to reduce statistical asymmetries. Data analysis in reports focuses on exports.
\r\n
\r\nCountry characteristics
\r\nGeographical and industrial characteristics of countries were used to estimate coefficients for ocean-economy activities.
\r\n
\r\nThe geographical characteristic retained is the level of proximity of each country to oceans, leading to the following country classification:
\r\n-    Landlocked
\r\n-    Coastal
\r\n-    Large island: island country greater than 100,000km2 (the closest country below this threshold is nearly half of it: Sri Lanka with 65,000km2). 10 countries match this criterion: Indonesia, Madagascar, Papua New Guinea, Japan, Malaysia, Philippines, New Zealand, UK, Cuba, Iceland.
\r\n-    Small Island: island country smaller than 100,000km2. 50 countries. Note: Singapore and Hong Kong are classified as small islands because they have similar characteristics given they are city ports.
\r\n
\r\nFor the tourism sector, two more country groupings are referred to:
\r\n-    Non-island Caribbean countries (Belize, Colombia, Costa Rica, Guatemala, El Salvador, Honduras, Mexico, Nicaragua, Panama)
\r\n-    Non-island Mediterranean countries (Albania, Algeria, Bosnia and Herzegovina, Croatia, Egypt, France, Greece, Israel, Italy, Lebanon, Libya, Malta, Montenegro, Morocco, Slovenia, Spain, State of Palestine, Syrian Arab Republic, Tunisia, Turkey)
\r\nExceptions to these categories are made based on the available literature.
\r\n 
\r\nThe industrial characteristic retained is whether a country is a shipbuilder, which influences the coefficients attributed for Sector E (Ships, port equipment and parts thereof). A shipbuilder is defined as a country with a national shipbuilding industry with a global market share over one percent OR the presence of large domestic cargo handling equipment manufacturers. The countries classified as shipbuilders are: China, Japan, South Korea, Germany, Poland, Italy, Finland, Norway, India, Turkey, US, Switzerland.
\r\n
\r\nSector characteristics
\r\nThe Sustainable Ocean Economy sectors are divided into single-source sectors, regrouping activities that are only ocean-based, and dual-source sectors, which contain activities that are both land-based and ocean-based.
\r\n
\r\nSingle-source sectors (A, B, C, E1, I, J, K1) have coefficients of 1 as they are only relevant to the ocean economy.
\r\n
\r\nLimitations: Sectors A (Marine fisheries) and B (Aquaculture and hatcheries) are included in UNCTADStat as one because trade flow data does not distinguish between aquaculture and fisheries. That said, FAO harvesting and production data could be used to differentiate between these two sectors at the national level but not in relation to trade flows.
\r\n
\r\nDual-source sectors (D, E2, F, G, L) have coefficients between 0 and 1 since they include both land-based and ocean-based activities. The coefficients estimating the share of the sector that is ocean-based are estimated using the available literature.

","name":"US.OceanServices","order":104}],"subFolders":[],"isParentFolder":false},{"id":46,"title":"Trade in non-plastic substitutes","reports":[{"id":2610,"version":10009,"title":"Non-plastics: Bilateral trade of non-plastic substitutes - Annual (analytical)","lastUpdatedDate":"2026-10-02T12:38:00","category":"Environment and related trade","keywords":"Plastics, trade, export, Non-plastic substitutes","notes":"

Disclaimer:
\r\nThis table, derived from UN Comtrade data, reflects values as reported by member states. This statistical series provided by UNCTAD offers insights into global non-plastic substitutes trade patterns, but it is still in its experimental phase and requires refinement. Labeled as experimental, ongoing testing and development may result in some uncertainties. The user's feedback is crucial for enhancing accuracy and reliability. Additionally, note that volume data reports may have discrepancies, requiring careful analysis, especially at the country level.

Keywords:
\r\nTrade, exports, imports, non-plastic substitutes, raw materials, products.

Description:
\r\nThis table presents data for trade in non-plastics substitutes, with a focus on trade by partner. It provides a comprehensive resource on natural, environmentally sustainable substitutes to plastics. These substitutes include materials derived from mineral, plant, animal, marine, or forestry sources with similar properties to plastics - excluding fossil fuel-based or synthetic polymers - bioplastics, and biodegradable plastics. Selected substitutes meet specific environmental standards, ensuring lower impacts throughout their life cycles and suitability for reuse, recycling, composting, or safe disposal as defined by national or international regulations.

Beyond environmental benefits, this shift towards plastic substitutes presents significant economic opportunities, particularly for developing economies. Non-plastic substitutes can foster new productive capacities, boost exports, and create higher-value employment while supporting gender equality and adaptive labor changes.

The hierarchy table used for the aggregation of Harmonized-System six-digit codes and subheading descriptions to the categories of product groups of the non-plastics substitutes trade are available on the Data Hub Classifications website.

Source:
\r\nUNCTAD calculation based on UNSD Comtrade database.

Notes:
\r\nUsers should be aware that some reported figures, particularly in trade volumes, may appear inconsistent or implausible. These anomalies can stem from a variety of factors, including differences in classification practices, the presence of transshipment or re-export activities, or challenges in accurately capturing unit values. In some cases, underreporting or undervaluation may reflect broader systemic or procedural issues.

Country group classifications may differ between the non-plastic substitutes and plastics data tables. As a result, the composition of some groups may vary slightly between the tables.

","name":"US.NonPlasticSubstsTradeByPartner","order":106}],"subFolders":[],"isParentFolder":false},{"id":24,"title":"Trade in plastic-based products","reports":[{"id":2581,"version":10017,"title":"Plastics: Bilateral trade across plastics life-cycle stages - Annual (analytical)","lastUpdatedDate":"2026-09-04T12:05:00","category":"Plastics trade","keywords":"Plastics, trade, export","notes":"

Disclaimer:

This table, derived from UN Comtrade data, reflects values as reported by member states. This statistical series provided by UNCTAD offers insights into global plastic trade patterns, but it is still in its experimental phase and requires refinement. Labeled as experimental, ongoing testing and development may result in some uncertainties. The user's feedback is crucial for enhancing accuracy and reliability. Additionally, note that volume data reports may have discrepancies, requiring careful analysis, especially at the country level.

Description:

This table presents data for trade in plastics across five stages of the life-cycle of plastics, with a focus on trade by partner. It presents data for trade by partner in primary forms of plastics, intermediate forms of plastics, intermediate manufactured plastic products, final manufactured plastic products, and plastic waste.

The hierarchy table used for the aggregation of Harmonized-System six-digit codes and subheading descriptions to the categories of product groups of the “associated” plastics trade is available on the UNCTADstat Classifications website.

Source:

UNCTAD calculation based on UN Comtrade database.

Notes:

Users should be aware that some reported figures, particularly in trade volumes, may appear inconsistent or implausible. These anomalies can stem from a variety of factors, including differences in classification practices, the presence of transshipment or re-export activities, or challenges in accurately capturing unit values. In some cases, underreporting or undervaluation may reflect broader systemic or procedural issues.

","name":"US.PlasticsTradebyPartner","order":108},{"id":2582,"version":10017,"title":"Plastics: Bilateral trade of plastics associated products - Annual (analytical)","lastUpdatedDate":"2026-09-04T12:05:00","category":"Plastics trade","keywords":"Associated plastics, trade, partner","notes":"

Disclaimer:

This table, derived from UN Comtrade data, reflects values as reported by member states. This statistical series provided by UNCTAD offers insights into global plastic trade patterns, but it is still in its experimental phase and requires refinement. Labeled as experimental, ongoing testing and development may result in some uncertainties. The user's feedback is crucial for enhancing accuracy and reliability. Additionally, note that volume data reports may have discrepancies, requiring careful analysis, especially at the country level.

Description:

This table presents data for two categories of "associated" trade flows that are relevant to the life cycle of plastics, namely raw materials and additives that may be intended for use in plastics.

The hierarchy table used for the aggregation of Harmonized-System six-digit codes and subheading descriptions to the categories of product groups of the “associated” plastics trade is available on the UNCTADstat Classifications website.

Source:

UNCTAD calculation based on UN Comtrade database.

Notes:

Users should be aware that some reported figures, particularly in trade volumes, may appear inconsistent or implausible. These anomalies can stem from a variety of factors, including differences in classification practices, the presence of transshipment or re-export activities, or challenges in accurately capturing unit values. In some cases, underreporting or undervaluation may reflect broader systemic or procedural issues.

","name":"US.AssociatedPlasticsTradebyPartner","order":109},{"id":2583,"version":10017,"title":"Plastics: Bilateral trade of plastic packaging, synthetic textiles and synthetic rubber - Annual (analytical)","lastUpdatedDate":"2026-09-04T12:05:00","category":"Plastics trade","keywords":"Plastics, trade, partner, hs, product","notes":"
Disclaimer:
 
This table spotlights data for trade flows for three selected topics in plastics trade: empty plastic packaging (in bulk intermediate and final forms); synthetic textiles (from primary products to final manufactured products); and synthetic rubber (from primary products to waste).
 
The hierarchy table used for the aggregation of Harmonized-System six-digit codes and subheading descriptions to the categories of product groups of these three selected topics in plastics trade is available on the UNCTADstat Classifications website.
 
Description:
 
This table presents data for two categories of "associated" trade flows that are relevant to the life cycle of plastics, namely raw materials and additives that may be intended for use in plastics.
 
The hierarchy table used for the aggregation of Harmonized-System six-digit codes and subheading descriptions to the categories of product groups of the “associated” plastics trade is available on the UNCTADstat Classifications website.
 
Source:
 
UNCTAD calculation based on UN Comtrade database.
 
Notes:
 
Users should be aware that some reported figures, particularly in trade volumes, may appear inconsistent or implausible. These anomalies can stem from a variety of factors, including differences in classification practices, the presence of transshipment or re-export activities, or challenges in accurately capturing unit values. In some cases, underreporting or undervaluation may reflect broader systemic or procedural issues.
 
","name":"US.HiddenPlasticsTradebyPartner","order":110}],"subFolders":[],"isParentFolder":false},{"id":47,"title":"Trade in green industrial goods","reports":[{"id":2446,"version":10001,"title":"Green industrial goods: Trade - Annual (Analytical)","lastUpdatedDate":"2026-06-12T13:45:00","category":"Environment and related trade","keywords":"Trade, exports, imports, green goods, sector, HS, products, industrial","notes":"

Description:

This table shows the trade value of exports and imports of green industrial goods (EG), expressed in thousands of United States dollars, grouped into different environmental sectors, as identified in the Combined List of Environmental Goods (CLEG), created by the Organization for Economic Co-operation and Development (OECD). See Sauvage (2014) and Garsous (2019) for the list of EG and detailed methodology. There is no international consensus on the definition of EG. These are understood as products that are thought to be more environmentally friendly. They are designed to be more energy-efficient, use fewer resources, and emit less pollution than their traditional counterparts. 
 
The CLEG consists of 248 products, classified according to the Harmonized System (HS) at the 6-digit level. One of the limitations of the CLEG is that it is based on the Harmonized System (HS) version of 2007, which means that the latest developments in green industrial goods are not duly included. Another limitation consists in a possible inclusion of products used for non-environmental purposes, partly due to the difficulty in identifying clear environmental content of a given product at a six-digit HS level and the large number of goods with multiple uses. 
 
OECD identified correspondent codes of EG in the HS versions up to HS 2017 (Garsous, 2019). Following the UN Statistics Division (UNSD) approach to convert a code of a given version into only one code of another HS version, UNCTAD identified correspondent codes of EG in the HS 2022. The retained code rule was used by UNCTAD to assign the HS 2022 code to the code itself, if one of the correlated subheadings is identical with the HS 2017 subheading. Otherwise, if no correlated codes were identical, the corresponding code with the largest trade share among the correlated codes in 2023 was manually assigned. The following HS 2022 codes, corresponding to the largest trade shares in 2023 within their respective groups of successor products derived from the original HS 2007 codes, were retained for the mapping from HS 2007 to HS 2022: 701919; 701969; 851419; 851439; 854143; 902789; 940511; 940529; 940542. Trade shares were calculated as the share of each HS 2022 product in the total 2023 trade value of the group of HS 2022 products originating from the same HS 2007 code. 
 
Variations in the volume of trade are possible for some categories of goods in the years of the update of the HS, due to changes in their definition in the HS. 
 
The correspondence table for the HS versions and environmental sectors used for the HS six-digit codes and subheading descriptions to the categories of product groups are available on the Data Hub Classifications website.
 
Given the absence of a harmonized international classification of green industrial goods and the use of unvalidated estimates for some economies, this table is released as analytical and should not be interpreted as official statistics.
 

Source:

UNCTAD calculation based on UNSD Comtrade database.

References:

Sauvage, J. (2014), “The Stringency of Environmental Regulations and Trade in Environmental Goods”, OECD Trade and Environment Working Papers, No. 2014/3, OECD Publishing, Paris, https://doi.org/10.1787/5jxrjn7xsnmq-en.

Garsous, G. (2019), “Trends in policy indicators on trade and environment”, OECD Trade and Environment Working Papers, No. 2019/01, OECD Publishing, Paris, https://doi.org/10.1787/b8d2bcac-en.

","name":"US.EnvironmentalGoodsTrade","order":112},{"id":2445,"version":10001,"title":"Green industrial goods: Revealed comparative advantage index - Annual (analytical)","lastUpdatedDate":"2026-06-12T13:45:00","category":"Environment and related trade","keywords":"Export, merchandise, goods, environment, green sector, trade, Revealed Comparative Advantage, RCA, environmental goods, green goods","notes":"
Description: 
Revealed comparative advantage (RCA) is based on Ricardian trade theory, which suggests that patterns of trade among countries are governed by their relative differences in productivity. Although such productivity differences are difficult to observe directly, an RCA metric can be readily calculated using trade data to "reveal" such differences. While the metric provides a general indication and first approximation of a country's competitive export strengths, it should be interpreted with appropriate caution (Balassa, 1965).
 
In the context of green industrial goods, RCA is used to assess a country’s relative export performance in this sector. For total environmental goods (TEG), the RCA is calculated using total exports of all products as the denominator, thereby indicating whether a country is more or less specialized in green industrial goods relative to the global average. For sub-groups of environmental goods - such as those related to air pollution control, heat and energy management, etc. - the RCA is computed using TEG exports as the reference point. This approach highlights patterns of specialization within the broader environmental goods sector and allows for more targeted insights.
 
When a country has a revealed comparative advantage for a given product (i.e., RCA > 1), it is inferred to be a relatively competitive producer and exporter of that product compared to the global average. In other words, a country with an RCA greater than 1 in environmental sector i is considered to have an export strength in that sector. The higher the RCA value, the stronger the inferred specialization or competitiveness in that particular good.
 
This table shows the RCA of environmental goods sectors.
 
Given the absence of a harmonized international classification of green industrial goods and the use of unvalidated estimates for some economies, this table is released as analytical and should not be interpreted as official statistics.
 
 
Source:
UNCTAD calculations based on the UNCTADstat table “Total trade in green industrial goods (analytical)”.
 
Notes:

Country A is said to have a revealed comparative advantage in TEG when the ratio of its total exports of green industrial goods to its total exports of all traded products is greater than the same ratio for the world:

\"RCA

Where

When a country has a revealed comparative advantage for a given product (RCA >1), it is inferred to be a competitive producer and exporter of that product relative to a country producing and exporting that good at or below the world average. A country with a revealed comparative advantage in product i is considered to have an export strength in that product. The higher the value of a country’s RCA for product i, the higher its export strength in product i.

 

","name":"US.EnvironmentalGoodsRCA","order":113},{"id":2447,"version":10001,"title":"Industrial wastewater treatment: Trade of associated products - Annul (ananlytical)","lastUpdatedDate":"2026-06-12T13:45:00","category":"Environment and related trade","keywords":"Trade, exports, imports, industrial wastewater, wastewater treatment, water treatment technologies, wastewater recovery, HS codes, products","notes":"

Description:

This table shows the trade value of exports and imports of selected products (and components) associated with industrial wastewater treatment and recovery technologies, expressed in thousands of United States dollars at current prices.

The “industrial wastewater treatment technologies” analytical group consists of 16 products, defined at the Harmonized System (HS) six-digit subheading level using HS 2022, covering integrated treatment systems and related parts and components.

The correspondence table for the HS versions used for the HS six-digit codes, subheading descriptions and product-group categories is available on the Data Hub Classifications website.

Source:

UNCTAD calculations based on the UNSD Comtrade database. The analytical product list draws on UNCTAD SMEP Programme research developed in the brief Beyond compliance: Trade as a channel for the adoption of industrial wastewater treatment in the Global South (2025).

 

Notes:

  1. The product list is analytical and indicative. The HS six-digit subheadings serve as proxies for selected products associated with industrial wastewater treatment and are defined using HS 2022. Trade values reflect the movement of these goods; they do not indicate end use, nor related services such as engineering, design and construction, installation, maintenance and operation or measure compliance, water reuse, pollution reduction or environmental performance.
  2. The codes do not distinguish industrial and non-industrial applications. In particular, HS 842121 covers water-filtering and purifying machinery, including both industrial and household water-treatment systems.
  3. Many parts and components, such as pumps, compressors and control panels, are used across a wide range of industries, so aggregate values may overstate trade that is specific to industrial wastewater treatment. Figures should therefore be read as trade in products relevant to industrial wastewater treatment, not as evidence of technology adoption or environmental impact.
  4. HS subheading 902789 was introduced in HS 2022. Where included in the published dataset, totals incorporating this subheading are not directly comparable before and after 2022, and time-series comparisons involving this subheading should be interpreted accordingly.
     
    5. \r\n

 

","name":"US.WastewaterTreatment","order":114}],"subFolders":[],"isParentFolder":false}],"isParentFolder":true},{"id":36,"title":"Digital economy and technology","reports":[],"subFolders":[{"id":19,"title":"Digital economy","reports":[{"id":2238,"version":10008,"title":"ICT producing sector: Workforce involved and value added - Annual","lastUpdatedDate":"2026-04-20T14:48:00","category":"Information Economy","keywords":"ICT producing sector, employment, value added","notes":"

Description:
\r\nThis table contains statistics on two core indicators of the ICT producing sector: the proportion (%) of total business sector workforce involved in the ICT sector, and value added in the ICT sector as a percentage of total business sector value added.

Sources:
\r\n- UNCTAD, Division on Technology and Logistics, Digital Economy Database
\r\n- Eurostat : https://ec.europa.eu/eurostat/databrowser/view/isoc_bde15ap__custom_11974213/default/table?lang=en
\r\nhttps://ec.europa.eu/eurostat/web/digital-economy-and-society/database
\r\nEconomies: Albania, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Malta, Netherlands, North Macedonia, Norway, Poland, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom
\r\n- OECD: Economies: Costa Rica, Israel, Republic of Korea, Serbia
\r\n- UNCTAD: Data provided by National Statistical Authorities
\r\nEconomies: Azerbaijan, Belarus, Brunei Darussalam, Ecuador, Georgia, India, Japan, Kenya, Malawi, Malaysia, Mauritius, Mexico, Mongolia, Paraguay, Peru, Russian Federation, South Africa, State of Palestine, Tunisia, Türkiye

Notes:
\r\nFor more information on definitions and on the core indicators, please refer to the UNCTAD Manual for the Production of Statistics on the Digital Economy 2020. The core indicators were developed by the Partnership on Measuring ICT for Development after a consultation process involving NSOs worldwide and were endorsed by the United Nations Statistical Commission (UNSC) at its 38th session in March 2007. The core list was revised in 2008, 2016 and 2020 and the latest version was presented to the UNSC at its 53rd session in 2022.

The ICT sector and the business sector are calculated using the ISIC Rev.3.1 and ISIC Rev.4 classification and definitions.

","name":"US.IctProductionSector","order":117},{"id":2533,"version":10002,"title":"ICT goods: Bilateral trade by category - Annual","lastUpdatedDate":"2025-12-01T13:16:00","category":"Digital economy","keywords":"Digital Economy, Information Economy, International Trade, ICT goods, Imports, Exports, share, bilateral flows","notes":"

Description: 
\r\nThis table presents bilateral exports, imports, re-exports and re-imports of information and communication technology (ICT) goods in accordance with the definition of ICT products given in the OECD Guide on Measuring the Information Society 2011. The ICT goods trade value is expressed in millions of dollars and also as a percentage of total merchandise import and export flows, these comprise two of the core indicators defined by the Partnership on Measuring ICT for Development.

Regional aggregates are also presented, as is a disaggregation of ICT goods into:

Source: 
\r\nUNCTAD, Digital Economy Database, based on UN DESA Statistics Division, UN Comtrade.


\r\nNotes:
\r\nThe underlying data are reported by national authorities with product details coded according to the World Customs Organization (WCO) Harmonized Commodity Description and Coding System (HS) 1992, HS 1996, HS 2002, HS 2007, HS 2012, HS 2017 or HS 2022, depending on the individual reporting country and year. In cases where multiple HS classifications are available for a given economy and year, preference is given to the latest HS edition. The data are downloaded from UN Comtrade and aggregated into ICT groups by UNCTAD. 

The product categorization specified in each successive edition of HS is assessed against the definition of ICT products (OECD, 2011) – “ICT products must primarily be intended to fulfil or enable the function of information processing and communication by electronic means, including transmission and display” – to identify which products should be categorized as ICT goods (see UNCTAD, 2023). This results in a separate list of ICT goods under each HS, available online at:
http://unctadstat.unctad.org/EN/Classifications.html
 
The cells of countries that did not report any data to UN Comtrade in a specific year are filled with a dash ("-”). Their values have been estimated for the calculation of regional aggregates. These country-level estimates are not sufficiently precise to be published. The regional aggregates are themselves only published if data are available for a sufficient proportion of underlying economies.
 
Trade in ICT goods is one of a suite of core indicators developed by the Partnership on Measuring ICT for Development through a consultation process involving national statistical offices worldwide. These indicators have been endorsed by UN member states through the United Statistical Commission. For more information, please refer to the UNCTAD Manual for the Production of Statistics on the Digital Economy 2020 (UNCTAD 2021).
 
 
References:
UNCTAD, 2023. “Trade in ICT goods statistics: Impacts of the 2022 update to the Harmonized Commodity Description and Coding System”, UNCTAD Technical Notes on Measuring ICT for Development No.20. Unedited, TN/UNCTAD/ICT4D/20. Available at:
https://unctad.org/system/files/official-document/tn_unctad_ict4d20_en.pdf
 
UNCTAD, 2021. “Manual for the production of statistics on the Digital Economy 2020”. Available at: https://unctad.org/publication/manual-production-statistics-digital-economy-2020 
 
UNCTAD and UNSD 2018. Updating the Partnership Definition of ICT Goods from HS 2012 to HS 2017, UNCTAD Technical Notes on Measuring ICT for Development No.10. Unedited, TN/UNCTAD/ICT4D/10. Available at:
https://unctad.org/en/PublicationsLibrary/tn_unctad_ict4d10_en.pdf
 
UNCTAD and UNSD 2014. Updating the Partnership Definition of ICT Goods from HS 2007 to HS 2012, UNCTAD Technical Notes on Measuring ICT for Development No. 2. Unedited, TN/UNCTAD/ICT4D/02. Available at: http://unctad.org/en/PublicationsLibrary/tn_unctad_ict4d02_en.pdf
 
OECD 2011. OECD Guide to Measuring the Information Society 2011, OECD Publishing, Paris. Available at: https://doi.org/10.1787/9789264113541-en
\r\n 
","name":"US.IctGoods","order":118},{"id":2347,"version":10009,"title":"ICT use in business: Core indicators - Annual","lastUpdatedDate":"2026-09-14T09:05:00","category":"Digital economy","keywords":"ICT indicators","notes":"
Description: 
This table contains statistics on 12 core indicators on the use of information and communication technology (ICT) in business differentiated by urban - rural location.
 
Source:
UNCTAD, Division on Technology and Logistics, Digital Economy Database
 
Notes: 
For more information on definitions and on the core indicators, please refer to the UNCTAD Manual for the Production of Statistics on the Digital Economy 2020. The core indicators were developed by the Partnership on Measuring ICT for Development after a consultation process involving NSOs worldwide and were endorsed by the United Nations Statistical Commission (UNSC) at its 38th session in March 2007. The core list was revised in 2008, 2016 and 2020 and the latest version was presented to the UNSC at its 53rd session in 2022.
 
Data are collected by UNCTAD from national statistical offices through an annual survey. Eurostat provides data for the Eurostat countries. OECD provides data for other than Eurostat countries. Other national data sources and estimates are also used.
 
Coverage:
The firm sizes and industries covered can vary across economies. For the following economies, micro-enterprises (firms with 0-9 persons employed) are not covered: Albania; Austria; Belgium; Bosnia and Herzegovina; Brazil; Bulgaria; Croatia; Cyprus; Czechia; Denmark; Egypt; Estonia; Finland; France; Germany; Greece; Hungary; Iceland; Ireland; Italy; Republic of Korea; Latvia; Lithuania; Luxembourg; Malta; Montenegro; Netherlands; Norway; Poland; Portugal; Romania; Serbia; Slovakia; Slovenia; Spain; Sweden; Türkiye; North Macedonia; United Kingdom.
 
","name":"US.IctUseTotal","order":119},{"id":2064,"version":10006,"title":"ICT use in business: Core indicators by location type - Annual","lastUpdatedDate":"2025-12-08T09:44:00","category":"Information Economy","keywords":"ICT indicators, enterprises, location","notes":"

Description:
\r\nThis table contains statistics on 12 core indicators on the use of information and communication technology (ICT) in business differentiated by urban - rural location.

Source:
\r\nUNCTAD, Division on Technology and Logistics, Digital Economy Database 

Notes:
\r\nFor more information on definitions and on the core indicators, please refer to the UNCTAD Manual for the Production of Statistics on the Digital Economy 2020. The core indicators were developed by the Partnership on Measuring ICT for Development after a consultation process involving NSOs worldwide and were endorsed by the United Nations Statistical Commission (UNSC) at its 38th session in March 2007. The core list was revised in 2008, 2016 and 2020 and the latest version was presented to the UNSC at its 53rd session in 2022.

Data are collected by UNCTAD from national statistical offices through an annual survey. Eurostat provides data for the Eurostat countries. Other national data sources and estimates are also used.

","name":"US.IctUseLocation","order":120},{"id":2266,"version":10008,"title":"ICT use in business: Core indicators by enterprise size - Annual","lastUpdatedDate":"2026-04-20T14:49:00","category":"Information Economy","keywords":"ICT indicators, enterprise size","notes":"
Description: 
This table contains statistics on 12 core indicators on the use of information and communication technology (ICT) in business by enterprise size class.
 
Sources:
- UNCTAD, Division on Technology and Logistics, Digital Economy Database
\r\n
- Eurostat: Economies: Albania, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Montenegro, Netherlands, North Macedonia, Norway, Poland, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Türkiye, United Kingdom
- OECD: Economies: Australia, Brazil, Canada, Colombia, Israel, Japan, Mexico, New Zealand, Republic of Korea, Switzerland, United Kingdom, United States of America
- UNCTAD: Data provided by National Statistical Authorities. Economies: Azerbaijan, Belarus, Bolivia (Plurinational State of), Brunei Darussalam, Chile, China, Macao Special Administrative Region, Dominican Republic, Ecuador, Egypt, Georgia, India, Indonesia, Kazakhstan, Kenya, Lebanon, Malawi, Mauritius, Mexico, Mongolia, North Macedonia, Oman, Paraguay, Peru, Philippines, Qatar, Saudi Arabia, Singapore, South Africa, State of Palestine, Switzerland, Thailand, Tunisia, Ukraine, United Arab Emirates, Uruguay
\r\n
\r\nNotes:
\r\nFor more information on definitions and on the core indicators, please refer to the UNCTAD Manual for the Production of Statistics on the Digital Economy 2020. The core indicators were developed by the Partnership on Measuring ICT for Development after a consultation process involving NSOs worldwide and were endorsed by the United Nations Statistical Commission (UNSC) at its 38th session in March 2007. The core list was revised in 2008, 2016 and 2020 and the latest version was presented to the UNSC at its 53rd session in 2022.

Data are collected by UNCTAD from national statistical offices through a survey. Eurostat provides data for the Eurostat countries. Other national data sources and estimates are also used.

","name":"US.IctUseEnterprSize","order":121},{"id":2250,"version":10009,"title":"ICT use in business: Core indicators by economic activity (ISIC Rev. 4) - Annual","lastUpdatedDate":"2026-04-20T14:52:00","category":"Information economy","keywords":"ICT indicators, industrial classification, economic activity, ISIC","notes":"

Description:
\r\nThis table contains statistics on 12 core indicators on the use of information and communication technology (ICT) in business by industrial classification of economic activity (ISIC Rev. 4).

Sources: 
\r\n- UNCTAD, Division on Technology and Logistics, Digital Economy Database
\r\n- Eurostat. Economies: Albania, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Montenegro, Netherlands, North Macedonia, Norway, Poland, Portugal. Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Türkiye, United Kingdom
\r\n- OECD. Economies: Australia, Brazil, Canada, Colombia, Israel, Mexico, New Zealand, Republic of Korea, Switzerland, United Kingdom, United States of America
\r\n- UNCTAD: Data provided by National Statistical Authorities. Economies: Andorra, Azerbaijan, Belarus, Bolivia (Plurinational State of), Brunei Darussalam, Chile, China, Dominican Republic, Ecuador, Egypt, Georgia, India, Indonesia, Japan, Kazakhstan, Kenya, Malawi, Malaysia, Mauritius, Mexico, Mongolia, Paraguay, Peru, Philippines, Qatar, Republic of Moldova, Russian Federation, Saudi Arabia, South Africa, State of Palestine, Switzerland, Thailand, Tunisia.
\r\n
\r\nNotes:
\r\nFor more information on definitions and on the core indicators, please refer to the UNCTAD Manual for the Production of Statistics on the Digital Economy 2020. The core indicators were developed by the Partnership on Measuring ICT for Development after a consultation process involving NSOs worldwide and were endorsed by the United Nations Statistical Commission (UNSC) at its 38th session in March 2007. The core list was revised in 2008, 2016 and 2020 and the latest version was presented to the UNSC at its 53rd session in 2022.

This table is prepared using ISIC Rev.4 for the classification of economic activities. The available data for ISIC Rev. 3.1 classification are presented in a separate table.

Data are collected by UNCTAD from national statistical offices through asurvey. Eurostat provides data for the Eurostat countries.

","name":"US.IctUseEconActivity_Isic4","order":122},{"id":2114,"version":10007,"title":"ICT use in business: Core indicators by economic activity (ISIC Rev. 3.1) - Annual","lastUpdatedDate":"2025-12-08T09:44:00","category":"Information Economy","keywords":"ICT indicators, Industrial Classification, economic activity, ISIC","notes":"

Description:
\r\nThis table contains statistics on 12 core indicators on the use of information and communication technology (ICT)  in business by industrial classification of economic activity (ISIC Rev. 3.1).
\r\n
\r\nSources:
\r\nUNCTAD, Division on Technology and Logistics, the Digital Economy Database

Notes:
\r\nFor more information on definitions and on the core indicators, please refer to the UNCTAD Manual for the Production of Statistics on the Digital Economy 2020. The core indicators were developed by the Partnership on Measuring ICT for Development after a consultation process involving NSOs worldwide and were endorsed by the United Nations Statistical Commission (UNSC) at its 38th session in March 2007. The core list was revised in 2008, 2016 and 2020 and the latest version was presented to the UNSC at its 53rd session in 2022. This table is prepared using ISIC Rev.3.1 for the classification of economic activities. Where the data is available for ISIC Rev. 4, is presented in a separate table.

Data are collected by UNCTAD from national statistical offices through an annual survey. Eurostat provides data for the Eurostat countries, but the Eurostat online database has more recent data.

 

","name":"US.IctUseEconActivity","order":123},{"id":2631,"version":10007,"title":"Digitally deliverable services: Trade - Annual (analytical)","lastUpdatedDate":"2026-09-29T13:47:00","category":"Information economy","keywords":"digital, digitally, deliverable, information, communication, technology, services, exports, imports, trade","notes":"

Description:
\r\nThis table presents annual statistics on international trade in digitally deliverable services of individual economies and various groups of economies. Export and import flows are included, but imports for groups of economies are presented only for total digitally deliverable services. Covering for missing values, the data set contains non-publishable and experimental estimates, which are included in total digitally deliverable services value for individual economies, as well as in regional and global totals. More detail on the methodology is described in the Notes below.

The figures are shown in four different measures:
\r\n- millions of current United States dollars
\r\n- annual percentage changes (growth rates)
\r\n- share of digitally deliverable services in total services
\r\n- share of digitally deliverable services sub-categories in total digitally deliverable services
\r\n
\r\nSources:
\r\nUNCTAD estimates, based on UNCTAD-WTO Trade in Services Data Set
\r\n
\r\nNotes:
\r\nDigitally deliverable services comprise those services which can be delivered remotely over computer networks. It is important to note that a product being digitally deliverable does not mean that it is always digitally delivered when traded internationally. Digitally deliverable trade will therefore be greater than trade that is actually digitally delivered.
\r\n
\r\nThese statistics are compiled by aggregating relevant products available in international services trade datasets as identified in Chapter 4 of the IMF-OECD-UNCTAD-WTO Handbook on Measuring Digital Trade (2023). The following EBOPS-2010* services categories are covered: insurance and pension services; financial services; charges for the use of intellectual property n.i.e.; telecommunications, computer and information services; research and development services; professional and management consulting services; architectural, engineering, scientific and other technical services; trade-related services; other business services n.i.e.; audio-visual and related services; health services and education services (excluding those consumed during international travel); heritage and recreational services.
\r\n
\r\nThe digital intermediation services provided by digital intermediation platforms (DIPs) should, in principle, be recorded within trade-related services. In practice, these may not be accurately accounted for in most countries. Furthermore, digitally deliverable services consumed while abroad (Mode 2 services supply) should also be included in principle. However, these are recorded within the EBOPS-2010 category “travel” and cannot be separated out from the available statistics.
\r\n
\r\nFor more information, see the IMF-OECD-UNCTAD-WTO Handbook on Measuring Digital Trade (2023)  https://unctad.org/publication/handbook-measuring-digital-trade
\r\n
\r\n*EBOPS-2010: Extended Balance of Payments Services Classification (2010)
\r\n
\r\nMethodological notes:
\r\nStatistics on trade in digitally deliverable services were compiled by aggregating relevant products from the base UNCTAD-WTO trade in services dataset (https://unctadstat.unctad.org/datacentre/dataviewer/US.TradeServCatTotal). (further referred to as “base data set”.) Where the product details needed to compile digitally delivered services are unavailable, estimations were undertaken.

The following services categories are well covered in the base data set: insurance and pension services; financial services; charges for the use of intellectual property n.i.e.; telecommunications, computer and information services; audiovisual and related services; research and development services; other business services; and personal cultural and recreational services (the latter two being parent-categories for sub-categories included in the digitally deliverable services.) Where figures for these services were missing, they were estimated by interpolation or extrapolation from values for other years for the same economy.

The following services categories have limited coverage in the base trade in services dataset: professional and management consulting services; architectural, engineering, scientific and other technical services; trade-related services; other business services n.i.e.; health services and education services; and heritage and recreational services. To enable the calculation of regional and global totals, missing values for these categories were estimated based on services items’ growth rates and shares calculated for respective regions. The estimated values for individual economies are not published. 
 
","name":"US.DigitallyDeliverableServices","order":124},{"id":2636,"version":10008,"title":"ICT services: Trade - Annual","lastUpdatedDate":"2026-09-29T13:43:00","category":"Information economy","keywords":"ICT, information, communication, technology, services, exports, imports, trade;","notes":"

Description:

This table presents annual statistics on international trade in ICT services of individual economies and various groups of economies.

The figures are shown in four different measures:

Export and import flows are included, but imports are available for individual economies only.

Sources:

UNCTAD calculations, based on UNCTAD-WTO Trade in Services data set.

Notes:

ICT services are an aggregation of computer and telecommunications services. ICT services were defined by UNCTAD in a technical note in 2015 as well as in a report of the 47th United Nations Statistical Commission in 2016.

The statistics presented correspond to the concepts and definitions of the IMF Balance of Payments and International Investment Position Manual, Sixth Edition (BPM6, 2009). Figures reported according to the fifth edition of the Manual (BPM5) have been adjusted to the BPM6 definitions, provided that such adjustment was possible. When possible, the values missing in principal international sources are estimated by using growth rates derived from the data available in national or other international sources.

References:

IMF, 2009. Balance of Payments and International Investment Position Manual, Sixth Edition (BPM6, 2009). Available at https://www.imf.org/external/pubs/ft/bop/2007/pdf/bpm6.pdf

UNCTAD, 2015. International Trade in ICT Services and ICT-enabled Services: Proposed Indicators from the Partnership on Measuring ICT for Development, Technical Note No 3 Unedited, TN/UNCTAD/ICT4D/03. Available at https://unctad.org/en/PublicationsLibrary/tn_unctad_ict4d03_en.pdf

UNSC, 2016. Report of the Partnership on Measuring Information and Communications Technology for Development: information and communications technology statistics. Note by the Secretary-General. E/CN.3/2016/13. Available at https://unstats.un.org/unsd/statcom/47th-session/documents/2016-13-Partnership-on-measuring-ICT-for-development-E.pdf

","name":"US.TradeServICT","order":125}],"subFolders":[],"isParentFolder":false},{"id":48,"title":"E-commerce","reports":[{"id":2656,"version":10010,"title":"E-commerce: Domestic and international sales - Annual (analytical)","lastUpdatedDate":"2026-10-14T15:16:00","category":"E-commerce","keywords":"e-commerce, digital ordering, digital trade, sales, business statistics","notes":"

Description:
\r\nThis table presents annual figures on the value of e-commerce sales by businesses, expressed in monetary terms and / or as a percentage of sales turnover. These are disaggregated by market (domestic / international), enterprise size, and economic activity when available.
\r\n
\r\nWarning:
\r\nThe figures in this table should be used with caution, especially when making comparisons across economies. There are important comparability limitations relating to the following:

Further explanations are given below and in individual economies’ information, with full details in the E-commerce Economy Notes metadata file.
\r\n
\r\nCompanion table:
For some economies, further details are available on the trading partner regions / economies concerned in international e-commerce flows. These are presented in the separate UNCTADstat table: "E-commerce: International digitally ordered trade, bilateral”.
\r\n
\r\nNotes:
\r\nE-commerce sales
\r\nE-commerce refers to “the sale or purchase of a good or service, conducted over computer networks by methods specifically designed for the purpose of receiving or placing orders” (OECD, 2009; 2025).

Payment and the ultimate delivery of those products do not have to be conducted online. Thus, the only defining feature of an e-commerce transaction is digital ordering.

The most widespread example of a “computer network” is the Internet. However, orders can also be placed and received via other networks, such as private connections between a business or government entity and its suppliers.

E-commerce includes orders placed and received via the seller’s own web site or apps, or via third-party web sites or apps (i.e. e-commerce marketplaces). These are together referred to as “Web sales”.
\r\nIt also includes “EDI-type sales”, in which orders are placed through the transfer of structured data, by agreed message standards, from one computer system to another without human intervention.
\r\nThe breakdown between “Web sales” and “EDI-type sales” is presented where available.

Orders made by telephone calls, facsimiles, and manually typed messages (including but not limited to e-mails) are excluded because, although the orders may happen over computer networks (i.e. the Internet), these are technologies for general purpose communications rather than "specifically designed for the puropose of receiving or placing orders".

It should be noted that some economies deviate from this definition by:

1.     Covering only orders placed over the Internet (i.e. excluding those placed via private networks), which implies that e-commerce may be under-estimated, and / or

2.     Including orders placed via manually typed messages (such those sent via email or messaging apps), which implies that e-commerce may be over-estimated.

These cases are marked with the note “definition differs” in economy information within this table. Relevant details can be found in the  the E-commerce Economy Notes metadata file. 
\r\n
\r\nThe 2025 guidelines for interpretation (OECD, 2025) further clarify that orders placed through "on-premises mechanisms (including but not limited to kiosks or QR codes)" should also be excluded as the buyer and seller are not remote from one another and so "while kiosks and QR codes automate transactions, similar to vending machines, they serve as technology-enabled substitutes for salespersons rather than constituting e-commerce". The inclusion / exclusion status of these orders is not known to UNCTAD but it is likely that they have been neither explicitly included or excluded in most cases.

It is common to also categorise e-commerce sales according to the following typology:
\r\n- B2C: business-to-consumer sales
\r\n- B2B: business-to-business sales
\r\n- B2G: business-to-government sales

Web sales may occur to any of these customers, while EDI-type ordering is used between organisations and hence can be thought of as a sub-component of B2B+G e-commerce.

For more information on the definition of e-commerce, see: OECD (2025), The 2025 OECD definition of e-commerce and guidelines for interpretation, OECD Publishing, Paris, https://doi.org/10.1787/2254f1de-en.

Market
\r\nThe market dimension indicates trading partner: whether e-commerce sales are domestic or international, based on where the customer is located.

At the highest level, domestic sales (where the buyer is located in the same economic territory as the seller) may be distinguished from international sales (where the buyer is located in a different economic territory to the seller). Note that this distinction may only be available for Web sales.

"International e-commerce" is synonymous with "international digitally ordered trade", as set out in the IMF-OECD-UNCTAD-WTO (2023), Handbook on Measuring Digital Trade, https://doi.org/10.1787/ac99e6d3-en. More specifically, international e-commerce sales are “digitally ordered exports”.

For some economies, further details are available on the trading partner regions / economies concerned. These details vary greatly and are presented in the separate UNCTADstat table: ”E-commerce: International digitally ordered trade, bilateral”.

Enterprise size
\r\nFigures in parentheses refer to the number of persons employed by the enterprise.

For some economies, the employment size bands implemented differ (in some cases considerably) from those indicated. These cases are marked with the note "enterprise size coverage" in the individual economies’ information notes. The E-commerce Economy Notes metadata file provides details.

"Enterprise Size: All" reflects the total scope of enterprises covered in the given economy and year. This varies across economies, and may also vary between years. It arises because economies implement differing limitations to the coverage of their e-commerce value statistics which may include employment thresholds, revenue thresholds, or other conditions such as registration with industry bodies or tax authorities. Care should be taken when using this total to make comparisons across economies. 

European Union (EU) members and partner economies implementing the European Community Survey of ICT usage and E-commerce in Enterprises (referred to as "Eurostat source" economies, see the table below for list):  

In most cases, "Enterprise size: All" refers to enterprises with 10+ persons employed. 
For monetary values only, and only for cells where Economic Activity = Total (all available), exceptions can arise due to the availability of figures for firms with <10 persons employed (Micro enterprises), which are included in the "Enterprise size: All" series whenever they are available.
 
For the following EU countries and years, "Enterprise Size: All" cells refer to enterprises of all sizes (i.e. 0+ persons employed): France (2021); Slovakia (2016); Spain: (all periods); Sweden (2024). For Belgium (2024) and Netherlands (2017 onward) enterprises with 2+ persons employed are covered (i.e. enterprises with 0-1 persons employed are excluded).
 
Where the coverage of "Enterprise size: All" changes from one year to the next this is indicated with the following cell-level notes: “Series break: micro enterprises included” when coverage increases due to availability of figures for micro enterprises, “Series break: micro enterprises excluded” when coverage decreases due to micro enterprise figures becoming unavailable. 
 
This approach is taken so that "Enterprise size: All", as far as possible, encompasses the total value of e-commerce transactions measured.
\r\n
\r\nEconomic activity
\r\nThe highest-level aggregate in the economic activity dimension is “Total (all available)”. It provides a figure representing all the economic activities covered in the source data available for a given economy. It offers the most complete view of the overall scale of e-commerce sales. However, the economic activities represented by the total may vary considerably across economies. In some cases there may also be variation between years. Care should be taken when using this total to make comparisons across economies. The table below gives an overview of the economic activities covered and excluded in different economies’ figures. In some cases, there may also be variation between years, with a break in series being indicated in the cell notes.
\r\n
\r\nDetails on coverage and related considerations can be found in the  E-commerce Economy Notes metadata file. 
 
Below the total level, a breakdown into International Standard Industrial Classification of All Economic Activities (ISIC), Rev. 4 Sections, and in some cases Divisions, is provided where possible. However, there may be limitations on the extent to which a given ISIC item is covered. These cases are indicated by "economic activity coverage" given in the individual economies’ information within this table.

Economies vary in the extent of economic activity detail they make available. To help facilitate comparisons, UNCTAD compiles various aggregates of ISIC divisions / sections. Figures calculated by UNCTAD are indicated by the cell note "Derived by UNCTAD".

Where figures are published according to a classification other than the ISIC (such as the North American Industry Classification System (NAICS) used by Canada, Mexico, and the United States), UNCTAD has mapped these to the closest corresponding ISIC Sections / Divisions. Effort has been made to record any deviations in coverage. See economy notes for details on mapping.
\r\n
\r\nCoverage of Economic Activity: Total (all available) across different economies

\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n

Economy

ISIC Rev.4 included

ISIC Rev.4 excluded

Australia

A-N, Q-T.

O, P, K642-3, K653, S94, T97.

Canada

B-N, P-S.

A, O, T, U.

China

B-J, L-O, P-S.

A, K, O (partially), T, U.

Colombia

C, G-J, L-N, P-S.

A, B, D-F, H49-51, K, M75, N813, O, P851-2, P854-5, Q879, Q88, R91, S94, S9512, S952, T, U.

Eurostat source *

C-J, L-N, S951.

A, B, K, M75 (until 2020), O-R, S94, S952, S96, T, U.

Hong Kong, Province of China

C-N, P-S.

A, B, O, T, U.

Indonesia

2018:  A-N, P-S. From 2022 onwards: A, C, G-J, M, N, P-S.

2018: O, T, U. From 2022 onwards: B, D-F, K, L, O, T, U.

Kazakhstan

A, G-N, P-S.

B-F, O, T, U.

Malaysia

A-N, P-S.

O, T, U.

Mexico

B-N, P-S.

A, O, T, U.

Philippines

A-N, P-S.

O, T, U.

Singapore

G-J, L-N, P-S.

A-F, K, O, T, U.

Switzerland

C-L, M71-72, N, S95-96.

A, B, M69-70, M73-75, O, P, Q, R, S94, T, U.

Thailand

C, G-N, P, R, S.

A, B, D-F, K64, K652-3, K66, M69, M71-2, M74-5, N80-81, O, Q, S94, T, U.

United Kingdom

C-J, L-N, S951.

A, B, K, M75, O-R, S94, S952, S96, T, U.

United States of America

C-E, G-N, P-S.

A, B, F, H491, H531, K642-3, K653, P8521, P853, P851/P8522 (partial), M701, O, S942, S949, T, U.

Republic of Korea, Web sales:  The collection covers operators of "online shopping malls" (including both seller's own website/apps and online marketplaces"), regardless of which ISIC they are classified in. Sales are classified by the following "groups of commodities",  which suggest fairly extensive coverage of economic activities: Computers and computer-related appliances; Home electric appliances,  electronic and telecommunication equipment; Books; Office appliances and stationery; Clothing; Footwear; Luggage; Fashion and accessories; Sports and leisure appliances; Cosmetics; Goods for infants and children; Food & beverages; Agriculture, livestock and fishery products; Household goods; Motor vehicle parts and accessories; Furniture; Pet products; Travel arrangement and transportation services; Culture and leisure services; E-coupon services; Food services; Miscellaneous services; Others.
\r\n 
* Eurostat source refers to countries which implement the European Community Survey on ICT usage and E-commerce in business and provide figures to Eurostat for publication: Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Montenegro, Netherlands, North Macedonia, Norway, Poland, Portugal, Romania, Serbia, Slovenia, Slovakia, Spain, Sweden. Includes % turnover figures for the United Kingdom. European Union aggregate is also provided. 


\r\nIndicator
\r\nE-commerce sales can be viewed in monetary values (millions of National Currency or U.S. Dollars) and / or expressed as a percentage of total sales turnover. The E-commerce Economy Notes metadata file provide an overview of the measures available for different economies.

National currency values have been converted to US$ by UNCTAD using IMF annual average exchange rates. For the following economies sourced from Eurostat, "national currency" presents figures in Euros rather than in actual national currency: Bosnia and Herzegovina, Czechia, Denmark, Hungary, Iceland, North Macedonia, Norway, Romania, Serbia, and Sweden.

The inclusion or exclusion of sales taxes (such as Value Added Tax and Goods and Services Tax) varies across economies. In most cases such taxes are excluded from the valuation of e-commerce sales. However, sales taxes are included in figures for China (standard VAT rate 13%), Indonesia (10-12%), Korea (Rep.) (10%), the Philippines (12%), and Thailand (7%).

There may also be variation in the treatment of price reductions, discounts, and rebates.
\r\nFurther details are presented in the economy notes.
\r\n
\r\nDerived by UNCTAD
\r\nIn some cases, e-commerce sales items, market details, enterprise size classes, economic activities, or indicator measures are not available directly from the economy source but can be calculated based on the information available. Figures calculated by UNCTAD are indicated by the cell-note "Derived by UNCTAD". The derivations made by UNCTAD are set out in the E-commerce Economy Notes metadata file.
\r\n
\r\nFurther considerations in using this table:

Sources:
\r\nThis table was compiled by UN Trade and Development (UNCTAD) on the basis of E-commerce value figures published by National Statistical Offices or other competent agencies, including those provided to Eurostat or the OECD.

The underlying data were gathered through various business surveys, with surveys of Information and Communication Technology (ICT) usage in business being most common. The data set is provided for analytical purposes. Care should be taken when making comparisons across economies.

Eurostat, “Monetary value of e-commerce sales by size class of enterprise”
\r\n(https://ec.europa.eu/eurostat/databrowser/product/view/isoc_ec_evalsm?category=isoc.isoc_e.isoc_ec)

Eurostat, “Monetary value of e-commerce sales by NACE Rev.2 activity”
\r\n(https://ec.europa.eu/eurostat/databrowser/product/view/isoc_ec_evaln2m?category=isoc.isoc_e.isoc_ec)

Eurostat, “Value of e-commerce sales by size class of enterprise”
\r\n(https://ec.europa.eu/eurostat/databrowser/product/view/isoc_ec_evals?category=isoc.isoc_e.isoc_ec)

Eurostat, “Value of e-commerce sales by NACE Rev.2 activity”
\r\n(https://ec.europa.eu/eurostat/databrowser/product/view/isoc_ec_evaln2?category=isoc.isoc_e.isoc_ec)

OECD, “ICT access and usage by business”
\r\n(https://oe.cd/dx/ecturnover)

National sources (source information can be accessed via the “i” button for each individual economy).

For more information, see:
\r\nUNCTAD (2023), Measuring the value of e-commerce, https://unctad.org/publication/measuring-value-e-commerce
\r\nUNCTAD (2024), Business e-commerce sales and the role of online platforms, https://unctad.org/publication/business-e-commerce-sales-and-role-online-platforms
\r\nIMF, OECD, UNCTAD and WTO (2023), Handbook on Measuring Digital Trade, https://doi.org/10.1787/ac99e6d3-en
\r\nOECD (2025), The 2025 OECD definition of e-commerce and guidelines for interpretation, https://doi.org/10.1787/2254f1de-en.

 

","name":"US.ECommerceTotal","order":127},{"id":2657,"version":10010,"title":"E-commerce: International digitally ordered trade, bilateral – Annual (analytical)","lastUpdatedDate":"2026-10-14T15:16:00","category":"E-commerce","keywords":"e-commerce, digital trade, digital ordering, sales, turnover, business statistics","notes":"

Description:
\r\nThis table presents annual figures on the value of international e-commerce sales by businesses expressed in monetary terms and / or as a percentage of sales turnover. These are disaggregated by trading partner (regions / economies), enterprise size, and economic activity, when available.

"International e-commerce" is synonymous with "international digitally ordered trade", as set out in the IMF-OECD-UNCTAD-WTO (2023), Handbook on Measuring Digital Trade, https://doi.org/10.1787/ac99e6d3-en. More specifically, international e-commerce sales are “digitally ordered exports”.
 
Warning:
\r\nThe figures in this table should be used with caution, especially when making comparisons across economies. There are important comparability limitations relating to the following:
Further explanations are given below and in individual economies’ information, with full details in the E-commerce Economy Notes metadata file.
\r\n
\r\nCompanion table:
\r\nTotal e-commerce sales (including domestic and international sales) are presented in a separate UNCTADstat table “E-commerce: Domestic and international sales, annual”.
 
Notes:
\r\nE-commerce sales
\r\nE-commerce refers to “the sale or purchase of a good or service, conducted over computer networks by methods specifically designed for the purpose of receiving or placing orders” (OECD, 2009; 2025).

Payment and the ultimate delivery of those products do not have to be conducted online. Thus, the only defining feature of an e-commerce transaction is digital ordering.

The most widespread example of a “computer network” is the Internet. However, orders can also be placed and received via other networks, such as private connections between a business or government entity and its suppliers.

E-commerce includes orders placed and received via the seller’s own web site or apps, or via third-party web sites or apps (i.e. e-commerce marketplaces). These are together referred to as “Web sales”.
\r\nIt also includes “EDI-type sales”, in which orders are placed through the transfer of structured data, by agreed message standards, from one computer system to another without human intervention.
\r\nThe breakdown between “Web sales” and “EDI-type sales” is presented where available.

Orders made by telephone calls, facsimiles, and manually typed messages (including but not limited to e-mails) are excluded because, although the orders may happen over computer networks (i.e. the Internet), these are technologies for general purpose communications rather than "specifically designed for the puropose of receiving or placing orders".

It should be noted that some economies deviate from this definition by:

1.     Covering only orders placed over the Internet (i.e. excluding those placed via private networks), which implies that e-commerce may be under-estimated, and / or

2.     Including orders placed via manually typed messages (such those sent via email or messaging apps), which implies that e-commerce may be over-estimated.

These cases are marked with the note “definition differs” in economy information within this table. Relevant details can be found in the  the E-commerce Economy Notes metadata file. 
\r\n
\r\nThe 2025 guidelines for interpretation (OECD, 2025) further clarify that orders placed through "on-premises mechanisms (including but not limited to kiosks or QR codes)" should also be excluded as the buyer and seller are not remote from one another and so "while kiosks and QR codes automate transactions, similar to vending machines, they serve as technology-enabled substitutes for salespersons rather than constituting e-commerce". The inclusion / exclusion status of these orders is not known to UNCTAD but it is likely that they have been neither explicitly included or excluded in most cases.

It is common to also categorise e-commerce sales according to the following typology:
\r\n- B2C: business-to-consumer sales
\r\n- B2B: business-to-business sales
\r\n- B2G: business-to-government sales

Web sales may occur to any of these customers, while EDI-type ordering is used between organisations and hence can be thought of as a sub-component of B2B+G e-commerce.

For more information on the definition of e-commerce, see: OECD (2025), The 2025 OECD definition of e-commerce and guidelines for interpretation, OECD Publishing, Paris, https://doi.org/10.1787/2254f1de-en.

Market
\r\nIndicates the digitally ordered export trade partner, based on where the e-commerce customer is located. Note that this detail may only be available for Web sales.

Details on the trading partner regions / economies concerned, if available, vary greatly across economies.
 
“European union (evolving membership)” comprises e-commerce sales made to customers in the European Union. These figures would reflect the membership of the European Union as it stood in the relevant reporting period. For EU member states, domestic sales are excluded.
 
For Canada, “World, non-specified” refers to Africa, Europe excluding EU and excluding United Kingdom, and Oceania.
 
Enterprise size
\r\nFigures in parentheses refer to the number of persons employed by the enterprise.

For some economies, the employment size bands implemented differ (in some cases considerably) from those indicated. These cases are marked with the note "Comparability limitations: [...] enterprise size coverage". The E-commerce Economy Notes metadata file provide details.

"Enterprise Size: All" reflects the total scope of enterprises covered in the given economy and year. This varies across economies, and may also vary between years. It arises because economies implement differing limitations to the coverage of their e-commerce value statistics which may include employment thresholds, revenue thresholds, or other conditions such as registration with industry bodies or tax authorities. Care should be taken when using this total to make comparisons across economies. 

European Union (EU) members and partner economies implementing the European Community Survey of ICT usage and E-commerce in Enterprises (referred to as "Eurostat source" economies, see the table below for list):  

In most cases, "Enterprise size: All" refers to enterprises with 10+ persons employed. 
For monetary values only, and only for cells where Economic Activity = Total (all available), exceptions can arise due to the availability of figures for firms with <10 persons employed (Micro enterprises), which are included in the "Enterprise size: All" series whenever they are available.
 
For the following EU countries and years, "Enterprise Size: All" cells refer to enterprises of all sizes (i.e. 0+ persons employed): France (2021); Slovakia (2016); Spain: (all periods); Sweden (2024). For Belgium (2024) and Netherlands (2017 onward) enterprises with 2+ persons employed are covered (i.e. enterprises with 0-1 persons employed are excluded).
 
Where the coverage of "Enterprise size: All" changes from one year to the next this is indicated with the following cell-level notes: “Series break: micro enterprises included” when coverage increases due to availability of figures for micro enterprises, “Series break: micro enterprises excluded” when coverage decreases due to micro enterprise figures becoming unavailable. 
 
This approach is taken so that "Enterprise size: All", as far as possible, encompasses the total value of e‑commerce transactions measured.
 
Economic activity
\r\nThe highest-level aggregate in the economic activity dimension is “Total (all available)”. It provides a figure representing all the economic activities covered in the source data available for a given economy. It offers the most complete view of the overall scale of e-commerce sales. However, the economic activities represented by the total may vary considerably across economies. In some cases, there may also be variation between years. Care should be taken when using this total to make comparisons across economies. The table below gives an overview of the economic activities covered and excluded in different economies’ figures. In some cases, there may also be variation between years, with a break in series being indicated in the cell notes. 
Details on coverage and related considerations can be found in the  E-commerce Economy Notes metadata file. 
 
Below the total level, a breakdown into International Standard Industrial Classification of All Economic Activities (ISIC), Rev. 4 Sections, and in some cases Divisions, is provided where possible. However, there may be limitations on the extent to which a given ISIC item is covered. These cases are indicated by "economic activity coverage" given in the individual economies’ information within this table.

Economies vary in the extent of economic activity detail they make available. To help facilitate comparisons, UNCTAD compiles various aggregates of ISIC divisions / sections. Figures calculated by UNCTAD are indicated by the cell note "Derived by UNCTAD".

Where figures are published according to a classification other than the ISIC (such as the North American Industry Classification System (NAICS) used by Canada, Mexico, and the United States), UNCTAD has mapped these to the closest corresponding ISIC Sections / Divisions. Effort has been made to record any deviations in coverage. 
\r\n
\r\nCoverage of Economic Activity: Total (all available) across different economies

\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n

Economy

ISIC Rev.4 included

ISIC Rev.4 excluded

Australia

A-N, Q-T.

O, P, K642-3, K653, S94, T97.

Canada

B-N, P-S.

A, O, T, U.

China

B-J, L-O, P-S.

A, K, O (partially), T, U.

Colombia

C, G-J, L-N, P-S.

A, B, D-F, H49-51, K, M75, N813, O, P851-2, P854-5, Q879, Q88, R91, S94, S9512, S952, T, U.

Eurostat source *

C-J, L-N, S951.

A, B, K, M75 (until 2020), O-R, S94, S952, S96, T, U.

Hong Kong, Province of China

C-N, P-S.

A, B, O, T, U.

Indonesia

2018:  A-N, P-S. From 2022 onwards: A, C, G-J, M, N, P-S.

2018: O, T, U. From 2022 onwards: B, D-F, K, L, O, T, U.

Kazakhstan

A, G-N, P-S.

B-F, O, T, U.

Malaysia

A-N, P-S.

O, T, U.

Mexico

B-N, P-S.

A, O, T, U.

Philippines

A-N, P-S.

O, T, U.

Singapore

G-J, L-N, P-S.

A-F, K, O, T, U.

Switzerland

C-L, M71-72, N, S95-96.

A, B, M69-70, M73-75, O, P, Q, R, S94, T, U.

Thailand

C, G-N, P, R, S.

A, B, D-F, K64, K652-3, K66, M69, M71-2, M74-5, N80-81, O, Q, S94, T, U.

United Kingdom

C-J, L-N, S951.

A, B, K, M75, O-R, S94, S952, S96, T, U.

United States of America

C-E, G-N, P-S.

A, B, F, H491, H531, K642-3, K653, P8521, P853, P851/P8522 (partial), M701, O, S942, S949, T, U.

Republic of Korea, Web sales:  The collection covers operators of "online shopping malls" (including both seller's own website/apps and online marketplaces"), regardless of which ISIC they are classified in. Sales are classified by the following "groups of commodities",  which suggest fairly extensive coverage of economic activities: Computers and computer-related appliances; Home electric appliances,  electronic and telecommunication equipment; Books; Office appliances and stationery; Clothing; Footwear; Luggage; Fashion and accessories; Sports and leisure appliances; Cosmetics; Goods for infants and children; Food & beverages; Agriculture, livestock and fishery products; Household goods; Motor vehicle parts and accessories; Furniture; Pet products; Travel arrangement and transportation services; Culture and leisure services; E-coupon services; Food services; Miscellaneous services; Others.
\r\n 
* Eurostat source refers to countries which implement the European Community Survey on ICT usage and E-commerce in business and provide figures to Eurostat for publication: Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Montenegro, Netherlands, North Macedonia, Norway, Poland, Portugal, Romania, Serbia, Slovenia, Slovakia, Spain, Sweden. Includes % turnover figures for the United Kingdom. European Union aggregate is also provided. 


\r\nIndicator
\r\nE-commerce sales can be viewed in monetary values (millions of National Currency or U.S. Dollars) and / or expressed as a percentage of total sales turnover. The E-commerce Economy Notes metadata file provide an overview of the measures available for different economies.

National currency values have been converted to US$ by UNCTAD using IMF annual average exchange rates. For the following economies sourced from Eurostat, "national currency" presents figures in Euros rather than in actual national currency: Bosnia and Herzegovina, Czechia, Denmark, Hungary, Iceland, North Macedonia, Norway, Romania, Serbia, and Sweden.

The inclusion or exclusion of sales taxes (such as Value Added Tax and Goods and Services Tax) varies across economies. In most cases such taxes are excluded from the valuation of e-commerce sales. However, sales taxes are included in figures for China (standard VAT rate 13%), Indonesia (10-12%), Korea (Rep.) (10%), the Philippines (12%), and Thailand (7%).

There may also be variation in the treatment of price reductions, discounts, and rebates.
\r\nFurther details are presented in the economy notes.

Derived by UNCTAD
\r\nIn some cases, e-commerce sales items, market details, enterprise size classes, economic activities, or indicator measures are not available directly from the economy source but can be calculated based on the information available. Figures calculated by UNCTAD are indicated by the cell-note "Derived by UNCTAD". The derivations made by UNCTAD are set out in the E-commerce Economy Notes metadata file.

Further considerations in using this table

Sources:
\r\nThis table was compiled by UN Trade and Development (UNCTAD) on the basis of E-commerce value figures published by National Statistical Offices or other competent agencies, including those provided to Eurostat or the OECD.

The underlying data were gathered through various business surveys, with surveys of Information and Communication Technology (ICT) usage in business being most common. The data set is provided for analytical purposes. Care should be taken when making comparisons across economies.

Eurostat, “Monetary value of e-commerce sales by size class of enterprise”
\r\n(https://ec.europa.eu/eurostat/databrowser/product/view/isoc_ec_evalsm?category=isoc.isoc_e.isoc_ec)

Eurostat, “Monetary value of e-commerce sales by NACE Rev.2 activity”
\r\n(https://ec.europa.eu/eurostat/databrowser/product/view/isoc_ec_evaln2m?category=isoc.isoc_e.isoc_ec)

Eurostat, “Value of e-commerce sales by size class of enterprise”
\r\n(https://ec.europa.eu/eurostat/databrowser/product/view/isoc_ec_evals?category=isoc.isoc_e.isoc_ec)

Eurostat, “Value of e-commerce sales by NACE Rev.2 activity”
\r\n(https://ec.europa.eu/eurostat/databrowser/product/view/isoc_ec_evaln2?category=isoc.isoc_e.isoc_ec)

OECD, “ICT access and usage by business”
\r\n(https://oe.cd/dx/ecturnover)

National sources (source information can be accessed via the “i” button for each individual economy).

For more information, see:
\r\nUNCTAD (2023), Measuring the value of e-commerce, https://unctad.org/publication/measuring-value-e-commerce
\r\nUNCTAD (2024), Business e-commerce sales and the role of online platforms, https://unctad.org/publication/business-e-commerce-sales-and-role-online-platforms
\r\nIMF, OECD, UNCTAD and WTO (2023), Handbook on Measuring Digital Trade, https://doi.org/10.1787/ac99e6d3-en
\r\nOECD (2025), The 2025 OECD definition of e-commerce and guidelines for interpretation, https://doi.org/10.1787/2254f1de-en.

 

","name":"US.ECommerceInternational","order":128}],"subFolders":[],"isParentFolder":false},{"id":23,"title":"Technology and innovation","reports":[{"id":1861,"version":10006,"title":"Technology: Frontier technology readiness index - Annual (analytical)","lastUpdatedDate":"2025-07-18T16:34:00","category":"Technology and innovation","keywords":"ICT, skills, information, communication, industry, technology","notes":"

Description:
\r\nThis table presents statistics on the frontier technology readiness index developed by UNCTAD.
\r\nIt includes technological capacities related to physical investment, human capital and technological effort, and covers national capacities to use, adopt and adapt these technologies:
\r\n•    Use – This requires basic capacities, passive skills, and effort along with infrastructure, and some
\r\ntechnological knowledge. This might involve, for example, following Artificial Intelligence (AI)-driven recommendation from an e-commerce website or using a chatbot.
\r\n•    Adopt – Active use for one’s own purposes requires more advanced capability levels. This could mean using AI to produce recommendations or run a chatbot for a business website.
\r\n•    Adapt – Modifying the technologies requires further advanced capabilities – such as for tailoring AI-driven recommendations or localizing the features of a chatbot.
\r\n
\r\nSources:
\r\nUNCTAD calculations, based on data retrieved from ITU, M-Lab, UNDP, ILO, Scopus, Patseer, World Bank and UNCTAD.
\r\n
\r\nNotes:
\r\nAs a result of a review of the literature, UNCTAD’s analytical and technical cooperation work, consultation with experts within and outside UNCTAD, as well as taking into consideration data availability, five building blocks were selected for the index to measure the capacity to use, adopt and adapt frontier technologies: ICT deployment, skills, R&D activity, industry activity and access to finance.
\r\n1.    ICT deployment – This is the level of ICT infrastructure. Using, adopting and adapting frontier technologies requires sufficient ICT infrastructure, especially since AI, Internet of Things, big data and blockchain are internet-based technologies. Two aspects of ICT infrastructure need to be considered: the prevalence to ensure that everyone has access and that no one is left behind; and the quality of infrastructure that allows for more advanced and efficient use. For these purposes, internet users as a percentage of the population captures the prevalence of internet infrastructure, while the mean download speed measures the quality of internet connection.
\r\n2.    Skills – Using, adopting and adapting frontier technologies needs people equipped with relevant skills. These may be advanced but are generally lower than those required to originate the technologies. Two types of skills need to be considered: skills acquired through education, and skills acquired in the workplace through practical training or learning-by-doing. The overall educational attainment of the population is measured through expected years of schooling, while the skill level in the labour market is measured by the extent of high-skill employment – defined by the ILO as the sum of managers, professionals and technicians and associate professionals following the International Standard Classification of Occupations (ISCO). These indicators need to be interpreted with caution, especially in developing countries, because of the emigration of highly trained or skilled people, the “brain drain”, as a result of which the actual skill level could be lower than the official estimate.
\r\n3. R&D activity – R&D activity is needed not just for the production of frontier technologies, but also for adoption and adaption, as these technologies often require adjustment or modification for local use. R&D activities are measured using the number of publications and patents filed on the 17 frontier technologies in a country. The publication and patent search queries used are the same as shown in the Technical note in Annex I in chapter I, of the Technology and Innovation Report 2025 while extending the year to 2023. The countries of publication of authors and patent assignees were analyzed. It should be noted that, especially in developing countries, there are informal R&D activities that do not result in a publication or patent so the R&D scores might not reflect the actual scale of activities.
\r\n4.    Industry activity – This building block aims to capture ongoing activities in an industry related to the use, adoption and adaption of frontier technologies. It considers three sectors that are early adopters: manufacturing, with high-tech manufacturing as the frontrunner; finance; and ICT, which tends to interact with other technologies. Then it uses export data, on high-technology manufactures, as well as on digitally deliverable services which cover both finance and ICT. However, especially in developing countries, activities are also undertaken by firms in the informal sector – which are often outside official statistics. The scores from these countries could therefore be lower than the actual activity.
\r\n5.    Access to finance – This assesses the availability of finance to the private sector. Better access to finance could accelerate the use, adoption and adaptation of frontier technologies. For this purpose, domestic credit to the private sector as a percentage of GDP was selected as part of the index. This indicator measures resources provided by financial corporations such as finance and leasing companies, money lenders, insurance corporations, pension funds and foreign exchange companies. It also includes various financial instruments including loans, purchases of non-equity securities, and trade credits and other accounts receivable. However, there could also be other, unconventional financing providers or financial instruments that are not covered sufficiently by this indicator.
\r\n
\r\nDue to updates in the dataset and corresponding changes in the weighting resulting from the Principal Component Analysis (PCA), the absolute values of the index in this new release may not be directly comparable to those from previous releases. However, it’s recommended to focus on the relative rankings of the economies within the index when making comparisons over time. This approach allows for a more consistent basis of comparison, despite potential variations in the index values.

While the methodology itself has remained unchanged, several notable ranking and score shifts between editions are due to updates in the underlying data. For example, higher values in the “Access to Finance” category reflect updated World Bank figures, while countries such as Zimbabwe saw significant upward movement in the R&D category due to updated publication counts. In contrast, declines in countries like Papua New Guinea and the United Republic of Tanzania were largely attributable to revised data on digitally-deliverable services exports.

","name":"US.FTRI","order":130}],"subFolders":[],"isParentFolder":false}],"isParentFolder":true},{"id":37,"title":"Population and inclusiveness","reports":[],"subFolders":[{"id":16,"title":"Population","reports":[{"id":2329,"version":10002,"title":"Population: Total - Annual","lastUpdatedDate":"2026-05-21T18:32:00","category":"Population and inclusiveness","keywords":"Population, urban, population growth rate","notes":"

Description:

This table provides estimates from 1950 to 2024 and projections (medium fertility variant) from 2025 to 2050 of total population and urban population, expressed in thousands and as a percentage of total population, respectively. It also provides the logarithmic growth rate of the total population. Population refers to de facto population in a country, area or region as of 1 July of the indicated year.
\r\n 

Sources:

Notes:

The figures for certain groups may differ from those published by above-mentioned sources, where UNCTAD definitions for the groups differ.
 
Urban population is defined using the geospatial framework Degree of Urbanization (DEGURBA), endorsed by the United Nations Statistical Commission in 2020 and introduced in World Urbanization Prospects 2025. Since national definitions of urban areas differ across countries, the DEGURBA approach improves international comparability. In this table, urban population refers to the population living in areas classified by DEGURBA as “Cities” and “Towns”. Urban population figures based on national definitions are available through the World Urbanization Prospects 2025 website.
 
The population growth rate refers to the logarithmic growth rate between the mid-year population of the previous year and the mid-year population of the reference year. 
Formula: Growth rate = ln(Population in reference year / Population in previous year) where:

 

","name":"US.PopTotal","order":133},{"id":2330,"version":10002,"title":"Population: Structure by gender and age group - Annual","lastUpdatedDate":"2026-05-21T18:32:00","category":"Population and inclusiveness","keywords":"population, structure, gender, age","notes":"

Description:

This table provides estimates of population by gender and age-group, from 1950 to 2024 and projections from 2025 to 2050 (medium fertility variant), expressed in thousands.
\r\n 

Sources:

UN DESA Population Division, World Population Prospects 2024


\r\nNotes:

De facto population in a country, area or region as of 1 July of the indicated year.
 
Estimates and projections by age group and sex are rounded to the nearest 0.5 person. As a result, the sum of the age groups or the sum of sexes may not match the published totals for all age groups and/or sexes.
 
","name":"US.PopAgeStruct","order":134},{"id":2331,"version":10002,"title":"Population: Dependency ratios - Annual","lastUpdatedDate":"2026-05-21T18:32:00","category":"Population and inclusiveness","keywords":"dependency, total, child, youth, old-age, elderly","notes":"

Description:

This table provides total, child and old-age dependency ratios from 1950 to 2050. Population data used for years 2025-2050 are those of medium fertility variant. 
\r\n 

Sources:

UNCTAD calculations based on:

- UN DESA Population division, World Population Prospects: Revision 2024.
\r\n 

Notes:

The total dependency ratio is defined as the number of children (0–14 years old) and older persons (65 years and over) per 100 persons in working age (15–64 years old).
 
The old-age dependency ratio is defined as the number of older persons (65 years or over) per 100 persons in working age (15–64 years old).
 
The child dependency ratio is defined as the number of children (0–14 years old) per 100 persons in working age (15–64 years old).
 
Calculations are based on de facto population in a country, area or region as of 1 July of the indicated year.
 
","name":"US.PopDependency","order":135}],"subFolders":[],"isParentFolder":false},{"id":30,"title":"Inclusive growth","reports":[{"id":1631,"version":10006,"title":"Inclusiveness: Inclusive growth index - Annual (analytical)","lastUpdatedDate":"2025-03-10T14:55:00","category":"Inclusive Growth","keywords":"Inclusive Growth Index, IGI, economic development, living conditions, social inclusion, environmental sustainability, equity","notes":"
Description
The UNCTAD’s Inclusive Growth Index (IGI) comprehensively measures a country's economic well-being and inclusivity across four categories: economy, living conditions, equality, and the environment. It assesses economic development, living conditions, social inclusion, and environmental sustainability to provide a holistic view of progress. The IGI summarizes 27 indicators organized in four categories into a single indicator, thereby offering a multidimensional perspective on growth that goes beyond traditional economic metrics.
 
The first category, economy, encompasses economies’ GDP, national income, power consumption, employment and trade using traditional economic indicators. 
 
The second category, living conditions, focuses on social and health conditions as well as logistics and finance, using indicators such as the under-five mortality rate, school enrollment, coverage of essential health services and percentage of people using safely managed drinking water services. 
 
The third category, equality, aims to measure equality using indicators such as the income concentration ratio, gender parity in school enrollment, ratio of female to male employment rate, difference in female to male labor force participation rate, and others. 
 
The fourth category, environment, focuses on natural capital protection and energy intensity using indicators such as carbon dioxide emissions, efficiency of water use and percentage of terrestrial protected areas.
 
The IGI is calculated as a geometric average of the indices for the four categories. Each category index is obtained from the principal components extracted from the underlying indicators, weighted by their capacity to explain the variance of the original data. The category indices are normalized into 1-100 intervals. The categories themselves are selected on the basis of their relevance to the conceptual and analytical framework for achieving inclusive growth.

\r\n 
Sources
UNCTAD calculations based on 27 indicators from international sources. The use of international sources ensures coherence and comparability across countries. The data sources are the following:
 
• World Bank, World Development Indicators
• SDG Global Database
• ILOSTAT Database
• United Nations Population Division. World Marriage Data 2019
• OECD/IEA Statistics
• UNU-WIDER, World Income Inequality Database (WIID)
• United Nations Population Division. World Population Prospects: 2022 Revision.
• United Nations Statistics Division. National Accounts - Analysis of Main Aggregates (AMA)
• UNESCO Institute for Statistics (UIS)
• U.S. Energy Information Administration
• European Commission, Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency (PBL). Emission Database for Global Atmospheric Research (EDGAR)
• Other tables in UNCTADstat
 
 
Notes
The first edition of the IGI refers to the earlier work by UNCTAD and the Eurasian Economic Commission, first published in 2019 in a pdf format and covers 96 economies (UNCTAD and EEC, 2019). It had three categories: economy, living conditions, and equality, with 21 indicators, including one indicator related to environmental impact, namely, CO2 emissions (Barnat et al., 2023). In 2022, the updated IGI expanded its focus on gender equality and introduced a new category dedicated to environmental issues, driven by improved data availability (UNCTAD, 2022). 
 
In September 2023, the overall IGI was compiled for 129 economies for which all information on 27 indicators needed for the 4 categories was available, compared to 96 economies covered in 2022. The 2025 edition was expanded to 134 economies. The changes in the ranking are mostly due to a significant revision of ILO employment data - Benin, Comoros, Cote d’Ivoire, Nigeria, Lesotho, Morocco, Moldova, Senegal. Some economies have revised data for environmental indicators such as CO2/GDP or primary energy use efficiency - Bhutan, Guinea, Lesotho, Morocco.
 
 
FAQ
How are indicators selected?
 
The indicators for this assessment of inclusiveness, particularly focusing on inclusive growth, were selected through a rigorous process that prioritized data availability and quality. The assessment identified gaps in inequality and governance indicators while adhering to the UNCTAD Statistical Quality Assurance Framework's criteria, including relevance, completeness, comparability, timeliness, accessibility, accuracy, and reliability. Out of a wide range of potential indicators, 27 were chosen for their strong relevance to inclusive growth and robust data across dimensions, countries, and time, including fourteen SDG indicators. The choice of specific indicators, such as employment rates by age and sex and the Gini index, aimed to capture nuanced aspects of inclusiveness. Incorporating environmental indicators posed significant challenges due to data scarcity, but also to the inherent trade-offs between economic growth and environmental sustainability. These complexities arise from the multifaceted interactions between inclusiveness, economic growth, and the environment, which can sometimes point in divergent directions. This underscores the intricate balance required when striving for economic growth while simultaneously ensuring environmental sustainability. In this first IGI published version, four indicators related to the environment have been included with a clear intent to reevaluate this approach as more robust and comprehensive environmental data becomes accessible in the future. 
 
Why was a fourth category created?
 
The decision to transition from three categories to four categories in the IGI was driven by a recognition of the importance of environmental sustainability in the broader context of inclusiveness. While the initial approach focused on economy, equality, and living conditions, the dynamics between economic growth, social equity, and the environment are deeply interconnected. Adding a dedicated sustainability category allows users to comprehensively assess how these elements interact, ensuring a more holistic understanding of inclusive growth. This adaptation reflects the commitment to providing a more nuanced and comprehensive evaluation of the complex factors contributing to inclusive development.
 
Why is the principal component analysis (PCA) used to determine weights?
 
A crucial step in calculating overall index scores is applying weights to individual indicators. By assigning appropriate weights, the index can accurately represent the significance of each indicator for the overarching concept. 
 
While equal weights for indicators are frequently considered when building a composite index, they might not be suitable when some source indicators are correlated or may be driven by common factors or trends. Combining correlated variables may potentially introduce an element of ‘double counting’ into the overall index.
 
For this reason, category weights are determined by quantifying the interconnections of source indicators using PCA. As the first principal component gives the weights that maximize the variance of the units, the final PCA weights are the most suitable for discriminating between units, and for capturing as much information from the underlying indicators.
 
Why is the minimum for category scores set to 1?
 
The minimum score for category scores is set to 1, ensuring representation for even the lowest-scoring categories. This maintains a scoring range from 1 to 100, striking a balance between comprehensiveness and sensitivity to differences in performance.
\r\n
\r\n
\r\n
References
UNCTAD (2022). In focus: Inclusive growth, Stark contrasts in inclusive growth – progress towards equal opportunities needed everywhere. In SDG Pulse 2022. Available at: https://sdgpulse.unctad.org/inclusive-growth/
 
Barnat, N., MacFeely, S., Cantu, F., Peltola, A., Khazhgerieva, A., Panteleev, A., & Ryabtsev, N. Compiling an inclusive growth index: Methodological challenges, considerations and conclusions. Statistical Journal of the IAOS, (Preprint), 1-21.
Available at: https://content.iospress.com/articles/statistical-journal-of-the-iaos/sji230025
 
UNCTAD and EEC (2019). Inclusive growth of the Eurasian Economic Union Member States: Assessments and opportunities. Available at:
http://www.eurasiancommission.org/ru/act/integr_i_makroec/dep_makroec_pol/Documents/Inclusive_growth_in_EAEU_Member.pdf (accessed 21 June 2022).
","name":"US.InclusiveGrowth","order":137}],"subFolders":[],"isParentFolder":false},{"id":32,"title":"Gender and trade","reports":[{"id":1315,"version":10001,"title":"Gender: Domestic value added in gross exports by sex and broad sector - Annual (analytical)","lastUpdatedDate":"2024-10-10T12:39:00","category":"Gender","keywords":"Trade, gender, gap, equality, employment, earnings, wages, domestic value added, export","notes":"
Disclaimer:
This table, derived from various international data sources, reflects values as reported by Member states. Although it is still in experimental phase and requires further refinement, this statistical series developed by UNCTAD offers insights into gender-in-trade patterns. The data set is labeled as experimental; ongoing testing and development may result in some uncertainties. The user's feedback is crucial for enhancing accuracy and reliability. Additionally, earnings and employment data reports may have discrepancies due to the differences in reporting of national data, requiring careful analysis, especially at the country level.
 
Description: 
This table presents data on estimated female and male contributions to domestic value added in gross exports (measured in current USD). Disaggregation by sex and selected broad sectors is available, as defined by economic activities in ISIC Rev.4: agriculture, industry, manufacturing and services. The table also includes direct and indirect components of domestic value added in gross exports. 
 
This data provides insights into whether female employees’ contribution to creating domestic value added in gross exports differs from male employees and whether differences exist based on the type of economic activity. 
 
Sources:
• ILO Employment by sex and economic activity (thousands) -- Annual, (Series code: EMP_TEMP_SEX_AGE_ECO_NB_A)
• OECD Trade in Value Added (TiVA) 2025 edition: Principal Indicators
 
Notes:
Domestic value added content of exports, by industry “i” in country/region “c” to partner country/region “p”, represents the exported value added that has been generated anywhere in the domestic economy (i.e. not just by the exporting industry). The domestic value added content of gross exports can be split further into three components, direct domestic industry value added, indirect domestic value added and re-imported domestic value added (Martins Guilhoto et al., 2022).
 
The OECD TiVA database is used to derive direct and indirect domestic value added content in gross exports for 2-digit ISIC Rev.4 industries and to link the ILO detailed industry-level employment data. Direct domestic value added content in gross exports refers to the value added by domestic industries directly involved in the production of exported goods and services, while indirect domestic value added in gross exports accounts for the broader economic contributions of the entire domestic supply chain involved in supporting the production of exported goods and services. Industries (defined at ISIC 2-digit level) are divided into broad sectors with respect to their exported value added. The corresponding employment numbers are aggregated up to the level of those sectors.
 
The statistical series assume that there are no differences in gender distribution between exporting and non-exporting firms. The proportions of male and female contributions to domestic value added are calculated assuming homogeneity in labour intensity, skills, etc., thereby stating that women represent a comparable share of value added to their proportion in employment. Without micro data linking, this is the most accurate approach available, maximizing coverage across countries and facilitating comparability of results.
 
References:
Martins Guilhoto J, Webb C and Yamano N (2022). Guide to OECD TiVA Indicators, 2021 Edition. OECD Science, Technology and Industry Working Papers, No. 2022/02. OECD Publishing. Paris.
 

 

","name":"US.Gender_DomesticValueAdded","order":139},{"id":1312,"version":10001,"title":"Gender: Employment and earnings by sex - Annual (analytical)","lastUpdatedDate":"2024-10-10T12:39:00","category":"Gender","keywords":"Trade, gender, employment, earnings, wages, inequality, female, male","notes":"
Disclaimer:
This table, derived from various international data sources, reflects values as reported by Member states. Although it is still in experimental phase and requires further refinement, this statistical series developed by UNCTAD offers insights into gender-in-trade patterns. The data set is labeled as experimental; ongoing testing and development may result in some uncertainties. The user's feedback is crucial for enhancing accuracy and reliability. Additionally, earnings and employment data reports may have discrepancies due to the differences in reporting of national data, requiring careful analysis, especially at the country level.
 
Description: 
This table presents sex-disaggregated data on employment and average monthly earnings in tradable and non-tradable industries to understand whether differences exist in participation and pay in international trade by sex.
 
Sources:
• ILO Employment by sex and economic activity (thousands) -- Annual, (Series code: EMP_TEMP_SEX_AGE_ECO_NB_A)
• ILO Employment by sex and economic activity -- ILO modelled estimates, Nov. 2025 (thousands) -- Annual (Series code: EMP_2EMP_SEX_ECO_NB_A)
• ILO Average monthly earnings of employees by sex and economic activity – Annual (Series code: EAR_4MTH_SEX_ECO_CUR_NB_A)
 
Notes:
Employment data provides summary statistics on the number of female and male employees in both tradable and non-tradable industries, by country and year. Data on wages represents average monthly earnings of female and male employees in tradable and non-tradable industries by country and year. Average monthly earnings are reported in current USD and 2021 Purchasing Power Parity (PPP) USD.
 
The key advantage of this indicator lies in its extensive coverage and comparability. We encompass a wide range of countries across the globe and offer a long series. Our assumption is that trade impacts both female and male employees, as evidenced by their employment proportions in tradable sectors.
 
We utilize ILO data with a 1-digit level of ISIC aggregation, ensuring maximum coverage and comparability across countries. The country-specific data are official, while country groupings are derived from ILO modelled estimates, including both ILO estimates and imputations. Classification of tradable and non-tradable sectors is derived based on OECD (2018).  
 
Tradable industries include agriculture (A), industry (B, C, D, E), transportation (H), information and communication (J), financial and insurance activities (K), and other services (R, S, T, U). Transportation is included among tradable sectors because international transport is a key enabler of international trade and because the ILO groups this sector together with information and communication.
 
Non-tradable industries include construction (F), distributives trade and repairs (G), accommodation and food services activities (I), and real estate activities (L), business and administrative services (MN), public administration (OPQ). Business services refer to professional, scientific and technical activities.
 
The country-specific data are official statistics as reported by countries. Earnings and employment statistics present several complications in terms of their international comparability, most of which arise from the variety of possible data sources. The various sources available -- establishment surveys, household surveys and administrative records -- differ in their methods, objectives and scope, which influence the results obtained. Thus, potential discrepancies can be identified due to a) discrepancies in data sources; b) classification of activities used by a country; c) informal employment; d) coverage of data source (geographical coverage, economic activities covered, types of establishments covered, etc.); e) working-age population definition.
 
References – tradable sectors:
OECD (2018) Chapter 2. Thinking global, developing local: Tradable sectors, cities and their role for catching up in OECD, Productivity and Jobs in a Globalised World: (How) Can All Regions Benefit?, OECD Regional Development Studies, OECD Publishing, Paris.
 
","name":"US.Gender_TradableIndustries","order":140}],"subFolders":[],"isParentFolder":false}],"isParentFolder":true},{"id":49,"title":"SDG Indicators","reports":[],"subFolders":[{"id":50,"title":"Goal 9","reports":[{"id":2254,"version":10003,"title":"Indicator 9.1.2: Freight loaded and unloaded - Annual","lastUpdatedDate":"2026-04-16T07:41:00","category":"SDG Indicators","keywords":"seaborne, international, maritime, trade, cargo, volume, SDG Indicator, SDG Goal 9, Indicator 9.1.2","notes":"
Context for SDG indicators:
This table is a set of tables presenting Sustainable Development Goal (SDG) indicators for which UNCTAD is custodian or partner agency. 
 
The data in this table is part of indicators to track SDG 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation. The data series is one of four series that make up SDG indicator 9.1.2 Passenger and freight volumes, by mode of transport. The series presented here is:
 
- IS_RDP_LULFRG: Freight loaded and unloaded, maritime transport (metric tons).
 
Also presented on UNCTADstat is:
 
- IS_RDP_PORFVOL: Container port traffic, maritime transport (twenty-foot equivalent units)
 
The two remaining series are:
 
- IS_RDP_FRGVOL: Freight volume (tonne kilometres), and
- IS_RDP_PFVOL: Passenger volume (passenger kilometres).
 
All SDG indicators and series can be accessed from the Global SDG Indicators Data Platform (https://unstats.un.org/sdgs/dataportal).  
 
Data in this table is also presented in the UNCTADstat Data Centre under the theme Maritime and other transport – Freight volumes, by sea.
 

Description:
\r\nThis dataset features international maritime trade volume in metric tons, by type of cargo at individual economy and economy grouping levels. Aggregates of groups of economies can differ from the sum of individual economies because data for some economies are not presented individually but included in the aggregates.

Figures reflect the imports and exports carried onboard ships and loaded (exports) and unloaded (imports) at ports worldwide. Data on international maritime freight volumes exclude transhipments and domestic maritime freight.

In the case of multimodal transport, seaborne trade volumes are recorded if one of the transport modes is maritime. Maritime traffic is attributed to an origin or a destination economy (e.g., landlocked country), not to economies where cargo handling ports are located (e.g., transit coastal economies). In other words, the seaborne trade data includes maritime freight volumes that originate from and are destined to the reference economy’s territory but that are loaded and unloaded through ports located outside the reference economy’s territory. For example, maritime freight volumes assigned to landlocked countries are handled at ports located in relevant transit coastal economies. The seaborne trade volumes do not necessarily match the international trade data by mode of transport reported in Comtrade Plus, which in the case of exports, records the first mode of transport while for imports it records the last mode of transport.

The seaborne trade data generally reflect data recorded at goods’ arrival time at the country’s external border. Therefore, for loaded (export) cargo, the trade volume reflects cargo that enters the territory of a destination economy in the report year.

Key variables and concepts underpinning the dataset are set out below.

  1. Dimensions
    2. \r\n
\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n

Year

Description

Year in which the imported goods enter the territory of the economy of destination, usually approximated by the year of the lodgement of the customs declaration.

Economy

Description

The economy that has reported an import volume under its international merchandise trade statistics reporting submitted to the United Nations Statistics Division (UNSD) for UN Comtrade, or the economy of origin as reported by the importer economy.

Categories

Economies, as defined by the UNCTAD classification of economies

Cargo Type

Description

Maritime cargo categorized based on the type of vessels used, separately for loading (exporting) and discharging (importing)

Categories

  • Total goods loaded
  • Crude oil (tanker) loaded
  • Other tanker trade loaded (including refined petroleum products, liquefied natural gas (LNG), liquefied petroleum gas (LPG), and chemicals)
  • Dry cargo loaded (transported on bulkers, container ships, and general cargo ships)
  • Total goods discharged
  • Crude oil (tanker) discharged
  • Other tanker trade discharged (including refined petroleum products, liquefied natural gas (LNG), liquefied petroleum gas (LPG), and chemicals)
  • Dry cargo discharged (transported on bulkers, container ships, and general cargo ships)
  • Freight loaded and discharged (Sum of Total goods loaded and Total goods discharged. SDG Indicator 9.1.2 [IS_RDP_LULFRG])
     
    • \r\n
  1. Indicator
    2. \r\n
\r\n\r\n\r\n\r\n\r\n\r\n

Metric tons

Description

The weight of the internationally traded goods net of packaging.

Unit of measure

Metric tons in billions

Metric tons in millions

Metric tons in thousands (default)

Metric tons in hundreds

Metric tons

Cell footnotes

All figures are based on aggregated bilateral flows involving the economy in a given direction.

Theoretically, two separately reported time series are available for each bilateral flow:

-          The import data reported by economy A for imports from economy B

-          The export data reported by economy B for exports to economy A

In this compilation, import data are preferred by default, unless significantly longer time series are available from trading partners’ corresponding export data.

That means that if all economies would report for all years, then

-          Cargo discharged for an economy would be the sum of imports by sea from all trade partners as reported by the economy (direct data)

-          Cargo loaded for an economy would be the sum of imports by sea reported by trade partners for imports from the economy (mirror data)

 

In practice, in some cases, the value is the sum of a mix of import and export data. When the availability of the mirroring export data is significantly better than the import data for the same bilateral flow, the export data is used.

The time series used for a bilateral flow may have gaps. Those gaps are filled via trends of the time series and/or growth rates of the mirroring time series.

That means that in broad terms the underlying bilateral flows can be a mix of

-          direct reported, that is reported by the economy itself,

-          mirror reported, that is reported by the trade partner, or

-          estimated using available data from direct or mirror data

                    

The following cell notes are used.

 

For cargo discharged (imports):

-          No note: The direct import data are used for all bilateral flows and the economy has reported data for the year.

-          Majority reported direct data: The direct import data are used for most bilateral flows, the other bilateral flows are either reported mirror export data or estimated.

-          All reported mirror data. The mirroring export data is the time series used for all bilateral flows and all partners have reported.

-          Majority reported mirror data: The mirror export data are used for most bilateral flows, the other bilateral flows are either reported direct import data or estimated.

-          Majority estimated: The majority of the used time series have a data gap for this year and have been estimated. Some bilateral flows are based on reported data (direct or mirror data).

-          All estimated: All of the used time series have a data gap for this year and have been estimated.

 

For cargo loaded (exports):

-          All reported mirror data: The preferred data source, the mirror import data reported by trade partners, is available for all partners.

-          Majority reported mirror data: The mirror import data reported by trade partners is available for most partners. The other bilateral flows are either export data reported by the economy or estimated.

-          Majority reported direct data: The export data reported by the economy itself is used for most bilateral flows. The other bilateral flows are either reported mirror data or estimated.

-          Majority estimated: The majority of the used time series have a data gap for this year and have been estimated. Some bilateral flows are based on reported data (direct or mirror data).

 

No cell footnotes are provided for the sum freight loaded and discharged. Cell footnotes are also not provided for country groups.

Sources:

 

Notes:

In, 2025 this table was expanded to include country level data. New methodology has been developed for this purpose as described below.

The UNCTAD secretariat generates the maritime trade data by volume and in metric tons by using UN Comtrade. UNCTAD divides the UN Comtrade data on merchandise trade volume into maritime and non-maritime freight volumes and by cargo type, using baseline maritime transport shares and cargo type shares. The methodology applied to generate maritime freight volumes from UN Comtrade data was developed by the UNCTAD secretariat in collaboration with MDS Transmodal (https://www.mdst.co.uk) and UN Statistical Division.

Data are aggregated from data reported in metric tons on bilateral trade flows at the subheading (6-digit) level of the Harmonized System (HS) nomenclature. Commodities are inferred to be transported as a particular maritime cargo type (e.g. containerized carried in containers on containerships or oil carried as bulk in tankers) or a non-maritime cargo type (e.g. containerized cargo carried in containers on trucks or oil carried in tanks onboard trucks). This required the application of base rates of cargo types and base rates of how these cargo types are transported. These base rates are specific to product and bilateral trade flow but fixed over time. The baseline cargo type shares and maritime transport shares are based on MDST’s World Cargo Database. However, some maritime transport shares are adjusted using information from UNCTAD Trade-and-Transport Dataset.

Commodity and cargo type are different concepts. Commodity refers to goods as classified under HS codes while cargo types reflect the condition under which the freight is being transported (containerized (dry or reefer/refrigerated containers), bulk liquid (tanker ships or, tanks), gas carriers, car carriers, dry bulk (e.g. iron ore, coal, grain, etc.). Thus, maritime freight volumes of some commodities can be divided into various cargo types. For example, a share of frozen meats can be carried as dry bulk cargo while another share of this same commodity can be carried in containers on a container ship.

The maritime trade data generated by UNCTAD are primarily based on importer-side data (i.e., data on bilateral trade flows reported by the importing country) in Comtrade. Mirror data (i.e., data on bilateral trade flows reported by the exporting country) in Comtrade are used only when the importer-side data are missing or deemed unreliable, particularly when exporter-side data offer a substantially longer time series or display greater stability over time. Missing data in UN Comtrade are imputed by selecting the best method, based on a pre-determined criterion such as minimum mean squared error, for each trading pair and each commodity and from several options including interpolation/ extrapolation using linear trend and growth rates in mirror data. When mirror data are available, priority is given to the growth rates in mirror data. There are no other sources of missing values in the compilation process.

UNCTAD secretariat developed a methodology that automatically detects and corrects UN Comtrade merchandise trade volume records that appear to be outliers and unreliable. The detection and correction of unreliable values are conducted for each bilateral trade flow of each commodity. The detection and correction are based on time series information of unit values (i.e. trade value/volume). If an inferred unit value deviates from its time series trend by a pre-determined threshold, the corresponding trade volume data is considered an unreliable value. The methodology also considers the possibility that a reported trade volume figure might be correct while the reported trade value figure might be incorrect. The time series trend is calculated by combining several types of trends, such as linear trend, log-linear trend, and invert of inverted trend, according to a pre-determined rule, to ensure in-sample and out-of-sample validity. To guarantee that the time series trend is not significantly affected by unreliable values, a first-round check of these values is conducted before calculating the time series trend by comparing deviations of unit values from the corresponding world median (of unit values of the corresponding commodity across all trading pairs). When the number of observations is considered insufficient to calculate the time series trend, the algorithm checks for the presence of a significant deviation of unit values from the corresponding world median. In addition to the automated detection and correction of outliers, a manual adjustment process is applied to selected data points when highly reliable alternative sources are available, such as detailed trade statistics published on official national government websites.

The measure of volume does not include the impacts of changes in quality of commodities (i.e., technological improvements) over time. As a result, the seaborne trade data generally indicates a weaker long-term trend compared to merchandise trade volume indices that deflated trade values by corresponding unit value indices, published by UNCTAD and other organizations. Even though the calculation of unit value indices, unlike price indices, does not make a specific effort to adjust for changes in quality of commodities, changes in quality of commodities are implicitly accounted for when value indices are calculated based on data at a disaggregated level. As a result, trade volume indices include the impact of changes in quality. In addition, bulky commodities such as iron ore tend to have higher shares in total seaborne trade volume than in trade volume indices because trade volume indices use trade value in base year as a weight of each commodity. This also contributes to the weaker long-term trend in the seaborne trade volumes expressed in metric tons compared to merchandise trade volume indices in some countries.

UNCTAD secretariat checks for the consistency between the estimated international maritime freight volumes and other comparable datasets published by international organizations such as International Monetary Fund and World Trade Organization as well datasets published by national governments, and maritime specialised sources, including Clarksons Research Services.

For more information, please contact the Review of Maritime Transport team at: unctad-rmt@un.org.

 

","name":"US.SDG_LULFRG","order":145},{"id":2264,"version":10003,"title":"Indicator 9.1.2: Container port traffic - Annual","lastUpdatedDate":"2026-04-15T15:06:00","category":"SDG","keywords":"ship, maritime, transport, container, port, throughput, TEU, SDG Indicator, SDG Goal 9, Indicator 9.1.2","notes":"

Context for SDG indicators:

This table is a set of tables presenting Sustainable Development Goal (SDG) indicators for which UNCTAD is custodian or partner agency.

The data in this table is part of indicators to track SDG 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation. The data series is one of four series that make up SDG indicator 9.1.2 Passenger and freight volumes, by mode of transport. The series presented here is:

Also presented on UNCTADstat is:

The two remaining series are:

All SDG indicators and series can be accessed from the Global SDG Indicators Data Platform (https://unstats.un.org/sdgs/dataportal). 

Data in this table is also presented in the UNCTADstat Data Centre under the theme Maritime and other transport – Freight volumes, by sea.

 

Description:

This table presents the estimated total number of containers handled, per country, expressed in twenty-foot equivalent units (TEUs). A TEU represents the volume of a standard 20 feet long intermodal container used for loading, unloading, repositioning and transshipment. A 40-foot intermodal container is counted as two TEUs.

Sources:
\r\nUNCTAD secretariat based on data collected by various sources, including

The above-mentioned data collectors, in turn, source their data from a variety of port authority reports and other industry sources. The collected data may in some cases depend on the ultimate source consulted.

Notes:

Data published are country totals which may conceal the fact that some minor ports are not included. The data includes container port throughput for ports with time series that are complete since 2010 or that can be made complete by repeating an observation for a maximum of four years after the original observation. This means that country level time series are comparable over time but that the coverage of ports may differ between countries. UNCTAD continuously seeks to improve coverage in updates to this table. Ports included are noted in the country notes.

Repeated port level observations are treated as estimates and country level figures that are made up of more than 20 per cent estimates are noted as such. Observations, where more than 40 per cent of the country level container port throughput is estimated, are not published but included in the group level totals. Revisions of more than 20% from the previous version of this statistical table are also noted. Notes and cell suppressions are only applied for individual economies.

For any questions related to the country data and methodology, please contact the Review of Maritime Transport team at: unctad-rmt@un.org.

 

","name":"US.SDG_PORFVOL","order":146}],"subFolders":[],"isParentFolder":false},{"id":53,"title":"Goal 16","reports":[{"id":2493,"version":10002,"title":"Indicator 16.4.1: Trade misinvoicing illicit financial flows, IFFs - Annual ","lastUpdatedDate":"2026-06-09T09:17:00","category":"SDG Indicators","keywords":"IFF, IFFs, Trade, misinvoicing, SDG, Tax, SDG","notes":"

Description:

The estimates are valid for the reporting of SDG Indicator 16.4.1 “Total value of inward and outward illicit financial flows (in current United States dollars)” and are published in the Global SDG Indicator Database (https://unstats.un.org/sdgs/dataportal).

Trade misinvoicing IFFs are illicit transactions linked to the deliberate falsification of quantity, quality or value of goods traded internationally, typically with the objective to evade customs duties, VAT and income tax, to shift profits, to circumvent currency controls, to smuggle illicit goods and launder proceeds of crime.

Formula to calculate inward IFFs from trade misinvoicing:

InwardIFFs c, r, p, t = OverinvoicedEX c, r, p, t + UnderinvoicedIM c, r, p, t

Formula to calculate outward IFFs from trade misinvoicing:

OutwardIFFs c, r, p, t = UnderinvoicedEX c, r, p, t + OverinvoicedIM c, r, p, t

Where:

The estimations were carried out following UNCTAD methodological guidelines to measure tax and commercial IFFs, presented in UNCTAD (2023) Towards a Statistical Framework for the Measurement of Tax and Commercial Illicit Financial Flows (https://unctad.org/publication/towards-statistical-framework-measurement-tax-and-commercial-illicit-financial-flows).

UNCTAD guidelines recommend two methods to measure trade misinvoicing IFFs, namely:

 

\r\n\r\n\r\n\r\n\r\n\r\n
MethodDescription
Method #1 – Partner country method “plus”
IFFs are calculated starting from the analysis of mirror trade statistics and the adjustment of trade asymmetries to account for legitimate sources of misalignment (CIF/FOB valuation, partner country attribution, time lag, trade system…) and other statistical errors. The “plus” part of the methodology involves also country-and product-specific adjustments and can reflect inputs from customs and other relevant authorities’ experts.
The method uses international and national aggregated data on merchandise imports and exports, from international and national data sources (UN Comtrade and/or national customs authority data).
Method #2 – Price filter method “plus”
The method uses a statistical price filter to detect suspicious transactions with abnormal prices, which is an indication of possible trade mispricing. Trade mispricing occurs when the unit price of a transaction declared is different from the arm’s length price of the transaction. The price filters are defined for each commodity HS code and include benchmark upper and lower bound prices allowing for variations in arm’s length price during a specified time. 
The “plus” part involves commodity, partner and/or time-specific adjustments, based on inputs from customs and other relevant authorities’ experts.
\r\nIt requires access to individual trade data, typically collected by customs authorities. 

 

The methodologies follow globally agreed definition and concepts presented in the UNCTAD-UNODC (2020) Conceptual Framework for the Statistical Measurement of Illicit Financial Flows (https://unctad.org/publication/conceptual-framework-statistical-measurement-illicit-financial-flows), endorsed by Member States at the UN Statistical Commission in 2022.

Results shall be interpreted with caution.

More information on the methodology application process and assumptions taken are available at:

Sources:

Estimates from pilot studies carried out by national authorities, with capacity building support from UNCTAD and UN Regional Commissions (UNECA, UNESCAP, UNECLAC).

For Burkina Faso, estimates were elaborated by the Institut National de la Statistique et de la Démographie (INSD) and by the Direction Générale des Douanes (DGD).

Notes:

 

","name":"US.IFF_TradeMisinvoicing","order":148},{"id":2492,"version":10002,"title":"Indicator 16.4.1: Crime-related illicit financial flows, IFFs - Annual","lastUpdatedDate":"2026-06-09T09:17:00","category":"SDG Indicators","keywords":"IFF, crime-related, Drug, smuggling, trafficking, SDG 16.4.1, financial, inwards","notes":"

Description:

The estimates presented are valid for the reporting of SDG Indicator 16.4.1 “Total value of inward and outward illicit financial flows (in current United States dollars)” and are published in the Global SDG Indicator Database (https://unstats.un.org/sdgs/dataportal).

Data refer to inward and outward illicit financial flows (IFF) from crime-related activities (illegal market activities, corruption, exploitation type and terrorism financing), obtained in the phase of income generation. The estimates presented do not cover flows from the income management phase.

Inward IFFs occur when illicit goods and services are exported abroad. These products can be either for final consumption (e.g. an online dealer sells synthetic drugs directly to a consumer in another country) or they are (licit or illicit) intermediate inputs to illicit productive processes in a foreign country (e.g. morphine is exported to another country to produce heroin). Outward IFFs occur when intermediate inputs are imported against payments from abroad (e.g. drugs imported for further domestic sale) or when final illicit goods and services are imported.

Illegal market activities include any illegal exchange (trade) of goods or services, often involving a degree of criminal organization aimed at creating profit. They include any type of trafficking in goods, such as drugs and firearms, or services, such as smuggling of migrants. IFFs can emerge from transnational trade in illicit goods and services, as well as from cross-border flows from managing the illicit income from such activities.

Exploitation-type activities are illegal activities that entail a forced and/or involuntary transfer of economic resources between two actors. Examples include slavery and exploitation, extortion, trafficking in persons and kidnapping. In all such cases, the focus is on the financial benefit that an individual (or a group) gains by exploiting somebody else and/or depriving somebody of some of his/her assets.

IFFs from income generation are the set of cross-border transactions that are performed in the context of the production of illicit goods and services, or the set of cross-border operations that directly generate illicit income for an actor during a non-productive illicit activity.

IFFs from income management are the set of cross-border transactions finalized to use the (illicit) income for investment in (legal or illicit) financial and non-financial assets or for consuming (legal or illegal) goods and services.

Methodologies to measure crime-related IFFs are presented in UNODC (2026) Working Guidelines for Measuring Crime-related Illicit Financial Flows, at: https://www.unodc.org/documents/data-and-analysis/statistics/IFF/Working_guidelines_for_measuring_crime-related_IFFs.pdf

The methodologies follow globally agreed definition and concepts presented in the UNCTAD-UNODC (2020) Conceptual Framework for the Statistical Measurement of Illicit Financial Flows (https://www.unodc.org/documents/data-and-analysis/statistics/IFF/IFF_Conceptual_Framework_FINAL.pdf ), endorsed by Member States at the UN Statistical Commission in 2022.

Sources:

UNODC estimates, based on national pilot studies and crop monitoring surveys.

Notes:

 

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