Access official WTO tariff and trade data for over 170 economies. Compare tariffs, imports, exports, and access downloadable data.

The WTO's Integrated Data Base (IDB) contains imports by commodity and partner countries and Most Favored Nation (MFN) applied and, where available, data on preferential tariffs at the most detailed commodity level of the national tariffs.

This statistic shows the share of China's contribution to the World Trade Organization's (WTO) budget from 2014 to 2024. The contributions are calculated according to each member's share of international trade including trade in goods, services and intellectual property rights. In 2024, China contributed around 11.18 percent to the consolidated budget of the World Trade Organization Secretariat and the Appellate Body Secretariat.

The WTO's Consolidated Tariff Schedule Data Base (CTS) contains WTO-bound tariffs, Initial Negotiating Rights and other indicators. The CTS reflects the concessions made by countries during goods negotiations (e.g., the Uruguay Round of Multilateral Trade Negotiations). The IDB and CTS are practical working tools and there are no implications as to the legal status of the information contained therein.

Recent research has shown that the General Agreement on Tariffs and Trade (GATT)/World Trade Organization (WTO), contrary to common perceptions, does not increase trade. We argue that the effect of the GATT/WTO on dyadic trade flows is conditioned by the strength of market-protecting institutions (MPIs), which are the fundamental determinant of transaction costs. Dyads with weak MPIs do not see an increase in trade from GATT/WTO membership while dyads that have strong MPIs do see an increase in trade from GATT/WTO membership. In the former case, the benefits of GATT/WTO membership are outweighed by the high risk of doing business under weak market protection, but when property rights are well protected, GATT/WTO membership contributes positively to international trade. Empirical analysis of bilateral trade flows from 1948 to 1999 supports this hypothesis.

Trade (% of GDP) in World was reported at 58.51 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - Trade (% of GDP) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

In 2022, the global trade value of goods exported throughout the world amounted to approximately 24.9 trillion U.S. dollars at current prices. In comparison, this figure stood at around 6.45 trillion U.S. dollars in 2000. The rise in the value of goods exported around the world reflects developments in international trade, globalization, and advances in technology.

Export trade
Global trade refers to the exchange of capital, goods and services between different countries and territories. The export of trade goods refers to goods sold internationally which were grown, produced, or manufactured in another country.

Who are the leading importers and exporters of trade goods?
In 2021, China was the largest source of goods exported around the world, with total merchandise exports valuing approximately 3.37 trillion U.S. dollars. That year, China was responsible for almost 15 percent of all trade goods exported around the world. The United States was the second largest exporters of goods that year. The United States was the leading importer of merchandise in the world as of 2021. That year, the global superpower accounted for 13 percent of the world’s merchandise imports.

The Investment Facilitation Index (IFI) provides information on the current adoption of investment facilitation measures at country level for 142 World Trade Organisation (WTO) Members. It was developed by the German Institute of Development and Sustainability (IDOS), previously known as the Deutsches Institut für Entwicklungspolitik / German Development Institute (DIE), in cooperation with the WTO. The IFI is a composite index measuring the adoption of investment facilitation measures in 2021 and applying a multiple binary scoring scheme. Departing from an earlier version of the index (Berger et al., 2021), it has been conceptually revised and extended regarding its country coverage. It now consists of 101 measures composing six regulatory dimensions and corresponds closely to the main policy areas and developments within current policy debates, including the newly negotiated Investment Facilitation for Development (IFD) Agreement among the WTO Members.

The data set provides the foundation for analysing specific facilitation hurdles in investment frameworks of a large number of economies. The fine grained data of the IFI can be used for investigating economic benefits and challenges of investment facilitation reforms, support the assessment of implementation gaps, as well as prioritisation of technical assistance and capacity development. It can also be used by investors seeking information on a country’s investment regime.

For a detailed description of the methodology and coding of the IFI, please have a look at the uploaded data documentation, contained in the file ifi_documentation.pdf. It provides information on the conceptual composition of the index, its evolution from the first version, as well as the coding, data generation and validation processes. In the annex, it also features a detailed overview of each measure contained in the index.

The file ifi_codebook.csv contains the codebook for the 101 investment facilitation measures included in the IFI. The file features six variables (columns):

Measure: The code of a measure under observation;

Area: Specification of the policy area a given measure belongs to;

Measure_Description: A short description of what investment facilitation feature is evaluated by a given measure;

Weight: Specification of the individual weight of a measure, the product of the allocated score (0, 1 or 2) and this weight denotes the contribution to the total score of a given measure;

Unit: The measurement unit for the answer of a given measure, it can take values "Score", meaning that the answer is directly measured by score from the multiple binary scoring scheme, or specify another measurement unit, e.g. number of documents, days, US Dollars, etc.;

Coding_0: Specifies the answer coding which allocates a score of 0 to this measure;

Coding_1: Specifies the answer coding which allocates a score of 1 to this measure;

Coding_2: Specifies the answer coding which allocates a score of 2 to this measure.

The file ifi_table.csv or ifi_table.xlsx (please choose your preferred file format) contains all 14484 data points resulting from the 101 measures coded for 142 economies. Moreover, it also contains the total score for each country calculated by applying the expert weighting scheme. The file contains the following variables (columns):

CountryCode: ISO-alpha3 code of a country for which a given measure is coded;

Country: Name of a country for which a given measure is coded;

Measure: Code of a measure that is coded in a given row;

Area: Specification of the policy area a given measure belongs to;

Measure_Description: A short description of what investment facilitation feature is evaluated by a given measure;

Answer: The answer coded for a given measure and country;

Score: The allocated score based on the answer, according to the definition of the measure (see codebook);

Unit: The measurement unit for the answer of a given measure;

Coding: The answer option coded for a given measure and country (corresponds to either Coding_0, Coding_1 or Coding_2 in the codebook);

Source: Source statement for the provided answer.

For further inquiries please contact the authors.

The World Bank and the Center for International Business, Tuck School of Business at Dartmouth College Global Preferential Trade Agreements Database provide information on preferential trade agreements (PTAs) around the world, including agreements that have not yet been notified to the World Trade Organization. This resource helps trade policy makers, research analysts, the academia, trade professionals and other individuals better understand and navigate the world of PTAs.

TR: Tariff Rate: Most Favored Nation: Weighted Mean: All Products data was reported at 5.370 % in 2016. This records a decrease from the previous number of 5.800 % for 2015. TR: Tariff Rate: Most Favored Nation: Weighted Mean: All Products data is updated yearly, averaging 4.980 % from Dec 1993 (Median) to 2016, with 21 observations. The data reached an all-time high of 7.900 % in 1993 and a record low of 3.720 % in 2006. TR: Tariff Rate: Most Favored Nation: Weighted Mean: All Products data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Turkey – Table TR.World Bank.WDI: Trade Tariffs. Weighted mean most favored nations tariff is the average of most favored nation rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database.; ; World Bank staff estimates using the World Integrated Trade Solution system, based on data from United Nations Conference on Trade and Development's Trade Analysis and Information System (TRAINS) database and the World Trade Organization’s (WTO) Integrated Data Base (IDB) and Consolidated Tariff Schedules (CTS) database.; ;

Our paper sheds light on Sanitary and Phytosanitary (SPS) cooperation among trading countries. We contribute to the existing literature a data-driven analysis on the effectiveness of various forms (in monetary value, duration, and diversification) for SPS-related technical assistance received by 33 countries from 1993 to 2015. The World Trade Organization's (WTO’s) SPS Agreement encourages biosecurity for countries to safeguard human health and productivity from contamination by biological hazards (pests, pathogens or invasive species) through technical assistance. Our panel model finds that WTO’s SPS program encourages simultaneously agricultural trade and biosecurity in traded goods. We implement a Multiple Indicator Solution (MIS) to correct bias from the endogenous technical assistance. The effectiveness of technical assistance depends on the level of development and geography among the heterogeneous countries in our data. This investment in biosecurity benefits both donors and recipients of technical assistance. Based on our results donors should be encouraged to invest in countries with below average resources and abilities.

When do international institutions promote economic cooperation among countries? The World Trade Organization (WTO) is central to the multilateral trade regime and a benchmark for international dispute resolution. Yet it remains unclear whether it has been effective in restoring trade cooperation. This article uses WTO disputes to examine the impact of domestic politics in the defendant country on compliance with adverse legal rulings. I build a novel data set on compliance. Using the method of synthetic case control, I estimate the effect of adverse rulings on trade flows between disputant countries using product-level time-series trade data. I infer the defendant complied if trade flows increased after the dispute, relative to estimated levels that would have occurred in the absence of the ruling. The results show domestic political divisions—measured by veto players—hinder compliance.

This data package includes the underlying data file to replicate the charts presented in The World Bank, the IMF, and the GATT/WTO: Which institution most supported trade reform in developing economies?, PIIE Working Paper 22-19.

If you use the data, please cite as: Irwin, Douglas (2022). The World Bank, the IMF, and the GATT/WTO: Which institution most supported trade reform in developing economies?, PIIE Working Paper 22-19. Peterson Institute for International Economics.

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in What Might a Trump Withdrawal from the World Trade Organization Mean for US Tariffs?, PIIE Policy Brief 18-23. If you use the data, please cite as: Bown, Chad P., and Douglas A. Irwin. (2018). What Might a Trump Withdrawal from the World Trade Organization Mean for US Tariffs?. PIIE Policy Brief 18-23. Peterson Institute for International Economics.

The Global Preferential Trade Agreements Database (GPTAD) provides information on preferential trade agreements (PTAs) around the world, including agreements that have not been notified to the World Trade Organization (WTO). It is designed to help trade policy makers, scholars, and business operators better understand and navigate the world of PTAs. The GPTAD contains the original text of PTAs that have been notified to the WTO as well as agreements that have not yet been notified. The database is updated on a regular basis and currently comprises more than 330 PTAs. Agreements in the database have been indexed using a classification consistent with the WTO criteria. The GPTAD is a unique online tool that allows users to search PTAs around the world by provisions or keywords and to compare provisions across multiple agreements.

WITS is a trade software tool giving access to bilateral trade between countries based on various product classifications, product details, years, and trade flows. It also contains tariff and non-tariff measures data, as well as analysis tool to calculate effects of tariff reductions. In addition, users have access to many visualization tools. - Periodicity: Quarter - Number of Economies: 219 - Trade imports, exports, re-imports, re-exports, gross exports, gross imports, MFN tariff rates, bound tariff raters, preferential tariffs. Non-Tariff Measure data with new UNCTAD classification now available. - Update Frequency: Quarterly - Access Option: API, Bulk download, Query tool

WTO Integrated Database (IDB) and Consolidated Tariff Schedule (CTS) contains annual imports (values) and tariff structures (current and final Bound, MFN Applied and Preferential tariffs, ad-valorem or not) since 1996 at the National tariff line level for WTO member countries only.

Accession to the World Trade Organization (WTO) is unlike accession to other global organizations. It is extremely demanding on applicant countries, time consuming and essentially power- rather than rule-based. This article argues that existing WTO members select themselves into the Working Party of applicant countries, the body which determines the timing and conditions of accession, in order to have the option to strategically delay membership by the applicant and/or extract concessions from it. Existing members will select themselves into a specific Working Party if their own trade interests are strongly affected, which will be the case if the existing member’s bilateral trade with the applicant country forms a large share of its income, unless both countries already have a preferential trade agreement (PTA) between them. Trade interests are also strongly affected if the existing member competes with the applicant in terms of export product and market structure. Conversely, where both member and applicant have more PTAs with third countries of large economic size in common, potential accession will affect the member’s trade interests less. An empirical analysis of Working Party membership since 1968 estimates to what extent these three different facets of trade interests are substantively important determinants of Working Party composition.

The TRade Analysis and Information System globally aims to increase the transparency in international trading conditions. TRAINS contains information from UNCTAD's Database on trade confirmation as well as other trade information components such as tariffs para-tariffs measures including TM's, PTM's and NTM's. The UNCTAD Database also contains information on the Generalized System of Preferences (GSP). TRAINS also has trade data involving imports obtained directly from the countries or indire ctly through organizations such as LAIA (Latin American Integration Association), EU(European Nation), IDB (Iinter-American Development) and WTO (World TRade Organization). There is also an alphabetical index of the Standard International Trade Classification (SITC), which enables one to find the corresponding SITC rev.3 code of an alphabetical list of commodities. Lastly TRAINS contains general documentation on various data elements including the import regimes, the GPA and other preferential schemes.

United States US: Tariff Rate: Most Favored Nation: Simple Mean: All Products data was reported at 3.570 % in 2016. This records an increase from the previous number of 3.560 % for 2015. United States US: Tariff Rate: Most Favored Nation: Simple Mean: All Products data is updated yearly, averaging 3.860 % from Dec 1989 (Median) to 2016, with 27 observations. The data reached an all-time high of 5.760 % in 1993 and a record low of 3.540 % in 2014. United States US: Tariff Rate: Most Favored Nation: Simple Mean: All Products data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Trade Tariffs. Simple mean most favored nation tariff rate is the unweighted average of most favored nation rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups.; ; World Bank staff estimates using the World Integrated Trade Solution system, based on data from United Nations Conference on Trade and Development's Trade Analysis and Information System (TRAINS) database and the World Trade Organization’s (WTO) Integrated Data Base (IDB) and Consolidated Tariff Schedules (CTS) database.; ;