CHELEM-TRADE contains bilateral flows of goods expressed in millions of current US dollars since 1967. Data from UN-COMTRADE and complementary sources are harmonized and made consistent in a multi-level classification spanning the entire world (95 CHELEM individual countries or statistical territories and zones and additional aggregates). Goods are detailed in 71 categories, non-ventilated products (NV) and total products (TT). The elementary product categories can be aggregated by production chains and stages, sections or sectors. Data are harmonized by country pairs and product category; exports from country A to country B are equal to imports of country B from country A.

CHELEM-TRADE contains bilateral flows of goods expressed in millions of current US dollars since 1967. Data from UN-COMTRADE and complementary sources are harmonized and made consistent in a multi-level classification spanning the entire world (95 CHELEM individual countries or statistical territories and zones and additional aggregates). Goods are detailed in 147 elementary (4-digits) categories, aggregated in 3-digits (79) and 2-digits (35), non-ventilated products (NV) and total products (TT). The elementary product categories can be aggregated by technological levels. Data are harmonized by country pairs and product category; exports from country A to country B are equal to imports of country B from country A.

Dataset

This dataset was created by valcho valev

Contents

BACI provides yearly data on bilateral trade flows at the product level. Products are identified using the Harmonized System (HS), which is the standard nomenclature for international trade, used by most customs. The Harmonized System was revised in 1992, 1996, 2002, 2007, 2012 and 2017.

This data is using the 2017 revision.

Main BACI files :

Additional files :

Source : http://www.cepii.fr/CEPII/en/bdd_modele/bdd_modele_item.asp?id=37

The data set contains dyadic link specific variables for use in gravity models

This dataset explores international trade data through the lenses of Network Analysis, in order to visualize the World Trade Network and describe the topology of the network of world trade. The details of such analysis are provided in a companion paper De Benedictis et al (2013).

The main advantage in using network analysis to explore international trade flows relies on the piece of information that networks provide. The network represents a dyad of counties ij; not the monads i or j, but the relationship between them. However, the specificity of networks is that the relation between i and j is not analyzed in isolation, but it is studied focusing on its structural dimension, that is taking into account the effect of z in the relation between i and j. Extending the effect of others, or in our case the z country effect, to the many z included in the set of possible trade relations, the resulting image is a network in its essence. The implication of this “structural view” is that the relation between i and j cannot be considered independent from the relation between i and z, and between j and z. Therefore the characteristic of interdependence is the hinge of networks.

Citation

De Benedictis, L., Silvia, N., Santoni, G., Tajoli, L. and Vicarelli, C. (2013), Network Analysis of World Trade using the BACI-CEPII dataset, CEPII Working Paper, N°2013-24. BibTex

Acknowledgement

Foto von Kelly Sikkema auf Unsplash

Code and Data needed to replicate the results of the paper "Production Relocation to the South and Within-Country Inequality". The ZIP file contains a folder structure that must be preserved when unzipping. Data on BACI folder is missing due to the restrictions. Please contact the Centre d’études prospectives et d’informations internationales (CEPII: http://cepii.fr) for accession, and then contact the authors.

Against the backdrop of a complex and volatile global trade environment, the entry into force of the Regional Comprehensive Economic Partnership (RCEP) has brought new opportunities for regional economic development. From a China-centric perspective, this paper constructs a two-dimensional analytical framework of tariff reduction and non-tariff barriers (NTB) reduction based on theories of regional economic integration and general equilibrium, and uses the GTAP-Dyn model to systematically examine the impact mechanisms and policy effects of RCEP implementation on agricultural trade among member states. The study finds: ①Ten years after RCEP’s implementation, it significantly promotes economic growth in all countries. Taking China as an example, NTB reduction contributes 68.4% to this growth, revealing the dominant role of non-tariff barriers (institutional coordination) in deep regional integration. ②The agricultural sector exhibits a “dual differentiation” feature: sensitive sectors face adjustment pressures (dairy output −21.55%) while domestic substitution effects emerge (aquatic product imports −88.09%). ③Policy effects show national heterogeneity: ASEAN countries experience low growth but high gains (GDP + 0.75%, terms of trade +8.88%), reflecting a complex game landscape. ④The interaction between tariff and non-tariff measures is asymmetric, with long-term dividends relying more on institutional openness. Based on these findings, China should build a composite open system, implement differentiated agricultural policies, and deepen cooperation pathways.

数据内容： 该数据集包含了全球范围内双边和单方面贸易伙伴关系的份额数据。具体而言，数据集包括以下字段： 1. Entity：标识数据对应的国家或地区。 2. Code：与Entity对应的编码。 3. Year：数据对应的年份。 4. Non-trading (Fouquin and Hugot (CEPII 2016))：非贸易伙伴关系的份额。 5. Bilateral trade partnerships (Fouquin and Hugot (CEPII 2016))：双边贸易伙伴关系的份额。 6. Unilateral trade partnerships (Fouquin and Hugot (CEPII 2016))：单方面贸易伙伴关系的份额。 数据来源：互联网公开数据 数据用途： 该数据集可用于多个行业的研究和分析，包括但不限于以下领域： 1. 国际贸易：分析全球贸易伙伴关系的变化趋势，研究双边和单方面贸易政策的影响。 2. 经济学研究：探讨贸易伙伴关系与经济发展的关系，评估贸易政策的效果。 3. 政策制定：为政府和国际组织提供数据支持，帮助制定更有效的贸易政策。 4. 数据分析：用于贸易数据的可视化和建模，支持商业决策和学术研究。 5. 全球经济研究：研究全球贸易格局的变化，预测未来贸易趋势。 标签：贸易伙伴关系,双边贸易,单方面贸易,CEPII研究,全球贸易分析,国际贸易数据,经济政策研究 行业分类： 1. 国际贸易 2. 经济学研究 3. 政策制定 4. 数据分析 5. 全球经济研究 分析： 该数据集包含多个关键字段，其中Year字段具有65种不同的值，表明数据覆盖了较长时间跨度。Non-trading, Bilateral trade partnerships, 和 Unilateral trade partnerships字段各有64种不同的值，说明数据反映了多个国家或地区在不同年份的贸易伙伴关系份额。这种多样性为研究全球贸易格局的变化提供了丰富的数据支持。

Semiconductor merchandise trade from an industrial chain perspective.

Metrics of the overall structure of the semiconductor trade network.

Community structure of the semiconductor trade network.

This dataset can be used for modeling gravity models or other research purposes, and also serves as a valuable resource for students in macroeconomics courses.

The dataset "Gravity & Hofstede Data | 2015 (49 Countries)" includes data from the following 49 countries:

• Argentina
• Australia
• Austria
• Bangladesh
• Belgium
• Brazil
• Bulgaria
• Canada
• Chile
• China
• Colombia
• Croatia
• Denmark
• El Salvador
• Estonia
• Finland
• France
• Germany
• Greece
• Hungary
• India
• Indonesia
• Ireland
• Italy
• Japan
• Latvia
• Lithuania
• Luxembourg
• Malaysia
• Malta
• Mexico
• Morocco
• Netherlands
• Norway
• Pakistan
• Peru
• Philippines
• Poland
• Portugal
• Singapore
• Slovenia
• Spain
• Sweden
• Switzerland
• Thailand
• Trinidad and Tobago
• Turkey
• Uruguay
• Vietnam

Source:

World Bank WITS - Trade Data: https://wits.worldbank.org/ CEPII Gravity Model Database: https://www.cepii.fr/CEPII/en/bdd_modele/bdd_modele_item.asp?id=8 Hofstede Insights - Dimension Data Matrix: https://geerthofstede.com/research-and-vsm/dimension-data-matrix/

The countries represent a diverse range of regions across the globe, rather than being concentrated in a single geographic area, in order to make the data more suitable for comprehensive research and analysis.

Hofstede data is presented as the absolute difference in levels between the exporting and importing countries; therefore, the closer the data is to 0, the more similar the countries are in terms of cultural dimensions.

This dataset provides a comprehensive framework for analyzing the relationship between productive capabilities and trade agreement formation across 95 countries from 2011 to 2017. It combines bilateral trade agreement status, productive capability measures, and sectoral productivity differentials for 8,575 unique country pairs, creating a novel resource for studying economic development and trade integration. The database merges information from multiple authoritative sources: trade agreement indicators from the CEPII Gravity database, maritime distances from CERDI-SeaDistance, productive capability measures from UNCTAD's Productive Capabilities Index (PCI), and sectoral productivity differentials from the RPROD database. Each observation represents a bilateral country relationship in a given year, with detailed information on trade agreement status, economic capabilities, geographic distance, and productivity measures. Key variables include:

-Binary indicators for active trade agreements -Productive Capability Index (PCI) for both origin and destination countries -Balassa-Samuelson effect measures capturing sectoral productivity differentials -Bilateral maritime distances and connectivity measures -Trade flow values and related economic indicators

This unified dataset is particularly valuable for researchers studying:

-Trade agreement formation and economic development -Productive capability thresholds in international trade -Regional patterns in economic integration -Development policy and trade liberalization sequencing

The data structure allows for both cross-sectional and longitudinal analyses, providing a rich foundation for investigating how domestic economic capabilities influence international trade relations. The time period (2011-2017) represents a relatively stable era in international trade, making it ideal for studying structural relationships without the confounding effects of major global disruptions.

Industry-level measures of export quality that take global input-output linkages into account and associated code; as described in Wacker et al. (2025). The main repository folders are: - codes/: a folder with python codes performing the four steps explained in the Usage Notes below. - data/QualEst/: a folder with zipped CSV files (QualEst_YYYY.zip, where YYYY stands for the year), storing the quality estimate for bilateral traded products () based on Trenczek and Wacker [15]. This folder will also store the quality aggregations generated as intermediate products in the codes. Those estimates are ultimately based on the BACI database for bilateral trade flows [13], version 202301-HS07. Quality estimates and trade flows are available for bilateral trade between countries and administrative regions for the entire world, and data for each year are provided in separated CSV files with the following columns: identifier of importing and exporting countries (i and j), product code in 6-digit HS2007 classification (hs6digit), value of export in thousand US dollars (v), and quality estimates (qual_idx). Country identifiers are integers that follow the classification in the BACI database; more details can be obtained from https://cepii.fr/CEPII/en/bdd_modele/bdd_modele_item.asp?id=37. - data/ICIO/: An empty folder, to which one should download and unzip the Regular ICIO input-output tables from the official OECD website (https://oe.cd/icio) and place them in this folder. We used the 2023 December release of Regular ICIO input-output tables from OECD. ICIO are available for 76 countries and regions, plus a "rest-of-world" entry covering all other economies, and 46 industries based on 2-digit ISIC Rev. 4 classification. The input-output tables are provided in CSV format. More details on the data structure and the definitions for rows and columns can be found in https://oe.cd/icio - data/keys/: folder with correspondence tables for country and product-industry identifiers used in the export quality dataset and ICIO input-output tables; both are in CSV format. The file for product-industry identifiers includes a column (TYPE) that describe the property of traded products: 0: intermediates, 1: mixed-use products, 2: final products for consumption or capital investment only. Further details can be found in the ReadMe.txt file placed in the root folder of the replication package.

This is a data repository that could be used to replicate the results found in the text: EU enlargements, Brexit and value-added trade. A structural gravity approach by Jan Hagemejer & Jakub Mućk. This dataset is based on the following datasets:OECD Trade in Value Added Database, https://www.oecd.org/sti/ind/measuring-trade-in-value-added.htmConte M, Cotterlaz P, Mayer T. The CEPII Gravity database. CEPII Working Paper N°2022-05. July 2022.The Hagemejer_Muck_2004.dta is a Stata file with aggregate-level variables.The Hagemejer_Muck_2024_Industry.dta is a Stata file that contains the sectoral data. dataset.txt contains the properties of the dataset and variables definitions.

Dataset Description: Purpose: Enables replication of empirical findings presented in the manuscript "EFFECTS OF TRADE RESISTANCES ON THE CAPITAL GOODS SECTOR: EVIDENCE FROM A STRUCTURAL GRAVITY MODEL FOR BRAZIL (2008–2016)". Nature and Scope: Quantitative panel dataset covering 2008-2016. Includes 144 countries with bilateral (exporter-importer) observations focused on the capital goods sector. Contains variables related to trade flows, trade policy, gravity determinants, macroeconomic indicators, and estimated model parameters. Content: The Stata dataset (20251019_submitted manuscript_data.dta) includes: bilateral capital goods import flows (imp); applied tariffs (tau, t_imp_Bra, t_exp_Bra); gravity variables (ln_DIST, contig, comlang, colony, rta); country-level macro data (ll, lk, lrgdpna); and estimated Multilateral Resistance terms (OMR/IMR). Origin: Data compiled from public sources: UN Comtrade, WITS, WTO (TRAINS, IDB, CTS), CEPII, PWT 9.1, Mario’s Larch RTA Database. OMR/IMR terms were generated via the estimation procedure detailed in the accompanying paper . Code Description: Purpose: This Stata do-file (20251019_submitted manuscript_dofile.do) contains the complete code necessary to replicate the empirical results presented in the manuscript "EFFECTS OF TRADE RESISTANCES ON THE CAPITAL GOODS SECTOR: EVIDENCE FROM A STRUCTURAL GRAVITY MODEL FOR BRAZIL (2008–2016)". Nature and Scope: The file is a script written in Stata command language. Its scope covers the entire empirical workflow. Content: The do-file executes the following main procedures: Loads the accompanying dataset (20251019_submitted manuscript_data.dta). Defines global macros and sets up the estimation environment. Runs the first-stage Poisson Pseudo-Maximum Likelihood (PPML) gravity model estimations with high-dimensional fixed effects (exporter-product-year, importer-product-year, bilateral pairs) using the ppmlhdfe command. Recovers the estimated fixed effects and constructs the Multilateral Resistance terms (OMR and IMR) based on the gravity model results. Merges the MR terms back into the main dataset. Runs the second-stage OLS regressions for the production function using the recovered OMR term. Runs the second-stage OLS regressions for the capital accumulation function using the recovered IMR term. Includes commands for diagnostic tests (e.g., RESET, MaMu variance tests, if applicable within the code). Dependencies: Requires Stata statistical software (version specified in the do-file or compatible) and likely requires user-written packages such as ppmlhdfe. Requires the accompanying dataset (20251019_submitted manuscript_data.dta) to be in the Stata working directory.

This statistic shows a comparison of projections of the effects of the Transatlantic Trade and Investment Partnership (TTIP) on the GDP of the European Union and the United States. The 2013 CEPII study model estimates TTIP will increase the EU GDP 0.3 percent over baseline.

The STATA do-files listed below allow the replication of all the tables and figures reported in the manuscript “Does Exporting Improve Matching? Evidence from French Employer-Employee Data”. The data used to obtain tables and figures have been acceded at CEPII via CASD secure remote access and are confidential. We are not allowed to disseminate these datasets.

Downloading the data will provide you with a .zip file.

These data are US imports from 1974 to 2020, by partner, transport mode and product at the 5 digit level in the "Hummels" data and by partner, transport mode, district of entry, district of unlading and product at the 10 digit level for the rest of the data.

All the data originally come from the Census Bureau "Foreign Trade" (see https://www.census.gov/foreign-trade/index.html) and belong to the public domain.

"Hummels_JEP _data" was downloaded from David Hummels’s website (https://www.krannert.purdue.edu/faculty/hummelsd/research/jep/data.html -- Hummels, David, "Transportation Costs and International Trade in the Second Era of Globalization", Journal of Economic Perspectives, Vol 21, No 3, pp 131-154. It covers 1974 to 2004.

The other main files were bought directly from the Census Bureau ("Annual Merchandise Trade files"). They cover 1997-1999 and 2002-2020.

Various files are included for the conversion of country codes (dist_cepii.dta, countrycodes_use.txt), product codes (HS2002_SITC2.txt) and to associate quantity units with hts codes (hts... and ..._hts_...).