The dataset contains import and export data of traded goods corresponding to ecoinvent products from the BACI database (https://www.cepii.fr/CEPII/en/bdd_modele/bdd_modele_item.asp?id=37), as well as production data from FAOSTAT and BGS/USGS and data used to estimation total production volumes from EXIOBASEv3.9.5.

The only purpose of this dataset is to be used as an input to the Regioinvent Python package which can be found here: https://github.com/CIRAIG/Regioinvent

v4 changes: Complete structure changed to accomodate for implementation of real production volume data, instead of only relying on the estimates from EXIOBASE. Also, now uses net exports to avoid problems wit re-exports. Imports were corrected in consequence to function be consistent with net exports.

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.

The CEPII BACI database provides harmonized bilateral trade data at the 6-digit HS level for over 200 countries.

# dc-economics-data
Data for the Economics curriculum of Data Carpentry.

## References
- CEPII, 2011. "GeoDist Database." Downloaded from http://www.cepii.fr/distance/dist_cepii.dta on 2019-06-05.
- Mayer, T. & Zignago, S. 2011. "Notes on CEPII’s distances measures: the GeoDist Database." CEPII Working Paper 2011-25. http://www.cepii.fr/CEPII/en/publications/wp/abstract.asp?NoDoc=3877
- World Bank, 2019. "World Development Indicators 2018." Downloaded from https://datacatalog.worldbank.org/dataset/world-development-indicators on 2019-06-05.

CHELEM-TRADE-INDIC contains 7 indicators on goods and services based on the CEPII International Trade, Balance of Payments and Gross Domestic Product databases: coverage ratio between the values of exports and imports; the degree of openness (average of exports and imports related to current GDP); market position (balance related to average of world exports and imports); exports, imports and balances related to current GDP; and comparative advantages (contribution to the balance). Data are displayed in a multi-level classification spanning the entire world (95 CHELEM individual countries or statistical territories and zones and additional aggregates). Goods are displayed in the 3 classifications of CHELEM-TRADE (CHEL, GTAP and ISIC), detailed in respectively 71, 43 and 147 categories, non-ventilated products (NV) and total products (TT), as well as production chains, stages, sections, sectors or technological levels. Services are detailed in 16 elementary items, without processing (already integrated in goods). All data are expressed in %, except comparative advantages which are in thousandths of current GDP and market position in % of World trade of goods and services.

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.

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 dataset contains panel data used to analyse the impact of Aid for Trade (AfT) on Nigeria’s agricultural export performance from 2005 to 2018. The data is structured at the bilateral level, capturing Nigeria’s trade with multiple partner countries over time. It includes detailed information on bilateral exports, disaggregated Aid for Trade flows (total, economic infrastructure, productive capacity, and trade policy), control variables such as GDP, distance, real and agricultural production, as well as dummy variables capturing trade agreements, linguistic ties, and regional integration (e.g. ECOWAS membership).

The dataset supports fixed effects gravity model estimations and includes relevant economic and institutional indicators drawn from a range of sources including UN COMTRADE, OECD CRS, World Bank World Development Indicators (WDI), CEPII, and the WTO.

This dataset is made available for academic and research purposes only. While every effort has been made to ensure the accuracy and consistency of the data, the compilers make no warranties regarding its completeness, reliability, or fitness for a particular purpose. Users are advised to consult original data sources (e.g. UN COMTRADE, OECD CRS, World Bank WDI, CEPII, WTO) for official statistics and definitions.

EU KLEMS Growth and Productivity Accounts: October 2012 Releaes, data in the ISIC Rev. 4 industry classification All series derived from this database need to be referred to as follows: O’Mahony, Mary and Marcel P. Timmer (2009), “Output, Input and Productivity Measures at the Industry Level: the EU KLEMS Database”, Economic Journal, 119(538), pp. F374-F403 For more details and analysis, see: Timmer, Inklaar , O'Mahony and van Ark, Economic Growth in Europe, Cambridge University Press, 2010 Introduction The 2012 EU KLEMS release follows up from the previous release in 2009 which showed detailed growth accounts up to 2007. This new release is similar in concepts and methodologies to calculate the various growth and productivity variables as its predecessors, but it also has a number of new features; It provides updates and data for additional years and revisions of longer time-series in case national statistical institutes (NSIs) provided these. For labour composition use has been made of the micro-data underlying the European Labour Force Survey (LFS) for recent years. New investment data has been provided by the EU KLEMS consortium partners. Most importantly, a new industrial classification is used based on the new international ISIC Revision 4 industry classification, which is consistent with the European NACE 2 industry classification. The National Accounts (NA) data in the new classification is typically provided for shorter time series than were previously available in the ISIC Rev. 3 (NACE 1) classification. We back-cast time series of output and labour data using growth rates from the earlier data in the ISIC Rev. 3 classification. These imputations are denoted in grey in the new release. Sources and methods The EU KLEMS updates in the new ISIC Rev. 4 industry classification are being done on a country by country basis. Sources and methods documentation is available separately for each country. The following institutes have partnered with us to construct these data, additional information can be found on their respective websites: STATSWE (Sweden) STATFI (Finland) FPB (Belgium) JIP (Japan) BEA/BLS (United States) CBS (Netherlands) NIESR (United Kingdom) DIW (Germany) ISTAT (Italy) WIFO (Austria) CEPII (France) IVIE (Spain) More information on additional EU KLEMS releases can be found on the website of the Groningen Growth and Development Centre (GGDC). The original website of the EU KLEMS project, on which these data were first released, has been archived, but is still available through the Internet Archive. This update of the EU KLEMS database is part of the INDICSER project. This project is funded by the European Commission, Research Directorate General as part of the 7th Framework Programme, Theme 8: Socio-Economic Sciences and Humanities. Grant Agreement no: 244 709 Any errors or omissions in this update of the EU KLEMS database are entirely the responsibility of the GGDC (Groningen Growth and Development Centre).

Metrics system for analyzing the overall structure of the network.

This is a strongly balanced panel dataset of 65 countries spanning the period from 1991 to 2020, with data from SWIID, ETH Zurich, the World Bank, and CEPII.Since the trade openness indicators for individual countries are missing from the World Bank database, the author supplements them by calculating and filling in the ratio of the corresponding GDP values in WITS to the total import and export trade. ratio of total import and export trade to supplement it. At the same time, to ensure the completeness of the data and the balance of the panel data, I linearly interpolate the missing GDP per capita values and the missing education indicator values for some countries.

EU KLEMS Growth and Productivity Accounts: March 2007 Release All series derived from this database need to be referred to as follows: Source: EU KLEMS Database, March 2007, see Marcel Timmer, Mary O'Mahony & Bart van Ark, The EU KLEMS Growth and Productivity Accounts: An Overview, University of Groningen & University of Birmingham; downloadable at www.euklems.net For a brief description of results on a country-by-country basis obtained from the EU KLEMS Growth and Productivity Accounts, please refer to: Bart van Ark, Mary O’Mahony and Gerard Ypma eds., The EU KLEMS Productivity Report, Issue 1, University of Groningen & University of Birmingham, March 2007. More information on additional EU KLEMS releases can be found on the website of the Groningen Growth and Development Centre (GGDC). The original website of the EU KLEMS project, on which these data were first released, has been archived, but is still available through the Internet Archive. Consortium members EU KLEMS Project: University of Groningen, Groningen Growth and Development Centre (GGDC, Groningen) National Institute of Economic and Social Research (NIESR, London) Centre d'études prospectives et d'informations internationales (CEPII, Paris) Centre for Economic and Business Research (CEBR, Copenhagen) Netherlands Bureau for Economic Policy Analysis (CPB, The Hague) Deutsches Institut für Wirtschaftsforschung (DIW, Berlin) Federaal Planbureau (FPB, Brussels) Information on the Istituto di Studi e Analisi Economica (ISAE, Roma) Instituto Valenciano de Investigationes Económicas (IVIE, Valencia) Helsinki School of Economics (HSE, Helsinki) Österreichisches Institut für Wirtschaftsforschung (WIFO, Vienna) Wiener Institut für Internationale Wirtschaftsvergleiche AMsterdam Institute for Business and Economic Research, Free University Amsterdam (AMBER) The Conference Board Europe (TCB, Brussels) Fachhochschule Konstanz (FK, Konstanz) University of Birmingham (UNI-BHAM, Birmingham) Pellervo Economic Research Institute (PTT, Helsinki) This project was funded by the European Commission, Research Directorate General as part of the 6th Framework Programme, Priority 8, "Policy Support and Anticipating Scientific and Technological Needs". More about this project.

Cette statistique montre une comparaison des projections des effets du Partenariat transatlantique de commerce et d'investissement (TTIP) sur le PIB de l'Union européenne et des États-Unis en 2018, 2025 et 2030. Le modèle de l'étude du CEPII de 2013 estimait ainsi que le TTIP devrait premettre au PIB de l'UE d'augmenter de 0,3 % par rapport à la ligne de base d'ici 2025.

This dataset is compiled from various public sources. The economic variables are compiled from OECD Statistics, Penn World Table 9.1, CEPII Gravity and Distance database, ILO Statistics and World Bank database. The language variable are compiled from Japan Foundation and Educational Testing Services.

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.