Research article

EU trade: deviations in multi-country input-output tables and their implications for trade policy

  • Published: 21 November 2025
  • JEL Codes: F13, F14, F17, C67

  • Over the past two decades, research institutions around the world have undertaken numerous projects to develop global multi-country input-output (MCIO) databases. This effort has resulted in the creation of several notable databases, including WIOD, Exiobase, GTAP-MRIO, OECD-ICIO, FIGARO, and Eora. These databases have allowed international organizations and countries to conduct comprehensive assessments of socioeconomic, environmental, and trade impacts. The trade-in-value -added indicators, in particular, have been widely used and have undergone detailed evaluations to compare the results across different databases. However, there has been limited examination of the compilation processes and underlying data that contribute to variations in indicators and trade values, and their implications for policy decisions. In this paper, we explored the significant discrepancies in trade policy-relevant indicators depending on the MCIO used, and delved into the reasons behind these deviations.

    Citation: Pablo Piñero, Zornitsa Kutlina-Dimitrova, José Manuel Rueda-Cantuche. EU trade: deviations in multi-country input-output tables and their implications for trade policy[J]. National Accounting Review, 2025, 7(4): 546-573. doi: 10.3934/NAR.2025023

    Related Papers:

  • Over the past two decades, research institutions around the world have undertaken numerous projects to develop global multi-country input-output (MCIO) databases. This effort has resulted in the creation of several notable databases, including WIOD, Exiobase, GTAP-MRIO, OECD-ICIO, FIGARO, and Eora. These databases have allowed international organizations and countries to conduct comprehensive assessments of socioeconomic, environmental, and trade impacts. The trade-in-value -added indicators, in particular, have been widely used and have undergone detailed evaluations to compare the results across different databases. However, there has been limited examination of the compilation processes and underlying data that contribute to variations in indicators and trade values, and their implications for policy decisions. In this paper, we explored the significant discrepancies in trade policy-relevant indicators depending on the MCIO used, and delved into the reasons behind these deviations.



    加载中


    [1] Abd Rahman MD, Los B, Owen A, et al. (2023) Multi-level comparisons of input–output tables using cross-entropy indicators. Econ Syst Res 35: 75–94. https://doi.org/10.1080/09535314.2021.1990869 doi: 10.1080/09535314.2021.1990869
    [2] Arto I, Rueda-Cantuche JM, Peters GP (2014) Comparing the GTAP-MRIO and WIOD databases for carbon footprint analysis. Econ Syst Res 26: 262–283. https://doi.org/10.1080/09535314.2014.939949 doi: 10.1080/09535314.2014.939949
    [3] Arto I, Dietzenbacher E, Rueda-Cantuche JM (2019) Measuring bilateral trade in terms of value added. Luxembourg: Publications Office of the European Union. Available from: https://op.europa.eu/en/publication-detail/-/publication/bbd49b67-8283-11e9-9f05-01aa75ed71a1/language-en.
    [4] Carrico C, Corong E, van der Mensbrugghe D (2020) The GTAP 10A Multi-Region Input Output (MRIO) Data Base. Research Memorandum No. 34, April. Available from: https://www.gtap.agecon.purdue.edu/resources/download/10043.pdf.
    [5] European Commission, International Monetary Fund, Organisation for Economic United Nations Co-operation, et al. (2009) System of National Accounts – SNA 2008. Available from: https://unstats.un.org/unsd/nationalaccount/docs/SNA2008.pdf.
    [6] Eurostat (2013) European System of Accounts – ESA2010. Luxembourg: Publications Office of the European Union. Available from: https://ec.europa.eu/eurostat/documents/3859598/5925693/KS-02-13-269-EN.PDF/.
    [7] Eurostat (2018) International trade statistics – background. Luxembourg: Publications Office of the European Union. Available from: https://ec.europa.eu/eurostat/statistics-explained/index.php?title = International_trade_statistics_-_background#Asymmetries.
    [8] Eurostat (2023) Employment by detailed industry (NACE Rev.2) - national accounts. Available from: https://ec.europa.eu/eurostat/databrowser/view/nama_10_a64_e/default/table?lang=en&category=na10.nama10.nama_10_dbr.
    [9] Eurostat (2024) International trade statistics - background. Explained Article. Available from: https://ec.europa.eu/eurostat/statistics-explained/SEPDF/cache/37447.pdf.
    [10] Fusacchia I, Salvatici L (2022) GTAP and TiVA: Differences Between the two Databases and their Implications for Trade in Value-Added Indicators. Collana Centro Rossi-Doria Papers, 5. Università degli Studi Roma Tre. Centro Rossi-Doria. Available from: https://romatrepress.uniroma3.it/wp-content/uploads/2022/08/RDP-5-22.pdf.
    [11] Huo J, Chen P, Hubacek K, et al. (2022) Full-scale, near real-time multi-regional input-output table for the global emerging economies (EMERGING). J Ind Ecol 26: 1218–1232. https://doi.org/10.1111/jiec.13264 doi: 10.1111/jiec.13264
    [12] Horvát P, Webb C, Yamano N (2020) Measuring employment in global value chains. OECD Science, Technology and Industry Working Papers, No. 2020/01. OECD Publishing, Paris. https://doi.org/10.1787/00f7d7db-en
    [13] International Monetary Fund (IMF), the Organisation for Economic Co-operation and Development (OECD), the United Nations Conference on Trade and Development (UNCTAD), et al. (2023) Handbook on Measuring Digital Trade. Second edition. Available from: https://www.oecd.org/content/dam/oecd/en/publications/reports/2023/07/handbook-on-measuring-digital-trade-second-edition_099afd2f/ac99e6d3-en.pdf.
    [14] Jones L, Wang Z, Xin L, et al. (2014) The Similarities and Differences among Three Major inter-Country input-output Databases and their implications for Trade in value-added estimates. Office of Economics Working Papers, no. 2014-12b, USITC. Available from: https://www.wto.org/english/res_e/statis_e/miwi_e/usitcwkpper.pdf.
    [15] Junius T, Oosterhaven J (2003) The Solution of Updating or Regionalizing a Matrix with Both Positive and Negative Elements. Econ Syst Res 15: 87–96. https://doi.org/10.1080/0953531032000056954 doi: 10.1080/0953531032000056954
    [16] Lenzen M, Moran D, Kanemoto K, et al. (2013) Building Eora: A Global Multi-regional Input-Output Database at High Country and Sector Resolution. Econ Syst Res 25: 20-49. https://doi.org/10.1080/09535314.2013.769938 doi: 10.1080/09535314.2013.769938
    [17] Lenzen M, Geschke A, Abd Rahman MD, et al. (2017) The Global MRIO Lab – charting the world economy. Econ Syst Res 29: 158–186. https://doi.org/10.1080/09535314.2017.1301887 doi: 10.1080/09535314.2017.1301887
    [18] Lenzen M, Geschke A, West J, et al. (2022) Implementing the material footprint to measure progress towards Sustainable Development Goals 8 and 12. Nat Sustain 5: 157–166. https://doi.org/10.1038/s41893-021-00811-6 doi: 10.1038/s41893-021-00811-6
    [19] Martins-Ferreira P (2018) QDR methodology: understanding trade flows in the EU. Eurostat Review on National Accounts and Macroeconomic Indicators (EURONA), 2/2018. Available from: https://cros-legacy.ec.europa.eu/system/files/euronaissue2-2018-article3.pdf.
    [20] Miao G, Fortanier F (2017) Estimating Transport and Insurance Costs of International Trade. OECD Statistics Working Papers, 2017/04. Available from: https://www.oecd-ilibrary.org/economics/estimating-transport-and-insurance-costs-of-international-trade_8267bb0f-en.
    [21] OECD (2021) OECD Inter-Country Input-Output Database. Available from: http://oecd/icio.
    [22] OECD, Directorate for Science, Technology, et al. (2023) Guide to OECD Trade in Value Added (TiVA) Indicators, 2023 edition. Available from: https://www.oecd.org/content/dam/oecd/en/topics/policy-sub-issues/trade-in-value-added/tiva-indicators-guide-2023.pdf.
    [23] OECD (2025) The OECD Balanced International Merchandise Trade Dataset (BIMTS). OECD Publishing, Paris. https://doi.org/10.1787/07518168-en
    [24] Owen A, Steen-Olsen K, Barrett J, et al. (2014) A Structural Decomposition Approach to Comparing MRIO Databases. Econ Syst Res 26: 262–283. https://doi.org/10.1080/09535314.2014.935299 doi: 10.1080/09535314.2014.935299
    [25] Piñero P, Banacloche S, Rueda-Cantuche JM, et al. (2018) Macroeconomic globalisation indicators based on FIGARO. Insights into the measurement of value added and employment in the EU – 2024 edition. Eurostat Statistical Working Papers. Available from: https://ec.europa.eu/eurostat/documents/3888793/20487140/KS-01-24-017-EN-N.pdf/121d6f41-b669-22b2-cf9c-c1ef
    [26] Remond-Tiedrez I, Rueda-Cantuche JM (2019) EU inter-country supply, use and input-output tables — Full international and global accounts for research in input-output analysis (FIGARO). Eurostat 2019 edition. Available from: https://ec.europa.eu/eurostat/web/products-statistical-working-papers/-/ks-tc-19-002.
    [27] Rueda-Cantuche JM, Remond-Tiedrez I, Bouwmeester MC (2018) Institutionalization of Inter-Country Input-Output Tables: Working Towards Harmonization and Standardization. J Ind Ecol 22: 485–486. https://doi.org/10.1111/jiec.12761 doi: 10.1111/jiec.12761
    [28] Rueda-Cantuche JM, Revesz T, Amores AF, et al. (2020) Improving the European input–output database for global trade analysis. J Econ Struct 9: 33. https://doi.org/10.1186/s40008-020-00208-2 doi: 10.1186/s40008-020-00208-2
    [29] Rueda-Cantuche JM, Durán JE (2023) Global Input-Output Accounts (GIANT): a collective initiative to harmonise input data entering ICIO tables published by international organisations. 30th International Input-Output Conference, Santiago (Chile). Available from: https://www.iioa.org/conferences/30th/papers/files/5209.pdf.
    [30] Stadler K, Steen-Olsen K, Wood R (2014) The 'Rest of the World' - Estimating the economic structure of missing regions in global multi-regional input-output tables. Econ Syst Res 26: 303–326. https://doi.org/10.1080/09535314.2014.936831 doi: 10.1080/09535314.2014.936831
    [31] Stadler K, Wood R, Bulavskaya T, et al. (2018). EXIOBASE 3: Developing a Time Series of Detailed Environmentally Extended Multi-Regional Input-Output Tables. J Ind Ecol 22: 502–515. https://doi.org/10.1111/jiec.12715 doi: 10.1111/jiec.12715
    [32] Tarne P, Lehmann A, Finkbeiner M (2018) A comparison of Multi-Regional Input-Output databases regarding transaction structure and supply chain analysis. J Clean Prod 196: 1486–1500. https://doi.org/10.1016/j.jclepro.2018.06.082 doi: 10.1016/j.jclepro.2018.06.082
    [33] Timmer MP, Dietzenbacher E, Los B, et al. (2015) An Illustrated User Guide to the World Input–Output Database: the Case of Global Automotive Production. Rev Int Econ 23: 575–605. https://doi.org/10.1111/roie.12178 doi: 10.1111/roie.12178
    [34] Timmer MP, Los B, Stehrer R, et al. (2016) GGDC research memorandum number 162. University of Groningen. Available from: https://www.rug.nl/ggdc/html_publications/memorandum/gd162.pdf.
    [35] UNECE (2015) Guide to Measuring Global Production. United Nations Economic Commission for Europe, Geneva. Available from: https://unece.org/DAM/stats/publications/2015/Guide_to_Measuring_Global_Production__2015_.pdf.
    [36] UN-ECLAC (2016) La matriz de insumo-producto de América del Sur. Principales supuestos y consideraciones metodológicas. Comisión Económica para América Latina y el Caribe (CEPAL), Santiago de Chile. Available from: https://repositorio.cepal.org/bitstream/handle/11362/40271/1/S1600691_es.pdf.
    [37] United Nations (2011) International Merchandise Trade Statistics: Concepts and Definitions 2010. Department of Economic and Social Affairs. Statistics Division. Statistical Papers, Series M, No. 52. United Nations, New York. Available from: https://unstats.un.org/unsd/trade/eg-imts/IMTS
    [38] United Nations (2016) Classification by Broad Economic Categories Rev.5. Statistical Papers, Series M, No. 53, Rev. 5. United Nations, New York. Available from: https://unstats.un.org/unsd/trade/classifications/SeriesM_53_Rev.5_17-01722-E-Classification-by-Broad-Economic-Categories_PRINT.pdf.
    [39] United Nations (2018) Handbook on Supply and Use Tables and Input Output-Tables with Extensions and Applications. United Nations, New York. Available from: https://unstats.un.org/unsd/nationalaccount/docs/SUT_IOT_HB_Final_Cover.pdf.
    [40] Valderas-Jaramillo JM, Rueda-Cantuche JM (2021) The multidimensional nD-GRAS method: Applications for the projection of multiregional input–output frameworks and valuation matrices. Pap Reg Sci 100: 1599–1624. https://doi.org/10.1111/pirs.12625 doi: 10.1111/pirs.12625
    [41] Yamano N, Guilhoto J, Alsamawi A, et al. (2022) Development of the OECD Inter-country Input-Output Database: Sources and Methods. WorldKlems Conf. Available from: https://www.worldklems.net/conferences/worldklems2022/paper_Webb.pdf.
    [42] Yamano N, Guilhoto J (2020) CO2 emissions embodied in international trade and domestic final demand: Methodology and results using the OECD Inter-Country Input-Output Database. OECD Science, Technology and Industry Working Papers, No. 2020/11. OECD Publishing, Paris. https://doi.org/10.1787/8f2963b8-en
  • Reader Comments
  • © 2025 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(849) PDF downloads(33) Cited by(0)

Article outline

Figures and Tables

Figures(3)  /  Tables(6)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog