Handbook on Extended Supply and Use Tables and Extended Input‑Output Tables

Foreword

An extended supply and use tables (ESUT) framework consistent with the System of National Accounts (SNA) is desirable to disaggregate macroeconomic statistics by type of enterprise. It would better measure the impact of globalisation while providing new insights including, for example, about the role and interconnectedness of small and medium-sized enterprises (SMEs) and multinational enterprises (MNEs) in global value chains (GVCs).

Background

The increasing international fragmentation of production, driven by technological progress, cost reduction and policy reforms, easing access to resources and markets, led to increasing demand for statistics about GVCs and related insights. The OECD-WTO Trade in Value Added (TiVA) initiative was the first initiative at an official level that allowed mapping complete GVCs, not only trade between two countries.

However, although it was an immense step forward, TiVA does not provide all the necessary insights since it only links industries and countries. It does not provide information about SMEs’ integration into GVCs, which typically face larger barriers to trade than larger enterprises. Similarly, TiVA does not provide insights on MNEs’ role in value chains, and so is also silent on the trade-investment-production nexus. Yet this type of information is essential to better understand the nature of GVCs.

Furthermore, policy makers sometimes focus on specific groups of enterprises such as SMEs, MNEs or exporters. There were no statistics on each enterprise type’s contribution to macroeconomic aggregates such as GDP, imports, exports and employment, nor about the interconnectedness of different types of enterprises in GVCs. Assuming that all enterprises in an industry are equal would be incorrect – there is considerable heterogeneity. This heterogeneity can be tackled using microdata (ideal case) or enterprise type aggregates by industry. For example, Ahmad et al. (2013[1]) show that using Turkish trade microdata improved TiVA measures.

In response to the increased interest in linking types of enterprises to macroeconomic aggregates, in 2014 the OECD launched an Expert Group on Extended Supply and Use Tables (EGESUT), comprising participants from national statistical offices and international organisations. The terms of reference (OECD, 2014[2]) called for this group to share experiences and develop a methodology for constructing ESUTs. This Handbook aims to combine the accumulated knowledge on how to compile ESUTs and extended input-output tables (EIOTs), covering presentations and discussions conducted within the expert group, to assist others with compiling the extended tables and help users to properly understand the results. Earlier, Ahmad (2023[3]) provided some general guidance and results.

The extended tables provide new insights and have other advantages as well. In addition to providing more granular data about globalisation by enterprise type (such as by size, ownership and export orientation), the quality of globalisation indicators (such as imports used to export) is better than in the case that one assumes that all enterprises in an industry behave the same. Furthermore, compiling ESUTs and EIOTs will improve the quality of the regular statistical system as well. In the process of balancing supply and use, one works at a more detailed level, which enables identifying the reason for imbalances at the same detailed level as well. This facilitates decision making in the adjustment process and will improve regular supply and use tables (SUTs).

This Handbook adheres to the recommendations of the Handbook on Supply and Use Tables and Input –Output Tables with Extensions and Applications (United Nations, 2018[4]), including the recommendation to compile SUTs at the establishment level. However, because of the local data situation, not every country can follow this principle. Therefore, it is recommended to use the same statistical unit for the ESUT as the statistical unit used for the regular SUT. For convenience, throughout this Handbook, a SUT is said to be broken down by enterprise characteristics into an ESUT.

The purpose of the Handbook

This Handbook builds on extensive consultations with a wide range of national statistical compilers, international organisations and other key stakeholders in the domain of statistics and policy analysis. It is at the frontier of statistical measurement and contributes to developing the domain of integrated macroeconomic and microeconomic statistics. The Handbook’s key objectives are to:

• Provide policy-related reasons and statistical reasons to compile ESUTs and EIOTs.

• Provide specific compilation guidance, documenting concepts, classifications, methodologies and data sources.

• Share best practices and case studies.

• Indicate possible solutions when data availability is limited.

• Explain how to communicate results to the general public.

The general recommendation is to build on data and methods already in use in the regular statistical production process to avoid extra data collection. Besides SUTs, existing data could include Structural Business Statistics, Trade by Enterprise Characteristics, Services Trade by Enterprise Characteristics Foreign Affiliates Statistics and Activities of Multinational Enterprises.

Target audience and using the Handbook

It follows from these objectives that this Handbook is intended for potential users of ESUTs, EIOTs and the related indicators, and for those involved in compiling the data.

The users of the statistics and indicators will often be policy makers, researchers and institutes that use macroeconomic statistics. For them, the Handbook aims to create a better understanding of the new information that ESUTs, EIOTs and derived indicators can provide; for example, production and value added by industry by type of enterprise, or value added at SMEs due to exports of large enterprises.

For those involved in collecting and compiling data and indicators, the Handbook provides a comprehensive reference, indicating best practices. The chapters build on existing compilation practices and have greatly benefited from inputs received from national compilers. Nevertheless, as the domain is still evolving, and some details of compilation practices are not yet well established, the authors of the Handbook recognise that a co-ordinated international effort is still required to address the remaining practical and conceptual challenges.

Furthermore, countries may face various challenges in putting the Handbook into practice, from applying the core concepts to the specific national context and the available data sources to compiling and disseminating the resulting statistics. Therefore, the Handbook also provides guidance for such cases. The transparency provided by explaining methodologies and highlighting remaining data-quality problems presents an opportunity for debate about what improvements can be made and how they can be achieved.

It is important to stress that this Handbook has been prepared to encourage and assist in compiling ESUTs, EIOTs and related indicators. It does not proclaim to be the final and definitive voice on the subject. Therefore, even though they reflect the current state of the art, some examples in the Handbook are “works in progress”. This is particularly relevant for Chapter 8, which discusses indicators that may be used to complement the regular SUTs countries publish. Improvements may be made following lessons learnt during their implementation.

Relation to other work

The work of the EGESUT was closely related to other initiatives in the field of national accounts, business statistics and globalisation. To name a few:

• The System of National Accounts 2025 (European Commission; IMF; OECD; UN, World Bank, 2025[5]) is the international macroeconomic statistical standard on compiling statistics on national accounts. It “is a statistical framework that provides a comprehensive, consistent and flexible set of macroeconomic accounts for policymaking, analysis and research purposes” (European Commission; IMF; OECD; UN; World Bank, 2009[6]). Among others, it contains chapters on SUTs, IOTs and globalisation. It refers to the current Handbook as a key tool to aid in measuring the impact of globalisation.

• The Handbook on Supply and Use Tables and Input-Output Tables with Extensions and Applications (United Nations, 2018[4]) aims “to provide step-by-step guidance for the compilation of supply and use tables and input-output tables and an overview of the possible extensions of SUTs and IOTs which increase their usefulness as analytical tools.” The current Handbook builds on this framework and introduces an extension to it.

• Accounting for Global Value Chains: GVC Satellite Accounts and Integrated Business Statistics (United Nations, 2021[7]) “provides a framework for the measurement of global value chains, which consists of a multi-country supply and use tables and related institutional sector accounts, and a framework for integrated business, trade, and investment statistics. It outlines how economic statistics can be made more accurate and relevant in measuring the effects of globalisation in national accounts, business, and trade statistics”.

• The Handbook on Integrating Business and Trade Statistics (United Nations, forthcoming[8]) provides “a conceptual and methodological framework for integrating business and trade statistics, including national best practices, and to provide guidance for building and strengthening capacities on microdata linking (MDL) to compile these statistics in an internationally comparable manner”.

• The Integrated Balance of Payments and International Investment Position Manual, seventh edition (BPM7) (IMF, 2025[9]) “serves as the standard framework for statistics on the positions, transactions, and other changes in financial assets and liabilities between an economy and the rest of the world”.

• The carbon footprint of foreign direct investment (FDI) is part of the Third Phase of the G20 Data Gaps Initiative. The usual method to estimate a carbon footprint is using input-output analysis. However, that type of analysis assumes that enterprises in an industry are homogeneous. Enterprises with FDI are, on average, very different from enterprises without FDI, hence this assumption does not hold. IMF (2024[10]) therefore recommends performing the analysis with an EIOT, where industries are split into a domestically owned part and a foreign-owned part.

• The Eurostat Integrated Global Accounts Expert Group (IGA EG) regularly discusses new methodology, data and indicators related to SUTs and IOTs. Due to globalisation, measurement challenges related to “traditional” macroeconomic aggregates such as GDP, gross national income, etc. appeared. The co‑operation of national accountants, the balance of payments statisticians and experts on business and other statistics in this expert group will lead to new methods and data to resolve the challenges.

• The OECD Expert Group on Accounting Frameworks for Measuring Economic Globalisation (EG-MEG) will, among others, tackle the measurement challenges related to both the macro (economy, industry-level) and micro (enterprise-level) perspectives. This is crucial to capture the complexity of economic globalisation in official statistics, for example when considering MNEs.

The structure of the Handbook

Chapter 1 introduces the concept of ESUTs and EIOTs, showing what they could look like. It notes that a statistical reason to compile these tables is that regular tables might not properly capture heterogeneity in industries. For example, in an industry, the exporters may also be the main importers, and the foreign value added in exports is underestimated using regular IOTs. This bias can be (partly) removed by breaking each industry up into exporters and non-exporters, such as metal manufacturing exporters and metal manufacturing non-exporters. The chapter also provides several policy reasons to compile ESUTs and EIOTs. It explains how to decide which breakdown of an SUT is relevant for a particular country. Several country examples show a country’s motivation for a particular breakdown, and the results that it obtained. The role of ESUTs and EIOTs as tools for policies is highlighted by the way several stakeholders used the new information.

Chapter 2 is the first of the chapters with compilation guidance. It discusses the three most common extension criteria: 1) trading status (e.g. exporter versus non-exporters); 2) ownership or group affiliation; and 3) enterprise size. It explains how to define the various categories of enterprises and how to classify the enterprises in practise, pointing out the various data sources.

Chapter 3 provides guidance to ESUT compilers that have access to detailed microdata, in particular at the enterprise level. In general, this includes the microdata underlying the construction of the regular SUT, such as the economic census, administrative data or specific surveys. The key idea in the approach described here is to construct an ESUT by disaggregating columns and rows of the regular SUT, using as much microdata as possible to capture the heterogeneity between different types of enterprises. The chapter explains step-by-step how to do this for an individual country, building on earlier experiences of national statistical offices which have compiled ESUTs.

Chapter 4 explains how to proceed in the case that one does not have the resources or access to the data to compile an ESUT and subsequently an EIOT. First, it points out that some of the recommendations in Chapter 3 might be followed up anyway. Subsequently, the chapter describes a step-by-step method to compile an EIOT. This approach requires the share of each enterprise type in output, value added, imports and exports by industry. It explains how to obtain that information in a robust way. Since some countries have an EIOT but do not have an ESUT, the chapter subsequently explains how to compile an ESUT from the EIOT in that specific situation (albeit it is recommended to go from an ESUT to an EIOT). Finally, a comparison is made between the “data-rich” and “data-scarce” approach (in Chapters 3 and 4, respectively) in terms of pros, cons and robustness.

Chapter 5 explains various mathematical methods to balance SUTs in the ESUT when there is lack of data to balance. It discusses various scenarios, providing recommendations on how to deal with each case. It also provides a checklist for designing automated balancing processes. Furthermore, it describes a method to backcast ESUTs, compiling a time series. The methods presented in this chapter are to be considered as second-best choices when there is no other way to improve sources of information and to tackle the main imbalances manually.

Chapter 6 covers the methodology of transforming an ESUT into an EIOT. It starts with a discussion of the underlying theory of IOTs then continues with the choice of dimension for such tables (product-by-product or industry-by-industry) and the construction methods for IOTs and EIOTs. It explains the various ways of doing so, each with its own assumptions, advantages and disadvantages. The chapter provides recommendations on how to adhere to best practices.

Chapter 7 provides compilation guidance on adding country detail to imports and exports in ESUTs and EIOTs. This can take several forms, depending on the information needs and data availability. A country may either wish for better trade in value added (TiVA) and other globalisation indicators, look for better integrating its own data into international data, or be interested in comparing a certain type of enterprises (e.g. MNEs) in the resident economy to that in other countries. The chapter provides methodology, data, concrete examples and references for each of the three common situations.

Chapter 8 provides advice on how to disseminate the new information to a broader public, varying from tables, infographics and video clips. It points out several indicators based on the new data that countries have compiled in the past and what information they convey.

Chapter 9 sets out several country examples of adding information from other domains to ESUTs and EIOTs. These additions vary from value added and income flows to employment, physical ESUTs and emissions. Subsequently, the chapter provides suggestions about the integration of ESUTs and productivity by industry statistics, regional ESUTs and digital SUTs.

References

[3] Ahmad, N. (2023), “Accounting frameworks for global value chains: Extended supply-use tables”, in Ahmad, N. et al. (eds.), Challenges of Globalization in the Measurement of National Accounts, Studies in Income and Wealth, 81, University of Chicago Press, New York, NY, https://www.nber.org/system/files/chapters/c14136/c14136.pdf.

[1] Ahmad, N. et al. (2013), “Using trade microdata to improve trade in value added measures: Proof of concept using Turkish data”, in Mattoo, A., Z. Wang and S. Wei (eds.), Trade in Value-Added: Developing New Measures of Cross-Border Trade, World Bank, Washington, DC.

[5] European Commission; IMF; OECD; UN, World Bank (2025), System of National Accounts 2025.

[6] European Commission; IMF; OECD; UN; World Bank (2009), System of National Accounts 2008, https://unstats.un.org/unsd/nationalaccount/docs/SNA2008.pdf.

[9] IMF (2025), Integrated Balance of Payments and International Investment Position Manual, seventh edition (BPM7), International Monetary Fund, Washington, DC.

[10] IMF (2024), Recommendation 3: Carbon Footprint of Foreign Direct Investment (FDI), Guilhoto, J.M. and K. Howell (eds.), International Monetary Fund, Washington, DC.

[2] OECD (2014), Terms of Reference OECD Expert Group on Extended Supply and Use Tables (EGESUT), OECD, Paris.

[7] United Nations (2021), Accounting for Global Value Chains: GVC Satellite Accounts and Integrated Business Statistics, Series F, No. 120, United Nations, New York, NY, https://unstats.un.org/unsd/business-stat/GVC/Accounting_for_GVC_web.pdf.

[4] United Nations (2018), Handbook on Supply and Use Tables and Input-Output Tables with Extensions and Applications, Series F, No.74, Rev.1, United Nations, New York, NY, https://unstats.un.org/unsd/nationalaccount/docs/SUT_IOT_HB_Final_Cover.pdf.

[8] United Nations (forthcoming), Handbook on Integrating Business and Trade Statistics, United Nations, New York, NY, forthcoming.