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UIBE GVC Indicators



1. Introduction


The UIBE-GVC-Indicators is a non-profit database for academic research. This database provides trade in value added indicators and global value chain (GVC) related indicators. At present, the UIBE-GVC-Indicators consists of three main categories of indicators, namely the GVC production decomposition based indicators (index1-Prod), the bilateral trade flow decomposition based indicators (index2-trade) and the GVC length decomposition based indicators (index3-Length). The UIBE-GVC-Indicators is currently saved in FangCloud. Click on the link of UIBE-GVC-Indicators for browse or download. (See instructions below for detailed download methods).

The three categories of indicators are introduced as follows. The detailed description (definition or calculation formulas) can be found in the technical documentation of the database.

a. GVC production decomposition based indicators (index1-Prod). Two types of decomposition are performed, namely the forward decomposition of production (production decomposition of value added/industry GDP) and backward decomposition of production (production decomposition of final product).

(1) The indicators of forward decomposition include: the decomposition of industry value added (industry value added=value added for total domestic production + value added for traditional final goods trade production + value added for simple GVC production + value added for complex GVC production), the value added export based on forward linkage measurement, the domestic value added in total export based on forward linkage measurement, RCA index based on value added trade, forward GVC participation (=simple forward GVC participation + complex forward GVC participation) and the industry value added export share of the total value added;

(2) The indicators of backward decomposition include: the decomposition of industry's final product (total value of the industry's final product = value from the total domestic final production + value from traditional final trade production + value from simple GVC production + value from complex GVC production), the foreign value added used in the production of the final product, the domestic value added used in the final product production, backward GVC participation (=simple backward GVC participation + complex backward GVC participation), the domestic value added share of final product, and the foreign value added share of final product.

b. Bilateral trade flow decomposition based indicators (index2_Trade). Using input-output analysis, the traditional trade volume can be broken down into four parts according to source country and source industry of the value added: domestic value added, returned domestic value added, foreign value added and pure double accounting. Such decomposition is mainly at the bilateral sector level, country-sector level and country level. At the same time, some widely used indicators of international trade are calculated based on the results of the decomposition. Those specific indicators are: bilateral gross trade (=bilateral final product trade volume + bilateral intermediate product trade volume), bilateral gross trade balance, bilateral value added trade (VAX), bilateral value added trade balance, VS&VS1 indicators for bilateral trade, bilateral gross trade decomposition indicators (8 or 16 components).

c. GVC length decomposition based indicators (index3-Length). This part mainly provides indicators related to the production length, production position and cross-border frequency in GVCs or international production process. The specific indicators include: average forward production length (=weighted average (forward production length of pure domestic value chain, forward production length of traditional trade value chain, simple forward GVC production length, complex forward GVC production length)), average backward production length (=weighted average (backward production length of pure domestic value chain, backward production length of the traditional trade value chain, simple backward GVC production length, complex backward GVC production length)), upstreamness index, downstreamness index, production position index based on APL, production position index based on TPL, GVC production position index, total production position index.


2. Background


With the rapid growth of intermediate goods trade and increasing expansion of international vertical specialization, global economy has entered the era of global value chains characterized by the fragmentation of production process and trade in intermediate goods. In recent years, following the research on enterprise-oriented global value chain in management domain, studies on industry-level and country-level global value chain have rapidly progressed. Among them, global value chain accounting (also known as value added trade accounting) is an emerging hot research field. It drives the rapid expansion of the research on global value chains from micro case studies to quantitative and macro-analysis based on economics and statistics.

The development of research on global value chain accounting has benefited from the release of well-developed inter-country input-output (ICIO) tables in recent years. For instance, the World Input-Output Database (Timmer et al, 2012) released in 2013, is a global ICIO table (known as the WIOD database) provides trade data on intermediates and final goods for 35 sectors among 27 EU countries and 13 major economies in the world from 1995 to 2011. The construction of this database has facilitated global value chain related studies in the field of international trade. In addition, the world ICIO databases also include the ICIO tables complied by OECD, EORA and Asian Development Bank. (For more information about ICIO Tables, see APPENDIX G in Taglioni and Winkler (2016). These databases have different characteristics in terms of economy coverage, industry classification, and time span)

In the area of global value chain accounting, representative studies include Timmer et al. (2013, 2014), Koopman et al. (2014, AER), WWZ (2013, NBER Working Paper) and WWYZ (2017a, 2017b, NBER Working Paper). Their work has made important contribution to economic theory and statistical methods, and promoted research at country and industry levels. Johnson & Noguera (2012) proposed the accounting method for value-added exports and value-added trade balance. Koopman et al. (2014) decomposed value-added in gross export under the framework of SNA and proposed the definition for double counted domestic value-added and foreign value-added in gross export. After that, Borin & Mancini (2015), Los et al. (2016), Nagengast & Stehrer (2016), Wang et al.(2018), Johnson (2018), Miroudot & Ye (2018), Los & Timmer (2018), Borin & Mancini(2019), and Miroudot & Ye(2020) proposed different value-added decomposition methods as well as different definitions for double-counting in gross export from different perspectives. These studies have provided important quantitative results for global value chain research and provided relevant policy implications for policy makers. In the future, these studies will promote research on other aspects of GVCs, and lay foundation for further in-depth research and extension. At the same time, the global value chain accounting methods at the industry and macro levels have continuously expanded in breadth and in depth. This resolves the shortcomings of traditional trade statistics to certain extent and gives answers to the problems that traditional supply chain and logistics management disciplines and GVC governance research cannot completely solve. In summary, a comprehensive literature review shows that the GVC accounting system has been established based on trade in value-added, industry competitiveness and the degree of participation in global value chains.

GVC accounting is the fundamental method for GVC studies at industry and country levels. To provide researchers the required GVC indicators for GVC studies and to avoid unnecessary duplicated workload of calculations, the research team for global value chains at University of International Business and Economics (UIBE) leading by Professor Zhi Wang have complied a set of accounting indicators, i.e. the UIBE-GVC-indicators. The construction of the indicator system is based on the representative studies on GVC accounting, which bridges the gap between international trade statistics and the system of national accounts (SNA), and uniforms all previous measures of vertical specialization in the literature (such as VS, VS1, RCA and VAX). Our aim is to promote studies on global value chains, to facilitate the use of accounting results in other areas, and to provide convenience for researchers in the fields of trade theory, empirical studies, as well as economic and policy analyses. The construction of UIBE-GVC-indicators is mainly based on the GVC indicators calculated by using the widely accepted methods of GVC accounting. Therefore, it is a secondary (derived) database, which is processed based on the public released ICIO tables. Considering that the accounting methods developed by KWW (2014), WWZ (2018), WWYZ (2017a, 2017b) and Borin & Mancini(2019) are relatively comprehensive and inclusive, the UIBE team primarily use these methods to construct the UIBE-GVC-Indicators. The detailed methods can be found in the following literatures (all the indicated working papers are the latest versions):

Robert Koopman, Zhi Wang and Shang-Jin Wei, “Tracing Value-added and Double Counting in Gross Exports”, American Economic Review, 104(2): 459-494, 2014.

Zhi Wang, Shang-Jin Wei, and Kunfu Zhu, “Quantifying International Production Sharing at the Bilateral and Sector Levels”. NBER Working Paper 19677, 2013.

Zhi Wang, Shang-Jin Wei, Xinding Yu and Kunfu Zhu, “Characterizing Global Value Chains: Production Length and Upstreamness”, NBER Working Paper 23261, 2017a.

Zhi Wang, Shang-Jin Wei, Xinding Yu and Kunfu Zhu, “Measures of Participation in Global Value Chains and Global Business Cycles”, NBER Working Paper 23222, 2017b.

Borin, A. and M. Mancini, 2019, “Measuring What Matters in Global Value Chains and Value-Added Trade”, Policy Research working paper. no. WPS8804.( Background Paper for World Development Report 2020)


3. Original data for indicator construction


The original data used to construct UIBE-GVC-Indicators are from the existing and internationally renowned world ICIO tables, which have different characteristics in terms of the number of economies covered, industry classification, time span, and whether or not distinguishing processing trade. Details of the original ICIO tables can be found on the corresponding website (WIOD, OECDICIO, GTAP, Eora). Their primary characteristics in terms of economy coverage and industry classification are as follows:

ICIO tables

Number of economies

Number of industries

Time periods
















2004, 2007, 2011




2000, 2007-2019





Notes: a. China and Mexico distinguish between processing trade and non-processing trade at sector level (or global manufacturing and non-global manufacturing).

b. This database is developed by RIGVC at UIBE on the basis of GTAP database in the same way as Koopman et al. (2014), characterized by the more detailed agricultural sectors (six agricultural sectors).

c. This database is developed by ADB (Asian Development Bank) and includes major Asian economies. The latest released ADB MRIO table (ADB-MRIO2021) has been updated to 2019 and the number of economies has increased to 63.

For each ICIO table (original database) the economy coverage and industry classification are different. See the economy code and industry classification in the original database for details. The doc subfolder with a description document of economy code and industry code can be found in the corresponding folder in UIBE-GVC-Indicators (e.g. WIOD or OECDICIO). These documents are copies from the original data website.

In addition, we added a hypothetical region called WLD to the WIOD and ADB IO tables to capture the international margins. See the interpretation in section 6 for details. For analytical purposes, the WLD region can be neglected. For purpose of checking the identities in bilateral export decomposition, the value-added sourcing from WLD should be taken into account.


4. Instructions for users


UIBE-GVC-Indicators are saved in FangCloud. You can get access to the database by clicking the link “UIBE-GVC-Indicators (or use the link:

https://v2.fangcloud.com/share/a26979974d538c7e5aeb24b55a?lang=en). You can also get access to the database by scanning the QR code.

When you click the link of the database, you will see a webpage page as shown below. First, users should select the ICIO database they would like to use to calculate GVC indicators and click on the corresponding folder to enter. In addition to the several folders of original data, the doc folder also provides detailed documentations (definition or formulas) of three categories of indicators for reference when using the database. You can also click on the language icon at the top right corner of the guide page to switch the language to your native language.

If you want to view the three categories of GVC indicators based on WIOD2016, click on the WIOD2016 folder. After entering in, you can see three folders with three categories of indicators. After clicking on one of these folders, you can see specific indicator or files on the new page. The doc folder gives the description of the economy code of the original ICIO tables for reference.

When downloading the data, select a specific indicator file, click the Download and then save it in the local hard drive. The indicator file is in csv format (R format files are also available for some indicators) and the size is less than 100M. You can save the selected files in your FangCloud account and then use the PC version of FangCloud to synchronize with your local hard disk. See https://www.fangcloud.com/?lang=en for information about FangCloud and how to download and synchronize data using the PC version.

We have very limited amount of assistance accounts, only available for users in RIGVC and our cooperators.


5. Copyright and citation


UIBE-GVC-Indicators are constructed and maintained by the Global Value Chain Research Institute of University of International Business and Economics, who owns the full copyright of the database. The database is open for researchers worldwide and free of charge. It is only for research purposes (not for commercial purposes), and research results (including but not limited to articles, reports, etc.) should indicate the source of data.

Download link: UIBE- GVC-Indicators © 2016, Research Institute for Global Value Chains, University of International Business and Economics.

Citation: RIGVC UIBE, 2016, UIBE GVC Index,



6. Interpretation for the decomposition of bilateral trade flows (index2_Trade)


For bilateral gross trade (“index2_Trade”), there are two types of indicators. Type 1: the WWZ decomposition (see NBER paper NO.19677) for bilateral trade Esr (see the WWZ file in index2_Trade); Type 2: the Borin-Mancini decomposition (see euqation10 in Borin & Mancini,2019) for bilateral trade Esr (see the Borin file in index2_Trade). The difference between these two decompositions is with respect to the definition of double counted foreign value-added. In the WWZ decomposition, the domestic value-added crossing domestic border more than two times is defined as double counted domestic value-added (relative to the GDP of exporting country). The foreign value-added crossing foreign border more than two times is defined as double counted foreign value-added (relative to the GDP of value-added sourcing country). In the Borin-Mancini decomposition, the double counted domestic value-added and foreign value-added are both defined based on the number of crossings of exporting countrys border. The UIBE GVC database provides the Borin-Mancini decomposition by using equation (10) in Borin & Mancini(2019), based on the source approach and exporting-country perspective.

Koopman et al. (2014) decomposed the value-added content in gross export and proposed the definition for double counted value-added caused by the border-crossings of intermediate goods. Afterwards, the WWZ decomposition is extended to the bilateral and sectoral levels. The WWZ decomposition is done under the SNA framework. Therefore, it can bridge the gap between trade statistics and national account. Popular indicators in international economics (such as VS and VS1 in Hummels et al., 2001; VAX in Johnson & Noguera, 2012) can be constructed based on the decomposition outcomes. The Borin-Mancini decomposition is useful for analyzing a countrys specialization and cooperation with other participants in GVCs.

For the WWZ decomposition, the 16 components are grouped into 8 components (see the technical documentation of UIBE-GVC-Index-System for details). For the Borin-Mancini decomposition, the bilateral gross export is decomposed into 4 components: domestic value-added (DVAinEsr), double counted domestic value-added (DDCinEsr), foreign value-added (FVAinEsr), and double counted foreign value-added (FDCinEsr).

The decomposition outcomes are organized in three dimensions: exporting country and industry, importing country, value-added sourcing country and industry. This multi-dimensional outcomes can be used for analyzing bilateral trade from both backward perspectives and forward perspectives. The decomposition outcomes across exporting-importing countries add up to the bilateral gross export (Esr).

In the indicator files, year and each component of decomposition is labeled in the file name; exporting country-industry and importing country is labeled in row headings (e.g. BRA.c5.AUT indicates that the c5 industry in BRA exports to AUT ); the value-added sourcing country-industry is labeled in the column headings. Each file records outcome for one value-added sourcing country and the name of the sourcing country as well as its sequence number in the IO tables is labeled in the file name.

This update is only done for WIOD2013, WIOD2016 as well as ADB MRIO 2021 (Jan). The updates for OECDICIO2018 and GTAPICIO have not been done yet.

Finally, an update is done for international margins in the WIOD and ADB data. In this updated version, international margins are not merged with value-added at basic price in our computation. Instead, a hypothetical region called WLD is added to the table to capture international margin services and assume this service has no intermediate input (i.e. vale added=gross output). If international margins are included in value-added, the outcomes would be upwards biased. This treatment for international margins, on the one hand, can keep the original information in the IO table. On the other hand, the identities in bilateral export decomposition can be checked in this framework. For analytical purpose, the WLD region can be neglected.


7. The R codes for computing UIBE-GVC-Indicators


To facilitate the application and use of UIBE-GVC-Indicators, we also provide R code for a 3-country and 4-sector model as an example to show the calculation method and process for each indicator. The R code presented is exactly the same as the actual operation. In practical operation, only the data read-in and preliminary processing section in 01.0.ReadingData.r need to be modified appropriately. In the UIBE-GVC-Indicators folder, a compressed file namely GVCin5R6S.zip can be found, which contains the example of 5-country and 6-sector model and the associated R codes.

When running the 01.0.ReadingData.r, the calculation will produce some intermediate results (A, B, L, Esr,..., see the inc folder). After that, the R programs for calculating various indicators can be called for directly. Run the subsequent R code programs and then one can obtain the GVC indicators. (Before run each R code, one needs to write a line of R code to set the working path given different editors, such as setwd (“d:/GVCin5R6S”))

As some data pre-processing procedure is required before calculating the indicators, we also provides the codes for this procedure and the outcomes based on WIOD 2013, which can be found in the main directory of the database (see WIOD2013.zip). This is helpful for understanding the treatment on international margins and the identity checking procedure.

8. Future work


So far, the UIBE-GVC-Indicators is mainly used for academic research closely related to GVC or international trade studies. The UIBE-GVC-Indicators has been successfully used in GVC report and the GVC training courses conducted by RIGVC, UIBE. In addition to researchers in academia, domestic institutions such as the Development Research Center of the State Council, Ministry of Commerce, the National Development and Reform Commission, Ministry of Finance and other ministries, and international institutions such as the World Bank, WTO, UNEP, Brookings and McKinsey have also used the UIBE-GVC-Indicators for policy analysis.

You are welcome to send us issues encountered in using our database and also suggestions to help us further improve the database. Please contact the Research Institute for Global Value Chain for questions and suggestions. When inquiring by emails, please send to all the following contacts.

Contacts: Zhi WANG (zwang36@gmu.edu)

Fei WANG (E-Mail: 01535@uibe.edu.cn)

Quanrun CHEN (E-Mail: qchen@uibe.edu.cn)

The current UIBE-GVC-Indicators have facilitated GVC related studies and saved calculation cost for researchers. However, it is still a “research database” to some extent. Some indicators interested by policy makers are needed further processing and therefore inconvenient for specific applications or policy analysis. As a future improvement direction, we will provide another database focusing on trade indicators that can be used directly with strong practical and policy applications for policy makers.

We plan to provide data copy services and data processing services in the future. Users can apply to copy the indicators and the original data (if copyright allows) in our database, as well as the intermediate results and all the R codes. Data copy services can save data download time and provide convenience for researchers. Data processing services will construct extended GVC indicators based on users’ requirements to assist the users to achieve their research goals.


9. Update record


4.0 Sept 2021, (1) the ADB data was updated to 2019; (2) added the Borin-Mancini decomposition for bilateral trade flows; (3) for the sake of accuracy, international margins are removed from value-added. Note: these updates have not been done for OECDICIO and GTAPICIO yet.

3.1 Aug 1, 2019, database was moved to FangCloud.

3.0 April 29, 2018: Improved the structure of the UIBE GVC index system. The five categories of indicators in the previous version have been aggregated to 3 categories. The big files have been broken down to small files to solve the downloading problems. Cooperating with WTO, the Amazon cloud service has been used to solve the downloading problem encountered by the foreign users. The Baidu cloud is used as an alternative choice for domestic users.

2.1 Mar 24, 2017: Adding two NBER Working Papers and their download links, which introduce the implications and computation equations of Index 1, Index 2 and Index 5 in the UIBE GVC Index System in detail.

2.0 Jan 15, 2017: Recalculating all the indicators based on WIOD2016, in line with the replacement of 2007 data in the original database (WIOD2016).

1.1 Dec 26, 2016: The UIBE GVC Index is officially open to the public.

1.0 Sep 20, 2016: The UIBE GVC Index is open to a small group of researchers.


Applications for selected indicators

(In Progress)



Baldwin, Richard & Javier Lopez-Gonzalez, 2013. "Supply-Chain Trade: A Portrait of Global Patterns and Several Testable Hypotheses," NBER Working Papers 18957, National Bureau of Economic Research, Inc.

Belotti, F., A. Borin and M. Mancini, 2020,Icio Economic Analysis with Inter-Country Input-Output Tables in Stata. Policy Research Working Paper No.WPS9156.

Borin, A. and M. Mancini, 2015, “Follow the value added: bilateral gross export accounting”, Economic Working Papers No. 1026, Bank of Italy.

Borin, A. and M. Mancini, 2019, “Measuring What Matters in Global Value Chains and Value-Added Trade”, Policy Research working paper. No. WPS8804.( Background Paper for World Development Report 2020).

Daria Taglioni and Deborah Winkler(ed)(2016), Making Global Value Chains work for Development, World Bank, Washington DC. http://www.nber.org/papers/w23261.

Hummels, D., Ishii, J., and Yi, K-M. (2001). “The Nature and Growth of Vertical Specialization in World Trade.” Journal of International Economics, 54(1), 75-96.

Johnson, Robert C., & Noguera, G., 2012, “Accounting for intermediates:production sharing and trade in value added”, Journal of International Economics, 86(2), 224-236.

Johnson, Robert, and Guillermo Noguera. 2012. “Accounting for Intermediates: Production Sharing and Trade in Value-added,” Journal of International Economics, 86: 224–236.

Johnson,Robert C, 2018, “Measuring Global Value Chains”, Annual Review of Economics.10:2017-36.

Koopman, R., Z. Wang, and S.J. Wei (2014) “Tracing Value-added and Double Counting in Gross Exports,” American Economic Review, 104(2): 1–37. or NBER wp18579(2012).

Los B., Timmer M.P. De Vries, G.J. 2014, How Global are Global Value Chains? A New Approach to Measure International Fragmentation. Journal of Regional Science.

Los Bart, Erik Dietzenbacher, Robert Stehrer, Marcel Timmer and Gaaitzen de Vries (2012), Trade Performance in Internationally Fragmented Production Networks: Concepts and Measures, WIOD Working Paper No.11, May 2012

Los, B. and M.P. Timmer, 2018, “Measuring Bilateral Exports of Value Added: A Unified Framework”, NBER Working Paper, w24896.

Los, B., M. P. Timmer, and G. J. de Vries (2016): “Tracing Value-Added and Double Counting in Gross Exports: Comment,” The American Economic Review, 106, 1958–1966.

Mattoo, Aaditya, Zhi Wang and Shangjin Wei,Trade in Value-Added — Developing New Measures of Cross Border Trade, co-edited with CEPR/World Bank, April 2013. http://documents.worldbank.org/curated/en/2013/01/18821638/trade-value-added-developing-new-measures-cross-border-trade

Miroudot, S., and M. Ye, 2018, “A simple and accurate method to calculate domestic and foreign value-added in gross exports”, MPRA Paper 89907, University Library of Munich, Germany.

Miroudot, S., and M. Ye, 2020, “Decomposing value added in gross exports”, Economic Systems Research, Volume 33, 2021 - Issue 1, Pages 67-87.

Nagengast, A.J. and R. Stehrer, 2016,Collateral imbalances in intra-European trade? Accounting for the dierences between gross and value-added trade balances” The World Economy.

Robert Stehrer (2012), Trade in Value Added and the Value Added in Trade, WIOD Working Paper No. 8, April 2012.

Robert Stehrer, Neil Foster, Gaaitzen de Vries (2012), Value Added and Factors in Trade: A Comprehensive Approach, WIOD Working Paper No.7, April 2012

Timmer, M.P. , B. Los, R. Stehrer and G.J. de Vries, 2013, "Fragmentation, Incomes and Jobs: An Analysis of European Competitiveness" Economic Policy, 28, 613-661.

Timmer, M.P., Erumban, A.A., Los, B., Stehrer, R., De Vries, G.J. 2014. Slicing Up Global Value Chains. Journal of Economic Perspectives, 28(2): 99-118.

Timmer, Marcel P.(ed), 2012, "The World Input-Output Database (WIOD): Contents, Sources and Methods", WIOD Working Paper Number 10, downloadable at http://www.wiod.org/publications/papers/wiod10.pdf.

Timmer, Marcel P., A. A. Erumban, J. Francois, A. Genty, R. Gouma, B. Los, F. Neuwahl, O. Pindyuk, J. Pöeschl, J. M. Rueda-Cantuche, R. Stehrer, G. Streicher, U. Temurshoev, A. Villanueva, and G. J. d. Vries (2012). The World Input-Output Database (WIOD): Contents, sources and methods. WIOD Background document available at www.wiod.org.

Timmer, Marcel P., Bart Los, Robert Stehrer, Gaaitzen de Vries (2012), Fragmentation, Incomes and Jobs. An analysis of European competitiveness, WIOD Working Paper No.9, November 2012.

Wang, Z., S. Wei and K. Zhu, 2018, “Quantifying International Production Sharing at the Bilateral and Sector Levels”, NBER Working Paper, No. 19677.

Zhi Wang, Shang-Jin Wei, Kunfu Zhu, 2013. Quantifying International Production Sharing At The Bilateral And Sector Level. NBER Working Paper 19677, http://www.nber.org/papers/w19677.

Zhi Wang, Shang-Jin Wei, Xinding Yu and Kunfu Zhu. (2017a). Characterizing Global Value Chains: Production Length and Upstreamness. NBER Working Paper 23261.

Zhi Wang, Shang-Jin Wei, Xinding Yu and Kunfu Zhu. (2017b). Measures of Participation in Global Value Chains and Global Business Cycles. NBER Working Paper 23222. http://www.nber.org/papers/w23222.