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UIBE GVC Indicators

 

 

  1. Introduction of the database

 

The 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 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 is currently saved in . Click on the link of 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.

 

  1. Background of the database

 

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. 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) and WWYZ (2017a, 2017b) are relatively comprehensive inclusive, UIBE team primarily use these methods to construct the . 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, 2018.

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, “”, NBER Working Paper 23222, 2017b.

 

3. Original data

 

The original data used to construct 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 (, , , ). Their primary characteristics in terms of economy coverage and industry classification are as follows:

 

ICIO tables

Number of economies

Number of industries

Time span

WIOD2013

40

35

1995-2011

WIOD2016

43

56

2000-2014

OECD-ICIO 2018a

64

34

2005-2014

GTAP-ICIO b

121

43

20042007, 2011

ADB-MRIO2018c

62

35

2000-2017

Eroa(pending)

189

26

1990-2015

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-MRIO2018) has been updated to 2017 and the number of economies has increased to 62.

 

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.

 

4. Browse and download

 

are saved in . You can get access to the database by clicking the link (or use the link:     

). 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 (csv format or R format), click the Download and then save it in the local hard drive. Since FangCloud has size limit on a single file (within 1G) when downloading from the web page, 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. At the same time, in order to facilitate users to download data, we prepare a number of external assistance accounts, thus you can send e-mails to contact with us. When receiving our email invitation, follow the requirements of FangCloud to register an external assistance account (free of charge) and then use the PC version to synchronize with the local hard drive. The registered external assistance account will be valid in one or two months and we will delete the old external assistance account after expiration in order to facilitate other users to download the data.

You can send email to 01535@makowave.net to apply for an external assistance account.

See http://www.fangcloud.com/?lang=en for information about FangCloud and how to download and synchronize data using the PC version.

5Copyright and citation of 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.

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