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GVC Participation and Trade

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Global Value Chains and Industrial Development

Part of the book series: SpringerBriefs in Economics ((BRIEFSECONOMICS))

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Abstract

This chapter investigates the dynamics of GVC participation in relation to trade. We initially present a method for analyzing GVC participation, which leverages international input–output data. The metrics covered in this chapter encompass both GVC backward and forward participation as well as GVC positional indicators. Insights from 2019 data reveal a non-linear correlation between GVC participation and income levels, highlighting a U-shaped trend between forward participation and real GDP per capita and an inverted U-shaped trend with backward participation. Detailed measures related to GVC participation, position, and trade-weighted distances are presented for eight global regions. The study suggests that specific industries hold distinct places in terms of GVC participation. This influences the overarching GVC engagement of the regions through their export configurations. Our econometric analysis revealed following findings. First, there is strong evidence supporting a U-shaped relationship between forward participation and GDP per capita. Second, an inverted U-shaped relationship was previously observed between backward participation and GDP per capita; however, this has been diminishing since the early 2000s. Third, maritime and aviation connectivity indirectly enhance GVC participation. Lastly, weighted degree centrality is posited as an effective metric for assessing a nation's connectivity within global maritime and aviation transport networks.

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Notes

  1. 1.

    For details regarding the international input–output tables, see Kuwamori et al. 2013 (AIO tables); Dietzenbacher et al. 2013 (WIOD); Yamano 2017 (OECD ICIO tables); Asian Development Bank 2016 (ADB MRIO tables); and Lenzen et al. 2013 (EORA MRIO tables).

  2. 2.

    Daudin et al. (2011) and Johnson and Noguera (2012) proposed VS* (i.e., domestic value added that is embedded in intermediate exports but returns home as part of final imports) and value-added exports (i.e., the amount of value added from a given source country that is consumed in each destination country), respectively. KKW showed that these measures as well as VS and VS1 measures comprise a decomposition of gross exports.

  3. 3.

    Although this part is somewhat technical, it is worth describing. As pointed out by BM, there is no unique method of decomposition. The formula may change depending on the accounting framework used in the analysis. For example, when a certain portion of value added crosses the border of the specific country at least twice, it must be assigned to a particular trade flow while it is recorded as double counted in the others. Therefore, the question then is when and where to consider it as value added or double counted in trade flows. Regarding the time issue, Nagengast and Stehrer (2016) proposed the source-based and sink-based approaches: the former accounts for the value added the first time it leaves the country of origin, whereas the latter considers it the final time it crosses the border. Regarding the geographical boundary, BM used the hypothetical extraction method to propose the country, the bilateral, and the world perspectives; in the country perspective, double counting is defined as the value added upon crossing the national border of the exporting country at least twice; in the bilateral perspective, it is defined as the value added upon crossing the bilateral border of the exporting country and its trade partner at least twice; and in the world perspective, it is defined as the value added upon crossing any national border at least twice. In this study, we adopt BM’s decomposition method, which is based on a combination of the source-based approach and the country perspective.

  4. 4.

    MY shows that KWW’s decomposition can be obtained when they adopt the world perspective. Moreover, MY revealed that, when this methodology is adopted, there is no FVA in the world perspective, and it has a disproportionally high share of the foreign double counted (FDC) value in comparison with that of the country perspective, although the allocation of values between the foreign content (= FVA + FDC) and domestic content (= DVA + DDC) does not change regardless of which of the two perspectives is adopted.

  5. 5.

    In Eqs. (2.1) and (2.2), \({\text{f}}\) is treated as the final demand vector for simplicity. However, as shown in Fig. 2.1, in reality, it consists of final demand vectors (or matrices) representing origin and destination countries.

  6. 6.

    In this study, GVC backward trade in Eq. (2.7) is used to calculate the backward participation measure, not GVC backward (total) trade in Eq. (2.5). This is because at the global level (aggregating across countries worldwide), the total GVC backward trade is equal to the total GVC forward trade, so the GVC position index is deemed a desirable attribute (Borin et al. 2021).

  7. 7.

    The GVC positioning measure introduced in Eq. (2.12) considers the net position of the country in GVCs relative to other countries, but it does not elucidate it in the entire production chain. The other strand of GVC positioning measures computes the implied upstreamness (or downstreamness) of specific industries and countries, capturing the distance (i.e., the number of production stages) from final consumption (or primary factors of production), respectively. This category of GVC positioning indexes includes average propagation length (Dietzenbacher et al. 2005); measures of production staging (Fally 2011); measures of upstreamness (Antràs et al. 2012); measures of output upstreamness and input downstreamness (Miller and Temurshoev 2015); and measures of production length and upstreamness (Wang et al. 2017).

  8. 8.

    As discussed in Sect. 2.3.2, the inverted U-shaped relationship between backward participation and GDP per capita has become less statistically significant in recent years. However, Fig. 2.3 shows that such a relationship remains when GVC participation measures are taken at the country level without taking the natural logarithm.

  9. 9.

    For details of these mechanisms, see Chap. 3.

  10. 10.

    To calculate the GVC participation share by export sector, part of the matrices in Eqs. (2.3)–(2.11) needs to be replaced with diagonal matrices (for details, see Borin et al. 2021).

  11. 11.

    During 1990–2019, the shares of Agricultural and Mining Products in exports increased from 8.9% and 26.5% to 20.9% and 32.7%, respectively, in Sub-Saharan Africa. In contrast, the export share of the manufacturing sector decreased from 37.7 to 26.5%.

  12. 12.

    Using Eq. (2.4), the forward participation of region P for destination region Q is calculated as \({\mathrm{GVC forward}}^{PQ}\)=\({\sum }_{s\in P}\sum_{r\in Q, r\ne s}{({\text{DVX}}}^{sr}-{{\text{DAVAX}}}^{sr})\). On the other hand, using Eq. (2.7), the backward participation of region P that is sourced from region Q is obtained as \({\mathrm{GV Cbackward}}^{QP}\)=\({\sum }_{s\in P}\sum_{t\in Q,t\ne s}{u}_{n}{A}^{ts}{L}^{ss}\sum_{r\ne s}^{m}{(f}^{sr}+{A}^{sr}{L}^{rr}{f}^{rr})\).

  13. 13.

    Southeast Asia had the second highest export share of Electrical Machinery (30.2%), while Latin America had the highest export share of Transport Equipment (11.99%).

  14. 14.

    In contrast, Northeast Asian countries were more successful in developing their upstream supplier industry. China, for example, used to be specialized in the downstream value chain, but it has rapidly changed its position. China’s backward participation share, which increased (from 4.9% to 14.6%) during 1990–2011, fell sharply (to 9.1%) in 2019. Simultaneously, China continued to increase its forward participation share (from 15.4% to 27.3%) during 1990–2019. This suggests that China could upgrade its export structure and has thus become a competitive exporter of intermediate goods and services after undergoing the phase of import-substitution.

  15. 15.

    Because of space limitations, we have plotted the backward and forward distances of only the manufacturing sectors. The other reason behind this is that it is difficult to interpret this aspect in service sectors, as their trade is not conducted directly in space, but is indirectly conducted and embodied in manufactured products.

  16. 16.

    The original source is the Penn World Tables, version 10.0. See Feenstra et al. (2015) for details.

  17. 17.

    Specifically, we exclude the top and bottom 1% of the dependent variables.

  18. 18.

    This specification may draw criticism because tariff is often treated as an endogenous variable (Fernandes et al. 2022), as two anonymous referees have pointed out. However, we will maintain this specification because tariff does not work well. In addition, as we will see later in Table 2.9, this specification passes the tests for weak instruments, namely, tests of under- and over-identification.

  19. 19.

    To avoid this problem, we define an alternative measure, proximity to market, to include the geographical factor into the set of determinants. We will explain the definition in the next paragraph.

  20. 20.

    In the computation, nominal GDP (ngdp) and the distance between capital cities (dist) are sourced from the World Development Indicators (WDI) and the GeoDist Database provided by CEPII (Mayer and Zignago 2011), respectively.

  21. 21.

    The prefix (ln_) means that the variable is logarithmically transformed, while the suffix (_sh) implies that the variable is measured as a share of the total.

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Author information

Authors and Affiliations

  1. Faculty of International Economic Studies, University of Niigata Prefecture, Niigata, Japan

    Ikuo Kuroiwa

  2. Institute of Developing Economies, Japan External Trade Organization (IDE-JETRO), Chiba, Japan

    So Umezaki

Authors
  1. Ikuo Kuroiwa
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  2. So Umezaki
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Corresponding author

Correspondence to Ikuo Kuroiwa .

Appendices

Appendix 1: Country Classification

The EORA tables initially listed 189 countries. However, 43 of these countries have been excluded for various reasons. First, 15 countries are omitted following the guidance of EORA because they are outliers. An additional two countries are removed for the same outlier concerns. Furthermore, 26 countries with populations below one million are excluded because these smaller nations often present extreme data, such as unusually high foreign dependency.

With these exclusions, the analysis focuses on 146 countries. These are divided into:

  • 9 Southeast Asian (SEA) countries

  • 7 South Asian (SSA) countries

  • 34 Sub-Saharan African (SSA) countries

  • 25 Latin American (LAC) countries

  • 7 North East Asian (NEA) countries

  • 32 European (EUR) countries

  • 2 North American (NAM) countries

  • 30 Rest of the World (ROW) countries.

Despite the removal of 43 countries from the original EORA dataset, this refined list still encompasses 98.53% of all export values in the world, as represented in the intermediate and final transaction matrices of the 146 selected countries.

Appendix 2: Sector Classification of the EORA Data

1

AGR

Agriculture

2

FIS

Fishing

3

MIN

Mining and quarrying

4

FOD

Food & beverages

5

TEX

Textiles and apparel

6

WOD

Wood and paper

7

PET

Petroleum, chemical, and non-metallic mineral products

8

MET

Metal products

9

ELQ

Electrical and machinery

10

TRQ

Transport equipment

11

OTM

Other manufacturing

12

REC

Recycling

13

EGW

Electricity, gas, and water

14

CON

Construction

15

MAI

Maintenance and repair

16

WHT

Wholesale trade

17

RET

Retail trade

18

HTR

Hotels and restaurants

19

TRN

Transport

20

PTL

Post and telecommunications

21

FIN

Financial intermediation and business activities

22

PUB

Public administration

23

EDU

Education, health, and other services

24

PVH

Private households

25

OTS

Others

 

26

REI

Re-export & re-import

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Kuroiwa, I., Umezaki, S. (2024). GVC Participation and Trade. In: Global Value Chains and Industrial Development. SpringerBriefs in Economics. Springer, Singapore. https://doi.org/10.1007/978-981-97-0021-9_2

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