metadata: authors: 0: author: Borin,Alessandro
1: author: Mancini,Michele
2: author: Taglioni,Daria
count: World
docna: 0: docna: Economic Consequences of Trade and Global
            Value Chain Integration : A Measurement Perspective
docty: Policy Research Working Paper
subtopic: International Trade and Trade Rules,Industrial and Consumer Services and Products,Transport and Trade Logistics,Legal Products,Legal Reform,Social Policy,Legislation,Foreign Trade Promotion and Regulation,Trade Law,Judicial System Reform,Regulatory Regimes,Textiles, Apparel & Leather Industry,Food & Beverage Industry,General Manufacturing,Construction Industry,Common Carriers Industry,Pulp & Paper Industry,Business Cycles and Stabilization Policies,Plastics & Rubber Industry
teratopic: Macroeconomics and Economic Growth,Social Development,Law and Development,Governance,Industry,Public Sector Development,International Economics and Trade,Transport
abstracts: cdata!: This paper presents a new approach to
            measuring Global Value Chain (GVC) participation, essential
            for informed policy-making. It introduces a tripartite
            classification of GVC involvement&#x2014;backward,
            forward, and two-sided&#x2014;extending beyond trade to
            include production data. GVCs, vital for global economic
            growth, are networks through which companies internationally
            produce goods and services. The advanced framework
            accurately assesses how different combinations of domestic
            output, trade, and GVC integration correlate with growth and
            output stability. The paper finds that traditional
            trade-based GVC metrics significantly underestimate global
            GVC activity and misrepresent participation in key sectors
            like services and upstream manufacturing. They also
            exaggerate risks during critical stages like early trade
            liberalization in large economies. Additionally, it shows
            that traditional backward-forward classifications
            overestimate backward linkages. The new metrics, applied to
            established models, effectively predict trade disruption
            impacts, indicating that GVC participation increases
            exposure to external shocks but also enhances overall output
            stability by mitigating local shocks. Furthermore, GVC
            participation is a key driver of the positive trade-income
            growth correlation. The complete dataset of these new
            measures is available on the World Bank&#x2019;s WITS
            Platform, and it is regularly updated, providing a key
            resource for GVC analysis.
repnme: repnme: Economic Consequences of Trade and Global
            Value Chain Integration : A Measurement Perspective
display_title: Economic Consequences of Trade and
            Global Value Chain Integration : A Measurement Perspective
listing_relative_url: /research/2021/09/33446594/economic-consequences-trade-global-value-chain-integration-measurement-perspective
topicv3: Industrial Clusters and Value Chains
            m1326062 1226,Global Value Chains(GVC) m1326472 767,Manufacturing Sectors m1326521 1281,Trade m1326924 698,Special Economic Zone m1327217 1016
publishtoextweb_dt: 2021-09-28T00:00:00Z
dois: 10.1596/1813-9450-9785
historic_topic: Macroeconomics and Economic Growth,Social Development,Law and Development,Governance,Industry,Public Sector Development,International Economics and Trade,Transport
origu: DECRG: Trade & Intl. Integration (DECTI)
no_of_pages: 50
author_keys: 0: author_key: NA
1: author_key: NA
2: author_key: 1155075
model_extractions: Policy Research Working Paper 9785 Economic Consequences of Trade and Global Value Chain Integration

# A Measurement Perspective

_Alessandro Borin Michele Mancini Daria Taglioni_ Development Economics Development Research Group September 2021
Policy Research Working Paper 9785

# **Abstract**

This paper introduces a new approach to measuring Global Value Chains (GVC), crucial for informed policy-making. It features a tripartite classification (backward, forward, and two-sided) covering trade and production data. The findings indicate that traditional trade-based GVC metrics significantly underestimate global GVC activity, especially in sectors like services and upstream manufacturing, and overstate risks in early trade liberalization stages.

Additionally, conventional backward-forward classifications over-estimate backward linkages. The paper further applies these measures empirically to assess how GVC participation mediates the impact of demand shocks on domestic output, highlighting both the exposure and stabilizing potential of GVC integration. These new measures are comprehensively available on the World Bank’s WITS Platform, providing a key resource for GVC analysis.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at dtaglioni@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Economic Consequences of Trade and Global Value Chain Integration: A Measurement Perspective

Alessandro Borin<sup>1</sup> , Michele Mancini<sup>2</sup> , and Daria Taglioni<sup>3</sup>

> 1Bank of Italy

> 2Bank of Italy

> 3World Bank

Originally published in the Policy Research Working Paper Series on _September 2021._ This version is updated on _April 2025._ To obtain the originally published version, please email prwp@worldbank.org.

**_Keywords:_** _Global Value Chains (GVCs); GVC Measures using Inter-Country Input-Output Tables; GVC Exposure; GVC Risk and Resilience Assessments._

**_JEL Codes:_** _E16, F1, F14, F15_

**_Online Appendix:_** _The Online Appendix is available here_
2

# **1 Introduction**

Securing market access and promoting exports are since long considered as primary drivers of economic growth and prosperity. The emergence of Global Value Chains (GVCs), which encompass production processes whose stages are spread across different countries, industries, and firms, complicate policy-making decisions. Global Value Chains introduce new opportunities and risks compared to traditional trade. On the one hand, GVC-led growth strategies feature additional gains from specializing in specific tasks or components and securing better access to inputs, technology, and skills through repeated firm-to-firm interactions (World Bank, 2019 and Antr`as, 2020). On the other hand, GVC participation presents countries with greater challenges, including more income inequality and heightened exposure to imported shocks and risks (due for example to dependencies on foreign partners, sourcing challenges, market concentration of critical inputs or stages of production, and new types of technology and intellectual property vulnerabilities).

Understanding how countries and sectors participate in global production networks is essential for analyzing the drivers of economic development, resilience to shocks, and the welfare implications of globalization. While trade in value-added and related GVC metrics have become mainstream in policy and academic circles, most rely on trade-based measures constructed from Inter-Country Input-Output (ICIO) tables, and bi-partite backward-forward classifications of linkages. These conventional measures, while insightful, fall short in some key respects: they often blur distinctions between participation modes, overlook indirect contributions of non-exporting sectors, and distort the true scale and nature of GVC integration.

This paper addresses these gaps by advancing both the methodology and interpretation of GVC participation metrics. It targets two fundamental questions: how much do countries and industries participate to traditional trade and to GVCs? And in what different ways? It argues that answering these questions requires a decomposition into three components: pure backward participation, pure forward participation, and a two-sided engagement strategically positioned between the extremes, involving elements of both backward and forward participation. Furthermore, the paper also advocates for extending the assessment of GVC participation to include both trade-based and output-based metrics. This approach deviates significantly from conventional metrics that decompose participation in backward and forward linkages and focus exclusively on trade measures. These two improvements allow to capture indirect and non-exporting contributions to global production and align measures more closely with real-world production structures.

Applying this methodology to major ICIO datasets (EORA, ADB MRIOT, OECD TiVA, and WIOD), we construct a comprehensive set of indicators covering nearly 190 countries and up to 56 industries starting in 1990, and updated to the most recent year of available data, which at the time of writing (March 2025) goes to 2023. The new metrics show that previous approaches systematically underestimate the extent and complexity of GVC participation, especially for services, upstream manufacturing, and countries with low exportto-output ratios. They also reveal distinct patterns of engagement that matter for resilience, risk exposure, and by extension for economic development.

By bridging conceptual gaps in existing GVC measures, this paper equips policymakers and researchers with more accurate and actionable tools for understanding globalization’s evolving landscape and their implications for economic growth, exposure to shocks, and development. For example, the paper shows that the risks associated with a country’s involvement in GVCs
3 during the initial phases of trade liberalization can often be exaggerated. This occurs when a country’s exports are heavily dependent on GVCs while forming only a minor portion of its total domestic output. Such a scenario was seen in early liberalizing China, for example. Measuring GVC participation against domestic output, in addition to trade, reveals that economies at such junctures are more shielded from international disturbances than previously thought. Moreover, the measures also allow to empirically show that GVC integration mediates the impact of demand shocks on domestic output in opposing directions, leading both to higher exposure and greater stabilizing potential for the domestic economy.

The rest of the paper is structured as follows: Section 2 provides a short literature review. Section 3 describes the tri-partite decomposition of GVC trade and it applies these concepts to GVC-related output. Section 4 applies these measures to multiple ICIO databases, highlighting how different countries and sectors participate in GVCs and comparing trade- and output-based metrics. Section 5 provides an empirical application of the new measures by examining how different types of GVC participation mediate the transmission of external demand shocks to domestic output. Section 6 concludes by summarizing key insights and outlining implications for research and policy. The full methodology describing the accounting framework and additional results are available in the supplementary online appendix, while the full set of new measures, provisioned from all major ICIO databases is available on the World Bank’s WITS Platform and described in Section S1 of the supplementary online appendix.

# **2 Related Literature**

Rigorous measures of GVC participation are needed to inform questions relevant to economic growth and development. Among these questions, there are two of primary importance: firstly, the extent of GVC involvement by different countries and industries; and secondly, the nature of their participation. In essence, good measures of GVC participation are needed to discern the importance of a given country’s involvement for the domestic economy, and whether it primarily manifests itself as input supply to downstream nations and industries, as utilization of foreign inputs, or as a combination of both. Assessing the extent to which the production and exchange of goods and services is interconnected globally is, however, a complex task. The most accurate and thorough approach requires data at the individual company level from numerous countries, especially when aiming for a global perspective. Obtaining such extensive and specific data poses a major hurdle, since very few countries generate the required data (Bems & Kikkawa, 2021). Owing to the shortage of such data, researchers have dedicated significant effort over the past twenty-five years to methodically combine transaction data from customs with national aggregate production data. They have developed accounting measures at the country-sector level based on global databases of inter-country input-output (ICIO) relationships. This approach ensures a scientific rigor in their analysis, despite well-known limitations due to assumptions in terms of homogeneity, proportionality and aggregation that such data require, as discussed by de Gortari (2019), Antr`as (2021) and Bems and Kikkawa (2021) among others. The resulting country-sector level measures have allowed to quantify the extent to which production processes have become global in the past decades, and how countries and sectors participate in GVCs, together with other features of participation, such as direct and indirect supply and demand inter-linkages. The study by Antr`as (2020) calls this body of work “the broad view of GVC participation” while Antr`as and Chor (2022) define it as the “macro-approach” to GVC
4 measurement.

A number of studies have laid the groundwork in understanding and quantifying production sharing and trade in value added following such “macro-approach”. Key contributions include Hummels et al. (2001), Johnson and Noguera (2012), Koopman et al. (2014), and Borin and Mancini (2015). These have been instrumental in conceptualizing this field and in developing sound measurement methodologies, that are now accepted as standard in the literature. In particular, Hummels et al. (2001) introduced a fundamental idea, suggesting that a minimal condition for trade to be considered GVC-related, is that it must cross at least two country borders.<sup>1</sup> Early measures have tended to concentrate on specific aspects of the trade-GVC relationship, providing foundational estimates of participation, which have been refined in later research for greater comprehensiveness, accuracy and statistical rigor. The ’vertical specialization’ index by Hummels et al. (2001), which gauges the import content of a country’s exports, was widely popular. However, as noted by the authors, it measures participation only partially, accounting for backward but not forward linkages. Koopman et al. (2014) introduced an accounting method to decompose a country’s gross exports by the source and final destination of their embedded value added. This approach, which is highly used in the literature, quantifies the extent of a country’s production included in other nations’ exports. It however does not trace whether exports undergo further processing in the importing or downstream countries, nor it aligns fully with theory (see Section S2 in the supplementary online appendix for further details). Another notable contribution is Johnson and Noguera (2012), which defined the value-added exports to gross exports ratio (VAX). The complement of this measure (1 _− V AX_ ) has been interpreted in some cases as a measure of the share of trade involved in GVCs. While changes in this indicator align closely with variations in international production fragmentation, especially globally (Johnson and Noguera, 2017), the indicator in levels tends to underestimate the significance of GVCs in trade, a point elaborated in Antr`as and Chor (2022).<sup>2</sup> Building on the definition by Hummels et al. (2001), Borin and Mancini (2015) showed how to calculate GVC-related trade using global input-output tables in a way that aligns with the original concept established by Hummels and co-authors in 2001. The accounting framework proposed by Borin and Mancini was the first to provide a quantitative assessment of trade crossing at least two borders. The method ensured that the measure of GVC-trade corresponds to the sum of two established metrics of cross-border GVC linkages: forward GVC participation (producing and exporting inputs for further re-export by the trading partner) and backward GVC participation (utilizing imported inputs for goods exported abroad). More recently, Borin and Mancini (2023) proposed a comprehensive methodology for value-added accounting of trade flows at the aggregate, bilateral, and sectoral levels, aligning different accounting perspectives to the economic questions they address best.

All the above-mentioned concepts and measures are now commonly accepted in academic

> 1In theory, value-added generated in the final stages of an internationally fragmented production process should be regarded as related to Global Value Chains (GVC), even if it only crosses one border. However, in practical terms, this segment cannot be distinguished from conventional trade using standard inter-country input-output (ICIO) tables. Due to this empirical constraint, the study Hummels et al. (2001) and all other macro-level measures (that is, at the country-sector level) of GVC-related trade in the wider literature concentrate on the “minimal” criterion for GVC trade, namely, crossing at least two borders.

> 2This comes from the fact that only a sub-portion of **VAX** is not GVC-related - see Section S2 in the supplementary online appendix for further details.
<!-- Start of picture text --> iB 8<br>aS 3<br>x=<br>—<br>2<br>oO<br>6 2 R<br>oO b=]<br>Tt N<br>1990 1995 2000 2005 2010 2015<br>t<br>——— GVC trade (LHS) ————_ 1-VAX/GX (RHS)<br>— 1-DAVAX/DVA (RHS) — \S, i.e. 1-DVA/GX (RHS)<br><!-- End of picture text -->
6 of GVC participation through the systematic exclusion of the contribution by industries not directly engaged in exporting activities despite being important suppliers of GVC-oriented industries; and it exaggerates the _relative_ exposure for countries whose traded sector is mostly GVC-related, but it constitutes a small share of overall domestic output.

When one applies the methodology proposed by this paper to the data (Section 4), it is possible to quantify more precisely the extent and modalities of countries’ and sectors’ engagement in trade and GVC activities. By improving the measurement of GVC exposure, the measures proposed in this paper are also well suited for discussions about GVC riskiness and resilience, two themes very current in discussions, and for exploring the relationship between trade and countries’ growth and development.

# **3 Methodology**

This section presents an accounting framework that allows for a tri-partite decomposition and the use of output-based measures of GVC participation - in addition to trade-related ones. The tri-partite decomposition allows to develop a precise and intuitive measure of how countries and industries participate in traditional trade and GVC-related activities. The application of these metrics to both trade and output data allows to capture more comprehensively the extent of participation, as mentioned shortly in the Introduction and at the end of Section 2. The discussion of this Section is streamlined for ease of understanding, and the main concepts are illustrated through diagrams. These latter have a direct correspondence with the underlying algebraic forms, whose full exposition, inclusive of definitions, adopted conventions, and mathematical derivations, is available in Section S2 in the supplementary online appendix.

## **3.1 Tripartite decomposition of GVC-related trade**

Characterizing linkages as either forward or backward fails to capture an important empirical regularity: GVC participation encompasses many activities that are linked simultaneously backward and forward to entities abroad. In Section 4 we will show that activities interlinked with both upstream suppliers and downstream buyers internationally could represent as much as two-thirds of all production related to GVCs, highlighting their substantial role. These activities may be critical drivers of economic growth and development, as suggested in Baldwin and Lopez-Gonzalez (2015), under the concept of I2E (import to export), underscoring their significance in the literature on international trade and development.

To more accurately represent international interdependencies, this paper introduces a nuanced accounting framework that identifies three separate modes of participation: _pure forward participation_ to describe activities that occur at the start of the value chain, where primary inputs are converted in intermediate goods; _pure backward participation_ to characterize activities at the end of the chain, which pertains to the final transformation of these goods into final products; and _two-sided or mixed participation_ for activities that are positioned in the intermediary stages of the chain.

Taking the perspective of the exporting sector, in this section we demonstrate the method for identifying the proportion of any trade flow that can be linked to global value chains and how to operate the above-mentioned tri-partite decomposition. In the next section (Section 3.2) we show how these concepts can be applied to output data too.
7

# **3.1.1 Definition of GVC-trade**

Consider a standard Inter-Country Input-Output (ICIO) model with _G_ countries and _N_ sectors. Given the _N ×_ 1 vector of gross exports from country _s_ to country _r_ , which we call **E** _sr_ , the level of GVC-trade at the sectoral level is: where **V** _s_ is the 1 _× N_ vector that incorporates the value-added shares embedded in each unit of gross output produced by country _s_ , **L** _ss_ is the _N × N local_ Leontief inverse matrix taking into account only the domestic chains, i.e. ( **I** _−_ **A** _ss_ )<sup>_−_1</sup> , with **A** _ss_ being the direct requirements matrix of country _s_ inputs in its own productions, **Y** _sr_ is the _N ×_ 1 vector of final goods and services produced by country _s_ and absorbed in country _r_ .<sup>5</sup> The term in equation (1) we subtract from gross exports _Esr_ corresponds to the **DAVAX** _sr_ – directly absorbed value-added in exports – which identifies, for each country _s_ and sector _n ∈ N_ of exports, the ‘traditional’ type of exports to country _r_ , as opposed to the international shipments that take place under the global sharing of production (‘GVC-related trade’). Traditional trade **DAVAX** _sr_ is the simplest form of trade between countries. In alignment with the concept established by Hummels et al. (2001), it traces the amount of value that crosses just one border, that is the one between the exporter and the importer. It consists of two types of flows: (i) the value of final goods produced entirely at home and consumed abroad � ( **V** _s_ **L** _ss_ **Y** _sr_ ); and (ii) the value of intermediate inputs (entirely) produced at home and used � by the importing country to produce final goods for its internal market ( **V** _s_ **L** _ss_ **A** _sr_ **L** _rr_ **Y** _rr_ ).

Accordingly, the measure of ‘GVC-related trade’, **GVC** _sr_ , includes all traded items that cross at least two international borders, i.e. that are re-exported at least once before being absorbed in final demand.<sup>6</sup> Characterizing **GVC** _sr_ as in Equation (1) presents two desirable features: _i_ ) once divided by gross exports, i.e. **GVC** _sr ⊘_ **E** _sr_ , the indicator is bound between 0 and 1, since it traces the _share_ of a trade flow value related to GVC activity; _ii_ ) the indicator **GVC** _sr_ is also additive along all dimensions and levels of aggregation (or disaggregation) of trade flows.<sup>7</sup> It should be noted that the broad definition in Equation (1) aligns with a definition initially presented in Borin and Mancini (2015) and discussed in Borin and Mancini (2023). The innovation of this paper is to divide it into the three distinct modes of participation discussed above, i.e. ‘pure forward’, ‘two-sided’, and ‘pure backward’.<sup>8</sup> 5 The hat notation is a standard way to transform a generic 1 _× N_ vector into its diagonal _N × N_ form. Further details are reported in Section S2 in the supplementary online appendix.

> 6In principle, also value-added produced in completion stages of the production process, even if it crosses only one border, should be labeled as GVC trade. However, it cannot be singled out using standard ICIO tables. Due to this empirical limitation, it is not considered as part of GVC trade, neither in this paper nor in any macro (country-sector level) measure of GVC trade discussed in the broader literature. Such convention is in line with the initial conceptualization and definition by Hummels et al. (2001).

> 7This means that the indicator can be aggregate in multiple ways and at different levels of aggregation in order to obtain the desired GVC participation measure i.e. exports and its portions (traditional trade, GVC trade, components of GVC trade) at world-level, and at country, country-pair, country-group level, for all industries, for groups of industries, and for individual sectors) by simple summation of the relevant elements

> 8The mapping of these measures with previous ones is as follows: “pure forward participation”, corresponds one-to-one to what Belotti et al. (2021) and Borin and Mancini (2017) label as “forward participation”. Instead, the sum of “pure backward” and “two-sided” participation corresponds to backward participation in those papers. See Section S2 in the supplementary online appendix for further discussion.
8

# **3.1.2 Pure forward GVC-trade participation**

‘Pure forward’ is close to the origin of the chain. It comprises the exports of sector _n_ of value-added generated within the domestic chains, which are then re-exported by the partner.

The pure forward participation is simply the difference between the entire domestic value-� added that is exported ( **V** _s_ **L** _ss_ **E** _sr_ ) and the one that is directly absorbed by the importer ( **DAVAX** _sr_ ). Schematically one can represent this as in Diagram 1

**Diagram 1: Pure-forward GVC-trade - Origin of the chain: first exporting sector**

<!-- Start of picture text --> Intermediates<br>n<br>s r<br><!-- End of picture text --> Notes: letters denote either countries (red) or sectors (black) traced by our accounting framework. Boxes represent countries while sectors are identified by circles. Arrows indicate sales, direct ones (solid arrows), or direct and indirect ones (dashed arrows).

The rest of GVC-related trade is given by the sum of the _pure_ backward participation and the _two-sided_ participation. This is what Hummels et al. (2001) call vertical specialization, i.e. the import content of exports. We discuss it, as well as its two components, in the next paragraphs.

# **3.1.3 Pure backward GVC-trade participation**

‘Pure backward’ is the portion of the import content of exports closer to the end of the chain: where **u** _N_ is the 1 _× N_ unit row vector whose purpose is to reduce the dimension of the matrices. Pure backward GVC-trade consists of imported inputs bought by sector _n_ in country _s_ directly from the foreign country _t_ or indirectly through domestic chains and exported by _G_ � the same sector to the final market _r_ , as final products � **Y** _sr_ or intermediates Å _t_ = _s_<sup>**u**</sup><sup>_N_</sup><sup>**A**</sup><sup>_ts_</sup><sup>**L**</sup><sup>_ss_</sup> ã _G_ � � **A** _sr_ **L** _rr_ **Y** _rr_ , as shown schematically in Diagram 2. Å _t_ = _s_<sup>**u**</sup><sup>_N_</sup><sup>**A**</sup><sup>_ts_</sup><sup>**L**</sup><sup>_ss_</sup> ã
9

**Diagram 2: Pure-backward GVC-trade - End of the chain: last exporting sector**

<!-- Start of picture text --> Final market<br>Intermediates + Final goods<br>n<br>t s r<br><!-- End of picture text --> Notes: letters denote either countries (red) or sectors (black) traced by our accounting framework. Boxes represent countries while sectors are identified by circles. Arrows indicate sales, direct ones (solid arrows), or direct and indirect ones (dashed arrows).

## **3.1.4 Two-sided GVC-trade participation**

Finally, the two-sided participation is given by all imported inputs embedded in the re-export of the bilateral partner. The measure represents a measure of both backward and forward participation. It consists of imported inputs in a country’s exports that are further re-exported by the bilateral partner (Diagram 3). It clearly excludes the value added captured by pure backward participation – i.e. imported inputs for a country’s exports to final markets - and the one captured by pure forward participation – i.e. exports of inputs produced with domestic value added and then re-exported by the bilateral partner.

**Diagram 3: Two-sided or intermediate position: other sectors have already exported the item, others will re-export it**

<!-- Start of picture text --> Intermediates<br>n<br>t s r<br><!-- End of picture text --> Notes: letters denote either countries (red) or sectors (black) traced by our accounting framework. Boxes represent countries while sectors are identified by circles. Arrows indicate sales, direct ones (solid arrows), or direct and indirect ones (dashed arrows).

# **3.2 GVC Concepts and Measures Applied to Output**

Traded activities represent the most visible manifestation of cross-country production linkages. However, global production networks extend beyond import-export dynamics and encompass both international and domestic production relationships.
10

# **3.2.1 Definition of GVC-related output**

The approach to measure GVC-related output mirrors the method used for GVC-related trade in Section 3.1. Also in this case, we focus on the three key moments that define a sector’s role in the supply chain: its contributions at the initial stage, its role in the intermediary phases, and its impact at the end of the production process. Here the emphasis is on the sector of production, not on the sector of exports, as in Section 3.1. Accordingly, we identify as “pure forward participation” the very first link of a chain, i.e. the activities related to the creation of value-added that will be exported by any sector and then re-exported by a direct trade partner. Activities related to the assembly of the final goods or services will instead fall in the “pure backward participation”, as they represent the last link of a chain. Final goods that are not exported will fall into this category too, if they are assembled using inputs that have previously crossed at least two borders. Everything in between, i.e. all the activities that encompass both buying and selling of inputs, will be categorized as two-sided participation.

# **3.2.2 Pure forward GVC participation in output**

Pure forward GVC output consists of value-added produced by sector _n_ that is sold abroad – directly by _n_ , or indirectly through other sectors that are part of the same domestic value – chains and subsequently re-exported by the partner country _r_ , hence crossing two borders or more. Formally, where **X**<sup>_exp_</sup> _r_ is the output of country _r_ further re-exported. The first term ( **A** _sr_ **X**<sup>_exp_</sup> _r_ ) represents direct sales to foreign country _r_ by sector _n_ , while the second term ( **A** _ss_ **L** _ss_ **A** _sr_ **X**<sup>_exp_</sup> _r_ ) indirect sales through domestic chains. Pure-forward participation is illustrated in Diagram 4.<sup>9</sup> Therefore, GVC pure forward participation in output corresponds to value-added crossing at least two borders traced in the sector of its origin.<sup>10</sup>

**Diagram 4: Pure-forward GVC-Output - Origin of the chain: sector where the value-added originates**

<!-- Start of picture text --> Intermediates<br>n<br>s r<br><!-- End of picture text --> Notes: letters denote either countries (red) or sectors (black) traced by our accounting framework. Boxes represent countries while sectors are identified by circles. Arrows indicate sales, direct ones (solid arrows), or direct and indirect ones (dashed arrows).

> 9Pure forward GVC participation in output can also be obtained by subtracting the portion of value added that is never exported and the one that crosses only one border from the total value added of a sector, as discussed in Borin and Mancini (2015).

> 10See Section S2 in the supplementary online appendix for a broader discussion.
11

# **3.2.3 Pure backward GVC participation in output**

Pure backward GVC output is defined as imported inputs bought by a sector _n_ directly from abroad or indirectly through domestic chains that are embedded in sector _n_ ’s final goods production. If imported inputs cross more than one border before being used by _n_ , they are part of pure backward GVC output even if they are sold to domestic consumers as final goods (Diagram 5). Instead, if imported inputs cross only one border before being used by _n_ , they are part of pure backward GVC output only if they are sold abroad as final goods (Diagram 6). This distinction is a crucial one. It makes sure that overall pure backward GVC output adheres to our broad definition of GVCs, i.e. items crossing at least two borders. It is defined as: where **B** _ks_ is the _global_ Leontief inverse matrix. Intuitively, the first term identifies imported intermediates embedded in final goods production carried on by sector _n_ , while the second one excludes from the first term those intermediates crossing just one border. Therefore, **GVC** _PureBacks_<sup>_X_consistsonlyofimportedintermediatesthatareembeddedinfinal</sup> goods and cross at least two borders.<sup>11</sup>

**Diagram 5: Pure-backward GVC-Output - End of the chain: case in which the good is completed and sold in the domestic market**

<!-- Start of picture text --> j<br>Final market<br>Final<br>j goods<br>n<br>k s<br>j<br><!-- End of picture text --> Notes: letters denote either countries (red) or sectors (black) traced by our accounting framework. Boxes represent countries while sectors are identified by circles. Arrows indicate sales, direct ones (solid arrows), or direct and indirect ones (dashed arrows).

> 11See Section S2 in the supplementary online appendix and Wang et al. (2017).
12

**Diagram 6: Pure-backward GVC-Output - End of the chain: case in which the good is completed and sold to foreign markets**

<!-- Start of picture text --> Final market<br>Final<br>goods<br>n<br>s z<br>j<br><!-- End of picture text --> Notes: letters denote either countries (red) or sectors (black) traced by our accounting framework. Boxes represent countries while sectors are identified by circles. Arrows indicate sales, direct ones (solid arrows), or direct and indirect ones (dashed arrows).

# **3.2.4 Two-sided GVC participation in output**

While pure forward and pure backward GVC output pertain to activities at the origin – value– – – added creation or at the end final goods production of a value chain, two-sided GVC output is found in all the other activities in an intermediate position. Intuitively, it consists of two terms. First, intermediates bought abroad (backward) and exported as intermediates (forward). This case is illustrated in Diagram 7. Second, domestic inputs bought from other sectors in the domestic market (backward), exported as intermediates, and further re-exported by the bilateral partner (forward), as per Diagram 8. Both terms share the usual property (‘crossing at least two borders’) but they are computed in different ways. The first term can be computed by subtracting the inputs originating from a direct trade partner and not reexported, and those being part of pure backward GVC output, from the total imported inputs. The second term instead follows the same rationale of GVC pure forward output in (5), but it is applied to domestic inputs instead of value-added. Formally, we write the corresponding equations as follows: where **X** _s_ is the _N ×_ 1 vector of gross output produced by country _s_ , and Therefore, two-sided GVC participation in output will be equal to **GVC** _TwoSide_<sup>_X_</sup> _s_<sup>=</sup> **GVC** _TwoSide_<sup>_ImpInp_</sup> _s_ + **GVC** _TwoSide_<sup>_DomInp_</sup> _s_ .

> 12More technical details can be found in Section S2 in the supplementary online appendix.
13

### **Diagram 7: Two-sided GVC-Output - sector buying foreign inputs and selling intermediates**

<!-- Start of picture text --> Intermediates<br>n<br>k s<br><!-- End of picture text --> Notes: letters denote either countries (red) or sectors (black) traced by our accounting framework. Boxes represent countries while sectors are identified by circles. Arrows indicate sales, direct ones (solid arrows), or direct and indirect ones (dashed arrows).

**Diagram 8: Two-sided GVC-Output - sector buying domestic inputs and selling intermediates**

<!-- Start of picture text --> Intermediates<br>n<br>s r<br><!-- End of picture text --> Notes: letters denote either countries (red) or sectors (black) traced by our accounting framework. Boxes represent countries while sectors are identified by circles. Arrows indicate sales, direct ones (solid arrows), or direct and indirect ones (dashed arrows).

# **3.3 Derived Measures: Aggregations, Complements of GVC Activity, and Forwardness Indices**

The above accounting decompositions for trade and output can be manipulated in three useful ways: they can be aggregated to allow different empirical applications; they can be used to look at the complement of GVC-related trade and output, i.e. domestic production and traditional trade; and they can be used to compute an index of relative participation in GVC-trade and GVC-output centered at zero, that we call _forwardness_ .

## **3.3.1 Aggregation across industries, countries and modes of participation**

Depending on the empirical application, it could be useful to consider broad measures of participation instead of pure ones. The accounting framework allows to aggregate by simple summation across all dimensions of the indicators, i.e. participation modes, industries, countries. More specifically:

- Broad measures of participation can be computed by simply summing up the three modes of participation into an overall measure of GVC-related trade or GVC-related output, respectively:
14

- **GVC** _sr_ = **GVC** _PureForwsr_ + **GVC** _PureBacksr_ + **GVC** _TwoSidedsr_

- **GVC**<sup>_X_</sup> _s_<sup>=</sup><sup>**GVC**</sup><sup>_PureForw_</sup> _s_<sup>_X_+</sup><sup>**GVC**</sup><sup>_PureBack_</sup> _s_<sup>_X_+</sup><sup>**GVC**</sup><sup>_TwoSidedX_</sup> _s_

- The overall indicators of GVC-related trade and GVC-related output, and their forward, backward and two-sided components can be computed by summation at any level of country-sector aggregation.

- At the aggregate world level, i.e. summing across exporters _s_ , importers _r_ and sectors _n_ , the ‘GVC pure backward-related trade’ is equal to the ‘GVC pure forward-related _G G_

- trade’, i.e. �<sup>=</sup> �<sup>Thesameprop-</sup> _s,r_ = _s_<sup>**u**</sup><sup>_N_</sup><sup>**GVC**</sup><sup>_PureBacksr_</sup> _s,r_ = _s_<sup>**u**</sup><sup>_N_</sup><sup>**GVC**</sup><sup>_PureForwsr_.</sup> _G G_

- erty holds for GVC-related output, i.e. � _s_<sup>=</sup> � _s_<sup>.</sup> _s_<sup>**GVC**</sup><sup>_PureForwX_</sup> _s_<sup>**GVC**</sup><sup>_PureBackX_</sup>

# **3.3.2 Regional GVC trade**

The GVC-related trade measures presented above can also be computed so to distinguish between intra-regional and extra-regional value chain participation. The same holds for its sub-components, i.e. pure forward, pure backward and two-sided participation presented in equations (2), (3) and (4).

More specifically, given a country _s_ , member of a region _K_ , for each exporting sector _n_ , intra-regional value-chain participation is defined as the sum of : _i_ ) domestic value-added of sector _n_ re-exported by a regional member (pure forward); _ii_ ) import content of inputs sourced directly from a regional member _t ∈ K_ and exported by sector _n_ to final markets (pure backward); _iii_ ) import content of inputs sourced directly from a regional member _t ∈ K_ and exported by sector _n_ to partners that re-export them (two-sided).<sup>13</sup> Extra-regional valuechain participation is then computed as the difference between the total GVC participation and the intra-regional one. In Section S2.2.2 of the supplementary online appendix we provide a detailed derivation of these terms.

# **3.3.3 Measures of Domestic and Traditional Trade Output**

The framework developed to single out GVC-related output allows also to compute the share of output that never crosses a border, i.e. purely domestic, as the sum of the domestic inputs� � and value-added that are not exported at all, **Dom**<sup>_X_</sup> _s_<sup>=</sup><sup>**V**</sup><sup>_s_</sup><sup>**L**</sup><sup>_ss_</sup><sup>**A**</sup><sup>_ss_</sup> **L** _ss_ **Y** _ss_ + **V** _s_ **L** _ss_ **Y** _ss_ , as well as output related to traditional trade, i.e. crossing only one border before being absorbed by final demand, **Trad**<sup>_X_</sup> _s_<sup>=</sup><sup>**X**</sup><sup>_s−_</sup><sup>**Dom**</sup><sup>_X_</sup> _s_<sup>_−_</sup><sup>**GVC**</sup><sup>_X_</sup> _s_<sup>.Inthisway,wehaveobtainedafull</sup> decomposition of the output produced by a country, i.e. **X** _s_ = **Trad**<sup>_X_</sup> _s_<sup>+</sup><sup>**Dom**</sup><sup>_X_</sup> _s_<sup>+</sup><sup>**GVC**</sup><sup>_X_</sup> _s_<sup>.</sup>

# **3.3.4 Forwardness Index**

Finally, to fully assess the extent and nature of involvement in GVCs, it’s possible to compute a relative measure of participation that we will label as “Forwardness Index”. This index ranges from _−_ 1 to +1, and takes a value equal to _zero_ at the global level. It is calculated by first subtracting the measure of “Pure Backward Participation” from that of “Pure Forward

> 13See Section S2 in the supplementary online appendix for detailed formulas.
15 Participation”, and then dividing this difference by overall GVC participation. This measure, when applied to individual country-pair-industries and GVC-trade, can be written as follows: where the vector **P** _sr_ measures the “forwardness” of country _s_ exports to country _r_ , for each sector _n_ , and each of its elements is bounded between -1 and 1. Forwardness at the country level can be obtained simply by summing across sectors and bilateral partners both the numerator and the denominator.<sup>14</sup> In the same way, a natural measure of the type of participation in output of countries and sectors in GVC can be straightforwardly obtained as At the global level, _P_ is equal to zero both for trade and output, given pure backward and pure forward participation are equal at that level of aggregation,<sup>�</sup><sup>_G_</sup> _s_<sup>_Ps_= 0.Thus,any</sup> index different from zero at any level of aggregation might be interpreted as a deviation from the world aggregate average.

The forwardness index offers a synthetic means of evaluating the primary mode through which a country or a sector engages in GVCs. Previously, a separate strand of the literature has developed specific indicators, such as “upstreamness”, “downstreamness” and “positioning”, to characterize more accurately the participation mode (Antr`as and Chor, 2019 and Wang et al., 2017; see Mancini et al., 2024 for a comprehensive database of such measures based on the most popular Inter-Country Input-Output tables). It is reassuring to see that the forwardness index proposed above shows a strong positive correlation with those measures of positioning.<sup>15</sup> Incorporating both the dimensions of the extent of GVC participation and the primary participation mode within a unified framework provides a coherent and easily comprehensible method for a thorough evaluation of a country’s or a sector’s engagement in GVCs. Additionally, the fact that the forwardness index is bounded between -1 and 1 and neutral at the world level streamlines assessments when moving from a global perspective to specific country-sector interactions. Collectively, the set of indicators we present constitutes a straightforward and comprehensive toolbox of GVC descriptive statistics, readily applicable for policy analysis.

# **4 Results: Assessing GVC Participation Through the New Measures**

We used the GVC participation measures from Section 3 on four popular Inter-Country InputOutput (ICIO) datasets: EORA, the Asian Development Bank MRIOT, OECD TiVA, and WIOD. This helped us create a detailed and up-to-date set of indicators for 189 countries and 26 to 56 industries (depending on the dataset) from 1990. The industry classification and time coverage vary based on the raw data available in each dataset. These measures are available in the World Bank’s WITS Platform, which also provides details on industry classification and time coverage. Using these measures, we can answer two main questions: First, how much

> 14See Section S2 in the supplementary online appendix for more details.

> 15See Section S3.4 in the supplementary online appendix.
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20 a standard multi-country, multi-sector model to show that the new GVC measures better predict the impact of trade shocks on output than traditional openness indicators. Section S4.2 documents a positive correlation between GVC participation and per capita income growth, with the new measures containing more informational content than existing ones.

# **Methodology and Empirical Estimation Details**

Our objective is to quantify how different dimensions of GVC participation affect the output response to demand-side fluctuations. The empirical deliberately builds on well-established empirical frameworks. It comprises three steps.

**_Step 1:_** _Estimating Exogenous Demand Innovations._ We estimate innovations in countrylevel demand using global Input-Output (I-O) data via a fixed effects model adapted from Alfaro et al. (2021) and Kramarz et al. (2020). Specifically we estimate:

Here, ∆ _yij,t_<sup>_r_indicatethechangeinfinalsalesfromcountry</sup><sup>_i_tocountry</sup><sup>_j_insector</sup><sup>_r_,</sup> expressed in percentage terms. The term _ηj,t_ captures the demand innovations in the importing country _j_ at time _t_ , while _γi,t_<sup>_r_accountsforexportingcountry-sector-timeeffects.16</sup> We interpret _ηj,t_ as a residual innovation in final demand that reflects both unexpected fluctuations and persistent shifts. Although our terminology refers to ”shocks,” these innovations may also include trend-like movements. Our interest lies in understanding the implications of exposure to such changes—regardless of whether they are transitory or structural—for output volatility.

**_Step 2:_** _Constructing Direct and Indirect Demand Shocks._ Using a shift-share instrument approach that builds on Ferrari (2023), we aggregate _ηj,t_ into sector- and country-specific shocks based on observed trade patterns:

- The _direct foreign demand shock_ for country _i_ , sector _r_ , at time _t_ , is:

where _φ_<sup>_r_</sup> _ij,t−_ 1<sup>isthelaggedshareoffinalsalesfrom</sup><sup>_i_to</sup><sup>_j_insector</sup><sup>_r_.17</sup>

- The _direct domestic demand shock_ for country _j_ is simply the demand innovation _ηj,t_ , applied when _i_ = _j_ .

- The _direct demand shock_ variable used in Table 1, Column (5), is the sum of the foreign and domestic components, interacted with the share of output not related to GVCs.

- The _indirect demand shock_ aggregates demand changes _ηj,t_ through downstream linkages based on IO structures (captured by weights _ψij_<sup>_r_thatmeasuretheexposureof</sup>

> 16Results throughout the paper are robust to the inclusion of different sets of fixed effects in the estimation of demand innovations. The estimation is carried out by excluding country _i_ from the sample when computing the _jt_ demand innovation that might affect it, to reduce concerns of endogeneity.

> 17 _φrij,t_<sup>isequalto</sup><sup>_y_</sup> _ij_<sup>_r/_�</sup><sup>_G_</sup> _j_<sup>_y_</sup> _ij_<sup>_r_;notethat</sup><sup>_φr_</sup> _ii,t_<sup>= 0.</sup>
21 country _j_ final demand for sales of intermediates exported by country _i_ in sector _r_ ).<sup>18</sup> This shock reflects second-order effects — that is, the influence of demand arising in sectors that are served by the immediate buyers of the intermediates exported by country _i_ .

We leverage the ability of this instrument to differentiate between direct demand shocks (related to sales of final products) and indirect demand shocks (related to sales of intermediate products) for testing the hypothesis that indirect demand shocks should be exclusively associated with the variables capturing GVC participation, and in particular forward and two-way participation (see Step 3).

**_Step 3:_** _Integrating GVC Metrics into the Analysis:_ We estimate the impact of demand shocks on output by interacting them with GVC participation metrics:

∆ _x_<sup>_r_</sup> _i,t_<sup>=</sup><sup>_α_+</sup><sup>_β_1</sup><sup>_DomesticDemandShock_</sup> _i,t_<sup>_r_+</sup><sup>_β_2</sup><sup>_DirectForeignDemandShock_</sup> _i,t_<sup>_r_+</sup> + _β_ 3 _IndirectDemandShocki,t_<sup>_r_+</sup> + _β_ 5 **GVC** _PureForwi,t_<sup>_r_</sup> _−_ 1<sup>+</sup><sup>_β_6</sup><sup>**GVC**</sup><sup>_TwoSidedr_</sup> _i,t−_ 1<sup>+</sup> + **GVC** _PureForwi,t_<sup>_r_</sup> _−_ 1<sup>_× IndirectDemandShock_</sup> _i,t_<sup>_r_+</sup> + **GVC** _TwoSided_<sup>_r_</sup> _i,t−_ 1<sup>_× IndirectDemandShock_</sup> _i,t_<sup>_r_+</sup> + supply controls + _. . ._ + _δt_ + _γi_<sup>_r_+</sup><sup>_εr_</sup> _i,t_<sup>_._</sup> (13) We focus on interactions between indirect demand shocks and forward/two-sided GVC participation, reflecting propagation through downstream sectoral networks. While an alternative approach could consider propagation through country-level networks, we prioritize sectoral links as they more directly correspond to intermediate input flows.

Direct demand shocks, by contrast, are not interacted with GVC metrics, as they affect all producers regardless of production structure. Instead, we examine whether the sensitivity of non-GVC output to these shocks differs, as shown in Table 1, Column (5).

We do not scale the domestic demand shock by the share of domestic sales, in contrast to foreign demand components. This maintains conceptual consistency with the estimation of _ηj,t_ which reflects aggregate final demand innovations. Results are robust to alternative specifications with scaled domestic components (available upon request).

Despite the focus of this empirical application is on the demand side, we also control for supply-side shocks in the analysis. We compute those by using almost the same procedure described above. The key assumption employed to identify supply shocks is that variations in sales of intermediate inputs of a certain sector across all producers and country-sector of usage are related to some supply perturbation specific to that sector. Additional details on the derivation of supply-side shocks can be found in Section S5 in the supplementary online appendix.

# **Results**

Table 1 presents the main regression results. We find:

> 18 _ψijr_<sup>iscalculatedas</sup><sup>_ψ_</sup> _ij,t_<sup>_r_=</sup> <u>Ä�</u> _Gk_ <u>�</u> _Ns_<sup>_b_</sup> _ik_<sup>_rsy_</sup> _kj_<sup>_s−l_</sup> _ii_<sup>_rsy_</sup> _ij_<sup>_s_</sup> ä _/_ Ä� _Gj_ � _Gk_ � _Ns_<sup>_b_</sup> _ik_<sup>_rsy_</sup> _kj_<sup>_s−l_</sup> _ii_<sup>_rsy_</sup> _ij_<sup>_s_</sup> ä, where _b_<sup>_rs_</sup> _ik_<sup>and</sup>

> _lii_<sup>_rsy_</sup> _ij_<sup>_s_aretheelementsoftheglobalandlocalLeontiefinversematrixrespectively.</sup>
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23

**Table 1:** WIOD GVC-Output

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||∆Out|∆Out|∆Out|∆Out|∆Out|
|Domestic Demand shock|0.338<sup>_∗∗∗_</sup>|0.338<sup>_∗∗∗_</sup>|0.281<sup>_∗∗∗_</sup>|0.281<sup>_∗∗∗_</sup>||
||(0.007)|(0.007)|(0.008)|(0.008)||
|Foreign Direct Demand shock|0.055<sup>_∗∗∗_</sup><br>(0.018)|0.035<sup>_∗∗_</sup><br>(0.017)|0.031<sup>_∗_</sup><br>(0.017)|0.026<br>(0.018)||
|Direct Demand shock|||||-0.006<br>(0.068)|
|Indirect Demand shock|0.083<sup>_∗_</sup><br>(0.044)|0.120<sup>_∗∗∗_</sup><br>(0.043)|0.095<sup>_∗∗_</sup><br>(0.044)|0.146<sup>_∗∗∗_</sup><br>(0.047)|0.119<sup>_∗∗∗_</sup><br>(0.041)|
|Indirect Supply shock|0.258<sup>_∗∗∗_</sup><br>(0.049)|0.210<sup>_∗∗∗_</sup><br>(0.048)|0.232<sup>_∗∗∗_</sup><br>(0.048)|0.212<sup>_∗∗∗_</sup><br>(0.048)|0.190<sup>_∗∗∗_</sup><br>(0.046)|
|Sectoral Supply shock|0.108<sup>_∗∗∗_</sup><br>(0.007)|0.097<sup>_∗∗∗_</sup><br>(0.007)|0.095<sup>_∗∗∗_</sup><br>(0.007)|0.093<sup>_∗∗∗_</sup><br>(0.007)|0.089<sup>_∗∗∗_</sup><br>(0.007)|
|GVC Pure Forward_t−_1|0.082<sup>_∗∗∗_</sup><br>(0.029)|0.074<sup>_∗∗_</sup><br>(0.035)|0.097<sup>_∗∗∗_</sup><br>(0.033)|0.433<sup>_∗∗∗_</sup><br>(0.091)|0.423<sup>_∗∗∗_</sup><br>(0.090)|
|GVC Two-Sided_t−_1|0.018<sup>_∗_</sup><br>(0.011)|0.033<sup>_∗∗_</sup><br>(0.015)|0.066<sup>_∗∗∗_</sup><br>(0.016)|0.132<sup>_∗∗∗_</sup><br>(0.040)|0.151<sup>_∗∗∗_</sup><br>(0.039)|
|GVC Pure Back_t−_1|-0.034<sup>_∗_</sup><br>(0.019)|0.024<br>(0.027)|0.050<sup>_∗∗_</sup><br>(0.026)|0.094<br>(0.069)|0.096<br>(0.069)|
|Indirect Demand shock _×_ GVC Pure Forw_t−_1|0.634<sup>_∗∗_</sup><br>(0.275)|0.641<sup>_∗∗_</sup><br>(0.277)|0.741<sup>_∗∗∗_</sup><br>(0.277)|0.694<sup>_∗∗_</sup><br>(0.295)|1.254<sup>_∗∗∗_</sup><br>(0.292)|
|Indirect Demand shock _×_ GVC Two-Sided_t−_1|0.611<sup>_∗∗_</sup><br>(0.239)|0.641<sup>_∗∗∗_</sup><br>(0.238)|0.626<sup>_∗∗∗_</sup><br>(0.236)|0.486<sup>_∗_</sup><br>(0.257)|0.634<sup>_∗∗_</sup><br>(0.282)|
|Indirect Supply shock _×_GVC Two-Sided_t−_1|0.274<sup>_∗_</sup><br>(0.165)|0.269<br>(0.166)|0.280<sup>_∗_</sup><br>(0.168)|0.439<sup>_∗∗_</sup><br>(0.185)|0.690<sup>_∗∗∗_</sup><br>(0.193)|
|Indirect Supply shock _×_ GVC Pure Back_t−_1|0.317<sup>_∗∗∗_</sup><br>(0.107)|0.328<sup>_∗∗∗_</sup><br>(0.108)|0.349<sup>_∗∗∗_</sup><br>(0.109)|0.387<sup>_∗∗∗_</sup><br>(0.107)|0.854<sup>_∗∗∗_</sup><br>(0.122)|
|Direct Demand shock _×_ Non-GVC Output_t−_1|||||0.374<sup>_∗∗∗_</sup><br>(0.077)|
|Country-sector FE|No|No||Yes|Yes|
|Country FE|No|No|Yes|||
|Sector FE|No|Yes|Yes|||
|Year FE|Yes|Yes|Yes|Yes|Yes|
|R2|0.457|0.466|0.488|0.525|0.524|
|N|30,966|30,966|30,966|30,964|30,964|

Standard errors in parentheses. Results based on WIOD data. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01
<!-- Start of picture text --> a OMMR OBRN<br>OLAO<br>oKHm RUS<br>82Q “ @BRA °<br>5 OARGopGR WA OKAZ<br>s[aSwo Korotha iOza© OOVNM jsto GRCO- LUXo. svK Estal°<br>~3 OMEX ° gRavz.° ° ° © OPOLo%<br>at % OcHN? © 6 Gemarcze<br>e "lo‘AN TWN JFraeBKG GBR___DE0 0 98NLD<br>a ISR USA°<br>.06 .07 o Indirect; demand.08 shocks .09 4<br>° Advanced economies~ © Developing~ economies~<br>——— 45 degrees line<br><!-- End of picture text --> <!-- Start of picture text --> +<br>°<br>2<br>TTaN25<br>3s<br>a<br>a<br>©<br>0 2 4 6 8 1<br>Normalized Herfindahl Index<br>——— GVC-output below median ———— GVC-output above median<br><!-- End of picture text -->