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Case study analysis on agri-food value chain: a guideline-based approach.

1. Introduction

2. conceptual framework of value chain analysis.

• ❖ Logistics: the activities on receiving, storing and disseminating inputs to the product, and the activities on collecting, storing, and distribution to buyers;
• ❖ Processing: the activities on transforming inputs into the final product form, such as packaging, assemblage, maintenance of fixed assets;
• ❖ Marketing and Sales: the activities on the buyers purchasing the product and increasing sales;
• ❖ Service: the activities on providing services to maintain the value of the product.
• ❖ Procurement: this refers to purchasing inputs, such as raw materials, consumable items, and assets of inventory;
• ❖ Technology Development: the use of technology and technology development;
• ❖ Human Resource Management: these are recruiting, hiring, training, and development of all types of personnel;
• ❖ Firm Infrastructures: this is the number of activities on planning, financing, accounting, legal affairs, relations with government, and quality management.

2.1. Institutional/Functional Analysis of the Value Chain

2.2. economic/financial analysis of value chain, 2.3. social analysis of value chain, 2.4. environmental analysis of value chain, 3. materials and research methodology, 4. review results on guidelines of value chain analysis, 4.1. institutional/functional analysis, 4.2. economic/financial analysis, 4.3. social analysis, 4.4. environmental analysis, 5. review of case study results on value chain analysis, 5.1. the results on institutional/functional analysis’ tools and indicators, 5.2. the results on economic/financial analysis’ tools and indicators, 5.3. the results on social analysis’ tools and indicators, 5.4. the results on environmental analysis’ tools and indicators, 6. conclusions.

• To train the farmers on sustainable farming practices such as organic, improved agricultural practices, and new technologies which diminish the over-exploitation of natural resources;
• To launch a support program on the cost of investment of new technologies, such as artificial irrigation and soil treatments;
• To establish a certification and labelling program for sustainable food products. The increasing complexity of the labelling landscape has raised concern about their efficiency and capacity to help food consumers make well-informed choices, particularly in favor of biodiversity.
• To address existing negative connotations, and educate people and increase awareness of the nutritional benefits of sustainable foods and products;
• To limit the increased food demand by adopting healthier diets and reducing food waste, as well as to limit the consumption of other material goods and services that affect biodiversity, such as forestry, energy, and freshwater supply;
• To create programs for advertising sustainable foods of interest, encourage their use in everyday cooking, promote their use as both food and medicine, and stimulate improvements in the culinary skills of consumers;
• To design effective nutrition promotion strategies to encourage healthy eating in adolescence, and targeting food supply and availability;
• To include principles of healthy diets and sustainable food consumption in public health programs, and to raise children’s awareness of healthier and more environmentally-friendly food consumption;
• To bring policy makers, nutritionists, and agronomists together to develop a food system which balances productivity, sustainability, and the community’s nutrition fulfillment to reinforce environmentally-friendly food consumption behavior.
• To provide information to consumers as part of environmental policy design, as findings from different countries highlighted that most consumers are still not ready to make food choices based on what is best for the environment.
• To increase taxes on less environmentally-friendly food products as a way to promote organic products.

Author Contributions

Data availability statement, acknowledgments, conflicts of interest.

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Akyüz, Y.; Salali, H.E.; Atakan, P.; Günden, C.; Yercan, M.; Lamprinakis, L.; Kårstad, S.; Solovieva, I.; Kasperczyk, N.; Mattas, K.; et al. Case Study Analysis on Agri-Food Value Chain: A Guideline-Based Approach. Sustainability 2023 , 15 , 6209. https://doi.org/10.3390/su15076209

Akyüz Y, Salali HE, Atakan P, Günden C, Yercan M, Lamprinakis L, Kårstad S, Solovieva I, Kasperczyk N, Mattas K, et al. Case Study Analysis on Agri-Food Value Chain: A Guideline-Based Approach. Sustainability . 2023; 15(7):6209. https://doi.org/10.3390/su15076209

Akyüz, Yarkın, Havva Ece Salali, Pelin Atakan, Cihat Günden, Murat Yercan, Lampros Lamprinakis, Signe Kårstad, Irina Solovieva, Nadja Kasperczyk, Konstadinos Mattas, and et al. 2023. "Case Study Analysis on Agri-Food Value Chain: A Guideline-Based Approach" Sustainability 15, no. 7: 6209. https://doi.org/10.3390/su15076209

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Global value chains: A review of the multi-disciplinary literature

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• Published: 25 February 2020
• Volume 51 , pages 577–622, ( 2020 )

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• Liena Kano 1 ,
• Eric W. K. Tsang 2 &
• Henry Wai-chung Yeung 3  

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This article reviews the rapidly growing domain of global value chain (GVC) research by analyzing several highly cited conceptual frameworks and then appraising GVC studies published in such disciplines as international business, general management, supply chain management, operations management, economic geography, regional and development studies, and international political economy. Building on GVC conceptual frameworks, we conducted the review based on a comparative institutional perspective that encompasses critical governance issues at the micro-, GVC, and macro-levels. Our results indicate that some of these issues have garnered significantly more scholarly attention than others. We suggest several future research topics such as microfoundations of GVC governance, GVC mapping, learning, impact of lead firm ownership and strategy, dynamics of GVC arrangements, value creation and distribution, financialization, digitization, the impact of renewed protectionism, the impact of GVCs on their macro-environment, and chain-level performance management.

Cet article passe en revue le domaine en pleine expansion de la recherche sur la chaîne de valeur mondiale (CVM) en analysant plusieurs cadres conceptuels très cités, puis en évaluant les études sur la CVM publiées dans des disciplines telles que l’ international business , le management général, la gestion de la chaîne logistique, la gestion de production, la géographie économique, les études régionales et de développement, et l’économie politique internationale. En s’appuyant sur les cadres conceptuels de la CVM, nous avons mené la revue en nous fondant sur une perspective institutionnelle comparative qui englobe les questions de gouvernance essentielles aux niveaux micro, de la CVM et macro. Nos résultats indiquent que certaines de ces questions ont suscité beaucoup plus d’attention de la part des chercheurs que d’autres. Nous proposons plusieurs sujets de recherche pour l’avenir, tels que les micro-fondations de la gouvernance des CVM, la cartographie des CVM, l’apprentissage, l’impact de la propriété et de la stratégie des entreprises chefs de file, la dynamique des arrangements des CVM, la création et la distribution de la valeur, la financiarisation, la numérisation, l’impact du protectionnisme renouvelé, l’impact des CVM sur leur macro-environnement et la gestion des performances au niveau de la chaîne.

Este artículo revisa el dominio de rápido crecimiento de la investigación sobre la cadena de valor global (GVC por sus iniciales en inglés) analizando varios marcos conceptuales altamente citados y luego evalúa los estudios sobre GVC publicados en disciplinas como negocios internacionales, gerencia general, gestión de la cadena de suministro, gestión de operaciones, geografía económica, estudios regionales y de desarrollo, y economía política internacional. Sobre la base de los marcos conceptuales de GVC, realizamos la revisión basada en una perspectiva institucional comparativa que abarca cuestiones críticas de gobernanza a nivel micro, GVC y macro. Nuestros resultados indican que algunos de estos asuntos han recogido mucha más atención académica que otros. Sugerimos varios temas de investigación futuros, tales como los microfundamentos de la gobernanza de las GVC, el mapeo de las GVC, el aprendizaje, impacto de la propiedad y estrategia de la empresa líder, la dinámica de los acuerdos de la GVC, la creación y distribución de valor, la financiarización, la digitalización, el impacto del proteccionismo renovado, el impacto de las GVC en su macroambiente y la gestión del desempeño a nivel de cadena.

Este artigo analisa o domínio em rápido crescimento da pesquisa sobre a cadeia global de valor (GVC) analisando vários modelos conceituais altamente citados e avaliando os estudos sobre GVC publicados em disciplinas como negócios internacionais, administração geral, gestão da cadeia de suprimentos, gerenciamento de operações, geografia econômica, estudo regionais e de desenvolvimento e economia política internacional. Com base nos modelos conceituais da GVC, realizamos a revisão com base em uma perspectiva institucional comparativa que abrange questões críticas de governança nos níveis micro, GVC e macro. Nossos resultados indicam que algumas dessas questões atraíram a atenção de acadêmicos mais significativamente do que outras. Sugerimos vários tópicos para futuras pesquisas, como microfundamentos da governança da GVC, mapeamento da GVC, aprendizado, impacto da propriedade e estratégia da empresa líder, dinâmica de acordos da GVC, criação e distribuição de valor, financeirização, digitalização, impacto do protecionismo renovado, impacto das GVCs no seu ambiente macro e gerenciamento de desempenho em nível de cadeia.

本文通过分析几个被高度引用的理论框架,回顾了快速成长的全球价值链(GVC)研究领域,然后评估了在国际商务、通用管理、供应链管理、运营管理、经济地理、区域及发展研究和国际政治经济学中发表的GVC研究。我们建议了一些未来研究主题,例如GVC治理的微观基础、GVC制图、学习、牵头公司所有权及战略的影响,GVC安排的动态性,价值创造与分配、金融化、数字化、新保护主义的影响、GVC对它们的宏观环境的影响,以及链层面上的绩效管理。

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INTRODUCTION

During the last few decades, the gradual liberalization and deregulation of international trade and investment, coupled with the rapid development and spread of information and communication technologies (ICT), have fundamentally changed how multinational enterprises (MNEs) operate and compete in the globalizing world economy. A clear and yet sophisticated pattern of organizationally fragmented and spatially dispersed international business activity has emerged, whereby offshore production sites located in low-cost developing countries are closely linked with lead firm buyers and MNEs from major consumer markets in North America and Europe (Coe & Yeung, 2015 ; Dicken, 2015 ; Gereffi, 2018 ). New MNEs have also emerged from developing economies, particularly those in East and Southeast Asia, as major strategic partners and manufacturing service providers for traditional MNEs from advanced industrialized economies (Yeung, 2016 ). This pattern signals a new divide in industrial organization on a worldwide scale: a transition from hierarchically organized MNEs, with their traditional focus on managing internalized overseas investments, to MNEs as international lead firms. These firms work with and integrate their geographically dispersed strategic partners, specialized suppliers, and customer bases into complex structures, referred to variously as global commodity chains (GCCs), global value chains (GVCs), global production networks (GPNs), or global factories.

Since Gereffi and Korzeniewicz’s ( 1994 ) collection in the early 1990s, this phenomenon of organizationally fragmented international production has been subject to investigation in a wide range of academic disciplines, including economic sociology, international economics, regional and development studies, economic geography, international political economy, supply chain management, operations management, and international business (IB) (Buckley, 2009a , b ; Coe & Yeung, 2015 , 2019 ; Funk, Arthurs, Treviño, & Joireman, 2010 ; Gereffi, 1994 , 2018 ; Gereffi, Humphrey, & Sturgeon, 2005 ; Henderson, Dicken, Hess, Coe, & Yeung, 2002 ). In economic sociology and development studies, the earliest work was concerned with global commodity trade and the governance structure of such commodity chains in labor-intensive and high-tech industries (Bair, 2009 ; Gereffi, 1999 , 2018 ; Gereffi & Korzeniewicz, 1994 ). This literature has developed a simple typology of buyer-driven and producer-driven GCCs on the basis of the power and control exerted by buyers (retailers and brand name firms) or producers (original equipment manufacturers [OEMs]) in governing their international suppliers and service providers.

In 2000, the Rockefeller Foundation funded a large-scale GVC convention, which marked the beginning of a rapid growth of GVC research (Gereffi, Humphrey, Kaplinsky, & Sturgeon, 2001 ). By the early 2000s – near the beginning point of our review – the GCC literature moved away from its earlier focus on commodities (e.g., clothing, footwear, automobiles) to examining value chains that connected spatially dispersed production activities. In their introduction to a special issue of IDB Bulletin on globalization, value chains, and development, Gereffi et al. ( 2001 ) identified several pressing challenges for value chain researchers and pushed for the use of GVC as a common terminology. Since then, GVC has become the primary focus of research and analytical attention in the social sciences and, lately, international policy communities. The economic sociology view of GVC remains concerned mainly with the social consequences of economic exchange, and with mapping the governance structures/developing typologies of GVCs and their consequences for local upgrading (Gereffi, 2018 ; Gereffi et al., 2005 ; Humphrey & Schmitz, 2002 ). The study of GVCs within the international economics literature focuses on efficiency of contractual organization and economic exchanges in GVCs, and on mapping the geography of international trade flows and value creation (Aichele & Heiland, 2018 ; Antràs & Chor, 2013 ; Grossman & Rossi-Hansberg, 2008 ; Johnson & Noguera, 2012 ; Lee & Yi, 2018 ). IB researchers are interested mainly in how firms can profitably strengthen and exploit their unique firm-specific advantages, and create value by forging business relationships across national borders through MNE activity in GVCs (Buckley, 2009a ; Kano, 2018 ; Laplume, Petersen, & Pearce, 2016 ; Mudambi, 2008 ).

Closely related to the GVC concept is the GPN construct. The GPN concept was developed in the late 1990s by a group of researchers in economic geography, and emerged from a growing dissatisfaction with existing theories of economic development that failed to account for the increasingly complex, networked nature of production activities, which spanned across national borders and led to uneven development in different regions and countries (Coe & Yeung, 2015 , 2019 ; Henderson et al., 2002 ; Hess, 2017 ; Yeung, 2009 , 2018 ). The idea of a GPN goes beyond the simple notions of trading and outsourcing, and highlights firm-specific coordination and cooperation strategies through which such relational networks are constructed, managed, and sustained, as well as the networks’ geographical reach in specific territories, such as sub-national regions and industrial clusters. It also considers the strategic responses of other corporate and non-corporate actors within the GPN, such as the state and business associations. This central focus on economic actors, such as MNEs and their strategic partners, and territorialized institutions, such as state agencies and business associations, also distinguishes GPN thinking from GCC research’s focus on a particular commodity or GVC research’s concern with the aggregation of different value chains into industries.

While the term “GPN” accurately reflects the fact that the firms involved often form intricate intra- and inter-firm networks (rather than linear chains), we propose to use the term “GVCs” in an inclusive fashion throughout this review, to reflect the fact that disaggregation and geographic dispersion presently occurs in various parts of the value chain and encompasses both primary and support activities, with increasingly sophisticated knowledge-intensive processes being offshored and outsourced (Gereffi & Fernandez-Stark, 2010 ). The term “GVC” thus not only refers to manufacturing firms but also characterizes a variety of modern MNEs, including service multinationals and the so called “digital MNEs,” (i.e., firms that use advanced technologies to generate revenues from dispersed foreign locations without investing in production in a conventional sense) (Coviello, Kano, & Liesch, 2017 ). Since the 2010s, the concept and terminology of GVCs have also resonated very well with the development practice and policy communities in many international and regional organizations. A 2010 World Bank report on the post-2008 world economy, for example, claims: “given that production processes in many industries have been fragmented and moved around on a global scale, GVCs have become the world economy’s backbone and central nervous system” (Cattaneo, Gereffi, & Staritz, 2010 : 7). To most observers in these international organizations, GVCs are now recognized as the new long-term structural feature of the global economy (Elms & Low, 2013 ; UNCTAD, 2013 ; World Bank, 2019 , 2020 ).

While we draw on the above complementary research streams and theoretical lenses, we conduct our review from an IB - centric perspective. Following Mudambi ( 2007 , 2008 ) and Buckley ( 2009a , b ), we define a GVC as a governance arrangement that utilizes, within a single structure, multiple governance modes for distinct, geographically dispersed and finely sliced parts of the value chain. In other words, a GVC is the nexus of interconnected functions and operations through which goods and services are produced, distributed, and consumed on a global basis (Coe, Hess, Yeung, Dicken, & Henderson, 2004 ; Coe & Yeung, 2015 ; Henderson et al., 2002 ). IB scholars have recently acknowledged that the rapid rise of GVCs represents one of the most salient features of today’s economy (Turkina & Van Assche, 2018 ), and great strides have been made within mainstream IB literature to understand GVCs (Buckley, Craig, & Mudambi, 2019 ; Gereffi, 2019 ). Yet, surprisingly, there has not been, to the best of our knowledge, a paper that systematically reviews the social scientific and management literatures on GVCs and suggests pointers for future research, specifically for IB scholars. Our review aims to fill this important void.

The rest of the paper is organized as follows. We start by developing an organizing framework to guide our systematic review of multidisciplinary literature. This framework is premised on an inclusive theoretical coverage of the seminal works on GVC governance, upgrading, competitive dynamics, and territorial outcomes, and follows comparative institutional analysis logic. We then discuss our review methodology, and present the results of the review of 87 empirical and conceptual studies, organized according to the framework developed. We conclude by assessing the body of literature reviewed, identifying knowledge gaps, and suggesting avenues for future research.

A COMPARATIVE INSTITUTIONAL FRAMEWORK FOR GUIDING LITERATURE REVIEW

Given the complexity of GVC-related phenomena and the resultant multifarious nature of published studies, a guiding conceptual framework is needed to help us systematically categorize and analyze these studies. We have adopted an IB-centric comparative institutional perspective, embodied in internalization theory/transaction cost economics (TCE) (Buckley & Casson, 1976 ; Hennart, 2009 ; Verbeke, 2013 ), as the foundation of our framework. We consider this approach particularly suitable for systematizing our review for two reasons. First, it focuses on comparative efficiency of various types of governance, and therefore explains under what circumstances GVC governance is preferable to other alternatives. Second, a comparative institutional approach incorporates and links together different levels of analysis, such as micro/individual, transaction/a class of transactions, firm, network, and macro environment; such an integrative approach to governance accurately reflects the multifacetedness and complexity of the GVC phenomenon. However, before we elaborate on this organizing framework for reviewing GVC studies, it is useful and necessary to revisit some of the seminal theoretical works on GVC governance and upgrading (Gereffi, 2018 ; Gereffi et al., 2005 ; Humphrey & Schmitz, 2002 ), and network organization and territorial development outcomes (Coe et al., 2004 ; Coe & Yeung, 2015 ; Henderson et al., 2002 ). These social science studies provided content for designing our IB-centric organizing framework.

Seminal Theoretical Works on GVCs and GPNs in the Social Sciences: From GVC to GPN 2.0

In the early 1990s, Gereffi ( 1994 , also 2018 : Chapter 2) developed the first original framework for explaining the organization of international production networks on the basis of the economic power of giant buyers (e.g., largest retailers, supermarkets, and brand-name merchandisers) and producers (e.g., OEMs in automotive and other high-tech industries) in driving these commodity chains. Attempting to move beyond the then national state-centric modes of analyzing the global economy, Gereffi, Korzeniewicz, and Korzeniewicz ( 1994 : 2) defined commodity chains as “sets of interorganizational networks clustered around one commodity or product, linking households, enterprises, and states to one another within the world economy. These networks are situationally specific, socially constructed, and locally integrated, underscoring the social embeddedness of economic organization.” Their idea was to promote a meso scale of analysis that could probe “above and below the level of the nation-state” and reveal the “macro–micro links between processes that are generally assumed to be discretely contained within global, national, and local units of analysis.”

To operationalize these conceptual ideas and the overall “drivenness” (buyer- or producer -driven) of particular commodity chains, Gereffi ( 1994 ) expanded on three main dimensions of commodity chains and networks: (1) an input – output structure that refers to a set of products and services connected together in a sequence of value-adding economic activities; (2) a territoriality that refers to the spatial configuration of the various actors involved, such as spatial dispersion or concentration of production and distribution networks; and (3) a governance structure that reflects the authority and power relationships within the chain, which determine the allocation and flows of materials, capital, technology, and knowledge therein. Despite this early theoretical development, many of the subsequent empirical studies suffered from a “theoretical deficit.” As argued by Dussel Peters ( 2008 : 14), “most research on global commodity chains approaches the GCC framework as a ‘methodology’ and not a ‘theory’. The result of this is vast quantities of empirical work on particular chains and the experiences of particular firms and regions in them, and relatively little theoretical work attempting to account for these findings in a systematic and integrated way.”

Since Gereffi ( 1994 ), nevertheless, much of GVC theory work in the next decade has been focused on the third dimension of commodity chains – inter - firm governance – through mapping GVC governance structures as independent variables and developing typologies of these structures in order to postulate their consequences for industrial upgrading , as dependent variables, at the firm level and in local/regional development (see recent reviews in Coe & Yeung, 2015 , 2019 ; Gereffi, 2018 : Chapter 1). 1 In their important theoretical formulation following Gereffi’s ( 1999 ) influential empirical work on East Asian apparel upgrading trajectories and Kaplinsky and Morris’s ( 2001 ) highly cited handbook for value chain research, Humphrey and Schmitz ( 2002 ) conceptualized four types of GVC-related upgrading in industrial clusters: process upgrading, whereby the production system is made more efficient, perhaps through superior technology; product upgrading, in which firms move into more sophisticated product lines; functional upgrading, in which they acquire new functions to increase their value added; and chain or inter - sectoral upgrading, whereby firms move into new categories of production altogether. More recently, Pietrobelli and Rabellotti ( 2011 ) further theorized the relationships between these upgrading possibilities and different learning mechanisms embedded in local and regional innovation systems.

The most significant theorization of GVC governance , as an independent variable shaping local and regional upgrading outcomes, was Gereffi et al.’s ( 2005 , also in 2018 : Chapter 4) conceptual typology that came a decade after Gereffi’s ( 1994 ) work. In this most cited conceptual GVC study, Gereffi et al. ( 2005 ) drew upon earlier theoretical work on production fragmentation in international business and trade economics, coordination problems in transaction cost economics (TCE), and networks in economic geography and economic sociology. To them, the then recent work by geographers, such as Dicken, Kelly, Olds, and Yeung ( 2001 ) and Henderson et al. ( 2002 ), “has emphasized the complexity of inter-firm relationships in the global economy. The key insight is that coordination and control of global-scale production systems, despite their complexity, can be achieved without direct ownership” (Gereffi et al., 2005 : 81). To theorize this complexity of inter-firm relationships, Gereffi et al. ( 2005 ) constructed a typology of value chain governance by intersecting the three supply-chain variables of complexity of transactions, codifiability of transactions, and the capabilities within the supply base. By ascribing only two values – high or low – to these three variables, they identified a fivefold typology of governance within GVCs. In addition to the pure forms of market and hierarchy , the authors distinguished modular , relational , and captive forms of governance that rely on intermediate levels of coordination and control. While highly influential, this conceptual typology is still arguably somewhat limiting, and underplays the extent to which governance is also shaped by place-specific institutional conditions and intra- and extra-firm dynamics (Coe & Yeung, 2015 ). Further theoretical work mobilized convention theory to focus on the different modes and levels of governance operating within GVCs, distinguishing between overall drivenness, different forms of coordination (the five types of governance noted above), and the wider normalization and standards-setting processes that operate along the value chain (e.g., Gibbon & Ponte, 2008 ; Ponte & Gibbon, 2005 ).

As noted in the Introduction, a parallel theoretical development in the social sciences was the GPN framework developed by Dicken et al. ( 2001 ) and Henderson et al. ( 2002 ). Table  1 offers a comparison between GVC and GPN theoretical approaches that enable the “modular” theory-building efforts proposed by Ponte and Sturgeon ( 2014 ). As part of these efforts, Henderson et al.’s ( 2002 ) GPN 1.0 schema emphasized the complex intra-, inter-, and extra-firm networks involved in any economic activity, and elaborated on how these are structured both organizationally and geographically. This theoretical framework for analyzing the global economy was intended to delimit the globally organized nexus of interconnected functions and operations of firms and extra-firm institutions through which goods and services are produced, distributed, and consumed. The central concern of any GPN analysis therefore should not simply be about considering the networks in their own terms, but should reveal the dynamic developmental impacts on locations and territories interconnected through these networks. GPN 1.0 thus extends beyond the above-mentioned GVC governance approach by (1) bringing extra-firm actors, such as state agencies, non-governmental organizations, and consumer groups, into GPNs; (2) considering firm–territory interactions at multiple spatial scales, from the local and the sub-national to the macro-regional and the global; (3) examining intersecting vertical (intra-firm) and horizontal (inter-firm) connections in production systems; and (4) taking a more complex and contingent view of how GVC governance is shaped by the wider regulatory and institutional contexts.

The most recent and comprehensive theorization of GVCs is found in Coe and Yeung’s ( 2015 ) monograph. This work seeks to develop a dynamic theory of GPNs by specifying the causal mechanisms that explicitly link earlier conceptual categories of value, power, and embeddedness to the dynamic configurations of GPNs and their uneven development outcomes. In this GPN 2.0 framework, the aim is to conceptually connect the structural capitalist dynamics that underpin GPN formation/operation to the on-the-ground development outcomes for local and regional economies. The underlying capitalist dynamics encompass key dimensions such as drivers of lowering cost-capability ratios, market development, financialization and its disciplining effects on firms, and risk management; together, these dimensions distil the inherent imperatives of contemporary global capitalism. These dynamics are key variables driving the strategies adopted by economic actors in (re)configuring their GPNs, and consequent value capture trajectories and developmental outcomes in different industries, regions, and countries. Interestingly, these competitive dynamics are not well theorized in the existing GVC literature, which is much more concerned with governance aspects of the operation of such chains and networks after they are formed. Coe and Yeung ( 2015 ) considered how these causal drivers shaped the strategies of different kinds of firms in GPNs. These firms organize their activities through different configurations of intra-, inter-, and extra-firm network relationships. Conceptually, these network configurations are shaped by different interactions of the underlying dynamics. The authors then examined the consequences of these causal mechanisms – comprising varying dynamics and strategies – for firms in GPNs.

Fuller and Phelps ( 2018 ) further explained how parent–subsidiary relationships in MNEs can significantly influence the way that these competitive dynamics shape their network embeddedness in and strategic coupling with specific regional economies (Yeung, 2009 , 2016 ). Departing from the industrial upgrading literature that often takes on a unidirectional pathway to upgrading (from process to value chain upgrading in Humphrey and Schmitz ( 2002 )), Coe and Yeung ( 2015 ) further developed the concept of “value capture trajectories” to frame in dynamic terms whether firms are able or not to capture the gains from strategic coupling in GPNs. Ultimately, this GPN 2.0 work seeks to understand the impacts on territorial development by exploring how firm-specific value capture trajectories can coalesce in particular places and locations into dominant modes and types of strategic coupling, with different potential for value capture in the regional and the national economies.

Similar to other theories in the social sciences, the GVC/GPN frameworks discussed above are primarily explanatory rather than predictive in nature. The validity of predictions depends upon ceteris paribus conditions, which do not apply in open systems where social phenomena occur. Hence, “it is unrealistic to assume that all relevant data will be consistent with a theory even if the theory is correct” (Lieberson, 1992 : 7). As such, the predictive power of social science theories is curtailed (see Bhaskar, 1998 for a detailed discussion).

A Comparative Institutional Framework on GVCs

The above brief review of foundational works in GVCs and GPNs has clearly pointed to the general tendency in the social science literature to examine GVC governance, upgrading dynamics, and territorial outcomes. Still, there is a limited conceptualization of how different actors – from MNE lead firms to their strategic partners, key suppliers and customers, and other related firms – (1) structurally organize their business transactions to exercise control and coordination, determine locational choices, and configure networks; and (2) strategically manage their firm-specific activities to enhance learning and knowledge accumulation, create advantageous impacts, and orchestrate GVCs for better performance outcomes. These firm-specific considerations fall within the core premise and competence of IB research that can add much value to the existing GVC theoretical frameworks. In particular, we suggest that comparative institutional analysis can help link social science and IB approaches in GVC research. Comparative institutional analysis, as applied in firm-level studies, builds on the premise that economic actors will make decisions about the most efficient governance mechanisms to conduct economic exchange or to organize a given set of transactions. For example, they may choose between organizing production activities within the firm or through the market, and select coordination and control methods, such as the market system versus managerial hierarchy versus socialization (Gereffi et al., 2005 ; Hennart, 1993 ). Comparative institutional analysis has a number of branches, including internalization theory (Buckley & Casson, 1976 ), which is most relevant for exploring GVCs. Internalization theory applies the economic essence of comparative institutional analysis in an international setting, arguing that economic actors will select and retain the most efficient governance mechanisms to conduct cross-border transactions (Verbeke & Kenworthy, 2008 ).

From a comparative institutional perspective, a GVC represents a distinct form of governance, which is likely to emerge and thrive only if it enables superior efficiency when compared to other real-world alternatives (e.g., vertical integration or market contracting). Efficiency is served by aligning governance systems (both structural and strategic ) with the attributes of transactions in a cost-economizing way (Hennart, 1993 ). Ultimately, competitive advantage arises from the firm’s ability to choose the most efficient, economizing mix of internal and external contracts as a function of various micro - and macro - level characteristics of transactions – decisions made by economic actors at the micro level and demand/technological/institutional characteristics at the macro level (Antràs & Chor, 2013 ; Gereffi et al., 2005 ; Hennart, 1994 ). The most efficient governance forms are those that are comparatively superior in terms of enabling the firm to: (1) economize on bounded rationality; (2) economize on bounded reliability 2 ; and (3) create an organizational context conducive to innovation in its entirety (Verbeke & Kenworthy, 2008 ). Further, the firm must adjust its economizing mix of contracts over time as a function of changes in the micro- and macro-environments. Finally, the firm continually impacts both its micro-level and macro-level environments through changes in governance. Such changes evolve in a continuous, mutually reinforcing cycle (Williamson, 1996 ).

We combine comparative institutional logic with foundational GVC work discussed in the previous section to build an organizing framework, which facilitates our subsequent review of a large number of empirical and conceptual studies of GVCs. This framework, presented in Figure  1 , arranges extant studies along the three main layers impacting the functioning of GVCs, and conceptually connects these layers with each other and, ultimately, with GVC governance and performance outcomes. While incorporating some of the key conceptual variables in Gereffi et al.’s ( 2005 ) governance typology and Coe and Yeung’s ( 2015 ) GPN 2.0 theory, this integrative framework seeks to highlight IB - specific issues in relation to not only GVC-level variables, but also, crucially, micro- and macro-level influences that shape the organization and performance outcomes of MNEs and other firms in GVCs.

A comparative institutional framework of GVC governance.

First, at the micro - level , we identify studies that explore specific assumptions about the behavior of decision-makers in both the lead firm and peripheral units, and ways in which these assumptions explain processes within the GVC; that is, how knowledge is exchanged and processed, how the hazards of reliability are managed, and how new capabilities are developed and obsolete ones are discarded. Second, at the GVC level , we discuss studies that focus on governance and performance of the GVC. Here, we identify six broad dimensions that constitute critical elements of GVC governance: control, location, network structure, learning, impact of the lead firm, and GVC orchestration. GVC performance outcomes, to the extent that they are explored in the reviewed studies, are also addressed at this level. In accordance with comparative institutional analysis principles, and consistent with conceptual foundations of much GVC research, we view overall GVC performance in terms of sustainability of GVC as a governance form or its success in delivering value to participants, including capability development and upgrading. Third, at the macro - level , we focus on studies exploring the relationships between the GVC and its environment, including cultural, institutional, geographic, and economic make-ups of both home and host locations. Studies that constitute this group address both macro-level impacts on GVC configurations and the GVCs’ impact on macro-environments within which they operate. In the following sections, we use this integrative framework to review 87 conceptual and empirical studies of GVCs.

METHODOLOGY

We focused on published journal articles and excluded books, because more often than not, authors of books also published journal articles that contained much of the reported results (e.g., Gereffi, 2018 ). We also excluded book chapters, which usually went through a less rigorous review process than journal articles and were less accessible digitally. We conducted a multi-disciplinary literature search that covered IB, general management, supply chain management, operations management, and a selected group of social science journals that published GVC research, namely economic geography, economic sociology, regional and development studies, and international political economy 3 . This extensive scope should cover most of the key GVC studies published in academic journals. We included leading journals of each discipline that attracted researchers to submit their best-quality GVC studies.

For each journal, we searched articles published in the past 20 years – the period characterized by rapid growth and increased sophistication of GVC research, as discussed in the Introduction. We used four search terms: global value chain, global commodity chain, global production network, and global factory. We shortlisted conceptual articles with GVCs as their major foci, and empirical articles, whether qualitative or quantitative, that had at least one of the search terms as a major variable. That is, we excluded articles that casually cited or had any of the four terms serving as a control variable. Moreover, shortlisted studies targeted at the firm or network level, instead of other units of analysis, such as international organizations (e.g., Haworth’s ( 2013 ) case study of the Asia Pacific Economic Cooperation), industries, or locations.

Since the social science journals have a very large number of publications on GVCs that amounted to several hundreds, we applied additional criteria to narrow down this considerable volume of literature to a proportionate number of articles. We started with identifying nine theoretical pieces that constituted the foundation of the theory section above. For empirical papers, we implemented three additional screening criteria. First, we included more recent papers published after 2005. Second, we focused on papers that were closest to the research interests of IB scholars. Third, we ensured that our selection covered a reasonable mix of authors from different disciplines, institutions, and geographical locations, and that selected studies included both GVC and GPN approaches with a variety of research methods, industry coverage, and empirical locations in both developed and developing countries.

Based on the above criteria, a total of 21 journals publishing 22 theory papers (including the nine foundational pieces mentioned above) and 65 empirical articles were included in our review, as listed in Table  2 . Notably we also searched the Academy of Management Journal , Administrative Science Quarterly , Journal of Management and Management Science (all commonly regarded as leading management journals), but failed to find any relevant articles. The same applies to the leading journals in sociology (e.g., American Journal of Sociology and American Sociological Review ) and political sciences (e.g., American Political Science Review and International Organization ).

The 33 shortlisted articles in mainstream IB journals (i.e., GSJ, IBR, JIBS, JWB, and MIR) provide the most comprehensive picture of our field’s current state of knowledge on GVCs. Articles published in these journals, however, constitute about 58% of the group of non-social science journals, indicating that GVC is an important research topic attracting the attention of researchers working in disciplines beyond the IB turf. In the group of social science journals during the review period, GVC and GPN research has been particularly influential in the fields of economic geography, economic sociology, and regional and development studies. Here, we included only a small selection of 30 articles published in the leading journals, based on the criteria discussed above.

We studied each article and extracted two to three key GVC-related findings with respect to our organizing framework presented in Figure  1 . Table  3 lists these 87 articles’ key information (year of publication, authors, journal abbreviation, research method, and sample characteristics) and their most significant findings. The sample spans the time period from 1999 to the end of July 2019; however, for the non-social science journals, the more recent articles published after 2010 represent the bulk of the sample, reflecting a broad upward trend in GVC publications in the last decade. There is almost an equal split of research methods between qualitative case studies and quantitative studies based on archival or survey data. There are both single-country and multi-country studies, together covering a wide geographic scope. Most of the studies analyze firms, networks or clusters in manufacturing industries. It is not surprising that the automotive industry is the most popular context for these studies, given the industry’s requirement for many suppliers, large and small, manufacturing various components of an automotive. The studies as a whole investigate a variety of IB-related issues, as described in the next section.

REVIEW OF GVC LITERATURE

Micro-level: microfoundational assumptions and their impact on gvc.

Microfoundations refer to generic human behavioral conditions that impact firm-level (and, in the case of GVCs, network-level) outcomes (Kano & Verbeke, 2019 ). Scholars have argued that individual-level characteristics, such as bounded rationality, bounded reliability, cognitive biases, and entrepreneurial orientation, impact GVC governance (Denicolai, Strange, & Zucchella, 2015 ; Kano, 2018 ; Levy, 1995 ; Verbeke & Kano, 2016 ), in terms of how transactions are organized and orchestrated. Therefore, systematic attention to microfoundations is necessary in order to meaningfully advance the GVC research agenda. However, few empirical studies directly observe or measure individual-level variables. Further, while certain behavioral assumptions are frequently implied – e.g., the nature of individual-level knowledge and capabilities is inherent in the idea of learning and upgrading; the need for knowledge sharing across units implies bounded rationality of individual actors and associated information asymmetries; the notions of power balance and the need for intellectual property (IP) protection assume a certain level of bounded reliability of actors involved – these assumptions are, for the most part, neither articulated explicitly nor examined empirically.

Only seven studies in our sample directly address the impact of microfoundations (either stated or implied) on GVC geographic configurations, knowledge acquisition and dissemination within the GVC network, and efficient functioning and orchestration of the network. In an early qualitative study of supply chain management, Akkermans, Bogerd and Vos ( 1999 ) discuss how bounded rationality, as expressed in supply chain partners’ diverging beliefs and goals, contributes to functional silos and erects barriers to effective value chain management. Lipparini, Lorenzoni and Ferriani ( 2014 ) argue that GVC networks that benefit the most from knowledge transfer among partners are those where partners share common identity and language. These features serve as safeguards against the potential threat of opportunism and allow participating firms to learn from partners with reduced risk of proprietary knowledge spillover outside of the immediate network. Eriksson, Nummella and Saarenketo ( 2014 ) suggest that individual-level cognitive and managerial capabilities of lead firm managers, such as cultural awareness, entrepreneurial orientation, global mindset, interface competences and analytical capabilities, constitute a critical building block for firm-level ability to successfully orchestrate cross-border transactions in a GVC. Seppälä, Kenney and Ali-Yrkkö (2018) focus on boundedly rational accounting decisions in lead MNEs, and argue that lead firms’ accounting systems may misrepresent where the most value is created in a GVC. This mismatch implies that GVC activities to which value is allocated may be selected somewhat arbitrarily, and this further impacts location decisions. Kano ( 2018 ) argues that bounded rationality and reliability of decision-makers in participating firms impact the efficiency of the GVC; as such, the role of lead firm managers is to control bounded rationality and reliability through a mix of relational mechanisms, so as to improve the likelihood that the GVC will be sustainable over time. Treiblmaier ( 2018 ) theoretically predicts structural and managerial changes introduced into GVCs by blockchain technologies, by analyzing four behavioral assumptions of major economic theories: bounded rationality, opportunism, goal conflict, and trust. Finally, Sinkovics, Choksy, Sinkovics and Mudambi ( 2019 : 151) explore the relationship between three variables – information complexity, information codifiability, and supplier capabilities – and knowledge connectivity in a GVC, and conclude that individual characteristics of lead firm managers – specifically, their risk perceptions and associated “comfort zones” – moderate this relationship.

GVC Level: Components of GVC Governance

The term “governance” refers to the organizational framework within which economic exchange takes place, including the processes associated with the exchange (Zaheer & Venkatraman, 1995 ). In the context of a GVC, governance includes the overarching principles, structures and decision making processes that guide the “checks and balances” in network functioning, so as to make sure that the interests of the entire network (and broader societal/environmental interests where relevant) are served above and beyond localized interests of participating firms and individual decision-makers within these firms. These principles, structures and processes encompass considerations related to boundaries of the network and its geographic make-up, control and orchestration mechanisms for economic activities performed within the GVC, value distribution, relationship management, and direction of knowledge flows. Outcomes of successful governance include meeting of individual participants’ performance goals, as well as, ultimately, long-term sustainability of the GVC as a whole.

Here, a distinction can be made between structural and strategic governance of the GVC, as shown in Figure  1 . The former refers to the actual structure governing economic activities, e.g., make versus buy decisions, organizational structure of the network (number of players, power balance, boundaries, etc.), geographic and functional allocation of activities, level of centralization of decision-making, and so on. In contrast, strategic governance is concerned with dynamics of actors’ behavior in respect to strategic decision making (Schmidt & Brauer, 2006 ; Zaheer & Venkatraman, 1995 ). In the context of GVCs, strategic governance is about orchestrating the usage of resources, through codified and uncodified routines and managerial practices, to ensure smooth functioning of the entire network (Kano, 2018 ). Our review identified six broad, interrelated conceptual dimensions (Figure  1 ) that constitute critical elements of structural and strategic governance of a GVC. These dimensions, as well as outcomes of governance practices, are discussed below.

Control decisions establish the governance structure of the GVC, that is, whether each value chain activity should be internalized, outsourced, or controlled through hybrid forms such as joint ventures (JVs) (Buckley et al., 2019 ). It has been argued that in a GVC, control of critical knowledge and intangible assets (e.g., brand names and technological platforms) takes precedence over ownership of physical assets (Buckley, 2011 , 2014 ; Mudambi, 2008 ), and ownership advantages can be exploited without internalizing operations (Strange & Newton, 2006 ). This core premise underlying the GVC is supported in Hillemann and Gestrin’s ( 2016 ) analysis of OECD data on foreign direct investment (FDI) and cross-border mergers and acquisitions (M&As), which shows that cross-border financial flows related to intangible assets continue to increase relative to those related to tangible assets. An analysis of about 25,000 Italian firms also suggests that control of GVC activities, as compared to ownership, yields benefits in terms of greater propensity toward innovation, increased productivity, and faster sales growth (Brancati, Brancati, & Maresca, 2017 ). The preference for control without ownership is enabled by increasing digital connectivity, which allows lead firms to influence various units in the GVC without directly managing them (Foster, Graham, Mann, Waema, & Friederici, 2018 ).

To some extent, control decisions are impacted by host countries’ regulatory environments, particularly when national political institutions create pressure for local content on MNEs that are trying to gain access to large downstream markets in emerging economies (Lund-Thomsen & Coe, 2015 ; Morris & Staritz, 2014 ; Sturgeon, Van Biesebroeck, & Gereffi, 2008 ). This is the case with “obligated embeddedness” (Liu & Dicken, 2006 : 1238) of automotive MNEs in China, where the government’s industrial policy dictates that inward FDI should take a JV form. Further, control decisions are linked to sectoral and functional factors – for example, lead MNEs operating in high- and medium-technology sectors and/or locating knowledge-intensive functions (e.g., innovation) in host markets are more likely to pursue ownership in jurisdictions that offer weaker IP protection (Ascani, Crescenzi, & Iammarino, 2016 ). Ownership allows the MNE to have better control over the creation, transfer and leakage of propriety knowledge, and is thus a pre-emptive measure for knowledge protection.

However, considerable heterogeneity in control decisions exists among lead firms operating in the same geographic regions and industry sectors, which suggests that firm-level strategic considerations, and not only macro-level forces, are powerful drivers of control patterns in GVCs (Dallas, 2015 ; Sako & Zylberberg, 2019 ). These considerations include lead firms’ levels of specialization, the nature of their relationships with partners, the need for flexibility versus stability in offshore operations, and the value of the operations to the lead firm (Amendolagine, Presbitero, Rabellotti, & Sanfilippo, 2019 ; Dallas, 2015 ; Kleibert, 2016 ). Control decisions can be also driven by the level of local adaptation required, whereby the lead MNE may need to source external expertise in order to perform the desired degree of customization. Here, a carefully designed mix of internalized and externalized, yet managerially or technologically linked, activities is argued to allow the lead firm to achieve the ultimate balance between integration and responsiveness (Buckley, 2014 ).

Location decisions determine the most advantageous geographical configuration of the GVC, namely, where activities should be located, and how they should be distributed in order to maximize the value created in and captured through the GVC. Location decisions encompass such considerations as the regional effect (Rugman & Verbeke, 2004 ), the nature of industrial clusters (Turkina & Van Assche, 2018 ), and the links between GVCs and local clusters. Location decisions are tightly intertwined with control decisions discussed earlier. For example, FDI (as opposed to market contracting) enables the MNE to construct a regional, or even global, network under its control to supply wide-ranging, differentiated and low cost products in a flexible manner. Chen’s ( 2003 ) study of electronics firms in Taiwan indicates that FDI often starts at a location close to the home base, where resources from domestic networks can be drawn, and subsequently moves on to more distant locations, after the lead firm has developed a regional sub-network to support its further expansion.

Location considerations are linked to macro-level characteristics of host and home countries, including level of economic development and corresponding factors such as cost of labor, technological environment, and institutional quality. Among these factors, favorable business regulations, IP protection, and significant education spending typically attract technologically and functionally sophisticated activities (Amendolagine et al., 2019 ; Ascani et al., 2016 ; Pipkin & Fuentes, 2017 ). Control of the GVC resides in the hands of technology and/or market leaders, which are typically (although not always) located in developed economies and extract value from their GVCs through global orchestration capabilities (Buckley & Tian, 2017 ). Countries with more advanced production technologies are naturally engaged more in the upstream segments of the GVC, and become key suppliers to other countries in the region, thus supporting regional integration of production (Amendolagine et al., 2019 ; Suder, Liesch, Inomata, Mihailova, & Meng, 2015 ).

Most empirical studies address location of production activities, whereby labor cost emerges as one of the core determinants for GVCs led by both advanced economy MNEs (AMNEs) and emerging economy MNEs (EMNEs). For example, Asian tier 1 suppliers to MNEs and OEMs become GVC lead firms in their own right by shifting production to lower cost locations in the region (Azmeh & Nadvi, 2014 ; Chen, Wei, Hu, & Muralidharan, 2016 ). Yet efficiency-seeking offshoring may create strategic issues, particularly when inefficient local institutions fail to prevent unwanted knowledge dissipation. Issues can also emerge on the demand side due to sustainability and ethical breaches in large MNEs’ value chains, as evidenced in multiple, recent instances of public backlash in response to poor working conditions in manufacturing factories in South and Southeast Asia (Malesky & Mosley, 2018 ). Funk et al.’s ( 2010 ) survey of US consumers suggests that developed economy consumers’ willingness to purchase is negatively affected by partial production shifts to animosity-invoking countries (countries with poor human rights records/with poor diplomatic relationships with the home country). As the wave of consumer movement spreads to less developed countries, it is in the best interest of the lead firm to evaluate carefully the undesirable attributes of a potential host country when making FDI decisions (Amendolagine et al., 2019 ; Morris & Staritz, 2014 ).

Desire to access large and fast-growing consumer markets drives production activities close to end markets, for example, when host country governments in emerging markets pressure MNEs for local operations (Sturgeon et al., 2008 ). Co-location of manufacturing and sales also allows lead firms to be more responsive to customer demands, and to off-set the costs of globally dispersed activities by reducing investment in transportation and logistics (Lampel & Giachetti, 2013 ).

Strategic asset seeking by lead firms and suppliers explains much of the geographic configuration of GVCs, whereby MNEs locate value chain activities in globally specialized units to exploit international division of labor (Asmussen, Pedersen, & Petersen, 2007 ). This is particularly pronounced in knowledge-intensive industries, where lead firms often locate operations in innovation hubs and global cities (Taylor, Derudder, Faulconbridge, Hoyler, & Ni, 2014 ). In their analysis of clusters in the aerospace, biopharma, and ICT industries, Turkina and Van Assche ( 2018 ) demonstrate that innovation in knowledge-intensive clusters benefits from horizontal connection to global hotspots, as opposed to labor-intensive clusters where innovation gains from vertical GVC connections.

While much has been written about fine-slicing and fragmentation of value chain activities in a GVC (Buckley, 2009a , b ), few empirical studies measure the costs and benefits of geographic diversification of operations within the same part of the value chain. Lampel and Giachetti ( 2013 ) address a relationship between international diversification of manufacturing and financial performance in the context of the global automotive industry, and find an inverted U-shaped relationship, whereby advantages of diversified manufacturing (i.e., greater flexibility and access to internationally dispersed strategic resources) are eventually off-set by increased organizational complexity and managerial inefficiencies. Further, location decisions are tied to firms’ strategic priorities beyond cost reduction – for example, increased needs for customer responsiveness and/or enhanced quality control. Focus on such priorities may prompt backshoring initiatives (Ancarani, Di Mauro, & Mascali, 2019 ). Yet, geographic diversification may serve strategic purposes such as IP protection. Gooris and Peeters’ ( 2016 ) survey of offshore service production units demonstrates that lead firms may opt to fragment their global business processes across multiple service production units, rather than co-locating processes, with the explicit purpose of reducing the hazard of knowledge misappropriation.

Finally, technological advances continue to shape geographic make-up of GVCs (MacCarthy, Blome, Olhager, Srai, & Zhao, 2016 ). Few studies in our sample measure the impact of digital technologies on location choice, but several studies address current and potential influences of technology indirectly and/or conceptually. Ancarani et al. ( 2019 ) suggest that adoption of labor-saving technologies leads to backshoring in instances when lead firms compete on quality, rather than on cost. While digital connectivity enables exploiting complementarities between geographically dispersed processes (Gooris & Peeters, 2016 ), it may limit participation by suppliers located in technologically underdeveloped regions (Foster et al., 2018 ). Further, the latest technology, such as 3D printing, is likely to impact GVCs of relevant industries by making them shorter, more dispersed, more local, and closer to end users (Laplume et al., 2016 ; Rehnberg & Ponte, 2018 ).

Network structure

Network structure refers to the structural make-up of a GVC and has been well theorized in some of the most cited GVC conceptual frameworks (e.g., Coe & Yeung, 2015 ; Gereffi, 2018 ; Gereffi et al., 2005 ; Henderson et al., 2002 ). While a GVC can typically be conceptualized as an asymmetrical or high centrality network with a lead firm at its centre (Kano, 2018 ), these networks can also be heterogeneous in terms of such characteristics as depth, density, openness, and the presence of structural holes (Capaldo, 2007 ; Rowley, 1997 ). These characteristics affect power relations in the GVC, the level of control afforded to the lead firm, and innovation and business performance. Not surprisingly, a large number of empirical studies in our review address various dimensions of the nature and/or role of network structures in GVC governance and performance outcomes.

The network structure in a typical GVC can be dyadic or multi-actor in nature, and can affect knowledge flows (Lipparini et al., 2014 ), new venture formation (Carnovale & Yeniyurt, 2014 ), and operational performance (Golini, Deflorin, & Scherrer, 2016 ). A firm with high centrality (i.e., most links in a network) has greater power over other firms in a dyadic or multi-actor network, whereby control can be exerted by the lead firm beyond its legal boundaries over independent – but captive – suppliers (Yamin, 2011 ). In supply chain management, Carnovale and Yeniyurt’s ( 2014 ) study of automotive OEMs and automotive parts suppliers shows that manufacturing JV formation between lead firms and potential partners can be enhanced by higher network centrality of either the lead firm or the potential JV partner. This network centrality is seen as a proxy for greater legitimacy and credibility within the network. However, the study found mixed outcomes in relation to network density. High network density is not necessarily favorable to new JV formation due to “lock-in” effects through structural homophily. This network structure in turn limits access of lead firms to a diverse set of potential partners and hinders learning and innovation. Similarly, the studies of manufacturing plants in various countries by Golini et al. ( 2016 ) and Golini and Gualandris ( 2018 ) demonstrate that a higher level of external supply chain integration (e.g., through GVC activities) can improve the operational performance of and the adoption of sustainable production by manufacturing MNEs due to information sharing, learning, and innovation through supply chain partners.

The density of network structure in GVCs, however, may change over time in relation to the emergence of new technologies and platforms, some of which may favor greater density in localized networks. In their perspective article on 3D printing and GVCs, Laplume et al. ( 2016 ) question if technological advancements can influence the relative density of globally dispersed and localized production networks. As more local firms can participate in the production of high-value components through 3D printing, their need for technological acquisition and/or specialized components through MNE lead firms in GVCs may be reduced, leading to what Rehnberg and Ponte ( 2018 ) call “unbundling” and “rebundling” of GVC activities towards regionalized or even localized GVCs. In this scenario for decentralized GVC network structure, local producers can engage in more transactions with each other, and thus localized production networks may get denser over time.

In addition to centrality and density, network structures in GVCs can also be distinguished by linkage heterogeneity – the mix of horizontal linkages (between firms with similar value chain specialization) and vertical MNE-supplier linkages (with different value chain specialization). This structural mix has significant influence on the innovation performance of firms in different industries (Amendolagine et al., 2019 ; Brancati et al., 2017 ). Drawing on a social network approach, Turkina and Van Assche’s ( 2018 ) study of industrial clusters shows that network structures underpinned by dense horizontal linkages among local firms tend to enhance innovation performance in knowledge-intensive industries, whereas strong vertical linkages between local firms and MNEs can promote innovation in labor-intensive clusters. The former network structure tends to promote innovation through intra-task knowledge capability development among horizontally linked firms. As to the latter case of local suppliers in labor-intensive industries, inter-task capability development can be better served through vertical and international linkages with global lead firms.

Finally, power relations among GVC actors play out very differently in different network structures (Dallas, Ponte, & Sturgeon, 2019 ; Grabs & Ponte, 2019 ). In one of the earliest studies of industrial upgrading through GVC participation, Humphrey and Schmitz ( 2002 ) observed that network structures characterized by quasi-hierarchical power relations in favor of one party – often global lead firms or global buyers – were generally not conducive to the upgrading of local firms. Sturgeon et al. ( 2008 ) followed up with this line of research by examining major American and Japanese automotive lead firms and over 150 suppliers in North America. They found that upgrading of local suppliers was more likely if the GVC network structure moved towards a relational form of power dynamics. Such a relational form of network structure tends to favor inter-firm cooperation and credible commitment (e.g., IKEA and its suppliers in Ivarsson & Alvstam, 2011 and tuna canning firms in Havice & Campling, 2017 ). Similarly, Khan, Lew and Sinkovics’s ( 2015 ) study of the Pakistani automotive industry shows that local firms are more likely to acquire technological know-how and develop new capabilities by participating in geographically dispersed rather than locally oriented networks. Through international JVs (IJVs) with global lead firms, these local firms can access different knowledge base and know-how in those international networks.

As noted earlier, network structures are embedded in different national and institutional contexts. Pipkin & Fuentes ( 2017 ) find that domestic institutional environment, such as state policies and support from business associations, is more significant than lead firms’ influence in shaping network dynamics in developing countries. Horner and Murphy’s ( 2018 ) study of manufacturing firms in India’s pharmaceutical industry shows that network structures characterized by firms from similar national contexts (e.g., the Global South) can be more open and cooperative in relation to production and quality standards, market access, and innovation. This greater openness in South–South GVCs entails different business practices toward their end markets due to lower entry barriers, lower margins, and higher volumes. The opportunities for learning in these GVCs are also different from those tightly controlled and coordinated by lead firms from the Global North. Another study of chocolate GVCs in Indonesia by Neilson, Pritchard, Fold and Dwiartama ( 2018 ) also points to the importance of contextual heterogeneity in shaping the influence of different network structures on lead firm behavior and relationships with suppliers and distributors. Drawing upon Yeung and Coe’s ( 2015 ) GPN 2.0 theory, Neilson et al. ( 2018 ) argue that network structures differ significantly between branded chocolate manufacturing and cocoa farming/processing in agrofood manufacturing. Owing to domestic industrial policy and international business lobbying, the role of national context is much more pronounced in the network structure of cocoa farming/processing that favors inter-firm partnership and cooperative learning.

Conceptual studies have identified knowledge diffusion and transfer as an important aspect of network governance (Ernst & Kim, 2002 ; Inkpen & Tsang, 2005 ). Empirical studies take note of this topic and examine various dimensions of learning in a GVC. Most of such studies in our sample focus on interfirm learning in the context of capability development, technological catch-up and upgrading by peripheral GVC actors – that is, emerging economy suppliers’ progression from OEM to original design manufacturing (ODM) and to own brand manufacturing (OBM). As touched upon in the previous section, macro-level conditions such as market forces and state policies, rather than lead firm initiatives, are argued to be the main force in spurring supplier upgrading (Pipkin & Fuentes, 2017 ). Upgrading initiatives can produce a wide range of results, from incremental to significant leaps in market position (Pipkin & Fuentes, 2017 ), depending on a number of factors. Eng and Spickett-Jones ( 2009 ) argue that upgrading hinges on suppliers’ ability to simultaneously develop three sets of marketing capabilities: product development, marketing communication, and channel management. Wang, Wei, Liu, Wang and Lin’s ( 2014 ) study of manufacturing firms in China indicates that the presence of MNEs alone does not guarantee knowledge spillovers, and may in fact have a negative impact on indigenous firms’ domestic performance due to increased competition. Hatani ( 2009 ) describes barriers to learning by emerging market GVC suppliers. Her study of autoparts suppliers in China suggests that excessive inward FDI limits interactions between lead firms and local suppliers and thus creates structural obstacles to technology spillovers to lower GVC tiers. Also researching the autoparts industry (but in Argentina rather than China), McDermott and Corredoira ( 2010 ) suggest that supplier upgrading is facilitated by regular, disciplined discussions with the lead firm about product and process improvement; in this context, a limited amount of direct social ties to international assemblers appears to be the most beneficial.

In a follow-up study, Corredoira and McDermott ( 2014 ) find that lead firms alone do not help process upgrading, but add value particularly when emerging market suppliers’ ties to MNEs are augmented with multiple, strong ties to non-market institutions (e.g., universities and business associations), which act as knowledge-bridgers and help suppliers tap into knowledge embedded in the home country. These types of ties are particularly useful for accessing knowledge for the development of exploitative innovation, while exploratory innovation is best achieved through participation in trade fairs and collaboration with international (rather than domestic) institutions, according to the study of Pakistani motorcycle part suppliers by Khan, Rao-Nicholson and Tarba ( 2018 ). Similarly, Jean’s ( 2014 ) study of new technology ventures in China indicates that firms that participate in trade shows and have strong quality control practices are more likely to develop requisite knowledge to pursue upgrading, while firms engaging in Internet-based business-to-business transactions are less likely to upgrade. Based on their studies of the garment and toy industries, Azmeh and Nadvi ( 2014 ) as well as Chen et al. ( 2016 ) describe alternative paths to upgrading: some OEMs invest in R&D to enter the ODM business, or invest in marketing and branding and move toward the downstream end of the value chain to become OBMs. Others achieve competitive gains by shifting production to different locations and learning how to effectively coordinate multiple production locations (see also detailed case studies of ODMs from Taiwan and Singapore and OBMs from South Korea in Yeung, 2016 ). Buckley ( 2009b ) suggests that both options – incremental upgrading within the established GVC and developing a new GVC under local control – are difficult in that they require mobilization of entrepreneurial abilities and development of sophisticated managerial skills. Successful upgrading hinges not only on suppliers’ acquisition of knowledge, but also on their ability to absorb it and transform it into innovation, which ultimately improves suppliers’ position in GVCs (Khan et al., 2019 ).

Specific knowledge acquisition strategies required for upgrading vary depending on the nature of home institutions and labor markets (Barrientos, Knorringa, Evers, Visser, & Opondo, 2016 ; Pipkin & Fuentes, 2017 ; Werner, 2012 ). Weak home institutions hinder the transformation of knowledge into actual innovative products and processes (Jean, 2014 ). This explains why catch-up and upgrading by GVC suppliers often mirrors the evolution of home institutions (Kumaraswamy, Mudambi, Saranga, & Tripathy, 2012 ): as institutions evolve toward liberalization, upgrading strategies change from upgrading technical competencies through licensing and collaborations, to upgrading internal R&D and developing strong relationships with lead firms. The weakness of local institutions can be overcome by gaining knowledge through participation in international networks and collaboration with global suppliers (Khan et al., 2018 ).

The nature of relationship among parties in GVCs matters for technological knowledge transfer, as network ties are channels through which knowledge flows. Khan et al.’s ( 2015 ) above-mentioned study indicates that IJVs represent a governance vehicle that facilitates the creation of social capital between focal MNEs and automotive parts suppliers located in emerging economies, and thus facilitate development and acquisition of complex technological knowledge by local firms.

Learning and knowledge accumulation and diffusion in the lead firm, as well as lead-firm initiated network-wide learning, garnered significantly less scholarly attention, with one notable exception. Through analyzing Italian motorcycle industry projects carried out via dyads of buyers and suppliers, Lipparini et al. ( 2014 ) develop a framework that addresses multi-directional, multilevel and multiphase knowledge flows in a GVC, and describe practices implemented by lead firms to successfully cultivate creation, transfer and recombination of specialized knowledge to facilitate network-wide learning. In such a dynamic and somewhat open context of knowledge sharing, the threat of opportunism is likely to be outweighed by the advantages of learning from other network members.

There appears to be consensus in the literature that strong linkages within the GVC – frequently referred to as embeddedness of actors in the network (Henderson et al., 2002 ) – are conducive to transferring various types of knowledge, including production processes, sourcing practices, technological knowledge, and innovation capabilities (Golini et al., 2016 ; Golini & Gualandris, 2018 ; Ivarsson & Alvstam, 2011 ). Such linkages are the most effective when purposefully facilitated by strong lead firms. Lead firms can impel capability upgrading on peripheral units by leveraging their central positions and complementary assets, as indicated by the acquisition of UK-based Dynex by China’s Times Electric (He, Khan, & Shenkar, 2018 ). Ivarsson and Alvstam’s ( 2011 ) case study of IKEA and its suppliers in China and Southeast Asia similarly shows that lead firms can contribute to peripheral units’ upgrading by fostering close, long-term interactions, and by offering technological support. Conversely, weak strategic coupling between lead firms and peripheral units hurts knowledge transfer and capability development (Yeung, 2016 ). For example, Pavlínek’s ( 2018 ) study of automotive firms in Slovakia suggests that weak and dependent supplier linkages between MNEs and domestic firms undermine the potential for technology and knowledge transfer from the former to the domestic economy.

Lead firms are often motivated to drive their suppliers’ capability upgrading, because they themselves benefit from suppliers’ enhanced capabilities through improved sourcing efficiency, higher-quality inputs, and more generally valuable knowledge diffusion throughout the GVC. In the next section, we discuss how characteristics of the lead firm impact its position and role in the GVC.

Impact of lead firm

Extant conceptual research has acknowledged that smooth and efficient functioning of the GVC is contingent on the lead firm’s ability to establish, coordinate and lead the network (Kano, 2018 ; Yamin, 2011 ; Yeung, 2016 ; Yeung & Coe, 2015 ). Buckley ( 2009a ) argues that the role of headquarters is more important in a GVC than in a conventional hierarchical MNE, because leading a GVC demands specific management capabilities such as the ability to fine-slice the value chain, control information, and coordinate strategies of external organizations. Yet few studies directly investigate the specific impact of lead firm characteristics on the boundaries, configurations and performance of the GVC. The studies that do use lead firm features as independent variables focus on such aspects of the lead firm as size (small versus large), industry sector (and associated sector-specific value chain strategies), location (headquarters location in a particular region/in emerging versus developed markets, and proximity to clusters), and technological leadership.

Lead firm size appears to be seen as a proxy for power and influence in a network. Eriksson et al. ( 2014 ), in a case study of a Finnish high-tech SME at the centre of a globally dispersed value chain, argue that SMEs face additional liabilities of smallness and newness when managing a GVC, and suggest that in order to manage successfully a GVC over the long term, the SME must develop three distinct yet related sets of dynamic capabilities: cognitive, managerial, and organizational. Dallas ( 2015 ) takes a finer-grained view of firm size as a determinant of GVC management strategy. While his analysis of transactional data of Chinese electronics/light industry firms uses size as a control, rather than independent, variable, he concludes that ways in which GVCs are organized vary not simply by lead firm size and productivity, but also by other heterogeneous firm level features, such as distinct governance channels available to lead firms. Dallas ( 2015 ) thus cautions GVC researchers not to make assumptions about the distinctiveness of large lead firms as a group, and to focus on other potential sources of heterogeneity, which can be linked to sector-specific features as well as firm-level strategies.

One of such sources of heterogeneity appears to be the level of economic development of home country, dichotomized in some GVC papers as emerging versus advanced. Two studies explore differences in GVCs led by EMNEs versus AMNEs. He et al. ( 2018 ), based on a case analysis of China’s Times Electric-led GVC, argue that power relationships in the GVC seem to be more balanced when EMNEs, rather than AMNEs, are in lead positions. Buckley and Tian ( 2017 ) compare internationalization patterns of top non-financial EMNEs and AMNEs, and find that AMNEs are more likely to achieve profitability through global GVC orchestration, while EMNEs’ ability to develop orchestration know-how is restricted by home institutions. Therefore, EMNEs are more likely to extract monopoly-based rents from internationalization, but to remain constrained to the periphery position in GVCs.

It follows, then, that control of the GVC is likely to remain in the hands of technology leaders (Buckley & Tian, 2017 ). Jacobides and Tae ( 2015 ) describe such technology leaders as “kingpins,” operationalized as firms with superior market capitalization and comparatively high R&D investment. In their study of firms active in various segments in the US computer industry, the authors show that “kingpins” impact value distribution and migration through the value chain. Technological and R&D capabilities, however, need to be accompanied by global orchestration know-how in order for lead firms to achieve profitability from fragmented, globally dispersed operations (Buckley & Tian, 2017 ). We address GVC orchestration in the next section.

GVC orchestration

Orchestration refers to decisions and actions by lead firm managers – a managerial toolkit – aimed at connecting, coordinating, leading, and serving GVC partners, and ultimately shaping the network’s strategy (Rugman & D’Cruz, 1997 ). Orchestration encompasses such elements as, inter alia , formal and informal components of each relationship within the network, the entrepreneurial element of resource bundling, interest alignment among parties achieved through strategic leadership by the lead firm, knowledge management 4 , and value distribution.

Formal orchestration tools – that is, codified rules, specific contractual choices to manage partner relationships, and price-like incentives and penalties – are typically easier to observe and operationalize than informal tools such as social mechanisms deployed by lead firms to govern relationships. Yet, only a few studies in our sample investigate contractual choices in a GVC. Lojacono, Misani and Tallman ( 2017 ) examine nuances of cooperative governance in the dispersed value chain of the home appliances industry, and find that more complex transactions requiring greater coordination are more likely to be governed through equity participation. Specifically, non-equity contracts are more efficient for coordinating offshore production, while equity JVs are preferable for managing local strategic relationships, such as production alliances whose primary objective is to serve local markets. Chiarvesio and Di Maria ( 2009 ) explore differences in GVC orchestration between lead firms located within industrial districts versus those located outside. Their quantitative study of Italian firms active in the country’s four dominant industries – furniture, engineering, fashion, and food – shows that there are subtle differences in ways district and non-district lead firms manage their GVCs to achieve optimal efficiency: while lead firms located within industrial districts rely more on local systems through subcontracting networks, non-district firms invest in national level subcontracting. Here, local subcontracting networks allow lead firms to exploit flexibility, and national subcontracting facilitates greater efficiency and acquisition of value-added competences through the GVC. Of note, these differences decrease as firm size increases. Finally, Enderwick ( 2018 ) conceptually studies responsibility boundaries in a GVC, and argues that the full extent of lead firm responsibility for actions of indirect GVC participants depends on whether indirect partners’ contracts are exclusive or non-exclusive.

Entrepreneurial guidance by the lead firm is an important component of GVC orchestration (Buckley, 2009a ), as it serves to redirect GVC resources and tasks toward creating innovation. While most research in our sample implicitly assumes the lead firm’s entrepreneurial role in generating value, two empirical studies take a close look at the process of entrepreneurial resource recombination in a GVC, initiated by the lead firm. In a multiple case study of engineering firms, Zhang and Gregory ( 2011 ) identify mechanisms of value creation in global engineering networks: efficiency, innovation, and flexibility. The efficacy of these mechanisms depends on which part of the engineering value chain is the core focus of the operations: product development/production, design/idea generation, or service/support. Ivarsson and Alvstam ( 2011 ) discuss how IKEA manages resources to generate greater value and stimulate innovation capabilities in its supply chain. Their case study reveals that IKEA provides access to inputs through global sourcing, shares business intelligence, implements management systems and business policies across the network, and fosters informal R&D collaborations with suppliers.

Relational governance, as perhaps the most important of the five types of GVC governance in Gereffi et al.’s ( 2005 ) typology, emerged as a key tool for network orchestration. There appears to be a broad consensus in our sample that cultivating informal relationships, as a means of network orchestration, has a potential to facilitate knowledge transfer, secure commitments, enhance innovation, respond to legislation, and improve overall GVC efficiency. In fact, Brancati et al. ( 2017 ) show, based on a survey of about 25,000 Italian firms, that GVCs comprised of firms with strong relationships and active decisional roles in the value chain have a 4-6% higher probability of engaging in innovation and R&D, and display greater productivity and sales growth. Benstead, Hendry and Stevenson ( 2018 ) argue that relational capital facilitates successful horizontal collaboration among GVC members, which allows participating firms to respond more effectively to modern slavery legislation in the textiles and fashion industry, and consequently improve reputation and performance. In a case study of major American and Japanese automotive lead firms and their suppliers, Sturgeon et al. ( 2008 ) find that relational governance is necessitated by rising product complexity, low process codifiability and a paucity of industry-level standards. These relational links explain continued dominance of regional structures in the industry.

Studies have described specific relational strategies deployed by lead firms. These include promoting regular communication between suppliers and buyers (McDermott & Corredoira, 2010 ), adapting communication strategies to cultural contexts where GVC partners are embedded (Griffith & Myers, 2005 ), involving multiple actors in establishing functioning principles for the GVC, facilitating shared identity and common language (Lipparini et al., 2014 ), extending the network to include non-market institutions (Corredoira & McDermott, 2014 ; Kano, 2018 ; Pipkin & Fuentes, 2017 ), investing into image building (Horner & Murphy, 2018 ), and establishing a long-term horizon for inter-unit relationships to facilitate repeated interactions (Ivarsson & Alvstam, 2011 ).

Finally, extant research identifies GVC value distribution as the responsibility of the orchestrating firm. The lead firm must ensure that partners receive an equitable share of value created in the GVC, as a function of their respective contributions to the network (Dhanaraj & Parkhe, 2006 ). In most studies in our sample, a power view of the GVC is assumed, whereby value distribution is seen to be a result of the power struggle between the lead firm and the periphery. Typically, lead firms – particularly those that possess valuable technological knowledge and/or intangibles such as brand names and patents – are argued to capture the lion share of the value (Jacobides & Tae, 2015 ), while most peripheral players appear in a subordinate position and under high cost pressures (Taplin, Winterton, & Winterton, 2003 ), and must deploy strategies to counter the power of the lead firm (Grabs & Ponte, 2019 ; Havice & Campling, 2017 ; Pipkin & Fuentes, 2017 ), including attempts to move up the value chain, as discussed above. This power imbalance appears to be more pronounced in GVCs led by AMNEs than those led by EMNEs, because lead EMNEs are likely to build their GVCs with a knowledge-seeking objective, by enlisting AMNEs that possess desired knowledge (He et al., 2018 ).

Some conceptual studies in our sample approach the issue of value distribution as a deliberate orchestration tool on behalf of the lead firm. Kano ( 2018 ) argues that equitable value distribution improves reliability of partners and enhances sustainability of the GVC over time. Of note, equitable value distribution undermines potential efficiency gains achieved through externalization of activities; however, as argued by Yamin ( 2011 ), such sacrifice in terms of loss of efficiency may be necessary in order to ensure legitimacy and survival of the network.

Governance and performance outcomes

A significant proportion of papers in our sample is concerned with developing typologies, mapping linkages in GVCs, analyzing configurations, and investigating processes, without an explicit focus on performance. Studies that addresses performance per se conceptualize and measure performance outcomes in a variety of ways, depending on research questions and units of analysis. Most studies focusing on GVC suppliers are concerned with upgrading as a performance goal, as evidenced by suppliers’ development of technological and/or branding capabilities, or by their ability to reconfigure activities so as to become lead firms in their own right (e.g., Azmeh & Nadvi, 2014 ; Buckley 2009b ; Chen et al., 2016 ).

Studies focusing on lead firms are more likely to use financial performance measures as indicators of GVC success: for example, value capture as measured by comparative market capitalizations of various industrial sectors (Jacobides & Tae, 2015 ), sales and profit growth (Griffith & Myers, 2005 ), and return on assets (Buckley & Tian, 2017 ; Lampel & Giachetti, 2013 ). Other conceptualizations of lead firm performance include, inter alia , its ability to exercise control over independent partners and coordinate division of labor (Casson, 2013 ; Strange & Newton, 2006 ), ability to minimize the total sum of transaction costs (Buckley, 2009a ), capability development (Eriksson et al., 2014 ), and corporate social responsibility (CSR) performance (Enderwick, 2018 ).

Studies concerned with performance of the GVC network as a whole naturally explore more complex aspects of performance, such as flexibility/dynamism of the production process, access to a wide range of resources, operational efficiency, cohesiveness/connectivity, innovation/ability to transform ideas into commercial products, and sustainability of the GVC over time (Akkermans et al., 1999 ; Buckley, 2011 ; Chen, 2003 ; Colotla, Shi, & Gregory, 2003 ; Kano, 2018 ; Karlsson, 2003 ; Sinkovics et al., 2019 ; Yamin, 2011 ; Zhang & Gregory, 2011 ). Notably, studies in the social sciences group may focus on development and sustainability outcomes of GVC governance, such as industrial/economic development and positive institutional change (e.g., Coe et al., 2004 ; Fuller & Phelps, 2018 ; Henderson et al., 2002 ; Lund-Thomsen & Coe, 2015 ; Pavlínek, 2018 ; Yeung, 2016 ). Due to its complexity and multifariousness, GVC-level performance is difficult to operationalize quantitatively, and is mostly addressed in qualitative and conceptual studies in our sample.

Macro-level: Interaction of Home and Host Environment Characteristics and GVC Governance

GVC organization is contingent on a number of location characteristics, including levels of economic development (Mudambi, 2007 ), IP and FDI protection regimes (Johns & Wellhausen, 2016 ), trade and tariff regimes (Curran, Nadvi, & Campling, 2019 ; Kim, Milner, Bernauer, Osgood, Spilker, & Tingley, 2019 ), regulatory environments and government policy interventions, labor costs, level of technological sophistication, and societal norms (Dunning, 1988 ). The role of the state, in particular, can significantly shape the organization and evolution of GVCs over time (Alford & Phillips, 2018 ; Coe & Yeung, 2019 ; Smith, 2015 ; Yeung, 2016 ). Macro-level impacts on GVC governance have been discussed in the preceding sections, but we summarize the key themes and findings below.

Institutional factors, such as trade regulations and the strength of local institutions, are major determinants of GVC governance attributes, including geographic and structural configuration, operating mode choices, power balance, and possibility of upgrading by peripheral players. Host country institutions can both attract investment by lead firms through policies encouraging local content and promoting local supplier linkages (Amendolagine et al., 2019 ; Dawley, MacKinnon, & Pollock, 2019 ; Liu & Dicken, 2006 ; Sturgeon et al., 2008 ; Yeung, 2016 ), and deter such investment due to insufficient IP protection and underdeveloped legal systems (Gooris & Peeters, 2016 ). However, the impact of host country institutional environment on GVCs is heterogeneous: while it is tempting to assume that lead firms are attracted by favorable local business regulations and strong institutions, this impact in fact varies across GVCs, depending on specific functions/activities being offshored, internationalization motives, and lead firm-level strategies and capabilities (Ascani et al., 2016 ; Morris & Staritz, 2014 ).

One conclusion that can be drawn from our review is that institutions greatly impact GVCs’ abilities to engage in, and profit from, innovation. Inadequate local institutions prevent domestic firms from transforming R&D into innovative products and services (Buckley & Tian, 2017 ; Jean, 2014 ), and thus effectively hinder supplier catch-up and upgrading. This likely explains why most GVCs are controlled by MNEs that stem from developed institutional environments and, consequently, display technological leadership. Peripheral players in GVCs can respond to this challenge by entering into international collaborations, engaging with international institutions, and more broadly becoming embedded in international networks that off-set the weakness of local institutions (Khan et al., 2015 , 2018 ; Pipkin & Fuentes, 2017 ). This is a crucial dimension of strategic coupling in GPN 2.0 theory (Coe & Yeung, 2015 ; Yeung, 2009 , 2016 ). It is important to note that the impact of institutions is dynamic. As trade, liberalization and economic development in emerging markets progress, so do suppliers’ strategies. Internal R&D becomes a dominant strategy for upgrading (Kumaraswamy et al., 2012 ), and suppliers with more advanced technologies become core players in their regional networks (Suder et al., 2015 ).

Economic factors, such as labor cost and supply, markets and competition (MacCarthy et al., 2016 ), impact GVC configurations and, more recently, determine further production shifts in GVCs, whereby tier 1 GVC suppliers begin disintegrating their own value chains, in search of both greater efficiency (as a response to rising labor costs) and better production capabilities (Azmeh & Nadvi, 2014 ; Suder et al., 2015 ). In the terminology of GPN 2.0 (Coe & Yeung, 2015 ), this simultaneous attainment of both cost efficiency and production capabilities is translated into lower cost-capability ratios in favor of strategic partners and suppliers of global lead firms. This strategy is an alternative to functional upgrading discussed above (Chen et al., 2016 ; Humphrey & Schmitz, 2002 ; Sako & Zylberberg, 2019 ), and represents a different type of upgrading, where major suppliers become MNEs in their own right, e.g., leading ODMs such as Quanta and Wistron and contract manufacturers such as Foxconn, Flex, and Venture from East Asian economies (Yeung, 2016 ).

The impact of macro-level cultural characteristics is considered in a smaller subset of studies, and mainly in relation to the lead firm’s strategic governance routines. Griffith and Myers ( 2005 ) suggest that host country cultural expectations impact GVC performance by affecting the lead firm’s ability to effectively deploy relational strategies across the network. They argue that cultural adaptation of relational governance results in improved performance. Sturgeon et al. ( 2008 ) discuss the impact of home country cultural characteristics on American and Japanese lead firms’ abilities to successfully engage in relational governance. Only one study (Funk et al., 2010 ) analyzes the broader impact of home country consumers’ cultural characteristics on GVC profitability, using Schwartz’s ( 2006 ) theory of values.

It is acknowledged that technology is one of the major macro-level factors impacting a GVC over its lifecycle (MacCarthy et al., 2016 ). In the prior section, we have discussed ways in which advanced technologies impact structural and strategic governance decisions in a GVC, mostly in the context of facilitating connectivity and determining innovation and power loci in the network. Some studies in our sample investigate a direct impact of the latest, advanced technologies on GVC configurations. Laplume et al. ( 2016 ) analyze potential impact of 3D printing technologies on GVC structure and geographic reach. Treiblmaier ( 2018 ) discusses potential implications of blockchain technology for various aspect of GVC management, including boundaries, structures and relationships.

GVCs are not only impacted by, but also influence the macro-environment; specifically, sustainability impacts of GVCs and associated policy implications have to date invited much scholarly and practitioner dialogue (Coe & Yeung, 2015 ; Gereffi, 2018 ). This interest is to some extent reflected in our sample, yet few studies explicitly address ways in which GVCs affect social, economic and environmental conditions in host countries. For example, labor standards have become one critical frontier of GVC organization (Hastings, 2019 ; Malesky & Mosley, 2018 ). Lund-Thomsen and Coe ( 2015 ) studied Nike’s main football supplier factory in Pakistan, and investigated whether CSR initiatives by the lead firm can facilitate or constrain labor agency in GVCs. Their results indicate that lead firms are limited in their ability to shape local labor agency, as it is impacted by wider economic forces, relationships with local and national actors, and local regulatory frameworks; these factors can place clear limits on lead firms’ efforts to facilitate responsible forms of GVC. Barrientos et al. ( 2016 ) address the impact of diffusion by global and regional supermarkets in “global South” – South Africa, Kenya, and Uganda – and find that entry by large global retailers provides new opportunities for strategic diversification to the most skilled local horticultural producers and workers. This facilitates economic and social upgrading; yet, persisting economic downgrading pressures mean that many less skilled suppliers are excluded from both global and regional value chains. Kleibert ( 2016 ) explores local impacts of the Philippinean offshore service offices’ participation in GVCs, and finds that the majority of these offshore offices are characterized by foreign ownership and a high degree of dependency. However, participation in the GVC increases the number and quality of jobs in the region, and creates new opportunities in the labor force – particularly for young college graduates, who suffer from a high level of unemployment in the region. Finally, in a longitudinal study of the international canned tuna industry, Havice and Campling ( 2017 : 309) argue that value chain governance and environmental governance are “mutually constituted”: lead firm power dynamic is inextricable from the environmental conditions of production, and interfirm strategies work not only with, but also through , environmental governance.

CRITICAL ASSESSMENT OF EXTANT LITERATURE AND FUTURE RESEARCH AVENUES

Conceptual underpinnings and the theory of the gvc.

Our systematic analysis of the GVC literature reveals the theoretical and empirical terrains that have been covered to date, and shows that a substantial body of work has been accumulated to advance our understanding of the GVC phenomenon. One observation that emerged in our review is a high degree of theoretical pluralism. This is to be expected due to the multidimensionality of the construct, and the multidisciplinary nature of the review. One of the more common theoretical approaches deployed in IB, management, and supply chain/operations studies is based on various forms of business network theory (Carnovale & Yeniyurt, 2014 ; Chen, 2003 ; Golini et al., 2016 ; Humphrey & Schmitz, 2002 ; McDermott & Corredoira, 2010 ). Many studies investigating capability development and upgrading rely on capability-based theories, such as dynamic capabilities, resource-based view (RBV), knowledge-based view and organizational learning (Chen et al., 2016 ; Corredoira & McDermott, 2014 ; Eriksson et al., 2014 ; Jean, 2014 ), as well as theories of innovation (Golini et al., 2016 ; Werner, 2012 ). Macro-level trade and development theories (Dallas, 2015 ; Seppälä et al., 2014 ), institutional theory (Hatani, 2009 ) as well as resource dependency theory (He et al., 2018 ; Suder et al., 2015 ) are invoked in several studies focusing on geographic and structural make-up of GVCs.

Several IB studies, particularly those conducted within the global factory research stream and those investigating host country governance mode dynamics, adopt an internalization theory perspective (Buckley & Tian, 2017 ; Eriksson et al., 2014 ; Gooris & Peeters, 2016 ; Hilleman & Gestrin, 2016 , Kumaraswamy et al., 2012 ). A number of other theoretical angles, perspectives or frameworks are used to address specific research questions. These include international entrepreneurship (Eriksson et al., 2014 ), cultural values and norms (Funk et al., 2010 ; Griffith & Myers, 2005 ), and theories of clusters and cities (Brown, Derudder, Parnreiter, Pelupessy, Taylor, & Witlox, 2010 ; Turkina & Van Assche, 2018 ). Some studies attempt to address the complexity of the GVC phenomenon by merging interdisciplinary theoretical lenses: for example, Turkina and Van Assche ( 2018 ) combine insights from IB theory, economic geography, and social network analysis to study innovation in knowledge-intensive clusters; Treiblmaier ( 2018 ) develops a framework to explain the role of blockchain technology in GVCs based on four theories: principle-agent theory, TCE, RBV, and network theory.

Yet, despite the impressive amount of research investigating the GVC phenomenon from a variety of theoretical angles, it appears that we do not yet have a dominant theory of GVC. A number of studies – particularly those in the economic geography and economic sociology research streams – refer to the GVC theory of Gereffi et al. ( 2005 ) (or, alternatively, GPN/GCC theory, see, for example, Blažek, 2015 ; Brancati et al., 2017 ; Hatani, 2009 ; Neilson et al., 2018 ; Sturgeon et al., 2008 ; in a recent review by Coe & Yeung, 2015 ). However, as mentioned above, existing GVC frameworks (e.g., Gereffi, 1994 ; Henderson et al., 2002 ) and typologies (e.g., Gereffi et al., 2005 ) do not provide detailed causal mechanisms (Bunge, 1997 ), and thus do not constitute predictive theory of GVC in a sense of offering “a statement of relations among concepts within a set of boundary assumptions and constraints” (Bacharach, 1989 : 496). Instead, they are useful organizing frames for empirical research on GVCs. Although Coe and Yeung’s ( 2015 ) recent book on GPN 2.0 theory comes closer to a causal approach to theory development, there is still a lack of empirical studies to test its generality, validity, and robustness (e.g., Coe & Yeung, 2019 ; Neilson et al., 2018 ). Overall, GVC is a complex construct that captures a particular empirical phenomenon, namely progressive disintegration and geographic dispersion of MNEs’ value chains. The studies reviewed here investigate various dimensions of this construct and establish links among select dimensions, but fall short of developing an overarching theory of GVC that can adequately explain the phenomenon, preferably with some predictive power. Admittedly, predictability is difficult to achieve in social science theories, where the validity of predictions depends upon elusive ceteris paribus conditions (Bhaskar, 1998 ). Yet, in an applied field such as IB, predictive capacity makes our theories actionable for managers, and therefore is viewed as a desirable (though hard to attain) outcome of theory development.

Here, our comparative institutional analysis-based model (Figure  1 ) can be used as an eclectic framework that integrates various theoretical perspectives in order to explain the functioning of the GVC, and, we hope, predict specific outcomes, in terms of benefits accrued to GVC participants and chain-level sustainability. From the internalization theory perspective, a GVC will be sustained over time only if GVC governance is comparatively more efficient than alternative governance forms. The lead firm thus must manage inefficiencies at the macro-level (e.g., institutional frailties, economic shifts, public push-back, technological complexities), at the GVC level (e.g., need for structural changes, shifting power dynamics among partners, unequitable value distribution), and at the micro-level (e.g., cognitive biases, information asymmetries, commitment failures), by economizing on bounded rationality and reliability involved in GVC-related transactions, and by fostering an environment conducive to value creation and capture in the GVC (Kano, 2018 ). The lead firm must select and implement structural features and strategic governance routines that best serve these economizing objectives.

Taken together, the studies in our sample address all elements of our comparative institutional framework, although some elements have garnered more scholarly attention than others. Our review reveals a number of knowledge gaps, which indicate promising research directions for IB, management studies, and the broader social sciences. We discuss these in the next section.

Knowledge Gaps and Direction for Future Research

Microfoundations of gvc governance.

The microfoundational aspect appears to be underrepresented in our sample. While microfoundational assumptions are frequently made, they are rarely articulated or examined empirically. This is concerning particularly because GVC configurations are essentially outcomes of managerial choice. Our ability to predict accurately these configurations hinges on our understanding of the individual, which is for the most part omitted in our sample. Even papers that examine learning are typically silent on the role of individual behavior. In particular, studies based on archival data often engage in what Tsang ( 2006 : 999) calls “assumption-omitted testing”; that is, although key behavioral assumptions may be made implicitly or explicitly for the purpose of developing hypotheses, such assumptions are not tested empirically.

It should be noted that this gap is particularly evident in IB and management literatures. Sociology, development studies and economic geography literature does address individual motivations and behavior, mostly through the case study and/or ethnographic methods. Yet, economics-based research tends to steer away from directly examining such psychological factors. The fact remains that few narratives at the individual level are published in the journals represented in our review.

Future IB studies could explicate individual-level assumptions, and examine specific links between these assumptions and various components of GVC governance, such as ownership and control decisions, geographic and structural configurations, knowledge management, and network orchestration. In particular, the largely under-researched aspects of value distribution in a GVC could be advanced by incorporating specific microfoundational assumptions. Current narrative on value distribution implies a certain level of bounded rationality and bounded reliability of decision-makers. First, managers find it difficult to identify accurately where the most value is generated in the network (Seppälä et al., 2014 ). Second, most studies that address value distribution assume the presence of a power struggle among the players, whereby each actor attempts to appropriate the greatest amount of value, frequently at the expense of other players – consider the proverbial case of large buyers in Gereffi’s ( 1994 ) buyer-driven commodity chains or Gereffi et al. ( 2005 ) captive mode of GVC governance. Here, large buyers are assumed to opportunistically squeeze their suppliers to the point where relentless downward cost pressure leads suppliers to make suboptimal, environmentally and socially detrimental choices. However, this power view is not universally applicable, as noted recently in Dallas et al. ( 2019 ). Inequitable value distribution may alienate critical partners and undermine the sustainability of the entire GVC arrangement (Levy, 2008 ; Yamin, 2011 ). It is in the interest of the lead firm to sustain the GVC over time, particularly in situations of bilateral dependence from core suppliers. Explicating and testing individual-level assumptions can help scholars understand mechanisms underlying value distribution in a GVC.

Geographic scope of GVCs and GVC mapping

Location emerged as one of the key variables in empirical GVC studies, yet few empirical studies in our sample attempt to measure the geographic dispersion of value chains investigated, in order to determine whether the scope of these value chains is in fact global, in a sense of a relatively equal distribution of activities across regions (Rugman & Verbeke, 2004 ). In fact, only two studies (Azmeh & Nadvi, 2014 ; Suder et al., 2015 ) directly address the regional effect in GVCs, although a larger number of empirical studies published in economic geography journals (Table  3 ) focus on GVC impacts on location-specific upgrading and regional development. It has been argued that very few truly global value chains are currently in existence, and that the label “global,” used either out of inertia or as a teaser, may in fact misrepresent the actual geographic reach of MNEs’ international networks (Verbeke, Coeurderoy, & Matt, 2018 ). It is therefore the responsibility of GVC scholars to measure systematically the geographic breadth and depth of relevant value chain activities, and to arrive at an accurate definition of what a GVC represents. Such goal could be accomplished through firm-level GVC mapping, namely, linking locations with detailed data on inputs, outputs, flows of services and skills, employment, revenue, and value creation and capture. Unlike international economics studies based on value-added trade data (Escaith, 2014 ; Johnson & Noguera, 2012 ; World Bank, 2019 , 2020 ), such firm-based GVC mapping not only clarifies the geographic scope of economic activity as global versus regional, but also serves an important managerial purpose of specifying the precise location of value creation and capture within the firm and its GVC. This potentially helps managers to appraise comparative efficacy of global, regional and local governance.

Learning in a GVC

As indicated in Inkpen and Tsang’s ( 2005 , 2016 ) conceptual discussion of social capital, networks and knowledge transfer, the topic is surely a challenging as well as fruitful one. A number of empirical studies have examined knowledge diffusion and transfer in a GVC, but knowledge management is discussed mostly in the context of upgrading, technological catch-up and moving up the value chain by peripheral firms and strategic partners. Reverse knowledge transfer and learning in the lead firm are less explored (with the notable exception of Lipparini et al., 2014 ). Further, while recent conceptual studies have called for a closer examination of specific mechanisms for knowledge transfer in a GVC (Pietrobelli & Rabellotti, 2011 ; Cano-Kollmann, Cantwell, Hannigan, Mudambi, & Song, 2016 ; Kano, 2018 ), few empirical studies have addressed this. Future studies can examine channels through which knowledge travels in a GVC in multiple directions, and specific behaviors in various parts of the network that aid or constrain these processes. Finally, the concept of organizational unlearning – getting rid of obsolete knowledge or routines – points to another promising research area that has been neglected. Given the rapid technological and environmental changes, knowledge possessed by members of a GVC has to be regularly updated. Organizational routines that used to be cost-saving may no longer be so. The extent to that GVC members individually or collectively can replace such outdated knowledge or routines partly determines the GVC’s performance or even long-term survival. Since unlearning at the organizational level and the individual level are intricately connected (Tsang & Zahra, 2008 ), attention to microfoundations of individual behavior, as suggested above, can help advance this research agenda.

Impact of lead firm ownership and strategy on GVC governance

Several studies in our sample analyze the impact of lead firm features, such as size, industry, location, and capabilities, on GVC governance. However, few studies (with the exception of, e.g., Morris & Staritz, 2014 ) examined the impact of ownership, meaning potential differences among GVCs led by private, public, state-owned, and family-owned MNEs. Of particular interest here is behavior of firms whose international strategy may be driven by non-economic objectives, such as state-owned enterprises (SOEs), government-linked corporations (GLCs) and family firms. The social and political goals of SOEs and GLCs may conflict with efficiency considerations (Grøgaard, Rygh, & Benito, 2019 ; Rugman, 1983 ), and may drive idiosyncratic GVC configurations. These idiosyncrasies may be enhanced by lead firms’ unique relationships with key macro-level actors, such as the state, regional and local institutions, and trade unions, and their comparatively greater ability to influence economic policies that govern international investment. For example, political transformation in developing countries can enable the strategic coupling of national economic actors, such as SOEs, GLCs and even sovereign wealth funds, with lead firms in different historical periods. Yeung’s ( 2016 ) comparative study details the politics of state transformation in South Korea, Taiwan, and Singapore since the 1990s and explains how this transformation has led to a strategic coupling shift of the development process from SOE-led industrialization to an assemblage of state-firm-global production networks in which SOEs and GLCs work closely with lead MNEs in a variety of industries, such as personal computers, semiconductors, automotive, ship building, and passenger aviation.

Similarly, family-owned MNEs’ international strategy may be driven by non-economic objectives of the controlling family, such as keeping the firm in the family, providing jobs for future generations, cultivating connections with “chosen” stakeholders, and building a reputation in the community (Miller, Wright, Le Breton-Miller & Scholes, 2015 ). The prevalence of these non-economic preferences gives rise to a dysfunctional governance feature that family firm scholars termed “bifurcation bias”: an affect-based decision rule, whereby family-based assets and capabilities are given de facto preferential treatment over non-family ones (Kano & Verbeke, 2018 ). In the context of GVC governance, bifurcation bias can impact, inter alia , location and control decisions and network composition. Lead family firms may be more likely to seek to protect family-based assets through internalization, and to ascribe a commodity status to non-family assets and govern those assets through contractual modes, regardless of their actual value and contribution to the GVC. Location decisions in bifurcation-biased family firms are also likely to be subject to affect logic; for example, a desire to create jobs for the local community may drive domestic production even when more efficient options exist. This decision dynamic was evident in the well-known case of the iconic Danish toy manufacturer LEGO, where the family’s excessive loyalty to its home community of Billund, Denmark, prevented it from achieving efficiency through offshoring (Bennedsen & Foss, 2015 ). The choice of network partners may also be unique in family firm-led GVCs, since family firms display a strong preference toward partnerships with “kin-controlled” suppliers (Memili, Chrisman, & Chua, 2011 : 53). These, and other idiosyncratic features of GVCs led by firms with alterative ownership, can be investigated in future studies.

The impact of the lead firm’s international strategy can also be explored further. No studies in our sample have addressed this relationship. However, we assume that the lead firm’s international strategy (defined according to, e.g., Bartlett and Ghoshal’s ( 1989 ) integration/responsiveness framework, Ghemawat’s ( 2003 ) aggregation/adaptation/arbitrage framework, or Verbeke’s ( 2013 ) administrative heritage framework) will influence structural and strategic governance of the GVC, particularly because organizing operations through the GVC is meant to aid the lead MNE in achieving the ultimate balance between integration and responsiveness (Buckley, 2014 ).

Temporal factors and dynamics of GVC arrangements

Temporal considerations, such as assignment duration and timing of changes in governance modes, have received limited attention in GVC studies to date, likely because they are typically subsumed within control and/or location decisions (Buckley et al., 2019 ). Only two studies in our sample (Brancati et al., 2017 ; Havice & Campling, 2017 ) examined temporal factors in a targeted manner. However, time considerations represent a key parameter of GVC governance, particularly because modern GVCs thrive on flexibility and adaptability of their governance structures. We propose that future IB studies focus on such temporal elements as optimal assignment duration for economic activities, flexibility/stability trade-offs, and associated knowledge accumulation and learning. Analyzing temporal dynamics of the GVC will likely shed light on the issue of backsourcing, inshoring, and reshoring (Bailey & De Propris, 2014 ; Kinkel, Rieder, Horvath, & Jäger, 2016 ; Vanchan, Mulhall, & Bryson, 2018 ), which also is not sufficiently addressed in extant research.

Value creation, capture, and distribution in a GVC

Despite significant scholarly attention to the issue of value in a GVC, the question of how lead firms should coordinate value creation, capture and distribution is as of yet unresolved. Here, interdisciplinary differences in approach are particularly evident. IB scholars tend to focus on lead firms as key actors responsible for value orchestration in a GVC, viewing these firms as residual claimants of the network’s value proposition (Kano, 2018 ). Social science-based GVC scholars consider more closely contestation over value creation and distribution among lead firms and their partners, and approach value distribution from the perspective of various forms of power asymmetries between the lead firm and suppliers (Dallas et al., 2019 ; Strange & Humphrey, 2019 ). Both approaches present conceptual and empirical challenges. First, the empirical reality is that lead firms cannot accurately account for where value is created in the GVC (Seppälä et al., 2014 ), which complicates their role as value distributors. Second, formal and informal connections and arrangements in modern GVCs continually change in response to economic, political, and technological processes (Benito, Petersen, & Welch, 2019 ); this dynamism impacts both power relationships in a GVC and loci of value creation. Future studies can fruitfully combine IB and social science approaches to further investigate value creation and distribution in a GVC (Benito et al., 2019 ).

Finance and financialization in MNEs’ participation and coordination of GVCs

Overall, we know little about how financial considerations affect MNE strategies, management of GVCs, and competitive outcomes. Earlier studies by Milberg ( 2008 ) and Milberg and Winkler ( 2013 ) examined how financial considerations (e.g., share prices) shaped GVC configurations. From being a relatively obscure factor in the early GCC literature during the 1990s, finance has come to the forefront of accounting for the evolutionary dynamics of lead MNEs and their GPNs in the 2010s. Coe and Yeung ( 2015 ) argue that the pressures and opportunities associated with financial market considerations have compelled lead MNEs to further develop and expand their international operations. MNEs' responses to financial dynamics produce different geographical and organizational configurations of networks. Lead firms, such as certain American MNEs, that succeed in meeting the demands of financial discipline through globalizing production, tend to perform well in the financial market in terms of stock price and executive rewards. This prompts further strategic shift toward a greater emphasis on finance-driven approach to corporate growth and governance in lead MNEs.

GVC impact on macro-environment

Extant research has long acknowledged that GVCs are embedded in, and co-evolve with, political, socio-economic and environmental systems (Alford & Phillips, 2018 ; Santana, Vaccaro, & Wood, 2009 ; Smith, 2015 ; Whittaker, Zhu, Sturgeon, Tsai, & Okita; 2010 ; Yeung, 2016 ). GVCs thus have a continued impact on these complex systems, both positive and negative, intended and unintended. These impacts are well documented. On the positive side, they include economic upgrading, namely income and employment growth and skill development in domestic firms. GVCs’ negative impacts on host communities have attracted even more attention, and include increasing inequality, deteriorating labor standards, environmental damage (Kolk, 2016 ; Kolk, Rivera-Santos, & Rufin, 2018 ), and, in extreme cases, large-scale crises such as the Rana Plaza disaster in Bangladesh. Lead MNEs’ efforts to address these impacts by enforcing strict labor standards throughout the chain and implementing partial re-internalization are not unambiguously helpful for host communities. These initiatives limit local enterprise growth and reduce employment prospects among the most vulnerable population, and thereby attenuate some of the above-mentioned positive effects of GVCs on local economies (Narula, 2019 ). Today, in the era of the rise of political populism, renewed protectionism and the growing skepticism toward globalization, the question of whether GVCs are paragons or parasites is hotly debated in the academe, in the business community, and among the general public.

It is therefore surprising that few studies in our sample directly address the impact of GVCs on various facets of their macro-environment (although many more papers in the social science literature have addressed this issue). The reason may be that operationalizing and measuring social, economic and environmental impact is a challenging task and a rapidly moving target, even if we put aside the problem of data availability. Nevertheless, studying GVC impacts on relevant societies is an important direction of inquiry, which presents one of the “grand challenges” of IB research. To make such research actionable, IB scholars are encouraged to “expand the firm-centric lens” (Gereffi, 2019 : 195) so as to incorporate broader views on international development. Engagement with policymakers and researchers from adjacent fields such as international economics can facilitate linkages between firm-level and macro-level perspectives and help IB researchers translate their findings into policy and development implications.

While host country institutional environments were factored into many investigations, few studies (e.g., Fuller & Phelps, 2018 ) examined feedback effects from GVC governance on host, home, and international institutions. Such impacts (e.g., improvement to legal frameworks, changes to local business institutions, development and enforcement of industry standards, changes to regulations to implement protectionist measures or to promote liberalization) present another interesting area for future research.

The impact of renewed protectionism

Protectionism, as expressed in governments’ measures to discriminate against foreign commercial interests through trade policies, is not a new phenomenon, and has been observed over the years through periods of crises and economic downturns (Evenett, 2019 ). Yet, the issue of protectionism is gaining renewed relevance today, especially in light of Brexit, President Trump’s foreign policies, and associated trade tensions and the wide-spread backlash against globalization. These developments naturally create risks for GVCs, particularly in regards to manufacturing activities offshored to low-cost countries. Lead firms may respond by reconfiguring their value chains and/or reshoring/repatriating production to home countries (Bailey & De Propris, 2014 ; Vanchan et al., 2018 ). While renewed protectionism certainly impacts GVC configurations and governance, the nature and extent of this impact is not yet clear. First, reshoring occurs for a number of reasons, including rising labor and transportation costs, currency fluctuations, technological developments, and strategic considerations (Ancarani et al., 2019 ; Vanchan et al., 2018 ). Second, reshoring, even in the face of, for example, US-China trade war, is difficult and may prove inefficient. Access to specialized skills, infrastructure, and large-scale manufacturing facilities presents serious barriers to reshoring. Repatriation of assembly and production of commodity components from China to high-cost home countries may be next to impossible, as no developed country can presently match China’s combination of scale, skill, infrastructure, and cost ( Economist , 2018 ). The impact of renewed protectionism is not directly addressed in our sample, likely because it will take some time to materialize, and the patterns and outcomes of GVCs’ responses are still in a state of flux. Further, available data on the impact of protectionism are presently limited (Evenett, 2019 ). That being said, the potential impact of various expressions of the renewed protectionism, such as Brexit and Trumpism, on GVC governance is a major avenue for future research, with significant implications for academics, practitioners, and regulators.

GVCs and digitization

Extant studies have addressed the impact of new technologies on GVC configurations (Laplume et al., 2016 ), however, future studies can answer the broader question of how digital technologies have transformed the basic governance structure of GVCs (Foster & Graham, 2017 ; Foster et al., 2018 ; Wu & Gereffi, 2019 ). Digital technology-enabled “platformization,” or “the shift from individual products or services to platforms as the basis for offering value” (Nambisan, Zahra, & Luo, 2019 : 1465), has considerable implications for GVCs, but these impacts are complex. On the one hand, platform MNEs facilitate connectedness among different groups of actors around the world in fundamentally new ways (Coviello et al., 2017 ; Stallkamp & Schotter, 2019 ). Digital platforms and associated ecosystems offer new venues for multifaceted innovation and value creation, and for transferring value across borders with added efficiency and flexibility. Digitization also allows MNEs to quickly change their business models by adding or subtracting network units, adjusting multi-sided platforms, or modifying existing links and interactions (Nambisan et al., 2019 ). For suppliers based in technologically advanced emerging economies such as China, digitization reduces barriers to upgrading and diversification and facilitates access to end consumers (Li, Frederick, & Gereffi, 2019 ). On the other hand, increasing digitization may put at a disadvantage or even exclude GVC actors located away from innovation hubs. Platforms and ecosystems provide young and small firms with access to infrastructure and opportunities to quickly reach geographically dispersed customers (Nambisan et al., 2019 ), yet they also prompt increasing standardization of inputs, which makes suppliers, especially SMEs, more interchangeable and consequently vulnerable. Lead MNE’s orchestration task in a digital environment is more challenging, as lead firms must coordinate, recombine resources, and establish cooperative relationships with actors that are loosely connected and may be situated far beyond the traditional boundary of the lead firm’s industry and beyond the scope of its expertise (Li, Chen, Yi, Mao, & Liao, 2019 ). Further, the growing importance of big data and data analytics led to the emergence of an entirely new form of value chain: a “data value chain” evolving around a firm that manages world-wide acquisition, storage/warehousing, modeling, analysis, and production of insights from data (UNCTAD, 2019 ). This type of value chain represents a fundamentally new business model, presently little understood by IB scholars.

The phenomenon of platformization presents a number of novel and fascinating research opportunities. A platform MNE can be seen as a global virtual value chain, with the lead MNE possessing critical technology, and with the flows of inputs and outputs being mostly intangible. Specific research questions to be explored include, inter alia , power dynamics in digital value chains, business model innovation enabled by platformization, monetization of raw data and ownership of value-added data, integration of digital and brick-and-mortar scenarios within the same network, the impact of home country Internet regulations on GVC governance (Wu & Gereffi, 2019 ), specialization versus standardization, integration versus responsiveness, consumer involvement in digital GVCs, e-commerce-enabled supplier upgrading (Li et al., 2019a , b ), relational governance in a digital environment, and building trust in the global virtual teams in a GVC (Foster et al., 2018 ; Jarvenpaa & Leidner, 1999 ). As technology continues to advance, future studies can investigate potential impacts of artificial intelligence, internet of things, and virtual reality on both traditional and digital GVCs (UNCTAD, 2019 ).

GVCs performance measurement

As discussed above, GVC-level performance measurement is a challenging task, due to the tremendous complexity of the fine-sliced, multi-layered, geographically dispersed network as well as the multiple and potentially diverging objectives of its members. We proposed here that sustainability of the GVC over time served as an indication of governance efficiency and could, therefore, be seen as the ultimate GVC performance outcome. Future research can elaborate on this measure, and propose other ways in which lead firms in GVCs can assess network performance.

To date, scholars from a range of disciplines have accumulated an impressive body of research on GVCs, yet this work is presently characterized by a number of knowledge gaps and a lack of a unifying theory. These gaps present exciting opportunities for GVC researchers, and we hope that our review may contribute to an integrative GVC research agenda. We have suggested a comparative institutional framework for GVC analysis, and identified a number of under-researched issues at micro, GVC, and macro levels, which we would like to further synthesize into what we see as three interrelated “grand challenges” of GVC research in IB. At the micro-level, we need to pay greater attention to individual behavior and motivations, and ways in which these individual characteristics play out as MNEs expand their value chains across geographies and product markets. At the GVC level, we need to engage in rigorous GVC mapping, by specifying relationships among all critical elements of structural and strategic governance of the GVC. At the macro-level, we need to investigate carefully and objectively the intermingling of GVCs and new technologies, and the complex impacts of GVCs on their surrounding societies and the natural environment. The latter point is particularly relevant in the present political climate. With critics of globalization increasingly – and irrationally – blaming GVCs (and, more generally, MNEs) for the demise of public goods and “the rise of global public bads” (Verbeke et al., 2018 : 1102), it becomes the social responsibility of GVC researchers to paint an accurate picture of GVCs that demonstrates the fundamental and non-reversible interconnectedness of today’s global economy.

We would like to conclude by suggesting that this task is best accomplished through interdisciplinary research. Our review showed that each discipline can contribute unique and useful angles, both theoretically and methodologically. In terms of achieving research objectives outlined above, sociology scholars can contribute their expertise in individual-level variables and network-level analysis; economic geographers can enrich the discussion through their superior command of location data, geographical scales of network configurations, and uneven development outcomes; organizational behavior researchers can enhance our understanding of the psychological aspects of managerial decision making and strategy formulation and execution, and IB scholars can bring to the table theoretical rigor and sophisticated treatment of MNEs and their cross-border networks. We advocate that scholars from different disciplines should communicate, collaborate, and gain from this cross-pollination of ideas, and we look forward to seeing more cross-disciplinary GVC research.

Gereffi ( 1999 ; also reproduced in 2018 : Chapter 3), for example, applied his buyer- and producer-driven commodity chains framework to analyze empirically the industrial upgrading pathways of East Asian firms and economies in the global apparel commodity chains led by US buyers. Similar to Hobday’s ( 1995 : Chapter 3) earlier work examining East Asian electronics firms, he identified four types of upgrading trajectories in the form of apparel exports based on basic assembly, OEM, OBM, and ODM roles, and introduced them into the GVC literature. Gereffi ( 1999 ) also highlighted the importance of organizational learning as a mechanism for achieving industrial upgrading in GCCs.

Bounded rationality implies that economic actors’ behavior is “intendedly rational, but only limitedly so” (Simon, 1961 : xxiv). Bounded reliability explains failure of economic actors to make good on open-ended promises, irrespective of intent (Kano & Verbeke, 2018 ). It is an extension of the narrower construct of opportunism – a central behavioral assumption in the Williamsonian version of transaction cost economics, defined as “self-interest seeking with guile” (Williamson, 1981 : 1545).

Hereafter, we refer to this latter group of journals as “social science journals.” We realize that management research also falls under the social sciences umbrella, however, we make a distinction between management journals and other social science journals for simplicity.

Due to the significant volume of work dedicated to examining knowledge management in a GVC, we analyzed it as a separate aspect of GVC strategic governance (see the section on learning above).

(Papers included in the review are marked with an asterisk.)

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Acknowledgements

We would like to express our sincere gratitude to Prof. Yadong Luo and three anonymous reviewers for their valuable guidance and support. We thank Haskayne School of Business at the University of Calgary and the National University of Singapore for funding the research for this paper through the Transformative Research Grant and Strategic Grant R109000183646, respectively.

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Kano, L., Tsang, E.W.K. & Yeung, H.Wc. Global value chains: A review of the multi-disciplinary literature. J Int Bus Stud 51 , 577–622 (2020). https://doi.org/10.1057/s41267-020-00304-2

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An extended approach to value chain analysis

Klemen knez.

Centre of International Relations, University of Ljubljana, Ljubljana, Slovenia

Andreja Jaklič

Metka stare, associated data.

The datasets analysed during the current study are available at http://www.wiod.org .

In the article, we propose a comprehensive methodology of value chain analysis in the international input–output framework that introduces a new measure of value chain participation and an extended typology of value chains, with the novel inclusion of domestic value chain to address the extent of fragmentation of purely domestic production. This allows for the simultaneous analysis of both global and domestic production fragmentation, the complex patterns of their evolution and their impact on economic development. The main contribution of the proposed methodology is conceptual: it permits the measurement of all value chain paths that pass through each country-sector from production to final consumption, whether the path includes downstream linkages, upstream linkages or their combination. Empirical application of this methodology shows the importance of including domestic fragmentation in value chain analysis: The fragmentation of both global and domestic levels of production has a significant positive correlation with economic growth. This implies that the effects of global production fragmentation must be analysed together with the changing structure of the fragmentation of domestic production to obtain the whole picture, one that might provide important information for policymaking and industrial policy.

Introduction

In recent decades, the growing complexity of the division of labour has been reflected in the fact that ever more production is occurring within value chains, both at home and abroad. Theoretical and empirical approaches to the analysis of value chains have advanced rapidly, yet are very eclectic and heterogeneous. The earliest definitions of commodity chains 1 date back to the world-systems 2 theory: “What we mean by such chains is the following: take an ultimate consumable item and trace back the set of inputs that culminated in this item— the prior transformations, the raw materials, the transportation mechanisms, the labour input into each of the material processes, the food inputs into the labour. This linked set of processes we call a commodity chain (Hopkins and Wallerstein 1977 )”. In the 1990s, the research programme of global commodity chains was first systematically outlined by Gereffi’s seminal contribution (Gereffi 1994 ) that defined three interlocking dimensions of the research: the input–output dimension, the spatial dimension, and the question of commodity chain governance 3 . This research period was characterised by moving away from a historical and macroeconomic perspective towards a special focus on industrial chains and the inter-firm cooperation perspective, with numerous case studies on value chains. The global value chain framework emerged early in the new century with the express aim of unifying the previous heterogeneous research (Gereffi 1999 ; Gereffi et al. 2001 ). On one hand, the global value chain approach increased the focus on the enterprise level and merged with the literature from international business and management 4 , while also drawing from the new institutional transaction cost approach 5 . On the other hand, the creation of international input–output tables 6 led to a revival of the aggregated macroeconomic approach to global value chains, albeit with a different focus than the world-systems approach. 7

In this article, we present a new methodology for measuring different value chain participation rates in the international input–output framework. Compared to the most widely used measurement of value chain participation introduced by Wang et al. ( 2017 ), we make two fundamental conceptual enhancements.

First, our methodology creates a single and consistent measurement of value chain participation on the country-sector level, as opposed to the two (upstream and downstream) participation rates that feature in Wang’s methodology. The argumentation and logic used to derive a single value chain participation share on the country-sector level is very similar to the approach of Arto et al. ( 2019 ), which combines the source- and sink-based approaches to export decomposition. The idea is that decomposition based on final demand (sink-based decomposition) is independent of the decomposition of downstream value added (source-based) and thus both can be linearly combined to grasp both the information regarding the source of value added as well as the path to final demand simultaneously. Methodologies of export decomposition have recently seen significant improvements (Arto et al. 2019 ; Borin and Mancini 2019 ; Miroudot and Ye 2021 ). However, the value chain participation rate methodologies either still chiefly rely on the value-added export matrix to describe the value flows between any two country-sectors in the economy (Johnson and Noguera 2012 ) and result in separate upstream and downstream participation rate measures or combine a sink- and a source-based measure in merely one-sided, forward-looking measures. Our approach to value chain decomposition no longer uses the value-added export matrix and instead breaks down the asymmetric value chain stemming both downstream and upstream from each country-sector concerned simultaneously . Creating a single consistent variable on the country-sector level that measures the overall level of participation in value chains enables the empirical testing of many research theses that were previously either limited to the aggregate level or had to be articulated separately in terms of measuring the impacts of upstream and downstream value chain integration.

Second, our methodology allows extensions of the value chain typology that are not possible with Wang’s approach to the decomposition of production activities or with export decompositions. We introduce a novel measure of the domestic value chain participation rate to measure the share of production which represents the extent of the fragmentation of domestic production. In place of a single and undifferentiated domestic component, we distinguish domestic production, which is fragmented (involving measurable cooperation among domestic firms), and domestic production, which is not fragmented (consisting of producing direct value for consumption without the cooperation of domestic firms). This makes our concept of the domestic value chain a completely new and different concept compared to Wang’s domestic component, which does not distinguish the two and combines both categories within a single undifferentiated concept. While Wang’s share of the domestic component is only a simple residual—a negation of the share of the fragmentation of global production and the global Ricardian trade share that does not provide information about the nature of the domestic economy, our novel methodology allows us to measure the extent of fragmentation of domestic production in addition to the usual study of the fragmentation of international production.

We aim to use our approach to provide methodological tools that facilitate exploration of the complex interrelationship of global and domestic value chains and their evolution over time. We believe this will add to understanding of the diverse patterns of the structural integration of various countries/sectors and the different effects of such patterns on economic development. While this is primarily a methodological contribution, we shall use elementary empirical data to try to show the possible link between the level of fragmentation of global and domestic production and overall economic growth.

The article is structured as follows: In Sect. 2 , we review the existing value chain indicators and address their shortcomings. In Sect. 3 , we present our methodology. In Sect. 3.1 , we present a new conceptualisation of value chain in the international I–O framework and define our object of disaggregation. A new value chain typology is presented in Sect. 3.2 where we also derive participation shares. In Sect. 4 , we present an example of empirical application and some basic empirical results of the new methodology to show the insights into economic structures that can be gained by using the new value chain measures and which links exist between value chain integration patterns and overall economic growth. Finally, we discuss the contributions of the paper, its limitations and possibilities for further research.

The most recent macroeconomic analyses of global value chains rely on the international input–output methodology. As international I–O data are essentially an integrated standard accounting data set harmonised on the sectoral level, information is lacking on the typology of value chain governance. This means the international I–O database cannot be the sole source for the study of production networks, which theoretically differ from purely open trade transactions by including at least some level of hierarchy, and which investigate the local embedding of production linkages (Buckley  2009 ; Henderson et al. 2002 ; Hess and Coe 2006 ; Hortaçsu and Syverson 2009 ). However, the general framework of global value chains can function without such distinctions and this makes the international I–O data set one of its most important sources of information. The key benefit of applying the I–O methodology in global value chain analysis is that aggregated information about the structure of value chains can be obtained, as opposed to isolated firm-specific case studies that can provide a more detailed understanding of different aspects of a given value chain. Thus, of the three dimensions of commodity chain research noted by Gereffi ( 1994 ), both the I–O aspect and the spatial dimension, can be considered in the international I–O approach, while the governance aspect cannot. Various aggregated and sectoral global value chain indicators, indices and measures have been proposed, all derived from the international I–O framework. GVC indicators may be roughly divided into measures of length 8 and participation rates, which we will discuss briefly.

Early I–O measures of the GVC structure were simple upstream and downstream indicators that corresponded to the measure of distance to final demand (upstream) and the Leontief measure of backward linkage (downstream) and were often referred to as the length of a value chain (Ahmad et al. 2017). Fally ( 2011 ) and Antràs et al. ( 2012 ) defined the downstream indicator to “reflect how many plants (stages) are involved in production one after the other” up to the point observed and the upstream indicator to “measure how many plants this product will pass through (e.g. by assembly with other products) before it reaches final demand (Fally 2011 , 10)”. Fally ( 2011 ) defined them as the number of vertical stages weighted by the value added of each stage, with the distance between each stage set to 1. 9 Since then, the average vertical distance has been the basic measure of the length of the value chain in the international I–O framework. Miller and Temurshoev ( 2015 ) further specified the existing measures by presenting upstream and downstream indicators in a matrix formulation using Ghosh’s forward and Leontief’s backward coefficient matrices (Ghosh 1958 ; Leontief 1936 ). These upstream and downstream measures are simple measures of the upstream and downstream length of value chains measured by the average vertical distance. Within this framework, further improvements were introduced by Muradov ( 2016 ), who focused on separating the domestic from the global production component while calculating the length of value chains.

The existing dominant conceptualisation of GVC participation measures is largely based on the work of Johnson and Noguera ( 2012 ), who produced a value-added export matrix that captures information on value flows in the economy between any two points (country-sectors) in the economy. This provides the basis for the disaggregation of value on the country-sector level, depending on whether the value was produced domestically for domestic consumption or involved cross-border transactions for either final or productive consumption (Koopman et al. 2014 ; Los et al. 2015 ; Wang et al. 2017 ). Since the value-added export matrix tells us about the source and destination of value added and covers all possible paths between any two country-sectors in the economy, there are two indicators of the share of GVC participation—the upstream and downstream share. The conception of the upstream participation share of participation starts from the value added of individual industries (country-sectors), disaggregating all possible paths leading to the realisation of their value, while the conception of the downstream share of participation starts with final consumption, disaggregating all possible paths of the downstream production linkages. Within this framework, disaggregation is defined on the domestic part, the “Ricardian trade” in finished goods, the simple GVC and the complex GVC, which is currently the most widely used accounting framework for GVC participation and thus far has been used by the best-known research on GVC carried out jointly by the WTO, the WB group, the OECD, IDE-JETRO, RCGVC-UIBE and the China Development Research Foundation (GVC Development Reports). Further improvements and clarifications of the framework were made by Borin and Mancini ( 2019 ), who derive their own measure of GVC-related bilateral trade flows by decomposing export to that attributable to traditional trade and GVC trade. Their indicator is composed of source-based backward and sink-based forward parts of their export decomposition, which can be calculated in a bilateral, country and world setting.

The development of I–O participation share measures of value chains, which are the primary interest of this article, evolved simultaneously with the development of methodologies of decomposing trade in value added (Johnson and Noguera 2012 ) as well as value added in trade (Arto et al. 2019 ; Borin and Mancini 2019 ; Miroudot and Ye 2021 ). However, despite similarities and some conceptual and formal mathematical overlapping, the fields of value chain participation share measures and value added in trade are driven by quite distinct research questions and research interests. On one hand, principal interest in decomposing exports is the correct evaluation of cross-border flows (properly removing double counting), assessing trade policy impacts and conducting overall impact analysis, either in a bilateral setting or with a focus on a specific country. On the other hand, value chain participation measures attempt to grasp the structure of an economy, sectoral and country interdependencies and the specific embeddedness of each production unit in different value chain structures, both at home and abroad. Value chain participation share measures usually correspond to a share of production, which statistically satisfies certain a priori criteria, such as “at least two cross-border transactions” or “at least one cross-border production sharing transaction”. The reviewed literature has contributed to better understanding of value chains and their I–O applied research, but still suffers two shortcomings that we try to address and improve with our approach.

The first main shortcoming of all current value chain participation share indicators is the lack of a single uniform measure for different value chain participation rates on the country-sector level. First, the value chain decomposition of Wang et al. ( 2017 ) results in downstream and upstream value chain participation rates, which provide two different types of information at the country-sector level. This is relevant for some types of analysis that deal with the relationship between upstream and downstream participation in GVCs, but there is a variety of situations where a common measure of GVC participation, defined uniformly on the country-sector level, is required either as the main object of the analysis or as a supplementary or control variable. 10 Second, GVC measures based on the decomposition of exports, even though they overcome the sink- and source-based decomposition in one unifying framework of export decomposition (Arto et al. 2019 ; Borin and Mancini 2019 ), are conceptually unable to offer a consistent solution to the question of a single country-sector value chain participation measure. That is because the criteria for export decomposition (separating domestic value added from foreign value added and the removal of double counting) do not correspond with the general criteria for different value chains on the country-sector level (the share of production with a certain number of cross-border transactions). Although export can be decomposed both with regard to the origin of the value added as well as the final demand, the very fact that the object of decomposition is export means it has a one-sided, forward orientation since export decomposition cannot address the fragmentation of production of a country-sector that has little or no exports (but can still form part of the fragmentation of a global value chain downstream). In this sense, the attempt by Borin and Mancini ( 2019 ) to provide a GVC measure of bilateral trade by decomposing exports cannot identify the share of production of a given country-sector which satisfies the criterion of a certain number of cross-border transactions, but only examines its forward part and is hence conceptually similar to Wang’s forward GVC measure. Our attempt to solve this issue demands the decomposition of the gross output (total output) of each country-sector to simultaneously account for both downstream and upstream value chain linkages.

The second major shortcoming of existing value chain indicators is the lack of a measure of domestic value chain fragmentation. The decomposition put forward by Wang et al. ( 2017 ) includes a broadly defined “domestic component”, which covers all of the value that does not comply with the GVC and Ricardian trade criteria. One of the major contributions of this article is to conceptually further divide this broad domestic component into a first part which comprises domestic production fragmentation (involving production sharing between at least two domestic firms) and the second part which does not. This yields new information regarding the share of production not involved in the fragmentation of global production, but is part of the fragmentation of domestic production and enables research into the role of domestic production fragmentation, which was impossible with the existing conceptualisations. As a result of the present disaggregation of participation shares into the “domestic component” and the GVC participation rates (and the Ricardian trade share) consisting of a simple duality that in its construction sums to 1, the share of the domestic component is never used in regressions (due to collinearity) and never even examined as a theoretical concept. It is simply a residual, a share that does not interest researchers given that all the information they disaggregate is included in their GVC participation rates. The existing approaches are used by researchers to focus exclusively on the international dimension of the fragmentation of production, neglecting the potential held by the international I–O methodology that allows analysis of domestic production fragmentation. Our approach is breaks ground in this area as it proposes a new concept of domestic fragmentation able to be measured on its own and according to its own definition and that is not collinear with the sum of the GVC participation rate.

Our methodological approach starts with the formal criteria, which is common for most of the GVC literature where value chains are defined according to certain transaction criteria (number of cross-border production-sharing transactions or similar). It is important to note that any such criteria are arbitrary and potential multiplicity of such criteria and hence value chain typologies can coexist and offer researchers some leeway in their empirical applications. 11 With a view to creating a uniform value chain measure on the country-sector level, we use the total output of each country-sector as the starting point of our disaggregation. Decomposing total output (as opposed to export or total value added) enables us to simultaneously grasp both the downstream and upstream value chain paths as well as the structure of the economy that is entirely domestic. Our decomposition begins with a set of the presented value chain tree matrices ( τ i ) which describe all of the value chain paths, from any country-sector of primary origin to any country-sector of production for final consumption that passes through (include a production stage of) a single particular country-sector. The logic of our approach is very similar to that of Arto et al. ( 2019 ) for combining the sink- and source-based decomposition of exports: because the decomposition of paths to final demand is independent of the decomposition of downstream value added, these decompositions can be linearly combined to capture both types of information in a single decomposition along two different dimensions at the same time. The big distinction with this approach is that object of decomposition is different—in our case, it is the total output (gross output) of each country-sector. Our choice of the object of decomposition is a prerequisite for properly capturing downstream linkages and, more importantly, properly accounting for the domestic structure of the economy. This formulation is the first attempt to capture information concerning the asymmetric value chain tree, which is a specific feature of each individual country-sector (Fig. ​ (Fig.1). 1 ). The proposed value chain tree matrices are unique in that they allow us to simultaneously capture the structure of the downstream and upstream value chain paths and to define value chain participation rates as a single measure for each country-sector. The crucial point of the proposed methodology is to enable the disaggregation of value chains based solely on the structure of value chain paths—taking into account whether these paths include only domestic production fragmentation, international production fragmentation or no production fragmentation at all. This allows us to introduce the concept of domestic value chain fragmentation that simply cannot be created within the existing framework of 2 separate participation indices. This multiplies the research opportunities offered by the value chain methodology based on the international input–output structure by permitting general analysis of the fragmentation of both domestic and global production and their interdependence along with any mutual effects of their development.

Value chain tree. Source: own conceptualisation and design. Arrows represent production-sharing transactions—buying and selling of intermediate products for production. Orange colour denotes production that does not involve any production sharing, while any combination of red or orange paths denotes domestic production fragmentation. Any value chain path which includes a cross-country production-sharing transaction (a black arrow) is part of a global value chain from the perspective of the particular unit in focus. The paths of value creating and value realisation in a general case continue to branch ad infinitum (three levels are chosen only for demonstration purposes)

Applying this methodology, we show that increasing fragmentation of global production in recent decades has been a general trend for most countries (with a backlash in later years), but different institutional arrangements and structural economic positions led to various types of global economic integration, bringing diverse effects for domestic fragmentation. With our methodology, we shall empirically demonstrate that in many countries with high growth and ever stronger global integration domestic fragmentation also increased. However, one can find cases where domestic fragmentation stagnated or even declined whereas fragmentation of the global value chain increased. The different types of integration in global value chains are the outcome of several structural and institutional developments. 12 On one hand, the simultaneous increase in domestic and global fragmentation might only be a consequence of the growing complexity and division of labour. Yet, on the other hand, the simultaneous rise in global fragmentation and drastic decline in domestic integration might be due to the fracturing of domestic vertically integrated companies, parts of which are integrated into global value chains as subsidiaries, or due to the gradual replacement of domestic suppliers by globally traded inputs, which may increase following a foreign takeover or privatisation. The wide range of possibilities mean that every production unit may hold a different structural position within global production as a whole, and different structural positions may imply varying levels of dependence, which can be a factor of economic performance, especially during a crisis (Horvath and Grabowski 1999 ).

The value chain tree

Conceptualisation.

We understand a value chain as a series of stages in the production of a product or service for the end user, where each stage adds value and the total value of the end product is the sum of the value added in each stage. For a value chain to exist, there must be at least two separate production stages. The existing GVC framework is analytically and empirically based on the idea that value is created in the production process and added to the value already present in the intermediate goods being used. The old value (value of intermediaries) is only transferred to the new product, while the newly created value is added linearly to the transferred value. The same idea also lies behind the elimination of double counting in standard gross trade statistics and exploration of the hidden underlying trade in value added, which provides insight into the international structure of trade (Arto et al. 2019 ; Johnson and Noguera 2012 ; Miroudot and Ye 2021 ). We make the same basic assumptions for value chain analysis.

We examine the structure of the economy from the perspective of a small unit 13 (country-sector) and capture its structural position within domestic and international production by measuring the degree of integration into domestic or global value chains. Each production unit is located within the production structure with a number of production-sharing transactions. On one side, the conditions of production are linked to the inputs produced by other firms in downstream linkages and, on the other, the final consumption of its product may only be reached after a series of upstream linkages in which its output is used as an input by other firms.

Accordingly, if one concentrates on a specific unit (country-sector) and aims to capture the upstream and downstream value chain linkages simultaneously , the value chain can be viewed as a tree, in contrast to the snake or spider analogy (see Fig. ​ Fig.1 1 ). 14 In the general case, the product is partly consumed immediately after production but also partly sent on to further stages of production and from each of these upstream stages it is further decomposed in the same way (etc., ad infinitum), spreading out like twigs and leaves until it ends completely in final consumption. Similarly, the primary value-creating activity can be represented by the structure of the roots, whereby value is only partially created in each stage since it requires pre-existing intermediates, which in turn are further decomposed in the same way ad infinitum.

To conceptualise and measure the value chain structure of each specific smallest unit of analysis (country-sector), we introduce the value chain path concept. From the perspective of a firm, a value chain path is a series of transactions between firms that lead from a value-adding process to final demand. While currently no data exist that would account for every transaction between all firms 15 , firm transactions still represent a basis for any I–O sectoral aggregation, which can help us detect tangible differences in the value chain path structure in different country-sectors. While it is impossible with the given limits of accounting data to follow a certain value chain path of each specific product of each specific firm, it is nevertheless possible to analyse the average sectoral structure of value chain paths subject to whether the aggregated transactions between firms (and to the final consumer) are domestic or global. Our use of the signifier “transactions between firms” and “production-sharing transactions” thus does not refer to individual transactions, but instead refers to the information captured by the aggregated sectoral international I–O data regarding the average structure of value chain transactions. Since we do not focus on following transactions for an individual product but distinguish domestic from cross-border transactions between production units, aggregated I–O data are a sufficient starting point. While the accounting rules require transactions between firms in the same sector and the same country to be formally accounted (represented in aggregated form by the purely diagonal elements of the international Leontief coefficient matrix), the same goes for transactions between domestic firms from different sectors (represented in aggregated form by the block diagonal elements of the international Leontief coefficient matrix with purely diagonal elements 0). In this aggregated setting, one can differentiate between domestic and cross-border transactions (quantitatively in terms of shares), which gives the basis for decomposing different value chain paths based on the criterion of the number of cross-border or domestic production-sharing transactions. As shown in Fig. ​ Fig.1, 1 , the value chain path can be decomposed with respect to two dimensions: Origin (where the value was primarily created) and the final stage of production (where the end product for consumption is finished).

Our goal of deriving a single value chain participation share measure on the country-sector level requires the derivation of an object able to track the value passing through a specific country-sector in focus along all possible paths from its origin to its end use. In this way, we decompose the value that forms part of the production process of a given country-sector along all its paths, which not only include the downstream paths leading to the country-sector under study and the upstream paths leading from it to final consumption, but also, and above all, the paths that combine upstream and downstream linkages and pass through that country-sector. In general, any value share can originate in any country-sector, and the same value share can also reach final consumption as a product of any country-sector. Compared to the approach of Johnson and Noguera, we add a third dimension 16 —the midpoint—the siphon through which the value from any origin to any final stage flows (Fig. ​ (Fig.1), 1 ), by combining decompositions based on value added and the final demand value chain path. This approach relies on similar reasoning as that of decomposing exports based on both value added and final demand (Arto et al. 2019 ).

The value chain tree of each country-sector is defined as the structure of the value chain paths, where this country-sector is the siphon via which the value chain paths pass. We show that each unit of analysis (country-sector) has a unique value chain structure that represents its structural position in the economy. Its output can be decomposed along every possible path within its value chain tree—i.e. along every value chain path that has its primary origin in any country-sector, passes through downstream linkages to the production stage of the country-sector which defines the value chain tree (the siphon), and ends in final consumption through upstream linkages as the final product of any country-sector.

Understanding the structure of value chains by empirically measuring all such paths of each country-sector (the smallest unit of analysis) is already an end in itself and can help with further understanding of the economy and its changing structure in terms of global integration, its specific regional and sectoral forms, and the complex interactions between domestic and global production fragmentation.

The object of disaggregation is a country-sector’s total output. Each country-sector’s total output is disaggregated along both downstream and upstream linkages that are unique to its specific value chain structure. Downstream disaggregation represents all possible value chain paths from the origin of production and upstream disaggregation all possible paths to satisfy the final demand, both with respect to the unique value chain tree of each country-sector. In this way, we disaggregate the same object—the total output of each country-sector—simultaneously along its downstream and upstream paths.

In contrast to approaches based on the matrix of value-added exports (Johnson and Noguera 2012 ; Wang et al. 2017 ) to cover all value-added flows between any two country-sectors in an economy, we propose a new object—a set of matrices that describe the value chain structure of each country-sector separately, covering all value chain paths from each primary origin to each final stage via the output of a single specific country-sector (Fig. ​ (Fig.1). 1 ). In this conceptualisation, each country-sector has a corresponding value chain tree described by the value chain tree matrix—while the value chain structure of the economy as a whole is described by the set of such matrices.

We derive our disaggregation within the static international Leontief demand-driven model. C , F and x are the main accounting datasets representing the intermediate consumption matrix, final consumption matrix and total output vector. The Leontief coefficient matrix is usually derived as A = C x ^ - 1 . The variables with hat are vectors transformed into diagonal matrices, f ^ represents a diagonal matrix of final demand and v ^ C a diagonal matrix of value-added coefficients. 17 The usual pairs of indices characterising the country and sector of origin ( s , i ) and the final destination ( d , j ) are replaced by a single index for each country-sector for more transparent notation. Since we are no longer working in the n × n dimensional space, but in the n × n × n dimensional space, we would need 3 pairs of indices, 1 pair for the country-sector of origin, 1 pair for the final stage and also 1 pair for the country-sector, which is the siphon through which all possible value chain paths characterise its specific value chain structure. Instead, we are working with only 3 indices, one for the country-sector of origin ( k ), one for the final stage country-sector ( j ) and one to characterise the country-sector value chain tree—the country-sector representing the siphon through which the value chain paths pass ( i ). 18

We start with the upstream part, by using standard Leontief’s derivation: x = ( I - A ) - 1 f , 3.3

Definition 1

Upstream output decomposition W :

W = x ^ - 1 ( I - A ) - 1 f ^ .

The matrix W represents the upstream output decomposition along all upstream value chain paths. Its element w ij represents the share of the total output of country-sector i that reaches final consumption as the end product of country-sector j , along all possible upstream production fragmentation paths in the economy. The i th row of W represents the disaggregation of the total output of the i th country-sector into output shares according to its final production stages that account for all direct and indirect paths of the upstream value transfers leading to the full realisation of total output (by being used directly or indirectly by other country-sectors as intermediate productive consumption). Each i th row of W may thus be characterised as a discrete probability distribution. On one hand, the upstream output shares of each country-sector i add up consistently to 1: ∑ j = 1 n w ij = 1 ∀ i . On the other hand, there is a clear economic interpretation of the probability distribution: w ij represents the probability that a randomly selected part of the total output of the i th country-sector will eventually be consumed as the final product of country-sector j , along any upstream value chain path.

For the downstream part, we begin with identity:

Definition 2

Downstream output decomposition Z :

Z = v C ^ ( I - A ) - 1 .

The matrix Z represents the downstream output decomposition along all downstream value chain paths. Its element z ki represents the share of the total output of country-sector i that is primarily created in country-sector k , along any possible downstream production fragmentation path in the economy. The i th column of Z represents the disaggregation of the total output of the i th country-sector into output shares, which represent all direct and indirect paths of the downstream value transfer from each country-sector that has contributed to the production of its output (through the direct or indirect production of intermediate productive consumption used by i ). Each i th column of Z may thus be characterised as a discrete probability distribution. On one hand, the downstream output shares of the individual country-sectors i add up consistently to 1: ∑ k = 1 n z ki = 1 ∀ i . On the other hand, there is a clear economic interpretation of the probability distribution: z ki represents the probability that a randomly selected part of the total output of the i th country-sector was produced by country-sector k , along any downstream value chain path.

The two matrices presented, W and Z , may appear as two sides of the same coin—similar to forward and backward decomposition, which has largely been exhausted in the international input–output literature. However, if we focus on a single country-sector ( i ), the i th column of Z and the i th row of W represent two probability distributions that take the transfers in the value chain into account, which result in two completely different and independent types of information. The i th column of Z contains information on the downstream structure of the value chain of the respective i th country-sector and the i th row of W contains information on the upstream structure of the value chain of the respective i th country-sector. For a given i th country-sector, the two probability distributions are asymmetrical. Most importantly, both probability distributions relate to the same object of investigation—the total output of country-sector i .

Using the total output of each country-sector seems to be the only way to disaggregate the same object into its upstream and downstream value chains. The object of decomposition of the upstream part (which is decomposed based on the paths to final demand) of a certain country-sector can be either its total output or total value added (even its export). However, the same is not possible for the downstream part (which is decomposed according to the origins of its value-added). The object of decomposition of the downstream part of a certain country-sector can only be its total output, which also makes up the totality of value-added shares along the whole downstream value chain. 19 In other words, the country-sector’s total output is an object that has both an upstream and a downstream path, while total value added and total export represent only that part of the output which has an upstream path, even if this upstream path is disaggregated by value-added origin. Using the total output share as the basis for disaggregating to the individual country-sector level is therefore a legitimate choice. This mainly explains why we derived the W matrix in terms of shares of total output ( 3.4 , 3.5 ) and not, as is usual, in terms of shares of value added—to make it perfectly clear that both upstream and downstream disaggregation have the same object—the total output of i , which includes both the value added of country-sector i and the total value added of the other country-sectors ( k ) downstream. The same object (total output) is then distributed along the upstream value chain paths (as determined by the i th row of W ) until it reaches final consumption along an upstream value chain path.

All input–output analyses assume the homogeneity of the smallest classification object (country-sector in our case). The level of detail of the data corresponds to the level of detail of the sector (and country) classification and within a country-sector there is no further information and quite strict homogeneity assumptions apply. We use the assumption of the homogeneity of production of each country-sector to combine the two probability distributions.

z ki represents the share of the total output of the i th country-sector, which was primarily produced by country-sector k . Due to the homogeneity of the total output of the i th country-sector, the w ij represents not only the probability that a random part of the total output of the i th country-sector reaches final consumption as a product of j , but also the probability that a random part of any share of the output of the i th country-sector reaches final consumption as a product of j . Since z ki is a share of the i th country-sector’s total output, its upstream decomposition is clearly and uniquely defined by the i th row of w .

The product w ij z ki thus simply represents the probability that a certain part of the total output of the i th country-sector is primarily produced in k and reaches final consumption as the product of j along any value chain path (upstream, downstream or a combination) passing through i . In other words, it represents the share of the total output of i that was produced by k and reached final consumption as a product of j . A simple multiplication of probabilities requires that the two events—a random portion of the total output of i produced by k and a random portion of the total output of i completed for consumption by j —are statistically independent. First, if certain parts of the total output of a particular country-sector were to behave differently from certain other parts of the same output, this would violate the homogeneity assumption, which is the basic assumption of the input–output structure and methodology. Second, at the level of economic theory it is relatively easy to argue about the statistical independence of the structure of upstream and downstream value chains: Nothing about the downstream structure of production in the i th country-sector implies anything about its upstream structure and vice versa . Both are calculated independently and provide completely different information: the downstream decomposition gives information about the inputs produced by other country-sectors used directly or indirectly in the production process of the i th country-sector, and the upstream decomposition gives information about how the product of the i th country-sector is consumed either directly or as part of the final product of other country-sectors.

Two separate vectors which disaggregate the value chain paths of the downstream ( i th column of Z ) and upstream value chain ( i th row of W ) thus span an entire matrix of total output shares that capture the value chain tree structure of the i th country-sector. We combine them with the direct product that defines the matrix of the value chain tree for each country-sector ( i ) by multiplying each element of Z e i → (the i th column of Z ) by each element of e i → T W (the i th row of W ).

Definition 3

Value chain tree matrix

τ i = Z e i → ⊗ e i → T W ; τ i ∈ R n × n , where e i → ∈ R n represents the standard orthonormal basis of R n .

This defines each element of the value chain tree matrix t ijk ∈ τ i as t ijk = w ij z ki . Each element of the value chain tree matrix τ i thus represents a share of the total output of country-sector i , which is primarily produced in country-sector k and consumed as an end product of country-sector j , along any upstream and downstream value chain path.

The main point of our derivation is not the expressed final value distribution of the total output of each country-sector along any of its upstream and downstream value chain paths, but the expression of the total output distribution (of the respective country-sector) along any value chain path, be it a downstream value chain path, an upstream value chain path or any combination of both paths at the same time.

The structure of the value chain tree matrices allows us to focus our disaggregation on the composition of the value chain paths covered by the two global Leontief inverses in the equation, the first representing all downstream parts of the value chain paths and the second representing all upstream parts of the value chain paths.

A single value chain path is determined by a series of concrete transactions between companies: It is a unique path from primary value creation (value created in production, not transferred from intermediate products) to value realisation (final consumption, not productive consumption of intermediate products), which passes through the production stage of the i th country-sector. The total output of i is not only disaggregated along all possible paths leading from any country-sector of origin via country-sector i to any country-sector of final stage production (as determined by τ i ), but is also disaggregated in much finer detail, along all the unique value chain paths that pass through i . That a concrete value chain path only forms part of the value chain tree matrix can easily be recognised if both inverses in τ i are replaced by an infinite series ( ( I - A ) - 1 = I + A + A 2 + ⋯ ). Such disaggregation then results in an infinite number of value chain paths, and the total output of the i th country-sector is distributed over all of these paths.

A certain value chain path share of the total output of i is determined by the Leontief technical coefficients a ij ∈ A . For example, take a value chain path consisting of value primarily produced in country-sector C S 1 20 , then used as an intermediate in C S 2 , which in turn is used as an intermediate in i (the country-sector whose value chain is broken down), and then sent as an intermediate to C S 3 , which is then sent as an intermediate to C S 4 , where it is finished and sold for consumption. This value chain path has an origin ( C S 1 ), a midpoint ( i ) and a final destination of production ( C S 4 ), as well as a concrete path with a length of 5 (5 country-sectors contribute to production from origin to final consumption). The share of the total output of the i th country-sector that may be attributed to this specific path is:

A specific unique value chain path of the i th country-sector’s value chain tree, that has its origin in k and final stage in j , can be written as:

Such a path has a downstream length of d and an upstream length of u - 1 - d and the path is determined by a unique set of production-sharing transactions from the origin to the final stage (from origin j = C S 0 , to C S 1 , to C S 2 , ..., to i = C S d , and further to C S d + 1 , C S d + 2 , ..., to k = C S u ). Leontief technical coefficients a C S p - 1 C S p determine each production-sharing transaction. The summation along the total output shares of i attributed to all such unique value chain paths, taking into account all permutations of possible transaction sequences and also all possible lengths (all possible length combinations of downstream and upstream lengths) as well as all possible origins and final stage destinations, results in a unit:

Our conceptualisation allows us to define decomposition criteria applicable to each value chain path of the value chain tree of the i th country-sector. Based on this property, we will decompose the value chain structure of each country-sector separately in the following section.

The value chain typology

Definitions.

The framework of the international I–O analysis allows the separate analysis of final transactions to consumers and transactions between companies. Based on this characteristic, we propose a typology of value chains based solely on the structure of linkages between enterprises, while adding a further decomposition with regard to different possible transactions to reach the final consumption post festum . 21 Each matrix τ i expressed by equation 3.13 represents the desegmentation of the total product of country-sector i along different downstream and upstream paths. When we refer to a value chain, we refer to the specific share of value (share of output) that corresponds to a particular value chain path. Path 22 of each value share generally includes any combination of domestic and cross-border production-sharing transactions, which can take place both downstream and upstream relative to the respective country-sector. Our criteria for the value chain typology thus refer to each specific value share corresponding to a single path within a value chain tree specific to each country-sector.

Definition 4

Domestic value chain

Domestic value chain (DVC) is a value that involves at least 1 domestic production-sharing transaction and involves only domestic production-sharing transactions along its path.

Definition 5

Global value chain

Global value chain (GVC) is a value that involves at least 1 cross-border production-sharing transaction along its path. We further distinguish two types of global value chains: simple and complex.

Definition 5.1

Simple global value chain

Simple global value chain (SGVC) is a value that involves exactly 1 cross-border production-sharing transaction anywhere along its path.

Definition 5.2

Complex global value chain

Complex global value chain (CGVC) is a value that involves more than 1 cross-border production-sharing transaction along its path.

Definition 6

No value chain

No value chain (NVC) is a value that does not involve any production-sharing transactions and has no value chain path within production.

A few brief comments are appropriate on our definitions and their interpretation. No material product or service belongs to a single classification of value chain, and no enterprise can be considered part of a single type of value chain. The output of each enterprise belongs to a variety of value chain paths. In general, one part of the output comprises many cross-border transactions, another part only domestic transactions, and yet another part their relatively complex interrelationship. Each product (or country-sector in our case) can be assigned different shares of the value chain paths. These shares are objects that provide information about the structure of the economy. For example, virtually no enterprise could be classified exclusively as part of a no value chain, but some enterprises that provide services (e.g. domestic services) have a relatively high share of output that has no value chain path, especially in services, where salaries account for almost all of the enterprise’s expenditure and where their product directly satisfies final demand. On one hand, enterprises that specialise in intermediate goods are always part of a value chain, whether domestic or global. On the other hand, even modern industries such as food-processing and pharmaceuticals, also have a certain (usually small) share of value added that is not part of any value chain (no value chain share), corresponding to the share of domestic value added in these industries that is also directly consumed (part of output that has no value chain path). The value chain shares and their changes are the object that provide information about the structure of the economy, whether on the sector or country level. As the economy develops, the division of labour also increases, which corresponds to the growing fragmentation of production, in particular international production fragmentation, and a decrease in shares where there is limited or no value chain fragmentation. Compared to the existing typology of value chains, this revised typology allows for analysis of the relationship between global and domestic fragmentation, which might prove especially relevant for the policies of developing countries.

The decomposition of paths

Our value chain typology is established according to criteria along the entire value chain. For this reason, we disaggregate the value chain tree matrices τ i in terms of criteria for different types of value chain paths. Our decomposition consists of the decomposition of two Leontief inverses, which may be interpreted as the decomposition of the downstream part and upstream part of each value chain path, as defined by equation 3.11 : τ i = v C ^ ( I - A ) - 1 e i → ⊗ e i → T x ^ - 1 ( I - A ) - 1 f ^ . The decomposition is constructed based on of the criteria of the number of cross-border and domestic production-sharing transactions that are consistent with the revised value chain typology.

First, we investigate the decomposition of only a single Leontief inverse (interpreted symmetrically with respect to our criteria in the upstream and downstream value chain) and only then do we analyse the decomposition of all value chain paths characterised by the two Leontief inverses. The international I–O data have a specific block matrix structure in which the block diagonal elements represent domestic production-sharing transactions and the block off-diagonal elements represent international production-sharing transactions ( A D denotes domestic—block diagonal—and A CB cross-border—block-off diagonal—part of A ), which allows us to decompose the Leontief inverse in the following way:

• I obviously represents that part of the output which contains no production-sharing transactions —no value chain linkages. In the upstream part, it represents the share of total output that directly satisfies final demand (i.e. no upstream value chain), while in the downstream part it represents the direct value added of the country-sector whose production is being decomposed (i.e. no downstream value chain).
• A D ( I - A D ) - 1 = A D + A D 2 + A D 3 + ⋯ represents that part of output which contains at least 1 domestic production-sharing transaction and contains only domestic production-sharing transactions .
• ( I - A D ) - 1 A CB ( I - A D ) - 1 represents that part of the output which contains at least 1 production-sharing transaction and contains exactly one cross-border production-sharing transaction somewhere along its value chain path. This can be demonstrated by paraphrasing the part as all possible combinations of a single cross-border transaction among any possible set of domestic production-sharing transactions that occur before or after the single cross-border production-sharing transaction: ( I - A D ) - 1 A CB ( I - A D ) - 1 = A CB + A CB A D + A CB A D 2 ⋯ + A D A CB + A D A CB A D + A D A CB A D 2 ⋯ + A D 2 A CB + A D 2 A CB A D + A D 2 A CB A D 2 + ⋯ + ⋮
• ( I - A ) - 1 - ( I - A D ) - 1 - ( I - A D ) - 1 A CB ( I - A D ) - 1 represents that part of the output which contains at least two or more production-sharing transactions , of which at least two are cross-border production-sharing transactions . This logically follows from the fact that parts (1), (2) and (3) cover the total output that contains less than two cross-border transactions, and that the full Leontief inverse covers the total output.

Value chain tree matrix decomposition

We proceed by disaggregating all of the value chain paths as they are structured in the value chain tree matrices. Using the decomposition of the Leontief inverse that we disaggregated in the previous subsection and inserting it into Eq. 3.11 , we obtain 16 components ( 4 × 4 product) for each matrix τ i . 23 This disaggregation along both the upstream and downstream paths is the basis for deriving value chain shares that correspond to our typology. We decompose each τ i matrix describing all possible value chain paths of the output of the i th country-sector into a matrix consisting of domestic value chain paths only, a matrix containing all possible global value chain paths (as well as simple and complex global value chain paths separately), and a matrix consisting only of the value that has no value chain path.

Definition 7

Domestic value chain tree τ i DVC

The domestic value chain tree represents all value chain paths of the output of each country-sector which, according to Definition 4 , are part of the domestic value chains. In Fig. ​ Fig.1, 1 , the domestic value chain paths are marked in red. Domestic value chain paths are defined as all paths that contain at least one red-coloured linkage (representing transactions between domestic enterprises) and include only red-coloured linkages and orange paths (representing the value creation or realisation in the respective country-sector in focus). The first part ( v C ^ A D ( I - A D ) - 1 e i → ⊗ e i → T x ^ - 1 f ^ ) covers the downstream domestic value added (downstream domestic path), which ends as the i th country-sector final stage (no upstream path), the second part ( v C ^ e i → ⊗ e i → T x ^ - 1 A D ( I - A D ) - 1 f ^ ) covers the value added of the i th country-sector (no downstream path) that is transferred via the upstream domestic value chain (upstream domestic path), and the third part ( v C ^ A D ( I - A D ) - 1 e i → ⊗ e i → T x ^ - 1 A D ( I - A D ) - 1 f ^ ) comprises the downstream domestic value added that is used as an intermediate product in the production of i and then used as an intermediary further in the upstream domestic value chain until it reaches final demand (both downstream and upstream domestic paths). All three cases meet the definition of a domestic value chain.

Definition 8

Global value chain tree τ i GVC

The global value chain tree represents all paths of the output of the individual country-sector, which form part of global value chains according to Definition 5 . In Fig. ​ Fig.1, 1 , the global value chain paths are represented by all paths containing at least one black-coloured linkage (representing cross-border transactions between enterprises). Global value chain paths can contain any number of red (domestic) and orange (no value chain) linkages provided there is at least one black (cross-border) linkage along their path. The first element ( v C ^ ( I - A D ) - 1 e i → ⊗ e i → T x ^ - 1 [ ( I - A ) - 1 - ( I - A D ) - 1 ] f ^ ) covers the downstream domestic and no value chain paths, which have global upstream linkages (simple or complex), the second element ( v C ^ [ ( I - A ) - 1 - ( I - A D ) - 1 ] e i → ⊗ e i → T x ^ - 1 ( I - A D ) - 1 f ^ ) covers downstream global linkages (simple or complex), which have a upstream domestic or no value chain path and the third element ( v C ^ [ ( I - A ) - 1 - ( I - A D ) - 1 ] e i → ⊗ e i → T x ^ - 1 [ ( I - A ) - 1 - ( I - A D ) - 1 ] f ^ ) covers the value that has global paths both upstream and downstream. All of these cases correspond to our definition of a global value chain.

Definition 8.1

Simple global value chain tree τ i SGVC

The simple global value chain tree represents all paths of the output of each country-sector that are part of simple global value chains as defined by 5.1 The first element ( v C ^ ( I - A D ) - 1 e i → ⊗ e i → T x ^ - 1 ( I - A D ) - 1 A CB ( I - A D ) - 1 f ^ ) covers a downstream domestic and no value chain path that has simple global upstream linkages and the second element ( v C ^ ( I - A D ) - 1 A CB ( I - A D ) - 1 e i → ⊗ e i → T x ^ - 1 ( I - A D ) - 1 f ^ ) covers downstream simple global linkages that have an upstream domestic or no value chain path. These are the only cases that fit our definition of a simple global value chain. A value chain path covering both downstream and upstream simple global linkages already has more than 1 cross-border transaction and is hence part of a complex global value chain.

Definition 8.2

Complex global value chain tree τ i CGVC

The complex global value chain tree represents all paths of the output of individual country-sectors that form part of complex global value chains as defined in 5.2 The first element ( v C ^ ( I - A D ) - 1 e i → ⊗ e i → T x ^ - 1 [ ( I - A ) - 1 - ( I - A D ) - 1 - ( I - A D ) - 1 A CB ( I - A D ) - 1 ] f ^ ) covers the downstream domestic and no value chain path, having complex global upstream linkages, the second element ( v C ^ [ ( I - A ) - 1 - ( I - A D ) - 1 - ( I - A D ) - 1 A CB ( I - A D ) - 1 ] e i → ⊗ e i → T x ^ - 1 ( I - A D ) - 1 f ^ ) comprises downstream complex global linkages, which have an upstream domestic or no value chain path, and the third element ( v C ^ [ ( I - A ) - 1 - ( I - A D ) - 1 ] e i → ⊗ e i → T x ^ - 1 [ ( I - A ) - 1 - ( I - A D ) - 1 ] f ^ ) represents combinations of global downstream and upstream paths (simple-simple, simple-complex, complex-simple, complex-complex). All of these elements meet our definition of a complex global value chain because the value in all cases crosses borders for production at least twice.

Definition 9

No value chain tree τ i NVC

A no value chain tree represents that part of the output of each country-sector which is not part of a value chain according to Definition 6 . In Fig. ​ Fig.1, 1 , a no value chain path is represented by the orange colour only (any other linkage represents a value chain path). Solely the share of value added produced in the respective country-sector in focus (no downstream stages) and also completed for final consumption (no upstream stages) in the same production phase satisfies this criterion. Since the I–O method distinguishes between a product used as an intermediate product within the same sector 24 and the product manufactured for final consumption, the use of this definition as no value chain does not depend on the level of detail of I–O data disaggregation. The cyclical effect of the production of intermediate goods within the same country-sector is already included in the domestic value chain tree and, after taking into account all of the defined value chain paths (domestic, simple and complex global value chain paths), a value share remains without a value chain path and with a simple representation as the value added of the country-sector which is also directly consumed. This represents a value that has no path in terms of transactions that represent the fragmentation of production.

This concludes the value chain tree decomposition, which can be written as:

The value chain participation rates

In Sect. 3.1 , we showed that a set of value chain tree matrices τ i represents all possible value chain paths of the output of each country-sector and that the summation along all shares of total output assigned to all such unique value chain paths yields a unity for each value chain tree (Eq. 3.14 ). Namely, we presented a unique disaggregation of the output of each country-sector along all of its value chain paths. In the same way, the summation along the two disaggregating dimensions of our decomposed set of matrices (global, domestic and no value chain tree matrices) captures the overall share of the total output of each country-sector i that meets the criteria by which the value chain paths were decomposed by including either only domestic value chain paths, only global value chain paths, or only values that have no value chain paths at all. In other words, the summation of the disaggregated value chain matrices along any origin and end stage represents the share of output of each country-sector that has either a domestic, a global or a no value chain.

Definition 10

Domestic value chain share DVCs

D V C s ∈ I R n ; D V C s i = ∑ j = 1 n ∑ k = 1 n t ijk DVC ; D V C s = 1 T τ 1 DVC 1 1 T τ 2 DVC 1 ⋮ 1 T τ n DVC 1 .

Domestic value chain share represents the share of each country-sector’s output that has a domestic value chain path.

Definition 11

Global value chain share GVCs

G V C s ∈ I R n ; G V C s i = ∑ j = 1 n ∑ k = 1 n t ijk GVC ; G V C s = 1 T τ 1 GVC 1 1 T τ 2 GVC 1 ⋮ 1 T τ n GVC 1 .

Global value chain share represents the share of each country-sector’s output that has a global value chain path.

Definition 11.1

Simple global value chain share SGVCs

S G V C s ∈ I R n ; S G V C s i = ∑ j = 1 n ∑ k = 1 n t ijk SGVC ; S G V C s = 1 T τ 1 SGVC 1 1 T τ 2 SGVC 1 ⋮ 1 T τ n SGVC 1 .

Simple global value chain share represents the share of each country-sector’s output that has a simple global value chain path.

Definition 11.2

Complex global value chain share CGVCs

C G V C s ∈ I R n ; C G V C s i = ∑ j = 1 n ∑ k = 1 n t ijk CGVC ; C G V C s = 1 T τ 1 CGVC 1 1 T τ 2 CGVC 1 ⋮ 1 T τ n CGVC 1 .

Complex global value chain share represents the share of each country-sector’s output that has a complex global value chain path.

Definition 12

No value chain share NVCs

N V C s ∈ I R n ; N V C s i = ∑ j = 1 n ∑ k = 1 n t ijk NVC ; N V C s = 1 T τ 1 NVC 1 1 T τ 2 NVC 1 ⋮ 1 T τ n NVC 1 .

A no value chain share represents the share of each country-sector’s output that has a no value chain path.

With this, we conclude our disaggregation of each country-sector’s total output with respect to its specific value chain integration based on production-sharing linkages. We can summarise our decomposition in the simple vector form: D V C s + G V C s + N V C s = 1 , 3.20

Decomposition of the transaction to the final consumer

Since all value chain paths within production are covered and decomposed, we still have one last transaction to the consumer to complete the value chain path from production to consumption. We can decompose the final transaction to the consumer upon the criterion of whether it is a transaction to domestic consumers or a cross-border transaction (export of the final product for consumption). Domestic consumption here refers to the country-sector in which the last stage of production took place and not the country-sector whose value chain we are analysing. Each country-sector has a unique value chain and a specific structure of value chain paths. The completion of each value chain path by a transaction to the consumer can be achieved by an additional cross-border transaction of exporting the final product or consumption in the country where the product was finalised. Such a further decomposition of the value chain paths allows a more detailed analysis of the value chains.

The I–O data include information on the transaction to final consumers within matrix F , which can be decomposed into its cross-border and domestic flows to final consumers ( F = F CB + F D ) due to its block vector structure. We construct a matrix of all cross-border final consumption flows and a matrix of all domestic consumption flows:

Every value chain path within production can thus be further decomposed with an additional criterion of a transaction to final consumers. Each set of disaggregated value chain matrices, defined by Eqs. 3.16 and 3.17 , can be separated on two matrices, one covering all of the production paths that end in domestic final consumption (no export - τ i NE ) and the other all of the production value chain paths that end with exporting for final consumption ( τ i E ).

Due to their simple additive properties of operation, all of the decomposed value chain tree matrices are similarly decomposed to ones with exporting or with no exporting as the final transaction.

The value shares that are part of each value chain path are thus further decomposed, as explained in Sect. 3.2.4 . The final decomposition of the output is thus a decomposition along each value chain, as defined by criteria that simultaneously take account of transactions related to the production fragmentation (different value chains) and the final transaction to the consumer. A share of value that has either a domestic, global or no value chain has as its final transaction to the consumer either an export or a no export transaction, which provides a detailed decomposition of the participation shares that can be used to construct different composite indices suitable for different research questions. D V C s NE + G V C s NE + N V C s NE + D V C s E + G V C s E + N V C s E = 1 3.26

Results and discussion

The proposed measures broaden the scope for empirical application and static analysis of international production and trade. The contribution of our approach entails the simultaneous insight into domestic and global value chains, which allows the study of their interaction and structural changes in economies. All elements of the new typology may vary over time, from country to country and sector to sector and are relevant research topics. The derived participation shares are also simple fragmentation measures, and each smallest unit of analysis (country-sector) is represented by a single measure (scalar share) that covers the extent of overall value chain fragmentation, as opposed to separate downstream and upstream indicators.

Due to the limitations of the paper and its chiefly methodological focus, we present only some very basic empirical results. First, we show the global averages of value chain participation rates based on WIOD 2016 data and the global average participation rates for the manufacturing and service sectors separately (Figs. ​ (Figs.2, 2 , ​ ,3 3 and ​ and4). 4 ). Using our methodological approach, we observe that the global average GVC share of world output consistently exceeds 20%, reached almost 24% at its peak before the global recession, and then stagnated slightly below this level until 2014 (Fig. ​ (Fig.2). 2 ). This suggests that the most recent estimates of GVCs’ share of production between 10 and 15% (Dollar 2017 , p. 2; Li et al. 2019 , p. 12) may be undervalued. As expected, the manufacturing sector is globally integrated to an above-average extent, with the share in the global value chain rising from 35 to over 40% before the crisis and then stagnating around this level after a brief recovery. The share of the complex global value chain shows the highest relative growth, while the average increase in global value chain integration exceeds the decline in domestic value chain integration. Interestingly, the decline in global integration in times of crisis had almost no impact on that part of the economy without value chain fragmentation, while domestic fragmentation increased almost in proportion to the decline in global integration. Hence, the crisis did not lead to a general decline in the fragmentation of production, but only to a decrease in its global character. For services, in contrast, less than 15% of total output has a global value chain path, although services show some increase in global integration, mainly due to decreasing domestic integration (which may be attributed to the globalisation of business services), while that part of the economy without a value chain appears relatively stable. For this reason, vulnerability to external financial shocks was much less pronounced in services during the crisis.

World average participation rates

Source: WIOD, 2016; own calculations.

World average of manufacturing.

Source: WIOD, 2016; own calculations

World average of services.

Source: WIOD 2016; own calculations

As the data for the world average conceal large differences between countries, we also show the value chain participation shares of manufacturing for China, the USA and the average of the economically most integrated new EU members—3 Baltic and 4 Visegrad countries (Figs. ​ (Figs.5, 5 , ​ ,6 6 and ​ and7), 7 ), which reveal structural differences and diverse patterns of development in global and domestic integration. China has on average a high share of domestic production integration (around 65%) and is one of the few economies where the share of domestic integration grew by almost 10 percentage points between 2004 and 2014. In the United States, the picture is reversed, while the already lower average share of domestic integration is steadily shrinking. A completely different pattern is seen in the Baltic and Visegrad European countries, which became EU members in the new millennium. On average, these countries’ integration into global value chains in the manufacturing industries rose from an already high 53 to 69% during the observed period. At the same time, there was a huge relative decline in the already below-average share of domestic fragmentation from 32 to 18%. Interestingly, almost all of the growth in the global value chain share in Central and Eastern EU countries was due to the increase in complex global value chain linkages, while simple global value chain linkages remain relatively stable.

China manufacturing participation rates.

New EU countries manufacturing.

USA manufacturing participation rates. creditSource: WIOD 2016; own calculations

Finally, we use the fact that we have created uniform participation rates by performing a simple between-effects regression to test the relationship between the level of domestic and global fragmentation and economic growth measured by GDP per capita. Since short-term productivity fluctuations can hardly be explained by an economic structure expressed in value chain shares, we use a cross-sectional approach to test the long-term effects of different levels of domestic or global fragmentation on economic growth. Our observations relate to the 43 countries included in the WIOD 2016 data, and the variables are their average annual growth, the average DVC and GVC shares, with the average logarithm of GDP per capita as a control for convergence, the average logarithm of the annual population as a control for the size of the country, and the EU control dummy for potential EU specifics. The main regression equation with between effects is derived in the usual way out of a general panel data model:

y it = α i + l o g G D P it β 1 + D V C it β 2 + G V C it β 3 + ϵ it ,

y i ¯ = α + l o g G D P i ¯ β 1 + D V C i ¯ β 2 + G V C i ¯ β 3 + ( α i - α + ϵ i ¯ ) .

To ensure a consistent estimator, α i must be random effects. y it is yearly growth of GDP per capita, l o g G D P it is a logarithm of GDP per capita, while D V C it and G V C it represent shares of domestic and global value chains as calculated by the proposed methodology. The number of countries is 43 and number of time units is 15 (from 2000-2014), making a total of 645 observations in the panel.

The regression results are shown in Table ​ Table1. 1 . The logarithm of GDP per capita is a significant variable and negatively related to growth. The result simply reflects the fact that higher GDP implies less potential for higher growth rates, as implied by the convergence literature. Taking this into account, both the DVC share and the GVC share are highly significant variables that have a positive effect on growth rates. Therefore, both domestic and global integration can have a significant impact on economic growth. The same result applies after the introduction of additional controls on country size and EU specifics. Due to the principally methodological orientation of the article, we refrain from a detailed interpretation of the regression results. Yet, it should be noted that it is difficult to separate cause and effect while applying econometric analyses—a country in recession for external reasons could experience a decline in global and domestic production fragmentation due to those same external reasons. In any case, there is a correlation between economic growth and the degree of production fragmentation, whether it is domestic or global. A country that experiences an overall decrease in production fragmentation (domestic fragmentation declines faster than global increases), regardless of an increase in global production integration, might experience a negative impact on economic growth compared to similarly developed countries, in line with our findings. 25 An increase only in participation in global value chains therefore does not necessarily enhance the growth due to various forms of integration 26 with different effects on domestic integration, which is also an important factor in determining economic growth. Further studies are needed to examine the relationship between domestic and global fragmentation and diverse patterns of structural integration that could also help in assessing the impact of unpredictable circumstances (e.g. COVID 19) on individual countries, regions or sectors.

Regression results

Source: WIOD, 2016; WB; own calculations

* p < 0.05 , ** p < 0.01 , *** p < 0.001

We have proposed a new methodology for measuring the participation shares of different types of value chains in the international input–output framework. We addressed the lack of a consistent unitary measure of value chain integration on the country-sector level by proposing a new concept of the value chain tree for each country-sector, covering all value chain paths from value creation (downstream linkages) through a single country-sector to final consumption (upstream linkages) simultaneously. By capturing the structure of all value chains in a series of value chain tree matrices, we add a new mathematical object that serves as a basis for deriving the proposed new indicator of value chain participation, which we contribute to the existing collection of indicators.

This methodology allows us to introduce an extended typology of value chains by distinguishing and disaggregating all production activity into the following types: no value chain, domestic value chain, and global value chain—further differentiated into simple and complex global value chains. The most important new conceptual subdivision in the extended typology relates to the subdivision of the existing ’domestic component’ into a no value chain and a domestic value chain. This subdivision, which is only possible with the proposed methodology, provides a better representation of domestic production interdependencies and permits comparative analyses of the simultaneous development of domestic and foreign production interdependencies, thereby enabling aggregated analyses of domestic and global production fragmentation and its interrelated development as influenced by outsourcing or offshoring. Another big change introduced by the new typology is its fundamental production-related character: all distinctions between different types of value chains are made only with regard to (potential) production fragmentation, with a separate examination of the transaction to the final consumer—which may or may not be cross-border. This affirms the concept of value chain as related primarily to the fragmentation of production, while the post festum differentiation is also derived based on the last transaction to the final consumer.

The proposed methodology and typology of value chains provides researchers with new opportunities to conduct future research on different levels of disaggregation, be it comparative geographical analysis (e.g. comparing the evolution of value chain measures between two countries or between groups of countries) or observing the evolution of value chains in different sectoral disaggregations. The preliminary illustration of the new methodology, which attempts to link both domestic and global production fragmentation with long-term growth rates, shows a positive correlation between both global and domestic production fragmentation with economic growth. This result may indicate that it is the general complexity of the division of labour, reflected in the general fragmentation of production, that is chiefly correlated with growth, irrespective of its global or domestic nature. Accordingly, the proposed measure and the new typology of value chains, in particular the novel conceptualisation of domestic value chain fragmentation, could bring to light important information that has been concealed in the existing typology, which conceptualises the domestic component only as a negation of the global value chain and thus did not allow research with explicit questions concerning domestic integration. The complex development of globalisation in recent decades and the shifts of late towards the localisation and regionalisation of economic integration caused by political, economic and external factors make this new approach increasingly relevant. The proposed measure, particularly in conjunction with data from other sources, could further deepen the theoretical discussion and empirical investigations.

In conclusion, we believe that our new methodological approach and the new extended typology of value chains associated with it provide fertile grounds for obtaining deeper insights into different types of value chains as well as a broader set of tools of use for various extensions of research.

Acknowledgements

The authors thank the editor and all reviewers for their comments and suggestions that helped improve this article.

Appendix A: Notations

n S ∈ I N Number of sectors.

n C ∈ I N Number of countries.

n ∈ I N ; n = n S ∗ n C Number of country-sectors.

1 ∈ I R n Vector of ones.

1 → ∈ I R n C vector of ones.

e i → ∈ I R n ; e i j = δ ij Standard orthonormal basis of I R n .

I ∈ I R n × n Identity matrix.

x ∈ I R n Total output vector.

x ^ ∈ I R n × n ; x ^ = d i a g ( x ) Total output matrix.

C ∈ I R n × n Intermediate consumption matrix.

F ∈ I R n × n C Final consumption matrix on the country level. 27

f ∈ I R n ; f = F 1 → Total final consumption vector.

f ^ ∈ I R n × n ; f ^ = d i a g ( f ) Total final consumption matrix.

A ∈ I R n × n ; A = C x ^ - 1 Leontief technical coefficient matrix.

G ∈ I R n × n ; G = x ^ - 1 C Ghosh technical coefficient matrix.

v ∈ I R n ; v T = x T - 1 T C = 1 ( x ^ - A x ^ ) = 1 T ( I - A ) x ^ Vector of total value added.

v ^ ∈ I R n × n ; v ^ = d i a g ( v ) Total value-added matrix.

v C ∈ I R n ; v C T = v T x ^ - 1 = 1 T ( I - A ) Vector of value-added coefficients – value-added share in total output.

v ^ C ∈ I R n × n ; v ^ C = d i a g ( v C ) Value-added coefficients matrix.

C , A and G have a block-matrix structure I R ( n S × n S ) × ( n C × n C ) , while F has a block vector structure I R n S × ( n C × n C ) . Diagonal block elements with respect to countries represent domestic intermediate transfers and domestic consumption and off diagonal block elements represent transactions that cross a border either for intermediate use or final consumption.

C = C CB + C D A = A CB + A D G = G CB + G D F = F CB + F D f CB ∈ I R n ; f CB = F CB 1 → Total final consumption by exporting.

f D ∈ I R n ; f D = F D 1 → Total final consumption by domestic transactions.

f ^ CB ∈ I R n × n ; f ^ CB = d i a g ( f CB ) Total final consumption by exporting matrix.

f ^ D ∈ I R n × n ;

f ^ D = d i a g ( f D ) Total final consumption by domestic transactions matrix.

Appendix B: τ i decomposition

We make a demonstration of the methodology on a simple 2 sector 2 countries numerical example. 28 This simple case of international economy has following intermediate consumption matrix and final demand:

Total output is the sum of all the intermediate and final demand:

Calculation of value added coefficients and Leontief technical coefficients:

We continue with separate upstream and downstream decompositions, W and Z :

Value chain tree matrices are calculated for each country-sector in the following manner:

For each type of value chain (DVC, GVC, NVC,...) we have 4 matrices, each covering all the value chain paths of each country-sector (we have 4 in our example) that conform to our value chain criteria.

The value chain participation shares are obtained by summation of all elements of the value chain tree matrices:

Authors' Contributions

KK contributed the methodological derivations and empirical results, all three authors contributed to the literature review, the discussion of the results and extensive proofreading.

The authors of this article acknowledge the financial support received from the Slovenian Research Agency (research core funding No. P5-0177 and No. 52075).

Availability of data and materials

Declarations.

Not applicable.

The authors declare that they have no competing interests.

1 The term global commodity chain is a predecessor of global value chain.

2 Embracing a historical and macroeconomic approach to the analysis of the global division of labour, the world-systems approach examines the unequal patterns of exchange along global commodity chains as well as different structural patterns of the international integration of the core, periphery and semi-periphery (Arrighi and Drangel 1986 ).

3 Governance was conceived as either consumer-driven (apparel sector) or producer-driven (automotive sector). This approach was further extended by Ponte and Sturgeon ( 2013 ).

4 Porter’s (1985) concept of the intra-firm value chain is often used to discuss the specialisation of enterprises, and core competencies and business literature on multinational enterprises overlap with the global value chain framework.

5 Which was used to extend the producer-driven and consumer-driven governance typology of commodity chain research to a more general typology of value chain linkages, from transactions in a completely free market to a strict hierarchy (Gereffi et al. 2005 ).

6 In international economics, use of the input–output methodology grew in importance as researchers of various international incentives integrated nationally based input–output tables into harmonised global input–output tables. The most prominent are the World Input–Output Database (Timmer et al. 2015 ), the OECD’s Trade in Value Added and the EORA (Lenzen et al. 2013 ).

7 While all heterogeneous approaches to value chains focus on a development issue, the recent GVC approach has been adopted by international institutions to highlight the gains from liberalisation and industrial upgrading, while the world-systems approach critically examines unequal rewards along the value chain and different structural integration patterns that may cause the perpetuation of unequal development (Gereffi 2018 ; Taglioni and Winkler 2016 ).

8 Relative position indices can easily be derived from length measures as simple ratios.

9 Using a method similar to that used to calculate the average propagation length required for the analysis of the dynamic response to shocks, defined by Dietzenbacher and Romero ( 2016 ).

10 It is also obvious that a simple solution, such as using the average of existing upstream and downstream indicators, cannot be justified in theory. If, for example, a given country-sector’s share in the upstream global value chain is high (close to 100%) and its share in the downstream global value chain is relatively low (close to 0%), then the average share in the value chain would be around 50%, which is misleading because the value chain as a whole is almost entirely global (using the criterion that the value crosses a border at least once). As far as value chain paths are concerned, despite the small share of downstream global value chain paths, a high share (close to 100%) of the same paths continues in the upstream global value chain such that production as a whole has a very high global share (close to 100%), while the use of the average of the upstream and downstream indicators does not correspond to the definition of the global value chain.

11 For example, in a forthcoming article we explore the decomposition of value chains based on the criterion of the number of domestic transactions subject to meeting the usual global value chain criterion of having at least one production-sharing cross-border transaction. In this setting, we decompose the global value chain share into a GVC with no domestic cooperation, a GVC with simple domestic cooperation, and a GVC with complex domestic cooperation, offering information on the specific pattern of the EU periphery’s integration.

12 For example, the concept of integrated periphery was introduced to describe a specific type of integration in the case of the Slovak and Czech car industries, characterised by their proximity to consumer markets, cheaper labour force, the absence of positive spillover effects and lack of domestic linkages (Oldřich and Vladan 2019 ; Pavlínek 2018 ).

13 In our derivation, which is consistent with most existing international I–O data, the country-sector is the smallest object of analysis. When we refer to our methodology and derive it, the reference to the country-sector refers to the smallest object of analysis given by the level of detail of the I–O data set. If the I–O data sets were built on a more detailed structure at the enterprise level (greatly increasing the dimension), the proposed methodology and measures would work in the same way, with the value chain still structured around the smallest possible unit—in this case the enterprise. Despite the starting point of analysis of value chain structure being the smallest units of analysis, the approach offers many different aggregation possibilities to capture the changing economic structure of production as a whole.

14 The vertical and horizontal fragmentation of production is often represented with metaphors of snakes (sequential value transfers from one firm to further stages in a linear sequence) and spiders (simultaneous value transfers from different firms to the same company) (Baldwin and Venables 2013 ).

15 Technically, that would require I–O matrices of a dimension as large as the number of all firms of all countries included in such an international I–O structure.

16 Formal addition of further n dimensions to the usual n × n dimensions.

17 Definitions of all notations are given in Appendix A .

18 The simplification consists only of the notation. We retain all the complexity of the block-matrix structure of the international I–O data and remove only the large number of indices, which would make the equations much more difficult to read.

19 Our downstream output decomposition formally coincides with the output decomposition of the approach that integrates output decomposition with a demand-driven decomposition of exports (Arto et al. 2019 ).

20 C S k represents an index for different country-sectors. a C S 1 C S 2 thus represents a single Leontief technical coefficient indicating that the value produced by C S 2 requires a a C S 1 C S 2 share of C S 1 input.

21 For example, Wang’s disaggregation into simple and complex GVCs uses the number of cross-border transactions, regardless of whether the value crossed a border for production or whether it is only an export to end users. Such a criterion mixes two conceptually different transactions, leading to unnecessary calculation complexity and the impossibility of further conceptual disaggregation. Existing definitions of the typology of value chains, like all such definitions, are constructed in a relatively arbitrary way. More important than strict adherence to the prevailing definitions is the clarity of the proposed revision and the presentation of the conceptual relationship of the new concepts with the old ones. Our proposal facilitates a more detailed decomposition that will allow researchers to construct an indicator better suited to their research questions. Since the revised typology is based on a more detailed decomposition compared to the currently prevailing typology, researchers can (by simply adding components of the revised decomposition) also replicate objects that correspond to existing studies.

22 Here we examine the path of production fragmentation, while the path to final consumption, which represents an additional transaction, is analysed in Sect. 3.2.5 .

23 Details of the disaggregation are given in Appendix B .

24 This is determined by the pure diagonal elements of the Leontief technical matrix A . Each a ii represents the portion of the total product of the i th country-sector that requires the use of the intermediate product of the same country-sector in the production process, thereby covering cyclical transactions within a sector. These cyclical transactions are of course included in the decomposition of the domestic value chain and not the no value chain since cyclical transactions represent the fragmentation of a domestic value chain.

25 The Greek and Italian economies, which experienced the longest recession in the EU during the period, experienced this very pattern (general reduction of production fragmentation, chiefly a reduction of domestic production fragmentation and increased integration into global value chains).

26 A variety of institutional and structural economic positions brings a range of effects of global integration on the country level.

27 In the international I–O framework, F is usually disaggregated on the country level as well as in an additional dimension of final consumption (household, government and non-profit consumption, fixed capital formation and changes in inventories), which in our derivation is irrelevant and left out. Disaggregation by countries is relevant for enabling the separation of domestic final consumption and export.

28 The decimal numbers are truncated on the fourth digit.

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• DOI: 10.56557/jobari/2024/v30i38792
• Corpus ID: 271578955

A Review on Value Chain Analysis of Millets

• V. N , S. M. , +3 authors D. M
• Published in Journal of basic and applied… 29 July 2024
• Agricultural and Food Sciences, Economics

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