Global value chain

A global value chain (GVC) refers to the full range of activities that economic actors engage in to bring a product to market. The global value chain does not only involve production processes, but preproduction (such as design) and postproduction processes (such as marketing and distribution).
GVC is similar to Industry Level Value Chain but encompasses operations at the global level. GVC is similar to the concept of a supply chain, but the latter focuses on conveyance of materials and products between locations, often including change of ownership of those materials and products. The existence of a global value chain (i.e. where different stages in the production and consumption of materials and products of value take place in different parts of the world) implies a global supply chain engaged in the movement of those materials and products on a global basis.
The first references to the concept of a global value chain date from the mid-1990s. Early references were enthusiastic about the upgrading prospects for developing countries that joined them. In his early work based on research on East Asian garment firms, Gary Gereffi, the pioneer in value chain analysis, describes a process of almost 'natural' learning and upgrading for the firms which participated in GVCs. This echoed the 'export-led' discourse of the World Bank in the 'East Asian Miracle' report based on the East Asian 'Tigers' success. In economics, GVC was first formalized in a paper by Hummels, Ishii and Yi in 2001. They defined GVC as the foreign component of imported intermediate inputs used to produce output, and some fraction of output is subsequently exported. Allowing for such a framework, Kei-Mu Yi showed in a 2003 paper that the growth of world trade can be explained with moderate changes in the trade costs and named this phenomenon "vertical specialization".
This encouraged the World Bank and other leading institutions to encourage developing firms to develop their indigenous capabilities through a process of upgrading technical capabilities to meet global standards with leading multinational enterprises (MNE) playing a key role in helping local firms through transfer of new technology, skills and knowledge.
Wider adoption of open source hardware technology used for digital fabrication such as 3D printers like the RepRap has the potential to partially reverse the trend towards global specialization of production systems into elements that may be geographically dispersed and closer to the end users (localization) and thus disrupt global value chains.
Global value chains are a network of production and trade across countries. The study of global value chains requires inevitably a trade theory that can treat input trade. However, mainstream trade theories (Heckshcer-Ohlin-Samuelson model and New trade theory and New new trade theory) are only concerned with final goods. It needs a New new new trade theory. Escaith and Miroudot estimates that the Ricardian trade model in its extended form has "the advantage" of being better suited to the analysis of global value chains. Shiozawa argued that global value chains can be treated by the new theory of international values, because it is a general theory of input trade with many-country, many-product economy. He contends that global value chains are a new transforming general purpose technology.
The lack of appropriate tool of analysis, the studies of GVCs were initially conducted mainly by sociologists like Gary Gereffi, and management science researchers. See for a genealogy Jennifer Bair (2009). This is changing, with more studies by means of global Input-Output Table starting by economists, and economic geographers outlining the case for proactive sub-national public policy on the engagement of GVCs.
GVCs become a major topic in development economics especially for middle-income countries, because the "upgrading" within GVCs became the crucial condition for the sustained growth of those countries.
GVC analysis views “upgrading” as a continuum starting with “process upgrading” (e.g. a producer adopts better technology to improve efficiency), then moves on to “product upgrading” where the quality or functionality of the product is upgraded by using higher quality material or a better quality management system (QMS), and then on to “functional upgrading” in which the firm begins to design its own product and develops marketing and branding capabilities and begins to supply to end markets/customers directly - often by targeting geographies or customers (which are not served by its existing multinational clients). Subsequently, the process of upgrading might also cover inter-sectoral upgrading.
Functional upgrading to high-value-added activities like design and branding is for developing country suppliers a key opportunity to achieve higher profits in GVC. Likewise, a 2017 review of the empirical literature highlighted that suppliers operating in unstable economies, like Pakistan and Bangladesh, face high barriers to reach functional upgrading in high-value-added activities.
This upgrading process in GVCs has been challenged by other researchers – some of whom argue that insertion in global value chains does not always lead to upgrading. Some authors argue that the expected upgrading process might not hold for all types of upgrading. Specifically they argue upgrading into design, marketing and branding might be hindered by exporting under certain conditions because MNEs have no interest in transferring these core skills to their suppliers thus preventing them from accessing global markets (except as a supplier) for first world customer. Other authors point to the benefits of considering upgrading at a regional, or sub-national level. This focus enhances the possibility that less-developed regions capture the gains from GVC insertion.
There are motivations behind renewed interest in global value chains and the opportunities that they may present for countries in South Asia. A 2013 report found that looking at the production chain, rather than the individual stages of production, is more helpful. Individual donors with their own priorities and expertise cannot be expected to provide comprehensive response to the needs identified, not to mention the legal responsibilities of many specialist agencies. The research suggests they adjust their priorities and modalities to the way production chains operate, and to coordinate with other donors to cover all trade needs. It calls for donors and governments to work together to assess how aid flows may affect power relationships.
In his 1994 paper, Gereffi identified two major types of governance. The first were buyer-driven chains, where the lead firms are final buyers such as retail chains and branded product producers such as non-durable final consumer products (e.g., clothing, footwear and food). The second governance type identified by Gereffi were producer-driven chains. Here the technological competences of the lead firms (generally upstream in the chain) defined the chain's competitiveness.
Current research suggests that GVCs exhibit a variety of characteristics and impact communities in a variety of ways. In a paper that emerged from the deliberations of the GVC Initiative, five GVC governance patterns were identified:
As capabilities in many low- and middle-income economies have grown, chain governance has tended to move away from quasi-hierarchical models toward modular type as this form of governance reduces the costs of supply chain management and allows chain governors to maintain a healthy level of competition in their supply chains. However, whilst it maintains short-term competition in the supply chain, it has allowed some leading intermediaries to develop considerable functional competences (e.g., design and branding). In the long term these have the potential to emerge as competitors to their original chain governor. Other study outlines the initiative to promote inclusive GVC, three GVC patterns were identified:
The theoretical concepts often considered firms as operating in a single value chain (with a single customer). Whilst this was often the case in quasi-hierarchical chains (with considerable customer power) it has become apparent that some firms operate in multiple value chains (subject to multiple forms of governance) and serve both national and international markets and that this plays a role in the development of firm capabilities. The recent trend in GVC research shows exploration of issue emerging from the interaction of different stakeholders from within and outside the GVC structures and their effects on the sustainability of the GVCs. For examples, the local governance institutions and production firms.
In 2013, UNCTAD published two reports on GVCs and their contribution to development. They concluded that:
Gender plays a prominent role in global value chains, because it influences consumption patterns within the United States, and thus affects production on a larger scale. In turn, specific roles within the value chain are also determined by gender, making gender a key component in the process as well. The increase in global sourcing of production has created more and more work in the informal sector as labor intensive assembly work is being put out to homeworkers in the informal sector where women work as self employed traders and producers, casual workers, or sub contract workers. Far more women than men are found in the informal sector, as self-employed workers or subcontractors, while specific jobs and broader fields of work differ between men and women. In more than 90 percent of Sub-Saharan African countries, 89 percent of Southern Asian countries, and 75 percent of Latin American countries, women are more exposed to informal work.
Education levels, legal barriers, and social norms, are all factors that contribute to women being largely concentrated in informal work across global value chains. Work in the informal sector is primarily held by low-skill workers with little to no education. Because labor in the informal sector requires low levels of skill, individuals who are looking for work but lack a strong education frequently find work in the informal sector. Women tend to receive less education than men, in sub-Saharan Africa, women only receive 70 percent of the schooling that men do. Women's concentration in the informal sector is also influenced by social norms. In Senegal, women spend six times as much time as men caring for their families and completing household chores. The time spend in unpaid care labor puts constraints on women's ability to find formal work within a global value chain. Women seeking jobs in the formal sector face extra challenges due to legal barriers. Many Sub-Saharan African countries, for example, prohibit women from signing work contracts or opening bank accounts without their husband's permission.
Within global value chains, the distribution of returns between firms in the formal sector and women in the informal sector is disproportionate. In Zimbabwe's non-traditional agricultural exports value chains (NTAE), women accounted for only 12% of total costs while exporters accounted for 30% importers for 12% and retailers for 46% of costs.
Sustainability is an increasingly important factor in global value chains, and there is a growing need to evaluate their performance based on social and environmental impact, as well as economic. Initiatives such as The United Nations Sustainable Development Goals encourage sustainable practices through its 17-goal blueprint, but there are few enforced policies which address sustainability with the urgency required to protect natural resources and reduce the impacts of climate change on a global scale.
Sustainability efforts in global value chains are often voluntary steps taken in the private sector, such as the use of sustainability standards and certifications, and ecolabels, but they can sometimes lack evidence of measurable sustainable impacts. For example, some ecolabels seek to address issues such as poverty. Yet in some cases, even if producers meet ecolabel standards, the burden of certification costs can end up reducing the overall income for these producers.
The measurement of sustainability in global value chains requires a multifaceted assessment which includes environmental, social and economic impacts, and it must also be standardized enough to be compared in order to generate sufficient learning and for scalability. Technologies that make these types of measurements are becoming both more available and essential for the public and private sector sustainability efforts. Besides, the recent finding shows that the local realities like governance system and institutions also play a significant role in economic sustainability of the global value chains. Implementation of sustainability policies at the global level demands the greening of supply chains in their entirety, as well as their comprehensive technological modernization to accommodate advancing trends such as digitization, artificial intelligence (AI), and big data.
As a central force in the Fourth Industrial Revolution, artificial intelligence is fundamentally transforming global value chains by reshaping five key dimensions: production efficiency, labor structure, product quality, innovation diffusion, and trade costs. Its integration into industrial systems has redefined how nations specialize, upgrade, and compete within increasingly fragmented and technology-intensive global production networks. For example, AI-driven automation enhances manufacturing efficiency and precision; shifts in employment demand favor high-skilled roles; and intelligent systems enable real-time logistics coordination and faster innovation. These advances provide firms and countries with new opportunities to move up the value chain. Yet, AI's benefits are far from evenly distributed. As several studies note, advanced economies—those with concentrated control over chip manufacturing, machine learning capabilities, and cloud infrastructure—are disproportionately positioned to capture the gains from AI-led GVC upgrading. In contrast, developing countries risk remaining in low-value segments or facing widening technological gaps. This duality underscores the need to assess both the opportunities and structural challenges that AI presents for global value chain transformation.
Artificial intelligence enhances production efficiency by automating repetitive tasks, reducing error rates, and enabling uninterrupted operation. These improvements are particularly evident in highly automated manufacturing sectors, where labor productivity has increased by an estimated 0.36 percentage points annually across 17 countries. The efficiency gain allows firms and countries to shift toward more advanced stages in global value chains, especially in competitive industries. AI also optimizes labor structures by replacing low-skilled jobs while generating new roles in engineering, programming, and management. This transformation raises the share of high-skilled labor, improves wage structures, and enhances human capital, all of which contribute to a country's ability to embed itself in higher segments of the GVC.
In addition, AI improves product quality through highly standardized and precise manufacturing processes. Products made by intelligent machines consistently achieve higher quality ratings—often exceeding 90%—compared to approximately 80% for human-made goods. This quality advantage is essential for meeting the strict standards of high-value GVC tiers. Furthermore, AI stimulates innovation and enables spillover effects by improving knowledge absorption and accelerating technology diffusion within and across industries. These effects are most visible through horizontal and forward linkages, strengthening the entire supply chain. Finally, AI reduces trade costs by improving supply chain transparency, optimizing logistics, and minimizing information barriers. Such improvements lower the entry threshold for international markets, especially benefiting firms in developing economies and supporting their efforts to move up the value chain.
Artificial intelligence does not exert a wholly positive impact on global value chains. Its rapid integration also brings a host of structural challenges. Firstly, research and development of AI technology remains highly concentrated in a small number of developed countries, resulting in a severe imbalance in how benefits are distributed across global value chains. For example, 90% of advanced semiconductor chips are manufactured by TSMC, Samsung, and Intel, creating formidable technological barriers for developing countries. Consequently, developed nations can leverage these core AI capabilities (such as chip fabrication, cutting-edge algorithms, and cloud computing) to secure higher-value segments of the GVC, while developing countries remain dependent on lower-value manufacturing and assembly. This imbalance allows high-income nations to reap substantial gains from AI-driven GVC upgrades, whereas middle- and low-income countries struggle to move up the value chain due to insufficient technological capacity. Moreover, the integration of AI into the global value chain is driving profound changes in global employment structures, as low-skill roles are increasingly being automated. For instance, repetitive tasks in manufacturing, customer service, and logistics are especially vulnerable to displacement by AI and robotics. By 2030, around 14% of the global workforce—an estimated 375 million people—could face unemployment due to automation. While AI adoption can boost corporate profits, it does not automatically create enough new jobs, thereby exacerbating social inequality. Furthermore, the AI era requires more high-skill talent (e.g., data scientists, AI engineers), yet education systems in many developing countries lack the agility to adapt rapidly. This widening technology gap is likely to persist, intensifying inequalities across different nations.
AI technologies significantly enhance global value chains by boosting efficiency, quality, and innovation through automation and data-driven optimization, enabling firms to move into higher-value activities. However, its benefits remain unevenly distributed, with developed nations capturing most gains while developing countries face technological dependency and job displacement risks. The concentration of AI capabilities in a few corporations and nations risks widening global inequalities, and automation threatens to disrupt labor markets without adequate reskilling. Balancing AI's transformative potential with inclusive policies remains critical for sustainable GVC development.
Global supply chain management is facing the increasing difficulty in predicting demand variability in different areas. In addition, managing the production and transportation of goods over large distances to meet the peak demand represents another challenge.
Integrating global value chains requires all actors to adapt to technological changes, which is capital-intensive. Therefore, it is safe to say that this trend significantly benefits developed countries rather than developing countries.
Shifting of production base by the lead firm raises the challenges of sustainability for the local firms (economy) and the labor (society).
OECD maintains Inter-Country Input-Output (ICIO) tables, the most recent update was November 2021. An earlier project started at the University of Groningen. GTAP maintains a software program with an included trade database. Open-source software in R includes the decompr and gvc packages.