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Positioning in global value chains: A new dataset available for global value chain analyses

Michele mancini, pierluigi montalbano, silvia nenci, davide vurchio.

Amid recent global shocks and increasing geopolitical tensions, the debate surrounding the future of global value chains and economic integration has intensified. This column introduces a new open-access dataset of ready-to-use positioning indicators, calculated for a wide array of countries, including most developing nations, and sectors, covering an extensive period. These indicators are derived from the most widely utilised Inter-Country Input-Output tables. The availability of this new dataset offers scholars and policymakers the opportunity to conduct further qualitative and quantitative analyses on the aggregate complexities of global value chains and the relative positioning of countries and sectors.

Despite the shocks impacting the global economy and straining supply networks, there have been no clear signs of outright deglobalisation so far (Goldberg and Reed 2023, Baldwin 2022, Antràs 2021). Instead, recent data point to an ongoing decoupling between specific trade partners, mostly the US and China (Freund et al. 2023, Alfaro and Chor 2023). In this context, timely and state-of-the-art data to assess the prospect of global economic integration are key. While trade data might provide a first assessment on reshaping patterns of trade, indicators based on Inter-Country Input-Output (ICIO) tables are essential to unravel the complex structure of supply and demand linkages and assess how interconnections between economies are evolving, with a look-through perspective (Baldwin et al. 2022).

To capture the dynamics of global value chains (GVCs) and inform policymaking, scholars have developed various indicators and measures of GVC participation (see, among others, Hummels et al. 2001, Johnson and Noguera 2012, Koopman et al. 2014, Borin and Mancini 2023). A parallel body of work examines country and/or sectoral positioning within GVCs, evaluating whether a given country or industry specialises in relatively upstream or downstream activities towards final demand (see, among others, Antràs et al. 2012, Fally 2012, Antràs and Chor 2013). This perspective on production staging is crucial as it sheds light on factors influencing productivity differences, as well as geography and firm organisational decisions, emphasising that such choices are not solely determined by marginal costs.

A new global open access dataset of ready-to-use indicators of global value chains positioning

While common indicators of GVC participation are readily accessible to researchers through up-to-date open-access platforms like the World Bank's WITS GVC (Borin et al. 2021) and the OECD-TIVA datasets (OECD 2023) or software for more detailed analyses (Belotti et al. 2021), GVC positioning indicators are not as readily available. We have addressed this gap in the literature by creating a new global dataset of ready-to-use upstreamness and downstreamness indicators. These measures are computed following the methodologies outlined by Antràs et al. (2012), Fally (2012), and Antràs and Chor (2013, 2019), utilising the most recent data from widely used inter-country input-output tables, including Asian Development Bank (ADB) MRIO , EORA (Lenzen et al. 2013), OECD TiVA (OECD 2023), WIOD (Timmer et al. 2015), and Long-run WIOD (Woltjer et al. 2021). This GVC positioning dataset is frequently updated as timelier ICIO tables are released and covers a wide range of countries, including many developing nations, and sectors. It is openly accessible here .

The primary contribution of this work lies in its ability to facilitate qualitative and quantitative research activities for scholars working on the topic of global value chains and belonging to different research disciplines—economics, sociology, international economics, economic geography, international political economy, supply chain management, and international business. They can now explore the evolution of GVC positioning over time at both the country and industry levels and conduct international comparisons without the need for technical expertise or extensive matrix calculations.

Stylised facts, evolution, and comparability of global value chain positioning measures

In Mancini et al. (2024), we explore this new GVC positioning dataset, presenting stylised facts and comparing the various ICIO data. Figure 1 provides a first overview of the GVC positioning indicators for the various ICIO datasets at the global level. Acknowledging that at the global aggregate level, upstreamness and downstreamness coincide and can be seen as a proxy for global production complexity (Antràs and Chor 2019), Figure 1 shows a common trend of increasing GVC complexity across all the datasets considered during the hyper-globalisation phase (1995–2008), followed by a 'slowbalisation' phase starting right after the Global Crisis. No signs of major drops in GVC complexity have been found in recent years across different datasets, despite global shocks and increases in protectionism and inward-looking policies. The post-2020 increase could even be a sign of GVCs lengthening as global trade patterns are reshaped by geopolitical tensions.

Figure 1 Trends in GVC complexity at the global level

Source : Mancini et al. (2024)

Measures of upstreamness and downstreamness computed on different ICIO tables exhibit a very strong correlation not only at the aggregate level, but also across countries. To gain insight into this, a comparative analysis of the dynamics of GVC positioning indicators for three key countries, namely China, Germany, and the US, is presented in Figure 2. Despite differences in the methodologies used and the statistics employed to derive the original ICIO tables, as well as differences in sectoral coverage, the level gaps and time trends for each country appear broadly consistent across datasets, albeit with some country-specific differences.

Figure 2 Comparing dynamics of GVC positioning across different datasets for China, Germany, and the US

A snapshot of this evolution over time of both upstreamness and downstreamness by income group, is offered by Figure 3. It uses the EORA data, which encompasses the largest subgroups of low-income countries. Looking at the position of countries with respect to the 45 degrees line, it is possible to identify which countries have changed their relative position in GVCs over the period. Both upstreamness and downstreamness plots show that, in general, high-income economies have shifted their production toward sectors with a more extreme position.

Figure 3 Changes in GVC positioning by income group

Additional insights for the empirical analysis can be gleaned from assessing the dynamics of the indicators by sectors. 1 To this end, Figure 4 provides a snapshot of sectoral comparisons of upstreamness and downstreamness indicators between WIOD and OECD TiVA datasets for the top ten most upstream and downstream sectors. It again highlights a noteworthy level of consistency in the GVC positioning indicators between the two datasets. All manufacturing sectors exhibit greater chain length in relation to both endpoints of the production chains, typically indicating an average of two stages of production away from each endpoint. This trend remains consistent across manufacturing sectors, exhibiting relatively limited cross industry variation. In both datasets, the most upstream sectors are Mining and Quarrying, and Basic Metals, two sectors usually involved in the first stages of GVCs. Basic Metals are also listed alongside Motor Vehicles and Electrical Equipment as some of the most downstream industries. This is consistent with Antràs and Chor’s (2019) puzzling correlation and, in this case, is also influenced by the relatively high level of sector aggregation.

Figure 4 Sectoral dynamics in GVC positioning

Conclusions

The dataset discussed in this column holds the potential to greatly facilitate research efforts concerning the analysis of GVCs and production networks. Scholars, practitioners, and policymakers could benefit from its extensive country coverage, including developing and emerging economies, as well as its detailed sectoral breakdown and historical perspective on GVC positioning over time. Such features would enhance both qualitative and quantitative evidence regarding the complexities of GVC dynamics at the aggregate, country, and sector-specific levels, offering a truly global perspective.

Authors’ note: This column does not necessarily reflect the views of the Bank of Italy.

Alfaro, L and D Chor (2023), " A perspective on the great reallocation of global supply chains ", VoxEU.org, 28 September.

Antràs, P, D Chor, T Fally and R Hillberry (2012), “Measuring the Upstreamness of Production and Trade Flows”, American Economic Review Papers & Proceedings 102(3): 412–16.

Antràs, P and D Chor. (2013), “Organising the Global Value Chain”, Econometrica 81(6): 2127–204.

Antràs, P and D Chor (2019), “On the Measurement of Upstreamness and Downstreamness in Global Value Chains”, In L Y Ing and M Yu (eds.), World Trade Evolution: Growth, Productivity and Employment , 126–94, London and New York: Routledge.

Antràs, P (2021), “De-globalisation? Global value chains in the post-COVID-19 age”, NBER Working Paper 28115.

Baldwin, R (2022), “ The peak globalisation myth: Part 3 ”, VoxEU.org, 2 September.

Baldwin, R, R Freeman and A Theodorakopoulos (2022), “Horses for Courses: Measuring Foreign Supply Chain Exposure”, NBER Working Paper 31820.

Belotti, F, A Borin and M Mancini (2021), “icio – Economic Analysis with InterCountry Input-Output tables”, The Stata Journal (21)3.

Borin, A and M Mancini (2023), “Measuring What Matters in Value-added Trade”, Economic Systems Research 35(4): 586–613.

Borin, A, M Mancini and D Taglioni (2021), “Economic Consequences of Trade and Global Value Chain Integration: A Measurement Perspective”, World Bank Policy Research Working Paper no. 9785.

Fally, T (2012), “Production Staging: Measurement and Facts”, Unpublished manuscript, University of California, Berkeley, CA, USA.

Freund, C, A Mattoo, A Mulabdic and M Ruta (2023), “ US-China decoupling: Rhetoric and reality ”, VoxEU.org, 31 August.

Goldberg, P K and T Reed (2023), “Is the Global Economy Deglobalizing? And If So, Why? And What is Next?”, Brookings Papers on Economic Activity , Spring.

Hummels, D, J Ishii and K M Yi (2001), “The Nature and Growth of Vertical Specialization in World Trade”, Journal of International Economics 54: 75–96.

Koopman, R, Z Wang and S Wei (2014), “Tracing Value-Added and Double Counting in Gross Exports”, American Economic Review 104(2): 459–94.

Mancini, M, P Montalbano, S Nenci and D Vurchio (2024), “ Positioning in Global Value Chains: World Map and Indicators, a New Dataset Available for GVC Analyses ”, The World Bank Economic Review , lhae005.

Johnson, R C and G Noguera (2012), “Accounting for Intermediates: Production Sharing and Trade in Value Added”, Journal of International Economics 86(2): 224–36.

Lenzen, M, D Moran, K Kanemoto and A Geschke (2013), “Building Eora: A Global Multi-regional Input-Output Database at High Country and Sector Resolution”, Economic Systems Research 25(1): 20-49.

OECD (2023), TiVA User guide.

Timmer, M P, E Dietzenbacher, B Los, R Stehrer and G J de Vries (2015), “An Illustrated User Guide to the World Input-Output Database: the Case of Global Automotive Production”, Review of International Economics 23: 575–605.

Woltjer, P, R Gouma and M P Timmer (2021), "Long-run World Input-Output Database: Version 1.1 Sources and Methods", GGDC Research Memorandum 190.

• The various ICIO datasets do not share a uniform sectoral classification and exhibit varying degrees of sectoral aggregation. Hence, to facilitate sectoral comparisons, it is necessary to harmonise all the datasets at the less granular level of disaggregation. For the details about this harmonisation process see Mancini et al. (2024).

A perspective on the great reallocation of global supply chains

• Laura Alfaro
• International trade

Decoupling from global value chains

• Bohdan Kukharskyy
• Gabriel Felbermayr
• Oliver Krebs
• Peter Eppinger

The peak globalisation myth: Part 3 – How global supply chains are unwinding

• Richard Baldwin

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Akintola, A., Antimiani, A., & Fusacchia, I. (2024). Harmonizing Horizons: Impact of Trade and Transport Reforms on Agrifood Value Chains in ECOWAS (Presented during the 27th Annual Conference on Global Economic Analysis (Fort Collins, Colorado, USA)). Purdue University, West Lafayette, IN: Global Trade Analysis Project (GTAP). Retrieved from https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=7189

Value Chain Analysis

Value chain analysis is a strategic analytical and decision-support tool that highlights the bases where businesses can create value for their customers. The framework can also be applied to identify sources of competitive advantage for businesses. Value chain is a set of consequent activities that businesses perform in order to achieve their primary objective of profit maximization.

Most sources explain the essence and application of value chain analysis assuming their audience is businesses aiming to increase the level of their competitiveness. Here, we adopt an alternative approach. Below is an explanation of value chain analysis for business students who have been assigned to apply this strategic analytical tool as part of assignment given by their educational institution.

Theory of Value Chain Analysis

The concept value chain analysis was introduced by Michael Porter in 1985 [1] and its significance and relevance to strategic management and marketing has not diminished during 30 years of its existence.

The framework divides activities that generate value into two categories – primary activities and support activities. Primary activities comprise a set of activities that contribute to the creation of value in a direct manner. Support activities consist of functions and tasks that are intended to support primary activities.

It is important to clarify that the relevance of value chain analysis is not limited to manufacturing businesses and the framework can be applied towards service firms as well.

Primary Activities

Inbound logistics  involve receiving and storing raw materials and their usage in manufacturing as the necessity arises.

Operations relate to the processes of transforming raw materials into finished goods. For businesses operating in services sector operations relate to the process of providing the service.

Outbound logistics is associated with warehousing and distribution of finished products.

Marketing and sales refer to the choice and implementation of marketing strategy to communicate the marketing message to the target customer segment and generation of sales.

Service relates to support provided to customers after the sale.

Support Activities

Infrastructure of a company comprises its organizational structure, its departments and committees, organizational culture etc.

Human Resource Management involves a wide range of activities related to employee recruitment and selection, training and development, appraisals, motivation and compensation.

Technology development involves the use of technology to increase the effectiveness of primary activities in terms of value creation.

Procurement relates to the purchasing practices of raw materials, tools and equipment.

Businesses need to engage in value creation via their primary and support activities in order to survive in the marketplace. Value can be created in one of the following two ways.

a) Cost advantage : Businesses can reduce the costs of activities wherever possible and use the cost benefit to reduce the price of their final products or services

 b)   Differentiation : businesses can focus on activities closely associated with their competitive advantage. Investing in activities adapted as sources of competitive advantage allows the business to increase the quality of their products and services and sell them for higher prices.

Advantages and Disadvantages of Value Chain Analysis

Application of value chain analysis offers the following advantages:

1. Value chain analysis can play an instrumental role in terms of detecting organizational, tactical and strategic issues related to the business.

2. The tool assists businesses to appreciate potential sources of competitive advantage.

3. The strategic framework can be applied to any type of business regardless of the industry and the size of the business.

As it is the case with any other theoretical framework or model, the concept of value chain is not free from limitations. These can be summarized into the following points:

1. The framework assumes that it is possible to achieve a clear separation of company operations into different primary and support activities. This may not be the case in real life taking into account increasing level of complexity of business operations.

2. Application of the tool in practice can be overly time-consuming process, since it requires a comprehensive analysis of all business operations.

3. It may be difficult to find all the required information in order to conduct value chain analysis in an appropriate manner.

Application of Value Chain Analysis

You can follow the following stages in order to conduct value chain analysis as a part of your assignment:

Stage 1: Explaining the theory and the essence of value chain analysis

Write a brief introduction to the theory of value chain analysis. You may want to add the figure of value chain analysis (do not forget appropriate referencing). Inclusion of a brief discussion about advantages and disadvantages of the framework as mentioned above will contribute to your mark.

Stage 2: Researching primary and support activities of your case study company

Value chain analysis can be applied in relation to a business unit, operating segment, business division or a company. If you have a choice, business unit is the most appropriate level for conducting value chain analysis from the practicality point of view.

Some students prefer to choose their employer as a case study. However, student employers often happen to be small or medium sized business and it is difficult find necessary amount of relevant data to conduct value chain analysis in an appropriate manner.

Selection of a multinational company is a more desirable scenario to be able to produce a quality value chain analysis due to the availability of needed company-specific data. Company annual report is the most comprehensive source of data and you can also find useful information on official website of the company.

Alternatively, you can purchase company reports on major multinational enterprises form this portal. Reports comprise detailed value chain analyses of respective companies to be used as examples and template. Reports are kept updated regularly.

Stage 3: Illustrating how each activity is facilitated by the case study company

This stage consists of two parts:

Firstly, write about the manners in which the business conducts each activity. For example, for inbound logistics primary activity you can mention about the nature of raw materials the company uses and write about the numbers and location of suppliers. Similarly, when writing about operations primary activity you can discuss specific nature of company’s operations, as well as, numbers and locations of manufacturing units.

Secondly, identify and discuss activities and sub-activities that create the most value for the company you are analyzing. This depends on the choice of cost advantage or differentiation business strategy by company. If the company benefits from cost advantage strategy, you have to specify which activities contribute the most to the achievement of cost leadership. In other words, you have to explain where the company saves the most cash and how.

Alternatively, if the company focuses on differentiation business strategy, you will need to find and discuss activities that the company has adapted as sources of its competitive advantage.  Remember to justify each argument by referring to relevant statistical or non-statistical data from reliable sources.

It has to be noted that there are also some general value adding strategies that can be used by businesses following both strategies – cost advantage AND differentiation. For example, Just-in-Time supply chain management system can be applied to create value in inbound logistics by businesses using cost leadership strategy, as well as, businesses using differentiation strategy with an equal level of efficiency.

Below you can find some of the most popular value creation strategies used by businesses following cost leadership and differentiation business strategies:

Primary activities

Support activities

You can find many examples for the application of value chain analysis using the case studies of famous global brands here .

[1] Porter, M.E. (1985) Competitive Advantage: Creating and Sustaining Superior Performance, Simon & Schuster

ORIGINAL RESEARCH article

Integrated techno-economic and life cycle assessment of hydroformylation in microemulsion systems provisionally accepted.

• 1 Technical University of Berlin, Germany

The final, formatted version of the article will be published soon.

This paper presents the first integrated techno-economic and life cycle assessment of microemulsion systems being applied for rhodium-catalyzed hydroformylation of long-chain alkenes at industrial scale. The case study describes a projected 150 kt/a production of tridecanal (US gulf coast, 2019). The industrial success of the hydroformylation of short-chain alkenes lies in the continuous recycling of the rhodium-containing water phase. Microemulsion systems can be applied to transfer this concept to long-chain alkenes by overcoming the miscibility gap between the aqueous catalyst phase and the unipolar alkene phase and, moreover, by generating a temperature-induced multi-phase system enabling the immobilization of the catalyst and its continuous recycling, as demonstrated in miniplant operations with dodecene and rhodium/SulfoXantPhos. Customizable simulation models have been developed for scale-up and assessment of the miniplant data. Surprisingly, a profitability-driven sensitivity study indicates a base case optimum at low residence time with low alkene conversion leading to large throughput streams and high raw material purge rates. The comparison to the industrial cobalt-based benchmark system shows an economic advantage regarding net present value (Rh: 68 M$; Co: 62 M$), while about half of the environmental indicators are in favor or equivalent. In a best-case scenario considering zero leaching of expensive rhodium the net present value increases by almost 40 % accompanied by a shift to overall lower environmental impacts than the benchmark. In conclusion, the investigated miniplant data suggest microemulsion systems to be competitive when applied in continuous processes at a large scale.

Keywords: Techno-economic assessment, Life Cycle Assessment, Process simulation, homogenous catalysis, green chemistry, microemulsion, Hydroformylation, Micellar catalysis

Received: 22 Mar 2024; Accepted: 30 Apr 2024.

Copyright: © 2024 Wunderlich, Kretzschmar and Schomaecker. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Reinhard Schomaecker, Technical University of Berlin, Berlin, 10623, Brandenburg, Germany

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