research proposal value chain analysis

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

The Official Journal of the Pan-Pacific Association of Input-Output Studies (PAPAIOS)

• Open access
• Published: 02 August 2021

An extended approach to value chain analysis

• Klemen Knez   ORCID: orcid.org/0000-0003-4772-7074 1 ,
• Andreja Jaklič 1 &
• Metka Stare 1  

Journal of Economic Structures volume  10 , Article number:  13 ( 2021 ) Cite this article

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Metrics details

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.

1 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 Footnote 1 date back to the world-systems Footnote 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 Footnote 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 Footnote 4 , while also drawing from the new institutional transaction cost approach Footnote 5 . On the other hand, the creation of international input–output tables Footnote 6 led to a revival of the aggregated macroeconomic approach to global value chains, albeit with a different focus than the world-systems approach. Footnote 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.

2 Background

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 Footnote 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. Footnote 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. Footnote 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. Footnote 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 ( \(\tau _i\) ) 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. 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.

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. Footnote 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 ).

3.1 The value chain tree

3.1.1 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 Footnote 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. 1 ). Footnote 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.

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)

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 Footnote 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. 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 Footnote 16 —the midpoint—the siphon through which the value from any origin to any final stage flows (Fig. 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.

3.1.2 Derivation

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. 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 \(A=C{\hat{x}} ^{-1}\) . The variables with hat are vectors transformed into diagonal matrices, \({\hat{f}}\) represents a diagonal matrix of final demand and \({\hat{v}}_C\) a diagonal matrix of value-added coefficients. Footnote 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 \(n\times n\) dimensional space, but in the \(n \times n \times n\) 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 ). Footnote 18

We start with the upstream part, by using standard Leontief’s derivation:

Definition 1

Upstream output decomposition W :

\(W={\hat{x}}^{-1}(I-A)^{-1}{\hat{f}}.\)

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: \(\sum _{j=1}^n w_{ij}=1\) \(\forall i\) . 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 :

\(Z= \hat{v_C} (I-A)^{-1}.\)

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: \(\sum _{k=1}^n z_{ki}=1\) \(\forall i\) . 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. Footnote 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 \(Z\vec {e_i}\) (the i th column of Z ) by each element of \(\vec {e_i}^{T}W\) (the i th row of W ).

Definition 3

Value chain tree matrix

\(\tau _i=Z\vec {e_i} \otimes \vec {e_i}^{T}W\) ; \(\tau _i\in \mathbb{R}^{n\times n}\) , where \(\vec {e_i} \in \mathbb{R}^n\) represents the standard orthonormal basis of \(\mathbb{R}^n.\)

This defines each element of the value chain tree matrix \(t_{ijk} \in \tau _i\) as \(t_{ijk}=w_{ij}z_{ki}\) . Each element of the value chain tree matrix \(\tau _i\) 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 \(\tau _i\) ), 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 \(\tau _i\) are replaced by an infinite series ( \((I-A)^{-1}=I+A+A^2+\cdots\) ). 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 \(a_{ij}\in A\) . For example, take a value chain path consisting of value primarily produced in country-sector \(CS_1\) Footnote 20 , then used as an intermediate in \(CS_2\) , 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 \(CS_3\) , which is then sent as an intermediate to \(CS_4\) , where it is finished and sold for consumption. This value chain path has an origin ( \(CS_1\) ), a midpoint ( i ) and a final destination of production ( \(CS_4\) ), 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 \(j=CS_0\) , to \(CS_1\) , to \(CS_2\) , ..., to \(i=CS_{d}\) , and further to \(CS_{d+1}\) , \(CS_{d+2}\) , ..., to \(k=CS_u\) ). Leontief technical coefficients \(a_{CS_{p-1}CS_{p}}\) 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.

3.2 The value chain typology

3.2.1 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 . Footnote 21 Each matrix \(\tau _i\) 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 Footnote 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.

3.2.2 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 \(\tau _i\) 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 : \(\tau _i=\hat{v_C}(I-A)^{-1}\vec {e_i} \otimes \vec {e_i}^{T}{\hat{x}}^{-1}(I-A)^{-1}{\hat{f}}\) . 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).

\(A_D(I-A_D)^{-1}=A_D+A_D^2+A_D^3+ \dots\) 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:

\((I-A)^{-1}-(I-A_D)^{-1}-(I-A_D)^{-1}A_{CB}(I-A_D)^{-1}\) 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.

3.2.3 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 ( \(4 \times 4\) product) for each matrix \(\tau _i\) . Footnote 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 \(\tau _i\) 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 \(\tau _{i}^{DVC}\)

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. 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 ( \(\hat{v_C}A_D(I-A_D)^{-1}\vec {e_i} \otimes \vec {e_i}^{T}{\hat{x}}^{-1}{\hat{f}}\) ) 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 ( \(\hat{v_C}\vec {e_i} \otimes \vec {e_i}^{T}{\hat{x}}^{-1}A_D(I-A_D)^{-1}{\hat{f}}\) ) 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 ( \(\hat{v_C}A_D(I-A_D)^{-1}\vec {e_i} \otimes \vec {e_i}^{T}{\hat{x}}^{-1}A_D(I-A_D)^{-1}{\hat{f}}\) ) 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 \(\tau _{i}^{GVC}\)

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. 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 ( \(\hat{v_C}(I-A_D)^{-1}\vec {e_i} \otimes \vec {e_i}^{T}{\hat{x}}^{-1}\big [(I-A)^{-1}-(I-A_D)^{-1}\big ]{\hat{f}}\) ) covers the downstream domestic and no value chain paths, which have global upstream linkages (simple or complex), the second element ( \(\hat{v_C}\big [(I-A)^{-1}-(I-A_D)^{-1}\big ]\vec {e_i} \otimes \vec {e_i}^{T}{\hat{x}}^{-1}(I-A_D)^{-1}{\hat{f}}\) ) covers downstream global linkages (simple or complex), which have a upstream domestic or no value chain path and the third element ( \(\hat{v_C}\big [(I-A)^{-1}-(I-A_D)^{-1}\big ]\vec {e_i} \otimes \vec {e_i}^{T}{\hat{x}}^{-1}\big [(I-A)^{-1}-(I-A_D)^{-1}\big ]{\hat{f}}\) ) 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 \(\tau _{i}^{SGVC}\)

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 ( \(\hat{v_C}(I-A_D)^{-1}\vec {e_i} \otimes \vec {e_i}^{T}{\hat{x}}^{-1}(I-A_D)^{-1}A_{ CB }(I-A_D)^{-1}{\hat{f}}\) ) covers a downstream domestic and no value chain path that has simple global upstream linkages and the second element ( \(\hat{v_C}(I-A_D)^{-1}A_{ CB }(I-A_D)^{-1}\vec {e_i} \otimes \vec {e_i}^{T}{\hat{x}}^{-1}(I-A_D)^{-1}{\hat{f}}\) ) 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 \(\tau _{i}^{CGVC}\)

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 ( \(\hat{v_C}(I-A_D)^{-1}\vec {e_i} \otimes \vec {e_i}^{T}{\hat{x}}^{-1}\big [(I-A)^{-1}-(I-A_D)^{-1}-(I-A_D)^{-1}A_{ CB }(I-A_D)^{-1}\big ]{\hat{f}}\) ) covers the downstream domestic and no value chain path, having complex global upstream linkages, the second element ( \(\hat{v_C}\big [(I-A)^{-1}-(I-A_D)^{-1}-(I-A_D)^{-1}A_{ CB }(I-A_D)^{-1}\big ]\vec {e_i} \otimes \vec {e_i}^{T}{\hat{x}}^{-1}(I-A_D)^{-1}{\hat{f}}\) ) comprises downstream complex global linkages, which have an upstream domestic or no value chain path, and the third element ( \(\hat{v_C}\big [(I-A)^{-1}-(I-A_D)^{-1}\big ]\vec {e_i} \otimes \vec {e_i}^{T}{\hat{x}}^{-1}\big [(I-A)^{-1}-(I-A_D)^{-1}\big ]{\hat{f}}\) ) 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 \(\tau _{i}^{NVC}\)

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. 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 Footnote 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:

3.2.4 The value chain participation rates

In Sect. 3.1 , we showed that a set of value chain tree matrices \(\tau _i\) 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

\(DVCs \in \mathrm{I\!R}^n\) ; \(DVCs_i = \sum _{j=1}^n \sum _{k=1}^n t_{ijk}^{DVC}\) ; \(DVCs= \begin{bmatrix} {\mathbf {1}}^T \tau _{1}^{DVC} {\mathbf {1}} \\ {\mathbf {1}}^T \tau _{2}^{DVC} {\mathbf {1}} \\ \vdots \\ {\mathbf {1}}^T \tau _{n}^{DVC} {\mathbf {1}} \end{bmatrix}.\)

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

\(GVCs \in \mathrm{I\!R}^n\) ; \(GVCs_i = \sum _{j=1}^n \sum _{k=1}^n t_{ijk}^{GVC}\) ; \(GVCs= \begin{bmatrix} {\mathbf {1}}^T \tau _{1}^{GVC} {\mathbf {1}} \\ {\mathbf {1}}^T \tau _{2}^{GVC} {\mathbf {1}} \\ \vdots \\ {\mathbf {1}}^T \tau _{n}^{GVC} {\mathbf {1}} \end{bmatrix}.\)

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

\(SGVCs \in \mathrm{I\!R}^n\) ; \(SGVCs_i = \sum _{j=1}^n \sum _{k=1}^n t_{ijk}^{SGVC}\) ; \(SGVCs= \begin{bmatrix} {\mathbf {1}}^T \tau _{1}^{SGVC} {\mathbf {1}} \\ {\mathbf {1}}^T \tau _{2}^{SGVC} {\mathbf {1}} \\ \vdots \\ {\mathbf {1}}^T \tau _{n}^{SGVC} {\mathbf {1}} \end{bmatrix}.\)

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

\(CGVCs \in \mathrm{I\!R}^n\) ; \(CGVCs_i = \sum _{j=1}^n \sum _{k=1}^n t_{ijk}^{CGVC}\) ; \(CGVCs= \begin{bmatrix} {\mathbf {1}}^T \tau _{1}^{CGVC} {\mathbf {1}} \\ {\mathbf {1}}^T \tau _{2}^{CGVC} {\mathbf {1}} \\ \vdots \\ {\mathbf {1}}^T \tau _{n}^{CGVC} {\mathbf {1}} \end{bmatrix}.\)

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

\(NVCs \in \mathrm{I\!R}^n\) ; \(NVCs_i = \sum _{j=1}^n \sum _{k=1}^n t_{ijk}^{NVC}\) ; \(NVCs= \begin{bmatrix} {\mathbf {1}}^T \tau _{1}^{NVC} {\mathbf {1}} \\ {\mathbf {1}}^T \tau _{2}^{NVC} {\mathbf {1}} \\ \vdots \\ {\mathbf {1}}^T \tau _{n}^{NVC} {\mathbf {1}} \end{bmatrix}.\)

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:

3.2.5 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 - \(\tau _i^{NE}\) ) and the other all of the production value chain paths that end with exporting for final consumption ( \(\tau _i^{E}\) ).

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.

4 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. 2 , 3 and 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. 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.

Source: WIOD, 2016; own calculations.

World average participation rates

Source: WIOD, 2016; own calculations

World average of manufacturing.

Source: WIOD 2016; own calculations

World average of services.

China manufacturing participation rates.

New EU countries manufacturing.

USA manufacturing participation rates. creditSource: 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. 5 , 6 and 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.

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:

\(y_{it}=\alpha _i + logGDP_{it}\beta _1+DVC_{it}\beta _2+GVC_{it}\beta _3+\epsilon _{it},\)

\(\overline{y_i}=\alpha +\overline{logGDP_i}\beta _1+\overline{DVC_i}\beta _2+\overline{GVC_i}\beta _3+ (\alpha _i-\alpha +\overline{\epsilon _{i}}).\)

To ensure a consistent estimator, \(\alpha _i\) must be random effects. \(y_{it}\) is yearly growth of GDP per capita, \(logGDP_{it}\) is a logarithm of GDP per capita, while \(DVC_{it}\) and \(GVC_{it}\) 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 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. Footnote 25 An increase only in participation in global value chains therefore does not necessarily enhance the growth due to various forms of integration Footnote 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.

5 Conclusion

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.

Availability of data and materials

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

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

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 ).

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

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.

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 ).

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 ).

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 ).

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

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 ).

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.

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.

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 ).

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.

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 ).

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.

Formal addition of further n dimensions to the usual \(n \times n\) dimensions.

Definitions of all notations are given in Appendix A .

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.

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 ).

\(CS_k\) represents an index for different country-sectors. \(a_{ CS _1CS_2}\) thus represents a single Leontief technical coefficient indicating that the value produced by \(CS_2\) requires a \(a_{ CS _1CS_2}\) share of \(CS_1\) input.

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.

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 .

Details of the disaggregation are given in Appendix B .

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.

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).

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

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.

The decimal numbers are truncated on the fourth digit.

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Acknowledgements

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

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

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Appendix A: Notations

\(n_S\in \mathrm{I\!N}\) Number of sectors.

\(n_C\in \mathrm{I\!N}\) Number of countries.

\(n\in \mathrm{I\!N}\) ; \(n=n_S*n_C\) Number of country-sectors.

\({\mathbf {1}}\in \mathrm{I\!R}^n\) Vector of ones.

\(\vec {1}\in \mathrm{I\!R}^{n_C}\) vector of ones.

\(\vec {e_i} \in \mathrm{I\!R}^n\) ; \({e_i}_j=\delta _{ij}\) Standard orthonormal basis of \(\mathrm{I\!R}^n\) .

\(I \in \mathrm{I\!R}^{n\times n}\) Identity matrix.

\(x \in \mathrm{I\!R}^n\) Total output vector.

\({\hat{x}} \in \mathrm{I\!R}^{n\times n}\) ; \({\hat{x}}=diag(x)\) Total output matrix.

\(C \in \mathrm{I\!R}^{n\times n}\) Intermediate consumption matrix.

\(F\in \mathrm{I\!R}^{n\times n_C}\) Final consumption matrix on the country level. Footnote 27

\(f \in \mathrm{I\!R}^n\) ; \(f=F\vec {1}\) Total final consumption vector.

\({\hat{f}}\in \mathrm{I\!R}^{n\times n}\) ; \({\hat{f}}=diag(f)\) Total final consumption matrix.

\(A \in \mathrm{I\!R}^{n\times n}\) ; \(A =C{\hat{x}}^{-1}\) Leontief technical coefficient matrix.

\(G \in \mathrm{I\!R}^{n\times n}\) ; \(G ={\hat{x}}^{-1}C\) Ghosh technical coefficient matrix.

\(v \in \mathrm{I\!R}^n\) ; \(v^T= x^T-{\mathbf {1}}^TC={\mathbf {1}}({\hat{x}}-A{\hat{x}})={\mathbf {1}}^T(I-A){\hat{x}}\) Vector of total value added.

\({\hat{v}}\in \mathrm{I\!R}^{n\times n}\) ; \({\hat{v}}=diag(v)\) Total value-added matrix.

\(v_C \in \mathrm{I\!R}^n\) ; \(v_C^T= v^T{\hat{x}}^{-1}={\mathbf {1}}^T(I-A)\) Vector of value-added coefficients – value-added share in total output.

\({\hat{v}}_C\in \mathrm{I\!R}^{n\times n}\) ; \({\hat{v}}_C=diag(v_C)\) Value-added coefficients matrix.

C , A and G have a block-matrix structure \(\mathrm{I\!R}^{(n_S\times n_S)\times (n_C\times n_C) }\) , while F has a block vector structure \(\mathrm{I\!R}^{n_S \times (n_C\times n_C)}\) . 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.

\(C=C_{CB} + C_{D}\) \(A=A_{CB} + A_{D}\) \(G=G_{CB} + G_{D}\) \(F=F_{CB} + F_{D}\) \(f_{CB} \in \mathrm{I\!R}^n\) ; \(f_{CB}=F_{CB}\vec {1}\) Total final consumption by exporting.

\(f_{D} \in \mathrm{I\!R}^n\) ; \(f_{D}=F_{D}\vec {1}\) Total final consumption by domestic transactions.

\({\hat{f}}_{CB}\in \mathrm{I\!R}^{n\times n}\) ; \({\hat{f}}_{CB}=diag(f_{CB})\) Total final consumption by exporting matrix.

\({\hat{f}}_{D}\in \mathrm{I\!R}^{n\times n}\) ;

\({\hat{f}}_{D}=diag(f_{D})\) Total final consumption by domestic transactions matrix.

Appendix B: \(\tau _i\) decomposition

We make a demonstration of the methodology on a simple 2 sector 2 countries numerical example. Footnote 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:

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Knez, K., Jaklič, A. & Stare, M. An extended approach to value chain analysis. Economic Structures 10 , 13 (2021). https://doi.org/10.1186/s40008-021-00244-6

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Received : 30 November 2020

Revised : 14 June 2021

Accepted : 18 July 2021

Published : 02 August 2021

DOI : https://doi.org/10.1186/s40008-021-00244-6

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• Value chain typology
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Value Chain Analysis: Overview, How To Use It (With Examples)

Every business wants to gain a competitive edge. But putting together a highly effective strategy to identify and capitalize on competitive opportunities is easier said than done.

Lowering costs or improving operational efficiency can be difficult if you aren’t clear about how your organization’s activities fit together to create value for your customers. 

Michael Porter's value chain analysis may be your solution. In this article, you’ll get an overview of the most important concepts and the step-by-step process you can use to conduct a value chain analysis.

• Value chain analysis is a framework that aids strategists in analyzing internal activities that impact business success.
• It can be used by businesses of all sizes to gain a competitive advantage through differentiation and low-cost strategies.
• Pros: Value chain analysis shows how the activities in a company’s value chain are linked to each other and influence competitive advantage.
• Cons: Businesses must have in-depth knowledge about their operations and market to effectively use value chain analysis.

What Is a Value Chain?   

Let’s start with the definition of the value chain. A value chain is a set of activities, processes, and inputs that go into the creation of the final product and deliver value to a  business’ customers. 

All companies have value chains. Here are some examples: 

• An e-commerce store’s value chain allows a person to visit a website, buy a product, and then have it arrive at their doorstep.
• A brewing company’s value chain turns hops, water, and barely into a bottled drink that can be sold at bars or stores.
• A professional services firm’s value chain turns expertise, methods, and experience into specialized tax advisory services for multinational enterprises. 

The output of any value chain is the value or utility it provides consumers. Organizations make their money from the difference between the costs of the value chain and the value they offer (profit margin).

An example of a value chain diagram illustrating the concept. Image source: Harvard Business School

What is value chain analysis? 

A value chain analysis is a strategic framework that helps you analyze nine business activities needed to create a product or service and deliver it to its customers. The goal is to discover gaps and identify opportunities to: 

• Increase operational efficiency
• Reduce wasted resources 
• Increase financial performance and profitability
• Increase the quality of a product and reduce costs simultaneously
• Identify a sustainable competitive advantage

A value chain analysis offers a different perspective for businesses engaging in strategic planning to increase their competitive advantage. Rather than focusing on each activity separately, businesses should look at all crucial activities together.

“Management should work to organize the set of activities so they reinforce each other rather than conflict or cancel each other out… This approach forces managers to look beyond the boundaries of their own unit or organizations and see themselves as part of a larger system. Managing interdependencies becomes as important as managing within the organization’s walls.” - Michael E. Porter, Junaid Nabi, and Thomas H. Lee.

What is a competitive advantage? 

According to the value chain framework, companies can increase their competitive advantage by differentiating products or services or lowering costs.

Both of these actions will increase value for consumers and a larger profit margin for the company.  

Cost advantage

A cost advantage strategy aims to increase competitive advantage by lowering the cost of manufacturing a product or offering a service. Businesses should consider this approach if they want to become a cost leader in the market or if there is very little room for product differentiation.

Some examples of companies include Amazon , Walmart , Ford , and Toyota .

Differentiation advantage 

A differentiation strategy aims to increase competitive advantage by increasing the perceived value in the customer’s mind and justifying a product’s price tag. As an example, let's look at Apple. When it comes to iPhone's perceived value, it means the difference between spending a thousand dollars on the iPhone instead of buying a rival's cheaper alternative (but not necessarily lower quality). Diversification is a good strategy for companies who are unable to compete on cost or want to get out of a race to the bottom.

Some additional examples of companies include Starbucks and Coca-Cola.

Primary and Support Activities

Porter’s value chain model divides businesses into nine internal activities under two categories: primary and secondary activities.

Primary activities 

Primary activities directly impact the success of products and services in the market. These activities include inbound logistics, operations, outbound logistics, and marketing and sales.

Inbound Logistics involves receiving, storing, managing, and transporting the materials or components needed for a product or service. For example, a steel manufacturer would need to secure iron ore, coal, silica, and other source materials needed to produce final products in their factories. 

Here are some examples of activities:

• Receiving materials
• Designating storage space
• Ensuring proper storage
• Managing inventory

Operations refer to business processes that turn source materials into finished products. This includes designing and manufacturing products or services. For example, a vehicle manufacturer’s operations could include raw material quality checks, assembly processes, and quality assurance during and after the production process.

Outbound Logistics involves storing, transporting, and distributing finished products to consumers on time and in line with market demand. For example, a semiconductor company based in China or Taiwan must get products to various companies worldwide to make money. This whole process might include activities such as:

• Logistical planning
• Distribution

Marketing and Sales are activities related to promoting, advertising, and pricing products and services. For example, a professional services firm must get its offer in front of the right audience that needs its services and will pay for them.

Example activities:

• Market research
• Digital marketing
• PR activities
• Selecting advertising channels
• Advertising campaigns

After-Sales Services are activities that ensure a positive customer experience after a sale has happened. For example, an appliance maker or car manufacturer must offer after-sale support and services to ensure their customers are happy. 

• Customer service
• Account management
• Returns and replacements
• Client success

Support activities

Support activities indirectly impact the value chain of products and services for businesses. 

Procurement activities deal with the sourcing, selecting, and purchasing of raw materials, equipment, and components for a business.

Infrastructure relates to managerial, financial, and legal systems that help an organization make decisions, operate, and manage resources.

Human Resource Management deals with an organization's people and includes recruitment, training, compensation, and employee management.

Technological Development relates to innovation research, development, and implementation at various value chain stages to improve efficiencies and products.

How to Perform a Value Chain Analysis in 6 steps

Your business should perform a value chain analysis on a regular basis. In an ever-changing market, it's crucial to keep an eye on how you stack up against your competitors. Follow these steps to assess your value and refine your operations.

1. Identify primary and support activities 

The first step is to determine what is the perceived value of your product or services, and identify the activities in the process chain that create the most customer value. Break down all business activities of the organization into either primary or support activities. Each activity should also be broken down into its basic elements. 

This involves a correct identification of direct activities (activities that generate value on their own), indirect activities (activities that support direct activities), and quality assurance (activities that ensure direct and indirect activities meet the requirements). 

For example, if you’re analyzing your inbound logistics activities, you’ll need to look at receiving, storage, warehousing, inventory management, etc. 

💡Tip: This step requires adequate knowledge of the company’s operations. Organize a brainstorming session and bring in various stakeholders from your organization. You’ll get access to different perspectives and insights that you might not be able to discover on your own.

2. Evaluate the cost of each activity

If you want to compete based on cost, you need to focus on the cost of each activity. Go through the business’s primary and support activities and answer these two questions:

• What does the activity cost the company?
• How much does it contribute to overall product cost?

Let’s say that you are a manufacturer of wooden chairs. The overall product cost of a chair is $85. In the next steps, you should identify each activity and percentage that contribute to the overall cost. 

Is there an activity that accounts for a large percentage of the cost? If so, you should look at opportunities to reduce the costs of that specific activity first. 

If your goal is to create a cost advantage, then you also need to understand the cost drivers. Michael Porter identified 10 cost drivers:

• Economies of scale
• Capacity utilization
• Linkages among activities
• Interrelationships among business units
• Degree of vertical integration
• Timing of market entry
• A policy of cost or differentiation
• Geographic location
• Institutional factors

Porter's 10 cost drivers are factors that can affect an activity's cost. By controlling these cost drivers, an organization can improve efficiency, create value, and differentiate itself from the competition.

3. Identify which activities create value for your customers

If you want to create an action plan based on differentiation, you should focus on a value chain analysis with a slightly different approach. When it comes to value, you should look at it from your customer's perspective. 

Think along the lines of:

• Product features
• Marketing & Branding
• Product design
• Services around your product that contribute to positive customer experience
• Customization
• Complementary products

There are many ways to differentiate a product, including improving its quality, offering faster delivery, or adding more features. Additionally, it can mean updating your packaging, changing how items are sold, and trying out new marketing strategies.

Does most of your product or service value come out of customer support or brand identity? If so, you might want to consider investing more resources and budget into those activities.

4. Analyze the relationship between different activities

Look at the links between each activity in the business value chain. A change in one activity might affect another's profitability. 

For example:

• Procuring higher-grade materials may lead to better product quality and fewer product returns. 
• Outsourcing specific tasks like accounting and customer service may allow you to reallocate internal resources elsewhere.
• Renting out warehouse space during quieter periods could help reduce your inbound and outbound logistics costs.

5. Identify your best opportunities for competitive advantage

If you chose a differentiation approach, ask yourself which parts of your value chain offer the best opportunity to achieve differentiation. The analysis may suggest that you need greater or more expensive resources to increase product value, create loyalty, or differentiate yourself from your competitors. Investments in additional resources must be justified by the value created. 

If your main goal is to create value through cost-cutting, take a look at each piece of your value chain through the lens of reducing expenses. Which steps could be more efficient? Some of the resulting opportunities may be as simple as negotiating with suppliers on raw material costs. Or identifying activities that are better served by outsourcing. In some cases, you can design a product, but outsource the manufacturing or building of the product.

Get as clear as possible about what strategy you intend to pursue and how. Identify who will have to execute chosen initiatives and where will these actions have the most impact on value. This is essential for the next step.

6. Execute your strategy

The Value Chain is a worthless exercise if it is not followed by an action plan and execution . After completing your value chain analysis, select a few quick wins and put them into motion right away. Don’t fall into a cycle of planning and revising. Start executing as soon as you have an idea of which strategy you want to pursue. 

However, you need to have in place a strong management structure that will help you monitor progress, hold teams accountable , and empower leaders to lead the change. 

As an example, Cascade helps you build your action plan and assign an owner to each initiative and objective. This will give you a high-level overview of the performance of your teams and identify any setbacks before they become a serious problem. This is important if your value chain strategy includes changes across multiple areas at the same time. You’ll be able to keep your cross-functional teams aligned and accountable for progress. 

A strategy execution platform like Cascade can streamline the process of communicating your action plans, measuring, and executing strategic initiatives.

Value Chain Analysis Example: Ikea

IKEA is one of the largest furniture manufacturers and retailers in the world. Here’s how a value chain analysis would look for this global brand.

Inbound Logistics 

• IKEA inbound logistics is a major source of value creation for the business . They have 1220 suppliers worldwide. The company has strategically placed distribution centers worldwide and trading offices near suppliers to minimize transportation costs. 
• A trading service office located near a supplier location allows the company to monitor production, negotiate prices, and check the quality of raw materials and products they purchase.
• With IKEA's flat pack-assembly principle, packaging costs are reduced and inbound logistics are simplified.

Operations  

• The company operates a franchise business with over 458 locations worldwide. This helped them to expand internationally faster and at lower costs. They selectively outsource manufacturing and sell unassembled items to reduce unnecessary overheads.
• IKEA has 40 furniture production units, most located in Europe, and two factories that produce furniture components (screws, plugs, etc.). With a large number of manufacturing units located in Eastern Europe and China, the company saves a significant amount of money on human resources.

Outbound Logistics 

• IKEA minimized its expenses by creating its own distribution system and distribution centers. Customers are responsible for costs associated with the transportation of goods purchased from IKEA stores. 

Marketing and Sales

• The company markets the IKEA brand through media advertising, promotions, product placements, social media, and digital marketing . In order to cut marketing costs, one of their strategies was to cancel physical catalogs. 

After-Sales Services

• IKEA’s after-sale services include a 365-day exchange/return policy, measuring, assembly, and installation assistance. They also offer assistance with sourcing spare parts and operate furniture removal and recycling services. However, they are not famous for superb customer service which might be a result of their cost-cutting initiatives.

Support activities 

Firm Infrastructure

• Corporate management is centralized in the Inter IKEA Group, which oversees the strategic direction of the business. They also have an operations management team to manage supply chains , while franchisees manage individual stores.

Human Resource Management 

• IKEA leaves in-store hiring and HR management to franchisees. However, they offer employees skills development, training resources, and general well-being support.

Technological Development 

• IKEA utilizes integrated cloud computing, improving inventory management and data storage to enhance various aspects of their value chain .

Procurement

• The company buys a combination of raw materials and finished products in volume to reduce purchase prices on key category choices and only utilizes large suppliers capable of delivering their required quantities.

📚 Recommended reading: Strategy study: How IKEA became a household name 

Benefits of Value Chain Analysis 

The main benefits of value chain analysis are: 

• It helps you to identify value gaps and carve out your competitive advantage so you can formulate your differentiation strategy 
• Gives a clear understanding of the interconnectedness of business activities and how they influence business success.
• Can help maintain alignment when planning and executing cost advantage and product differentiation strategies.
• Allows leaders to systematically examine business units for strengths and weaknesses that can be addressed.

Cons of Value Chain Analysis 

The main cons of value chain analysis are: 

• The goal of value chain analysis is to break things down. Finer details are important, but if you rely on them too heavily, you may lose sight of the big picture.
• It can be difficult to apply value-chain analysis to complex business structures.
• Getting accurate, up-to-date data and metrics to perform a value chain analysis can take time and effort.

Value Chain Analysis + Strategy Execution = 🚀 

Value chain analysis helps businesses understand their operations better and identify sustainable competitive advantage. However, the key is to turn insights into action or you won't see any results.

How to do it? Using a centralized platform to manage your strategic initiatives in one place. This will help you keep your teams aligned and quickly adapt when disruptions occur. 

Thousands of teams around the world rely on Cascade's strategy execution platform to see faster results from their strategies. 

Curious to see it in action? Take it for a spin for free or book a call with a Cascade expert.

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What Is a Value Chain Analysis? 3 Steps

• 03 Dec 2020

Successful businesses create value with each transaction —for their customers in the form of satisfaction and for themselves and their shareholders in the form of profit. Companies that generate greater value with each sale are better positioned to profit than those that produce less value.

To evaluate how much value your company is creating, it’s critical to understand its value chain. Below is an overview of what a value chain is, why it’s important to understand, and steps you can take to conduct one and help your company create and retain more value from its sales.

Access your free e-book today.

Understanding the Value Chain

The term value chain refers to the various business activities and processes involved in creating a product or performing a service. A value chain can consist of multiple stages of a product or service’s lifecycle, including research and development, sales, and everything in between. The concept was conceived by Harvard Business School Professor Michael Porter in his book The Competitive Advantage: Creating and Sustaining Superior Performance .

Taking stock of the processes that comprise your company’s value chain can help you gain insight into what goes into each of its transactions. By maximizing the value created at each point in the chain, your company can be better positioned to share more value with customers while capturing a greater share for itself. Similarly, knowing how your firm creates value can enable you to develop a greater understanding of its competitive advantage .

Components of a Value Chain

According to Porter’s definition, all of the activities that make up a firm's value chain can be split into two categories that contribute to its margin: primary activities and support activities.

Primary activities are those that go directly into the creation of a product or the execution of a service, including:

• Inbound logistics : Activities related to receiving, warehousing, and inventory management of source materials and components
• Operations : Activities related to turning raw materials and components into a finished product
• Outbound logistics : Activities related to distribution, including packaging, sorting, and shipping
• Marketing and sales : Activities related to the marketing and sale of a product or service, including promotion, advertising, and pricing strategy
• After-sales services : Activities that take place after a sale has been finalized, including installation, training, quality assurance, repair, and customer service

Secondary activities help primary activities become more efficient—effectively creating a competitive advantage—and are broken down into:

• Procurement : Activities related to the sourcing of raw materials, components, equipment, and services
• Technological development : Activities related to research and development, including product design, market research , and process development
• Human resources management : Activities related to the recruitment, hiring, training, development, retention, and compensation of employees
• Infrastructure : Activities related to the company’s overhead and management, including financing and planning

What Is Value Chain Analysis?

Value chain analysis is a means of evaluating each of the activities in a company’s value chain to understand where opportunities for improvement lie.

Conducting a value chain analysis prompts you to consider how each step adds or subtracts value from your final product or service. This, in turn, can help you realize some form of competitive advantage, such as:

• Cost reduction , by making each activity in the value chain more efficient and, therefore, less expensive
• Product differentiation , by investing more time and resources into activities like research and development, design, or marketing that can help your product stand out

Typically, increasing the performance of one of the four secondary activities can benefit at least one of the primary activities.

How to Conduct a Value Chain Analysis

1. Identify Value Chain Activities

The first step in conducting a value chain analysis is to understand all of the primary and secondary activities that go into your product or service’s creation. If your company sells multiple products or services, it’s important to perform this process for each one.

2. Determine Activities' Values and Costs

Once the primary and secondary activities have been identified, the next step is to determine the value that each business activity adds to the process, along with the costs involved.

When thinking about the value created by activities, ask yourself: How does each increase the end user’s satisfaction or enjoyment? How does it create value for my firm? For example, does constructing the product out of certain materials make it more durable or luxurious for the user? Does including a certain feature make it more likely your firm will benefit from network effects and increased business?

Similarly, it’s important to understand the costs associated with each step in the process. Depending on your situation, you may find that lowering expenses is an easy way to improve the value each transaction provides.

3. Identify Competitive Advantage Opportunities

Once you’ve compiled your value chain and understand the cost and value associated with each step, you can analyze it through the lens of whatever competitive advantage you’re trying to achieve.

For example, if your primary goal is to reduce your firm’s costs, you should evaluate each piece of your value chain through the lens of reducing expenses. Which steps could be more efficient? Are there any that don’t create significant value and could be outsourced or eliminated to substantially reduce costs?

Similarly, if your primary goal is to achieve product differentiation, which parts of your value chain offer the best opportunity to realize that goal? Would the value created justify the investment of additional resources?

Using value chain analysis, you can uncover several opportunities for your firm, which can prove difficult to prioritize. It’s typically best to begin with improvements that take the least effort but offer the greatest return on investment .

One Piece of the Puzzle

Value chain analysis can be a highly effective means of understanding and contextualizing your business’s processes, but it’s just one tool at your disposal. There's a host of other frameworks and concepts that can help you evaluate organizational performance, craft winning strategies, and be more effective in your role.

Ready to learn additional frameworks that can enable you to make smarter business decisions? Explore our eight-week course Economics for Managers and other online Strategy courses , and find out more about how to develop effective pricing strategies.

About the Author

The Straightforward Guide to Value Chain Analysis [+ Templates]

Published: May 31, 2023

What's your business' competitive edge? A value proposition helps businesses identify what sets them apart from competitors.

But how can you tell if your business activities create the most value for your customers and result in a strong profit margin? One way to get there is through conducting a value chain analysis.

Let's take a deeper look into value chain analysis and learn how you can analyze your business activities.

We’ll cover:

What is a value chain?

• What is a value chain analysis?
• Porter’s Value Chain Analysis

How to Create a Value Chain Analysis

Value chain analysis example, value chain analysis templates.

• A value chain is used to describe all the business activities it takes to create a product from start to finish (e.g., design, production, distribution, and so on). A value chain analysis gives businesses a visual model of these activities, allowing them to determine where they can reduce costs.

What is Value Chain Analysis?

Value chain analysis is a strategic process where a firm evaluates its internal activities to identify how each contributes to the firm's competitive advantage. The ultimate goal of a value chain analysis is to pin down the practices and processes that differentiate a firm from its competitors — for better or worse.

Value chain analysis is a way for businesses to analyze the activities they perform to create a product. Once the activities are analyzed, a business can use the results to evaluate ways to improve its competitive advantage.

While one of the goals of value chain analysis is to improve operational efficiency, its final and most important goal is to establish an advantage over competitors.

Competitive Advantage

Competitive advantage is what sets your business apart from competitors. You'll need a clear idea of your target market to develop an advantage.

If you're an entrepreneur interested in clearly defining your business' target audience, find the ideal niche market for launching or selling your products. You’ll also need to know the benefit your product provides to the target market and understand your competitors’ offerings.

As you complete your value chain analysis, you’ll identify the edge you’re trying to gain over the competition. Firms typically choose between two types of competitive advantage: cost advantage and differentiation advantage.

Cost Advantage

A cost advantage strategy aims to become the lowest-cost provider in your industry or market. Companies that excel with a low-cost strategy have extreme operational efficiency and use low-cost materials to reduce the overall price of their product or service.

Examples include McDonald's and Walmart.

Differentiation Advantage

When using a differentiation strategy, you offer a unique or highly specialized product to gain a competitive advantage. The business needs to dedicate time and resources to innovation, research, and development.

A successful differentiation strategy allows the business to set a premium price for its product or service. Examples include Starbucks and Apple.

It's best to pick a single competitive advantage to focus your efforts on. Depending on which competitive strategy you choose, the goal of your value chain analysis will be to either reduce costs or differentiate to improve margins.

Then, you'll have a clear idea of your business' goals and how you plan to provide value. It also narrows the scope of changes that need to be made to improve efficiency.

Value Chain Analysis Template

Use this template to outline and visualize your:

• Outbound Logistics
• Technology Development
• Inbound Logistics

You're all set!

Click this link to access this resource at any time.

Fill out the form to access the free template.

Porter's value chain analysis.

But how would you choose which competitive advantage to go for? Using Porter’s value chain model, you can look at your business activities, pinpoint a unique value proposition , and determine the best way to establish dominance over your competition.

Michael Porter, a Harvard Business School professor, introduces a simple value chain model in his book Competitive Advantage . He lays out the steps for performing a value chain analysis and places business activities into two categories: primary and support.

Identifying the primary and support activities is critical in creating a value chain analysis. You’ll know where you spend the most resources, where your business can improve, and where your competitors may have an edge over you.

Let’s take a look at these activities below.

Primary and Support Activities

Primary and support activities are the processes and systems a business uses to develop its offering. The five primary activities are inbound logistics, operations, outbound logistics, marketing and sales, and services. Support activities are firm infrastructure, HR management, technology development, and procurement.

Primary Activities

There are five primary activities, including all the actions that go into creating a business' offering. Let's explore them below.

• Inbound logistics. This is how materials and resources are gained from suppliers before the final product or service can be developed. In your analysis, take a look at the locations of your suppliers and shipping costs from their facility to yours.
• Operations. Operations are how the materials and resources are produced, resulting in a final product or service. Here, you may look at the cost of running your warehouse, machinery, and assembly lines.
• Outbound logistics. Once a product is finished, it needs to be distributed. Outbound logistics describes this process. Consider your shipping costs to consumers, warehousing fees, distributor relations, and order processing operations.
• Marketing and sales. This is how your product or service is presented and sold to your ideal target market. In your analysis, take into account advertising costs, promotional costs, reach, and cost-per-acquisition .
• Services. This is the support a business provides for the customer, which can include support and training for the product, warranties, and guarantees. You’ll look at repair costs, product training costs, product adjustment frequency, and more.

Support Activities

Support activities help the primary activities in creating an advantage over competitors. They include:

• Firm infrastructure. This entails all the management, financial, and legal systems a business has in place to make business decisions and effectively manage resources.
• Human resource management . Human resource management encompasses all the processes and systems involved in managing employees and hiring new staff. This is especially important for companies that provide in-person service.
• Technology development. Technology development helps a business innovate. This can be used in various steps of the value chain to gain an advantage over competitors by increasing efficiency or decreasing production costs.
• Procurement . This is how the resources and materials for a product are sourced. The goal is to find quality supplies that fit the business' budget.

Value chain analyses require research and can take time to develop. Below are the general steps it takes to create a value chain analysis.

Value Chain Analysis Steps

• Determine the business' primary and support activities.
• Analyze the value and cost of the activities.
• Refer to your competitors' value chains.
• Understand your customer base's perception of value.
• Identify opportunities to gain a competitive advantage.

It’s now time to bring it all together in a unified process to create a value chain analysis. Let’s get started.

1. Determine the business' primary and support activities.

Together, the primary and support activities make up the value chain. They include each action required in developing a product or service, from raw material to final product.

2. Analyze the value and cost of the activities.

The team tasked with creating the value chain analysis should brainstorm ways each activity provides value to customers and the business as a whole. Compare the activity to the competitive advantage you're trying to achieve and see if it supports the goal.

After the value analysis is complete, look at the cost of the activities. Is the activity labor-intensive? How much does X raw material cost?

Asking similar questions will help identify which activities are cost-effective and which are not. This is where areas for improvement can be identified.

3. Refer to your competitors' value chains.

A value chain analysis improves your competitive advantage, so any business that conducts one should keep that information close to the chest. In all likelihood, you won't happen upon a nuanced, in-depth picture of your competitors' primary and support activities.

Still, you can get some concept of your industry peers' value chains through competitive benchmarking — using relevant metrics to compare your company to competitors’. The practice is multifaceted and is used for three primary functions:

• Strategic benchmarking , comparing business models and strategies.
• Process benchmarking , comparing business and operational processes.
• Performance benchmarking , comparing business outcomes based on a set of metrics.

Once you've identified the benchmarking category you'd like to pursue, you can pick the competitors you'd like to measure yourself against. Then, you'd choose metrics that you can realistically collect data for and leverage resources that enable the relevant research.

4. Understand your customer base's perception of value.

The customer is always right. So however valuable your customers perceive your product or service to be is exactly how valuable it actually is. Customer perception might be the most crucial factor in framing your competitive advantage, so you need to have a pulse on it.

Customer surveys, digging into any, and doing anything that will cue your target market’s perception of you are central to conducting a fully realized value chain analysis.

5. Identify opportunities to gain a competitive advantage.

Once the value chain analysis is complete, the primary stakeholders in the business can see an overview of where the business is excelling and where improvements can be made operationally.

Begin with the improvements that take minor changes and provide high-impact results. After the easy wins are identified, you and your team can tackle the bigger challenges that might be hindering efficiency.

The value chain analysis gives businesses a clear idea of how to adjust their actions and processes to provide the most value to their target market and increase profit margins for the company.

Still not sure how it all works? Let’s take a look at an example.

Completing a value chain analysis allows businesses to examine their activities and find competitive opportunities.

For example, McDonald's mission is to provide customers with low-priced food items. The analysis helps McDonald's identify areas for improvement and activities that add value to their products and services.

Below is an example of a value chain analysis for McDonald's and its cost leadership strategy.

• Inbound Logistics : McDonald's has pre-selected, low-cost suppliers for the raw materials for their food and beverage items. It sources suppliers for items like vegetables, meat, and coffee.
• Operations : The business is a franchise, and each McDonald's location is owned by a franchisee. There are more than 39,000 McDonald's locations worldwide.
• Outbound Logistics : Instead of formal, sit-down restaurants, McDonald's has restaurants that focus on counter-service, self-service, and drive-through service.
• Marketing and Sales : Its marketing strategies focus on media and print advertising, including social media posts, magazine advertisements, billboards, and more.
• Services : McDonald's strives to achieve high-quality customer service. It provides its thousands of employees with in-depth training and benefits so they can best assist their customers.
• Firm Infrastructure : The McDonald’s corporation has both C-suite executives and Zone Presidents who oversee the firm’s operations in various regions, with a general counsel overseeing legal matters.
• Human Resource Management : It maintains a career page where job seekers can apply to both corporate and restaurant roles. It pays hourly and salaried rates and promotes its tuition assistance program to attract talent.
• Technology Development : The restaurant has invested in touch kiosks to facilitate ordering and increase operational efficiency.
• Procurement : The firm uses Jaggaer , a digital procurement firm, to establish relationships with key suppliers across various regions of the world.

Here are a few value chain analysis templates to help you develop your own.

1. HubSpot's Value Chain Analysis Template

Available via Google Sheets and Google Slides, this interactive version of Porter's Value Chain Analysis can be customized to outline your company's value chain. Get your free copy .

2. Porter's Value Chain Analysis Model

This Porter's value chain analysis template provides a general overview of business activities.

3. Template for Cost Profit Margin

If you're analyzing the cost versus expected profit margin from your primary and support activities, this template's for you.

4. Template for Educational Institutions

Rather than analyzing the activities that go into creating a product or service, this model looks at the value chain involved in developing academic research.

5. Template for Products

Use this template to analyze the activities it takes to create a product from raw material to finished product.

6. Template for Financial Acquisitions

Grow Your Business with Value Chain Analysis

Your value chain analysis will help you identify areas for improvement and the activities that provide the most value to your customers and your business as a whole. Eliminating inefficient business activities speeds up production, improves your competitive advantage, and increases profit margins.

Editor's note: This post was originally published in November 2018 and has been updated for comprehensiveness.

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Outline your value chain analysis with this free template.

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3.2. Value Chain Analysis

Introduction.

Value chain analysis is a process that requires four interconnected steps: data collection and research, value chain mapping, analysis of opportunities and constraints, and vetting of findings with stakeholders and recommendations for future actions. These four steps are not necessarily sequential and can be carried out simultaneously.

The figure to the right is a simple graphic illustrating the analysis process and components. The value chain team collects data and information through secondary and primary sources by way of research and interviews. Mapping helps to organize the data, and highlights the market segments, participant/actors, their functions and linkages. The collected data is analyzed using the value chain framework to reveal constraints within the chain that prevent or limit the exploitation of end market opportunities. The resulting analysis of opportunities and constraints should be vetted with stakeholders through events such as workshops, focus groups or “reporting-out” days.

Each of these components are described in further detail below.

Prior to performing value chain analysis, some preparation is required.

Step One: Data Collection

Good value chain analysis begins with good data collection, from the initial desk research to the targeted interviews . The value chain framework—that is, the structural and dynamic factors affecting the chain—provides an effective way to organize the data, prioritize opportunities and plan interventions. To review the framework and its role in value chain analysis, click here .

The desk research consists of a rapid examination of readily available material. The aim is to familiarize the team with the industry, its market and the business environment in which it operates, as well as to identify sources for additional information. Information such as statistics on exports/imports, consumption reports, global trade figures, etc., can be obtained through the Internet, phone calls and documents from trade, commerce and industry ministries, specialized industry journals, and professional and trade association newsletters. Once the desk research is conducted, an initial value chain map can be drafted for refinement during the primary research phase.

Interviews are conducted with 1) firms and individuals from all functional levels of the chain, and 2) individuals outside the value chain such as writers, journalists or economists. In addition to providing information about the movement of product and the distribution of benefits, the interviews should inform on value chain actors’ current capacity to learn; how information is exchanged among participants; from where they learn about new production techniques, new markets and market trends; gender dynamics that affect value chain performance; and the extent of trust that exists among actors. Interviews can help to identify where chain participants see opportunities for and constraints to upgrading. Missing or inadequate provision of services necessary to move the value chain to the next level of competitiveness can be identified locally, regionally or nationally.

In addition to individual interviews, focus group discussions are a useful way to explore concepts, generate ideas, determine differences in opinion between stakeholder groups and triangulate with other data collection methods. The group may consist of 7-10 people who perform the same or a similar function in the value chain. Guided discussion better captures the social interaction and spontaneous thought processes that inform decision making, which is often lost in structured interviews. Please see the Guide to Focus Group DiscussionsGuide to Focus Group Discussions (https://www.marketlinks.org/resources/microreport-138-guide-focus-group-discussions) for more information.

Click here to see a summary of the advantages and disadvantages of four primary research tools (interviews, focus groups, surveys and observation). Other useful resources on techniques for data collection include the following:

• The SEEP Network's Technical Note, "An Inventory of BDS Market Assessment Methods for Programs Targeting Microenterprises"SEEP Network's Technical Note on market assessment methods (http://www.bdsknowledge.org/dyn/bds/docs/446/Inventory%20of%20Methods%20Final%20PDF.pdf)
• The SEEP Network's "Building a Team for BDS Market Assessment and Key Issues to Consider When Starting BDS Market Assessment"SEEP Network (https://seepnetwork.org/Blog-Post/Building-a-Team-for-Market-Assessment-and-Key-Issues-to-Consider-When-Starting-a-Market-Assessment) 
• The ILO's "Guide to Market Assessment for BDS Program Design"http://www.bdsknowledge.org/dyn/bds/docs/377/Guide%20to%20BDS%20MA%20for%20Program%20Design%20Miehlbradt.pdf

The qualitative data gathered by these methods will reveal dynamic factors of the value chain such as trends, incentives and relationships. To complement this, quantitative analysis of the chain is necessary to provide a picture of the current situation in terms of the distribution of value-added, profitability, productivity, production capacity and benchmarking against competitors. Analyzing these factors highlights inefficiencies and areas for reducing cost.

Step Two: Value Chain Mapping

Value chain mapping is the process of developing a visual depiction of the basic structure of the value chain. A value chain map illustrates the way the product flows from raw material to end markets and presents how the industry functions. It is a compressed visual diagram of the data collected at different stages of the value chain analysis and supports the narrative description of the chain.

The purpose of a visual tool in the analysis process is to develop a shared understanding among value chain stakeholders of the current situation of the industry. The mapping exercise provides an opportunity for multi-stakeholder discussions to reveal opportunities and bottlenecks to be addressed in subsequent stages of the project cycle . Maps also help to identify information gaps that require further research.

A two-phased process for developing the value chain map is recommended, as follows: a) initial basic mapping and b) adjusted mapping. Initial mapping is based on the information derived from desk research and knowledge at the outset of the analysis. The second phase includes revisions based on interviews and feedback from firms and individuals brought into the analysis process. As value chain maps are representations of a complex system, the analysis must balance the need to generalize with the desire to charge the map with details. Mapping is a dynamic process; therefore, adjustments should be made as needed.

Step Three: Analysis of Opportunities and Constraints Using the Value Chain Framework

Step three uses the value chain framework as a lens through which the gathered data is analyzed. The framework is a useful tool to identify systemic chain-level issues rather than focus on firm-level problems. While interviews give the value chain team the chance to gather information from individual firms, the value chain framework helps to organize this information in such a way that the analysis moves from a firm-level to a chain-level perspective. If the chain cannot be competitive, the success of individual firms is compromised. Therefore, taking a systemic approach is key to sustaining the competitiveness of the chain and the MSEs operating within it.

The factors affecting performance of the chain are further analyzed to characterize opportunities and constraints to competitiveness. These factors are:

• end markets
• business enabling environment
• vertical linkages
• horizontal linkages
• supporting markets
• value chain governance
• inter-firm relationships

Each plays a role in influencing value chain competitiveness. Using a table format, these factors of the value chain framework can be evaluated in terms of offering opportunities for upgrading and the constraints to taking advantage of these opportunities. Click here for further discussion on the factors and how to analyze them.

Step Four: Vetting Findings of Chain Analysis through Stakeholder Workshops

Value chain analysis helps develop a private-sector vision to reflect stakeholders’ interest in improving the efficiency and competitiveness of the chain. The fourth step, vetting findings, uses value chain analysis through a structured event (or series of events) like a workshop or reporting-out day to facilitate discussion with and among selected participants.Participatory Approaches to Value Chain Development Briefing Paper (https://www.marketlinks.org/resources/participatory-approaches-value-chain-development-briefing-paper)

The objective of these events is to bring participants together who are responsible for critical market functions, service provision, and the legal, regulatory and policy environment. The goal is to have these participants—who have an incentive to drive investments in upgrading—to develop and assist in implementing a private sector-led competitiveness strategy . To develop this strategy, the stakeholders will need to prioritize the opportunities and constraints identified during the value chain analysis. With an open format, such structured events foster buy-in to the analysis process.

Participants are selected based on the role they play in the value chain, or their responsibility for critical market functions. There should also be MSE, medium and larger firm and association representatives who, during the interview phase, exhibited an understanding of the issues related to the value chain (especially the opportunities), a strong interest in the types of questions posed during the interview, and leadership skills among peers or the community.

Vetting events can take on several forms from simple one day reporting-out sessions to more structured workshops that stretch to two or three days. The events are planned to reinforce the importance of knowing and understanding the end market. In presenting the findings of the value chain analysis, workshop leaders should stress that to remain competitive, stakeholders and other participants must continuously learn what end markets demand in terms of product specifications, quality, and other requirements.

It can be powerful to have a series of buyers present at the workshop. Where not possible, a phone call or pre-recorded video interview can be an effective means for stakeholders to see and hear directly from the buyer. Pre-recorded interviews were used successfully for the Haiti handicraft value chain vetting workshopHandmade in Haiti: The Perspectives of Global Buyers (https://www.marketlinks.org/library/handmade-haiti-perspective-global-buyers) and for the Tanzania high-value vegetables stakeholder workshop.Tanzania High-value Vegetables Value Chain Stakeholder Workshop (http://www.tzdpg.or.tz/fileadmin/_migrated/content_uploads/Vegetables_-Executive_Summary_FFV_Studies_pdf_1_.pdf)

The event should include facilitated discussions, review and adjustments of value chain map and a review of the analysis table mentioned above. For this exercise, it is recommended that the completed table be projected on a screen, and additions and modifications made during discussions inserted with the computer projecting the table. This assures a participatory process and on-the-spot adjustment witnessed by attending participants. If changes are made, the updated table can be rapidly printed and distributed to participants before they leave.

In environments characterized by a number of donor partners working with the same group of firms, burn-out and skepticism particularly among the most important change drivers is likely. In some instances, the firms most important to driving change may not attend a full-day workshop even though they may be highly committed to the upgrading process and strategy for making the industry more competitive. If time allows, the analysis team can meet with these firms in advance of the workshop to convince them of the value of the competitive planning process. If this is not possible, the analysis team should meet with these firms as soon after the workshop as possible to vet findings and secure buy-in or commitment to the industry competitiveness planning process.

See an example of value chain analysis

Department of Agricultural, Food, and Resource Economics Innovation Lab for Food Security Policy, Research, Capacity and Influence

Prci southeast asia workshop for developing cassava value chain analysis proposals.

Melissa Hill <[email protected]> - March 21, 2022

Kasetsart University (KU) in collaboration with IFPRI conducted a PRCI Southeast Asia workshop for developing cassava value chain analysis proposals

To develop cassava value chain analysis proposals in Southeast Asia, the USAID funded Innovation Lab for Food Security Policy Research, Capacity and Influence (PRCI) Southeast Asia in coordination with Kasetsart University and the International Food Policy Research Institute (IFPRI), held a morning workshop with educational sessions and presentations by partnering countries. The objective of the workshop was to provide an overview of value chain analysis concepts and current research gaps in the region. During the webinar, given on Feb 18, 2022, four researchers gave educational presentations on the process and importance of value chain analysis as well as reviewing research on the cassava value chain. Research proposal presentations were made by PRCI’s Laos, Thailand, and Cambodia country partners.  

Before the research proposal presentations were made, there were four educational sessions about the process of value chain research. The first session was a welcome on behalf of PRCI by Suresh Babu and Ora Napasintuwong . Suresh Babu gave a brief overview of the PRCI project and Ora  Napasintuwong discussed the program agenda for the workshop and the past research activities undertaken on value chain needs assessment process under PRCI. Next, Bart Minton of IFPRI presented on how food value chain transformation occurs in African and Asian developing countries. 

The third session was led by Ben Belton , Associate Professor at the Department of Agricultural, Food and Resource Economics (AFRE), on designing and implementing agri-food value chain surveys. The presentation included research objectives, how to frame value chain research, and detailed explanations of the step-by-step process of value chain analysis. The final educational session was presented by Jonathan Newby, of the International Center for Tropical Agriculture (CIAT). He went into detail about the cassava value chain in Southeast Asia clarifying what is already known and where there are still gaps in the research.  You can find the visual presentation materials here .

Following the educational sessions, each partnering country was given ten minutes to present their research proposal on the cassava value chain. First, two research teams represented Thailand, from the Department of Agricultural and Resource Economics, KU, and the Department of Agro-Industrial Technology Management, KU. Both teams identified key issues, including the dependency of the Thai cassava industry on domestic ethanol industry, labor challenges, low investment in research, and dependency on China as an export market. Potential value chain upgrades were suggested, including gluten-free products, organic products, bioethanol, bio plastic and global trade.

Cambodia presented next, represented by a team from the   Cambodia Development Resource Institute (CDRI). They pointed out that Cambodia is in the top ten cassava producers worldwide and laid out a literature review of previous research.  They then developed two research questions based on their review of the research, current policies, and cassava value chain data. The Institute for Industry and Commerce (IIC) represented Laos and presented a case study on the determinants of Lao agricultural export performance.

Discussion of their respective countries’ concept notes on conducting cassava value chain analysis was fruitful and the feedback received from the workshop will be used by country partners to further develop and finalize their research proposals.

View the visual materials here .

See the recording of the webinar here .

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COW MILK MARKET VALUE CHAIN ANALYSIS AND RAW MILK ADULTERATION TEST IN GORODOLA DISTRICT, GUJI ZONE, OROMIA REGION, ETHIOPIA.

Milk market value chain analysis and raw milk adulteration test were assessed in Gorodola district Guji zone of Oromia regional state with the objective of identifying main actors and constraints of milk marketing. The study was undertaken in purposely selected three rural and two peri urban kebeles of Gorodola district; and a total of 215 producers, consumers, retailers and Hotels and small cafes were randomly selected. The study was carried out using household survey, market monitoring and group discussion. One major milk market sites (Harekelo market) was selected based on accessibility. Milk market was monitored over two seasons; the wet and dry seasons and a total of 104 milk samples were checked using lactometer. The major actors of the dairy value chain include input suppliers, service providers, milk producers, wholesalers, retailers and consumers. There was weak link between producer and input supplier and service provider. The majority of producers indicated that they didn’t get, enough extension service, credit and technological packages from the concerned institutions. Four milk marketing channels were identified. Three subsidiary milk and milk product markets were identified in and around Gorodola district. Negele, Genale and Bitata market were received on average (302.8 ± 3.9) and (178.4± 3.4), (168.9 ± 3.9) and (90.8 ± 1.8), (117.7 ± 1.6) and (75.9 ± 1.34) litre of milk from Harekelo in each market day during rainy and dry season respectively. There was also price variation between season and the price of milk was (9.2 ± 0.1) and (16.9 ± 0.3) Birr during the rainy and dry season respectively. The producer travel 1-20 km and which indicated the house hold who are near to the market can have a chance to sell the milk frequently than which are far from the market. The main transportation services identified for dairy products were women backs, animal transportation, motor bikes, and public buses. About 58.7% of the sampled milk were adulterated. This indicated that most of the milk brought to the market was adulterated with water or other substances and adulteration was high during dry season due to shortage of milk in the dry season. The major constraints for milk marketing included long distance to market, seasonality of milk and milk products, short shelf life of fresh milk and absence of milk cooperative. Large cattle population, long indigenous knowledge in livestock holding and high participation of women in milk production was among the opportunities for development of dairy. Key words: Milk market, value chain, Adulteration, Market constraint

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