Low Interest Rates and Bank’s Profitability – Update May 2019
My last post on this important topic was in 2017. Since then several new articles and research papers have been published. I have compiled them in this post. Please see references.
In my posts I have shown how many trends in economics for the last thirty years can be explained by unintendend consequences of US Federal Researve monetary policy of lowering interest rates to boost economic growth.
Rise of Shadow Banking – MMMF
Rise of International capital flows in USA
Growth of Consumer credit – Credit Cards and Housing Loans
Decline in Net Interest Margins of the Banks
Risk taking by banks to maintain and increase their profits
Rise of Non interest income of Banks
Rise of Non core business of banks
Rise of Mergers/Acquisitions/Consolidation in Banking sector
Related to these are:
Business Investments by Production side of economy
Increase in Market concentration of Products
Increase in Mergers and Acquisitions/consolidation among Product market businesses
Decreasing monitory policy effectiveness
Wrong economic growth forecasts
Secular Stagnation Hypothesis
Rise of Outsourcing and global value chains
Free Trade agreements
Increase in Ineqality of wealth and Income
Increase in corporate profits and equities market
Increase in corporate savings
Increase in share buybacks, and dividends payouts
I have yet to see an effort by economists and policy makers to analyze these trends in an integrated manner.
To be prepared for any future crisis in economic/financial system, collective efforts have to be made to understand non linear sources of complexity and fragility.
Increasing Market Concentration in USA: Update April 2019
In this post, I have compiled recent articles and papers on the issues of:
Increased Market Power
Increased Market Concentration
Increased Corporate Profits
Anti Trust Laws and Competition policy
Interest rates and Business Investments
Interest rates and Mergers and Acquisitions
Stock Buybacks, Dividends, and Business Investments
Outsourcing, and Global Value Chains
Corporate Savings Glut
Slower Economic Growth
From Low Interest Rates, Market Power, and Productivity Growth
How does the production side of the economy respond to a low interest rate environment? This study provides a new theoretical result that low interest rates encourage market concentration by giving industry leaders a strategic advantage over followers, and this effect strengthens as the interest rate approaches zero. The model provides a unified explanation for why the fall in long-term interest rates has been associated with rising market concentration, reduced dynamism, a widening productivity-gap between industry leaders and followers, and slower productivity growth. Support for the model’s key mechanism is established by showing that a decline in the ten year Treasury yield generates positive excess returns for industry leaders, and the magnitude of the excess returns rises as the Treasury yield approaches zero.
Material flow analysis (MFA) is a systematic assessment of the flows and stocks of materials within a system defined in space and time. It connects the sources, the pathways, and the intermediate and final sinks of a material. Because of the law of the conservation of matter, the results of an MFA can be controlled by a simple material balance comparing all inputs, stocks, and outputs of a process. It is this distinct characteristic of MFA that makes the method attractive as a decision-support tool in resource management, waste management, and environmental management.
An MFA delivers a complete and consistent set of information about all flows and stocks of a particular material within a system. Through balancing inputs and outputs, the flows of wastes and environmental loadings become visible, and their sources can be identified. The depletion or accumulation of material stocks is identified early enough either to take countermeasures or to promote further buildup and future utilization. Moreover, minor changes that are too small to be measured in short time scales but that could slowly lead to long-term damage also become evident.
Anthropogenic systems consist of more than material flows and stocks (Figure 1.1). Energy, space, information, and socioeconomic issues must also be included if the anthroposphere is to be managed in a responsible way. MFA can be performed without considering these aspects, but in most cases, these other factors are needed to interpret and make use of the MFA results. Thus, MFA is frequently coupled with the analysis of energy, economy, urban planning, and the like.
In the 20th century, MFA concepts have emerged in various fields of study at different times. Before the term MFA had been invented, and before its comprehensive methodology had been developed, many researchers used the law of conservation of matter to balance processes. In process and chemical engineering, it was common practice to analyze and balance inputs and outputs of chemical reactions. In the economics field, Leontief introduced input–output tables in the 1930s, thus laying the base for widespread application of input–output methods to solve economic problems. The first studies in the fields of resource conservation and environmental management appeared in the 1970s. The two original areas of application were (1) the metabolism of cities and (2) the analysis of pollutant pathways in regions such as watersheds or urban areas. In the following decades, MFA became a widespread tool in many fields, including process control, waste and wastewater treatment, agricultural nutrient management, water-quality management, resource conservation and recovery, product design, life cycle assessment (LCA), and others.
Substance Flow Analysis
From Feasibility assessment of using the substance flow analysis methodology for chemicals information at macro level
SFA is used for tracing the flow of a selected chemical (or group of substances) through a defined system. SFA is a specific type of MFA tool, dealing only with the analysis of flows of chemicals of special interest (Udo de Haes et al., 1997). SFA can be defined as a detailed level application of the basic MFA concept tracing the flow of selected chemical substances or compounds — e.g. heavy metals (mercury (Hg), lead (Pb), etc.), nitrogen (N), phosphorous (P), persistent organic substances, such as PCBs, etc. — through society.
An SFA identifies these entry points and quantifies how much of and where the selected substance is released. Policy measures may address these entry points, e.g. by end‐of‐pipe technologies. Its general aim is to identify the most effective intervention points for policies of pollution prevention. According to Femia and Moll (2005), SFA aims to answer the following questions:
• Where and how much of substance X flows through a given system?
• How much of substance X flows to wastes?
• Where do flows of substance X end up?
• How much of substance X is stored in durable goods?
• Where could substance X be more efficiently utilised in technical processes?
• What are the options for substituting the harmful substance?
• Where do substances end up once they are released into the natural environment?
When an SFA is to be carried out, it involves the identification and collection of data on the one hand, and modelling on the other. According to van der Voet et al. (OECD, 2000), there are three possible ways to ‘model’ the system:
Accounting (or bookkeeping) The input for such a system is the data that can be obtained from trade and production statistics. If necessary, further detailed data can be recovered on the contents of the specific substances in those recorded goods and materials. Emissions and environmental fluxes or concentration monitoring can be used for assessing the environmental flows. The accounting overview may also serve as an identification system for missing or inaccurate data.
Missing amounts can be estimated by applying the mass balance principle. In this way, inflows and outflows are balanced for every node, as well as for the system as a whole, unless accumulation within the system can be proven. This technique is most commonly used in material flow studies, and can be viewed as a form of descriptive statistics. There are, however, some examples of case studies that specifically address societal stocks, and use these as indicator for possible environmental problems in the future (OECD, 2000).
Static modelling is the process whereby the network of flow nodes is translated into a mathematical ‘language’, i.e. a set of linear equations, describing the flows and accumulations as inter‐dependent. Emission factors and distribution factors over the various outputs for the economic processes and partition coefficients for the environmental compartments can be used as variables in the equations. A limited amount of substance flow accounting data is also required for a solution of the linear equations. However, the modelling outcome is determined largely by the substance distribution patterns.
Static modelling can be extended by including a so‐called origin analysis in which the origins of one specific problematic flow can be traced on several levels. Three levels may be distinguished:
• direct causes derived directly from the nodes balance (e.g one of the direct causes of cadmium (Cd) load in soil is atmospheric deposition);
• economic sectors (or environmental policy target groups) directly responsible for the problem. This is identified by following the path back from node to node to the point of emission (e.g. waste incineration is one of the economic sectors responsible for the cadmium load in soil);
• ultimate origins found by following the path back to the system boundaries (e.g. the extraction, transport, processing and trade of zinc (Zn) ore is one of the ultimate origins of the cadmium load in soil).
Furthermore, the effectiveness of abatement measures can be assessed with static modelling by recording timelines on substances (OECD, 2000).
Dynamic modelling is different to the static SFA model, as it includes substance stocks accumulated in society as well as in various materials and products in households and across the built‐up environments.
For SFA, stocks play an important role in the prediction of future emissions and waste flows of products with a long life span. For example, in the case of societal stocks of PVC, policy makers need to be supplied with information about future PVC outflows. Today’s stocks become tomorrow’s emissions and waste flows. Studies have been carried out on the analysis of accumulated stocks of metals and other persistent toxics in the societal system. Such build‐ups can serve as an ‘early warning’ signal for future emissions and their potential effects, as one day these stocks may become obsolete and recognisably dangerous, e.g. as in the case of asbestos, CFCs, PCBs and mercury in chlor‐alkali cells. As the stocks are discarded, they end up as waste, emissions, factors of risks to environment and population. In some cases, this delay between inflow and outflow can be very long indeed.
Stocks of products no longer in use, but not yet discarded, are also important. These stocks could include: old radios, computers and/or other electronic equipment stored in basements or attics, out‐of‐use pipes still in the ground, obsolete stocks of chemicals no longer produced but still stored, such as lead paints and pesticides. These ‘hibernating stocks’ are likely to be very large, according to OECD estimates (2000). Estimating future emissions is a crucial issue if environmental policy makers are to anticipate problems and take timely, effective action. In order to do this, stocks cannot be ignored. Therefore, when using MFA or SFA models for forecasting, stocks should play a vital part. Flows and stocks interact with each other. Stocks grow when the inflows exceed the outflows of a (sub)‐system and certain outflows of a (sub)‐system are disproportional to the stocks.
For this dynamic model, additional information is needed for the time dimension of the variables, e.g. the life span of applications in the economy; the half life of compounds; the retention time in environmental compartments and so forth. Calculations can be made not only on the ‘intrinsic’ effectiveness of packages of measures, but also on their anticipated effects in a specific year in the future. They can also be made on the time
it takes for such measures to become effective. A dynamic model is therefore most suitable for scenario analysis, provided that the required data are available or can be estimated with adequate accuracy (OECD, 2000).
Life Cycle Analysis (LCA)
What is Life Cycle Assessment (LCA)?
As environmental awareness increases, industries and businesses are assessing how their activities affect the environment. Society has become concerned about the issues of natural resource depletion and environmental degradation. Many businesses have responded to this awareness by providing “greener” products and using “greener” processes. The environmental performance of products and processes has become a key issue, which is why some companies are investigating ways to minimize their effects on the environment. Many companies have found it advantageous to explore ways of moving beyond compliance using pollution prevention strategies and environmental management systems to improve their environmental performance. One such tool is LCA. This concept considers the entire life cycle of a product (Curran 1996).
Life cycle assessment is a “cradle-to-grave” approach for assessing industrial systems. “Cradle-to-grave” begins with the gathering of raw materials from the earth to create the product and ends at the point when all materials are returned to the earth. LCA evaluates all stages of a product’s life from the perspective that they are interdependent, meaning that one operation leads to the next. LCA enables the estimation of the cumulative environmental impacts resulting from all stages in the product life cycle, often including impacts not considered in more traditional analyses (e.g., raw material extraction, material transportation, ultimate product disposal, etc.). By including the impacts throughout the product life cycle, LCA provides a comprehensive view of the environmental aspects of the product or process and a more accurate picture of the true environmental trade-offs in product and process selection.
The term “life cycle” refers to the major activities in the course of the product’s life-span from its manufacture, use, and maintenance, to its final disposal, including the raw material acquisition required to manufacture the product. Exhibit 1-1 illustrates the possible life cycle stages that can be considered in an LCA and the typical inputs/outputs measured.
Methods of LCA
Economic Input Output LCA
From Life cycle analysis (LCA) and sustainability assessment
Material Input Output Network Analysis
PIOT (Physical Input Output Tables)
MIOT (Monetary Input Output Tables)
WIOT (Waste Input Output Tables
MRIO (Multi Regional Input Output)
SUT (Supply and Use Tables)
From Industrial ecology and input-output economics: An introduction
Although it was the pioneering contributions by Duchin (1990, 1992) that explicitly made the link between input–output economics and industrial ecology, developments in input– output economics had previously touched upon the core concept of industrial ecology.
Wassily Leontief himself incorporated key ideas of industrial ecology into an input– output framework. Leontief (1970) and Leontief and Ford (1972) proposed a model where the generation and the abatement of pollution are explicitly dealt with within an extended IO framework. This model, which combines both physical and monetary units in a single coefficient matrix, shows how pollutants generated by industries are treated by so-called ‘pollution abatement sectors.’ Although the model has been a subject of longstanding methodological discussions (Flick, 1974; Leontief, 1974; Lee, 1982), its structure captures the essence of industrial ecology concerns: abatement of environmental problems by exploiting inter-industry interactions. As a general framework, we believe that the model by Leontief (1970) and Leontief and Ford (1972) deserves credit as an archetype of the various models that have become widely referred to in the field of industrial ecology during the last decade, including mixed-unit IO, waste IO and hybrid Life Cycle Assessment (LCA) models (Duchin, 1990; Konijn et al., 1997; Joshi, 1999; Nakamura and Kondo, 2002; Kagawa et al., 2004; Suh, 2004b). Notably, Duchin (1990) deals with the conversion of wastes to useful products, which is precisely the aim of industrial ecology, and subsequently, as part of a study funded by the first AT&T industrial ecology fellowship program, with the recovery of plastic wastes in particular (Duchin and Lange, 1998). Duchin (1992) clarifies the quantity-price relationships in an input–output model (a theme to which she has repeatedly returned) and draws its implications for industrial ecology, which has traditionally been concerned exclusively with physical quantities.
Duchin and Lange (1994) evaluated the feasibility of the recommendations of the Brundtland Report for achieving sustainable development. For that, they developed an input–output model of the global economy with multiple regions and analyzed the consequences of the Brundtland assumptions about economic development and technological change for future material use and waste generation. Despite substantial improvements in material efficiency and pollution reduction, they found that these could not offset the impact of population growth and the improved standards of living endorsed by the authors of the Brundtland Report.
Another pioneering study that greatly influenced current industrial ecology research was described by Ayres and Kneese (1969) and Kneese et al. (1970), who applied the massbalance principle to the basic input–output structure, enabling a quantitative analysis of resource use and material flows of an economic system. The contribution by Ayres and Kneese is considered the first attempt to describe the metabolic structure of an economy in terms of mass flows (see Ayres, 1989; Haberl, 2001).
Since the 1990s, new work in the areas of economy-wide research about material flows, sometimes based on Physical Input–Output Tables (PIOTs), has propelled this line of research forward in at least four distinct directions: (1) systems conceptualization (Duchin, 1992; Duchin, 2005a); (2) development of methodology (Konijn et al., 1997; Nakamura and Kondo, 2002; Hoekstra, 2003; Suh, 2004c; Giljum et al., 2004; Giljum and Hubacek, 2004; Dietzenbacher, 2005; Dietzenbacher et al., 2005; Weisz and Duchin, 2005); (3) compilation of data (Kratterl and Kratena, 1990; Kratena et al., 1992; Pedersen, 1999; Ariyoshi and Moriguchi, 2003; Bringezu et al., 2003; Stahmer et al., 2003); and (4) applications (Duchin, 1990; Duchin and Lange, 1994, 1998; Hubacek and Giljum, 2003; Kagawa et al., 2004). PIOTs generally use a single unit of mass to describe physical flows among industrial sectors of a national economy. In principle, such PIOTs are capable of satisfying both column-wise and row-wise mass balances, providing a basis for locating materials within a national economy.3 A notable variation in this tradition, although it had long been used in input–output economic studies starting with the work of Leontief, is the mixed-unit IO table. Konijn et al. (1997) analyzed a number of metal flows in the Netherlands using a mixed-unit IO table, and Hoekstra (2003) further improved both the accounting framework and data. Unlike the original PIOTs, mixed-unit IOTs do not assure the existence of column-wise mass-balance, but they make it possible to address more complex questions. Lennox et al. (2004) present the Australian Stocks and Flows Framework (ASFF), where a dynamic IO model is implemented on the basis of a hybrid input–output table. These studies constitute an important pillar of industrial ecology that is generally referred to as Material Flow Analysis (MFA).4
Although the emphasis in industrial ecology has arguably been more on the materials side, energy issues are without doubt also among its major concerns. In this regard, energy input–output analysis must be considered another important pillar for the conceptual basis of ‘industrial energy metabolism.’ The oil shock in the 1970s stimulated extensive research on the structure of energy use, and various studies quantifying the energy associated with individual products were carried out (Berry and Fels, 1973; Chapman, 1974). Wright (1974) utilized Input–Output Analysis (IOA) for energy analysis, which previously had been dominated by process-based analysis (see also Hannon, 1974; Bullard and Herendeen, 1975; Bullard et al., 1978). The two schools of energy analysis, namely process analysis and IO energy analysis, were merged by Bullard and Pillarti (1976) into hybrid energy analysis (see also van Engelenburg et al., 1994; Wilting,1996). Another notable contribution to the area of energy analysis was made by Cleveland et al. (1984), who present a comprehensive analysis, using the US input–output tables, quantifying the interconnection of energy and economic activities from a biophysical standpoint (see Cleveland, 1999; Haberl, 2001; Kagawa and Inamura, 2004). These studies shed light on how an economy is structured by means of energy flows and informs certain approaches to studying climate change (see for example Proops et al., 1993; Wier et al., 2001).
What generally escapes attention in both input–output economics and industrial ecology, despite its relevance for both, is the field of Ecological Network Analysis (ENA). Since Lotka (1925) and Lindeman (1942), material flows and energy flows have been among the central issues in ecology. It was Hannon (1973) who first introduced concepts from input–output economics to analyze the structure of energy utilization in an ecosystem. Using an input–output framework, the complex interactions between trophic levels or ecosystem compartments can be modeled, taking all direct and indirect relationships between components into account. Hannon’s approach was adopted, modified and re-introduced by various ecologists. Finn (1976, 1977), among others, developed a set of analytical measures to characterize the structure of an ecosystem using a rather extensive reformulation of the approach proposed by Hannon (1973). Another important development in the tradition of ENA is so-called environ analysis. Patten (1982) proposed the term ‘environ’ to refer to the relative interdependency between ecosystem components in terms of nutrient or energy flows. Results of environ analysis are generally presented as a comprehensive network flow diagram, which shows the relative magnitudes of material or energy flows between the ecosystem components through direct and indirect relationships (Levine, 1980; Patten, 1982). Ulanowicz and colleagues have broadened the scope of materials and energy flow analysis both conceptually and empirically (Szyrmer and Ulanowicz, 1987). Recently Bailey et al. (2004a, b) made use of the ENA tradition to analyze the flows of several metals through the US economy. Suh (2005) discusses the relationship between ENA and IOA and shows that Patten’s environ analysis is similar to Structural Path Analysis (SPA), and that the ENA framework tends to converge toward the Ghoshian framework rather than the Leontief framework although using a different formalism (Defourny and Thorbecke, 1984; Ghosh, 1958).
From Materials and energy flows in industry and ecosystem netwoks : life cycle assessment, input-output analysis, material flow analysis, ecological network flow analysis, and their combinations for industrial ecology
From Regional distribution and losses of end-of-life steel throughout multiple product life cycles—Insights from the global multiregional MaTrace model
From Feasibility assessment of using the substance flow analysis methodology for chemicals information at macro level
From Hybrid Sankey diagrams: Visual analysis of multidimensional data forunderstanding resource use
Sankey diagrams are used to visualise flows of energy, materials or other resources in a variety of applications. Schmidt (2008a) reviewed the history and uses of these diagrams. Originally, they were used to show flows of energy, first in steam engines, more recently for modern systems such as power plants (e.g. Giuffrida et al., 2011) and also to give a big-picture view of global energy use (Cullen and Allwood, 2010). As well as energy, Sankey diagrams are widely used to show flows of resources (Schmidt, 2008a). Recent examples in this journal include global flows of tungsten (Leal-Ayala et al., 2015), biomass in Austria (Kalt, 2015), and the life-cycle of car components (Diener and Tillman, 2016). More widely, they have been used to show global production and use of steel and aluminium (Cullen et al., 2012; Cullen and Allwood, 2013), and flows of natural resources such as water (Curmi et al., 2013). In all of these cases, the essential features are: (1) the diagram represents physical flows, related to a given functional unit or period of time; and (2) the magnitude of flows is shown by the link1 widths, which are proportional to an extensive property of the flow such as mass or energy (Schmidt, 2008b). Creating these diagrams is supported by software tools such as e!Sankey (ifu Hamburg, 2017), and several Life Cycle Assessment (LCA) and Material Flow Analysis (MFA) packages include features to create Sankey diagrams.
From Hybrid Sankey diagrams: Visual analysis of multidimensional data for understanding resource use
Visualization of energy, cash and material flows with a Sankey diagram
The most popular software for creating Sankey diagrams. Visualize the cash, material & energy flow or value streams in your company or along the supply chain. Share these appealing diagrams in reports or presentations.
Physical Input Output (PIOT) Tables: Developments and Future
Materials and energy flows in industry and ecosystem netwoks : life cycle assessment, input-output analysis, material flow analysis, ecological network flow analysis, and their combinations for industrial ecology
USA and China: What are Trade in Value Added (TiVA) Balances
Changes in Global Trade
Global Value Chains
Value added content of Trade
FROM INTERCONNECTED ECONOMIES : BENEFITING FROM INDUSTRY GLOBALISATION
From Domestic Value Added in Chinese Exports
From Measurement and Determinants of Trade in Value Added
From OECD WTO TIVA
Ongoing TiVA Projects
OECD TIVA Initiative
EU FIGARO Initiative
NA TIVA Initiative
APEC TiVA Initiative
There is also OECD TiVA – MNE Project which incorporates Intra Firm trade of MNEs.
From An Overview on the Construction of North American Regional Supply-Use and Input-Output Tables and their Applications in Policy Analysis
Trade-in-Value Added (TiVA) is a statistical approach used to measure the interconnectivity and marginal contribution in production of participating economies in global value chains (GVCs) (Degain and Maurer, 2015). The advantage of TiVA over traditional trade statistics is that TiVA measures trade flows consistent with internationally, vertically integrated global production networks, often called GVCs. TiVA statistics allow us to better analyze three aspects of international trade: measuring the contribution of domestic versus foreign intermediates in the exports, tracing production across countries to their final destination, and finally quantifying how individual industries contribute to producing exports (Lewis, 2013).
TiVA statistics allow us to map and quantify the interdependencies between industries and economies, and help us develop better estimates of the contribution from each country in the production processes and, consequently, better measure the impact from GVC engagement for domestic economies. However, it is necessary to highlight the underlying compilation methodology of TiVA in order to better understand the characteristics, scope and interpretation of TiVA. Hence, it is important to remember that TiVA statistics are estimated statistics that are derived, in part, from official statistics. TiVA statistics are meant to complement but not to replace official statistics.
Measuring trade flows in value added as opposed to gross value of trade flows has become increasingly important as the influence that GVCs has on international trade continues to rise. (Johnson, 2014; Ahmad and Ribarsky, 2014). The proliferation of GVCs means that production has become increasingly fragmented and vertically integrated across countries (Jones and Kierzkowski, 1988; Hummels, Ishii, and Yi, 2001; OECD, 2013). At the micro level, this means that many firms in disparate countries are interconnected. Across international borders, these firms take part in particular stages of the production process, together forming a global supply chain. As a result, intermediate inputs may cross international borders several times before being used to produce final consumable goods. This matters for several reasons. First, when goods cross multiple borders multiple times, they are exposed to more trade costs, which accumulate and compound before the goods are sold for final consumption. Additionally, traditional gross trade flows are overstated because gross trade flows may count intermediates multiple times. Relatedly, gross trade flows obscure the marginal contributions of countries along GVCs. TiVA measures the flows related to the value that is added at each stage of production by each country and maps from where value is created, where it is exported, and how it is used, as final consumption or as an input for future exports. How we understand gains from trade from trade flows is fundamental, and value-added approaches lead to better understanding of GVCs and their role in international trade.
There are two ways to capture TiVA. The first method is a direct approach, which decomposes existing data on trade statistics. Johnson (2012) introduce a TiVA indicator using value-added to output ratios from the source country to compute the value-added associated with the implicit output transfer to each destination. Koopman, Wang, and Wei (2014) build on the literature in vertical specialization (e.g. Hummels, Ishii, and Yi 2001) and the literature on TiVA (e.g. Johnson and Noguera, 2012; Daudin, Rifflart, and Schweisguth, 2011) to implement a complete decomposition of a country’s gross exports by value added components. This work has evolved into a second, indirect method of capturing TiVA. The indirect method is employed in the regional North American supply-use table (NASUT) and the regional North American inter-country input-output table (NAIOT). Estimating TiVA this way relies on national and international input-output tables as well as bilateral trade statistics to derive the international intermediate and final supply-demand matrices. These matrices reveal the origin and use of goods and services produced and exchanged among the countries and industries within the table domain. Other major international input-output tables include the Asian International Input-Output (AIO) Tables published by the Institute of Developing Economies Japan External Trade Organization (IDE-JETRO), the Inter-Country Input-Output (ICIO) Tables published by the OECD, the World Input-Output Tables (WIOT) published by the World Input-Output Database (WIOD) project, and the Eora Multi-region Input-Output Database (Eora MRIO).
The studies based on the above two approaches have revealed a trend of rising foreign value-added content in international trade flows and the resulting implications for trade policies. Johnson and Noguera (2016) find that value-added exports are falling relative to gross exports, which means that double-counting is increasingly more common in trade flows. This is consistent with increased GVC activity. Hummels, Ishii, and Yi (2001) show that vertical specialization has grown about 30 percent and accounts for about one-third of the growth in trade from about 1970 to 1990.
In recent years, more than half of global manufacturing imports are intermediate goods and more than 70 percent of global services imports are intermediate services (OECD, 2013). This is relevant because tariffs (and other trade costs) have a higher impact on the cost of GVC activity. Each time an intermediate input crosses an international border as part of the production process, the input incurs trade costs. As first observed by Yi (2003), trade costs are compounded when intermediate goods cross borders multiple times to complete the production process. Rouzet and Miroudot (2013) demonstrate that small tariffs can add up to a significant sum by the time a finished product reaches its consumers. Other trade costs such as non-tariff measures also have such accumulative effect on downstream products.
What the literature indicates the trends in GVCs mean for trade flows, generally, are two-fold. First, with the growth of GVC activity, gross value of trade flows will continue to be larger than the value of final goods that cross borders. Second, trade policy designed with respect to gross trade flows could have the potential to be overly restrictive or even impose costs indirectly on domestic production. Trade-in-Value Added thus provides a supplementary, relevant reference for evaluating the economic effect of trade policies.
In this paper, we introduce the North American Trade-in-Value Added (NA-TiVA) project, a trilateral, multiyear initiative that aims to produce a regional TiVA database that maps the value chains connecting Canada, the United States, and Mexico. Furthermore, we introduce and discuss the project’s deliverables, the agencies involved, how the NA-TiVA project complements other ongoing TiVA initiatives around the world, the technical framework for producing a regional inter-country input-output table for the NA region, and the value of this work to resolving open policy questions within international trade.
Ongoing TiVA Initiatives
Currently there are three major ongoing global and regional TiVA projects that are related to the North America TiVA project. They are the World Input-Output database (WIOD), OECD-WTO TiVA, and APEC TiVA initiatives.
The World Input-Output database (WIOD): The official WIOD project ran from May 1, 2009 to May 1, 2012, as a joint effort of eleven European research institutions. It was funded by the European Commission. Under the official WIOD project, the accounting framework and methodologies of constructing the TiVA databases, as well as the first version of the World Input-Output database were developed. The database was officially launched in April 2012. Since then, two additional versions of WIOD databases, namely the 2013 and 2016 Releases, were published. The 2016 Released database covers 28 EU countries and 15 other major economies in the world for years 2000-2014 with 56 industries.
The OECD-WTO TiVA database: The Organization for Economic Cooperate and Development (OECD) and World Trade Organization (WTO) undertook a joint initiative on TiVA in 2013. Since then, two versions of TiVA databases have been released (2013 and 2015 release). The 2015 release of OECD-WTO TiVA database covers 61 countries and 13 regions, with 34 industries, for years 1995, 2000, 2005, 2008-2011.
APEC TiVA initiative: In 2014, APEC economic leaders endorsed the APEC TiVA database initiative, a four-year project co-led by China and the United States. Under this project, an APEC TiVA database would be constructed by the end of 2018, covering 21 APEC economies.
Each of these three major global and regional TiVA initiatives include Canada, Mexico, and the United States. In the light of this, why is there still a need for constructing the NA TiVA database? What kind of additional value can the NA TiVA project bring to this global and regional network of TiVA initiatives?
The NA-TiVA project was motivated by regional statistical developments and continuous improvements in compiling TiVA databases. The 2003 Mexican input-output table distinguishes trade flows by domestic producers and production undertaken in Maquiladoras, a tax-free, tariff-free special processing zone, which allowed the estimates of separate production coefficients and thus TiVA measures for these two distinctive zones in Mexico (Koopman, Powers, Wang, and Wei, 2010; De la Cruz, Koopman, Wang, and Wei, 2011). The government of Canada further highlighted the importance and relevance of global value chains in the publication of a book assessing the impact and implication of GVCs (Foreign Affairs and International Trade Canada, 2011); and as of the 2015 edition of the OECD’s ICIO tables, Mexico is broken out as Mexico Global Manufacturers and Mexico Non-Global Manufacturers. This NA TiVA project builds off of these developments.
Constructing inter-country input-output tables, or so called TiVA databases, requires the harmonization of national supply-use tables (SUTs) or input-output tables (IOTs) as well as bilateral trade statistics from different countries. However, the data produced by countries often vary greatly in the level of detail and differ in industry and product classifications. Thus, the more countries are included in a global or regional TiVA project, the higher level of aggregation would be required for the purpose of harmonization. With only three countries involved, it is feasible for the NA TiVA database to include more products and sectors than other global and regional TiVA projects.
Moreover, other factors, such as all three countries adopt the same industry and product classifications (e.g. using the North American Industry Classification System (NAICS)), and produce SUTS at similarly detailed levels, would ensure the compatibility of data components, and thus lead to better quality of the resulting NA TiVA database.
Finally, the NA TiVA project could synthesize the ongoing trilateral trade statistics reconciliation effort and produce better-quality balanced bilateral trade data to feed into other global and regional TiVA initiatives. One of the key inputs for constructing TiVA databases is balanced bilateral trade statistics. However, countries rarely report symmetric bilateral trade statisticsone country’s reported exports rarely equals its trading partner’s reported imports, and vice versa. To reconcile such asymmetries to produce balanced bilateral trade statistics, joint effort by both trading countries is warranted, including investigating the causes of asymmetries at detailed product level and making corresponding adjustment mechanically. However, global and regional TiVA initiatives often have to consider an incredible number of country pairs, making such an elaborate reconciliation practice rather infeasible. Thus, global and regional TiVA initiatives often turn to economic modelling to balance bilateral trade statistics which could be applied in a systematic way to all countries. Although such approach can be mathematically sound, the resulting data often require additional scrutiny, validation, and adjustment, as they do not always reflect the reality accurately. Canada, Mexico, and the United States have ongoing bilateral trade reconciliation. This NA TiVA project provides additional motivation and framework for this effort.
The History, Scope, and Major Objectives of the NA TiVA Initiative
In October 2014, the representatives from the United States, Canada, and Mexico met and kicked off the idea of constructing the NA TiVA database at a UN conference in Mexico. The main objective of this project is to construct the NA TiVA database by 2021 covering three NA countries with more detailed industry and firm information, and to improve the quality of TiVA measures for the value chains in the NA region.
The NA-TiVA project involves eight government agencies across the three NA countries: for Canada, Statistics Canada (STATCAN) and Global Affairs Canada; for Mexico, Instituto Nacional de Estadística y Geografía (INEGI) and Banco de Mexico; and for the United States, the Bureau of Economic Analysis (BEA), the U.S. Census Bureau (CENSUS), the U.S. International Trade Commission (USITC), and the Office of the U.S. Trade Representative (USTR).
In addition, because the resulting NA-TiVA database would be eventually integrated into the OECD-WTO TiVA database to improve the quality of information on the North American region, participants of the NA-TiVA project regularly meet with OECD representatives to harmonize TiVA database compilation methodologies, exchange data to synthesize the effort and ensure consistency across countries, and discuss best practices. Other international organizations, such as United Nations Statistics Division (UNSD), and WTO, are often consulted as well for national account and trade statistics related issues.
Under the NA-TiVA initiative, three parallel work streams have been established: The trade in goods and services reconciliation team, which is tasked to produce balanced bilateral trade statistics for goods and services; the SUT team, whose goal is to harmonize the national SUTs and compile the regional NASUTs and NAIOTs; and the White Paper team, the goal of which is to produce documentation that outlines the conceptual methodology, identifies major technical issues, describes policy applications of a NA-TiVA initiative, and details project outputs as well as future work.
FROM INTERCONNECTED ECONOMIES :BENEFITING FROM INDUSTRY GLOBALISATION
From Supply-Use Tables, Trade-in-Value-Added Initiatives, and their Applications
US Trade Wars with Emerging Countries in the 21st Century: Make America and Its Partners Lose Again
Antoine Bouët (International Food Policy Research Institute, Washington, D.C., and Groupe de Recherche en Économie Théorique et Appliquée [GREThA], University of Bordeaux, France)
David Laborde (International Food Policy Research Institute)
Measuring Value Added in the People’s Republic of China’s Exports: A Direct Approach.
ADBI Working Paper 493. Tokyo: Asian Development Bank Institute
International Trade Costs, Global Supply Chains and Value-added Trade in
Gerard Kelly and Gianni La Cava
Trade in Value Added Revisited: A Comment on R. Johnson and G. Noguera,
Accounting for Intermediates: Production Sharing and Trade in Value Added
There have been several developments in economics in last 20 years. Although these have been developed by different groups of economists, there are common relations among all of them. Because of Institutional silos, many of these developments are not shared. My attempt is to compile them here in this post and other previous related posts.
IMF Balance Sheet Approach (BSA) – From-Who-To-Whom
Balance Sheet Economics/Asset Liability Matrices (ALM) of Tsujimura
Financial Input-Output Analysis (F-IO Tables)
F-SAM ( Financial Social Accounting Matrix)
Interrelated Balance Sheet Approach of Perry Mehrling and Zoltan Pozsar
Stock Flow Consistent Modeling – Marc Lavoie, G Zezza, W Godley
Extended Supply and Use Tables (E-SUT)/UN SEIGA Initiative
Supply Chain Finance/Financial Supply Chain Management/Operations and Finance
Trade Finance/Global Value Chains/Accounting for Global Value Chains
Integrated Macroeconomic Accounts – NIPAs and Financial Accounts
From Balance Sheets, Transaction Matrices and the Monetary Circuit
Chapter 2 of book Monetary Economics by M Lavoie and W Godley 2007
The lack of integration between the flows of the real economy and its financial side greatly annoyed a few economists, such as Denizet and Copeland. For Denizet, J.M. Keynes’s major contribution was his questioning of the classical dichotomy between the real and the monetary sides of the economy. The post-Keynesian approach, which prolongs Keynes’s contribution on this, underlines the need for integration between financial and income accounting, and thus constitutes a radical departure from the mainstream. 1
Denizet found paradoxical that standard national accounting, as was initially developed by Richard Stone, reproduced the very dichotomy that Keynes had himself attempted to destroy. This was surprising because Stone was a good friend of Keynes, having provided him with the national accounts data that Keynes needed to make his forecasts and recommendations to the British Treasury during the Second World War, but of course it reflected the initial difficulties in gathering enough good financial data, as Stone himself later got involved in setting up a proper framework for financial flows and balance sheet data (Stone 1966).2
In trade relations, if goods flow from X to Y, then Money (Payments) flows from Y to X.
Intra Firm amd Inter Firm relations between Accounts Receivables and Accounts Payable
UNITED NATIONS STATISTICS DIVISION SEIGA Initiative
Under System of Extended International and Global Accounts (SEIGA) Initiative
United Nations is developing:
UN Handbook on Accounting for Global Value Chains
Presentation from 2017 Seminar on Accounting for Global Value Chains
From Financial input-output multipliers
From Sectoral interlinkages in balance sheet approach
From STANDARD DEFINITIONS FOR TECHNIQUES OF SUPPLY CHAIN FINANCE
There are two Areas where FSCM/SCF names are used but in different contexts.
Inter firm FSCM
Intra firm FSCM
Inter firm F-SCM
Supply Chain Finance (SCF)
Value Chain Finance
Inter firm Finance
Collaborative Cash to Cash Cycles Management
During 2008 global financial crisis, the trade financing dried up resulting in decline in trade of goods and services.
Since the crisis, Financial De-globalization and Decline of Correspondent Banking has also made availability of financial credit harder.
Cash flow and working capital management is helped by inter firm collaboration among Suppliers and Buyers.
Financial Institutions which provide trade credit also benefit from inter firm collaboration.
From SUPPLY CHAIN FINANCE FUNDAMENTALS: What It Is, What It’s Not and How it Works
What Supply Chain Finance is Not
The world of trade finance is complex and varied. There are numerous ways to increase business capital on hand and, in many cases, the differences are slightly nuanced. Given this landscape, it’s not just important to understand what supply chain finance is; it’s also important to understand what it is not.
It is not a loan. Supply chain finance is an extension of the buyer’s accounts payable and is not considered financial debt. For the supplier, it represents a non-recourse, true sale of receivables. There is no lending on either side of the buyer/supplier equation, which means there is no impact to balance sheets.
It is not dynamic discounting or an early payment program. Early payment programs, such as dynamic discounting, are buyer-initiated programs where buyers offer suppliers earlier payments in return for discounts on their invoices. Unlike supply chain finance, buyers are seeking to lower their cost of goods, not to improve their cash flow. Dynamic discounting and early payment programs often turn out to be expensive for both suppliers (who are getting paid less than agreed upon) and buyers who tie up their own cash to fund the programs.
It is not factoring. Factoring enables a supplier to sell its invoices to a factoring agent (in most cases, a financial institution) in return for earlier, but partial, payment. Suppliers initiate the arrangement without the buyer’s involvement. Thus factoring is typically much more expensive than buyer-initiated supply chain finance. Also, suppliers trade “all or nothing” meaning they have no choice to participate from month-to-month to the degree that their cash flow needs dictate. Finally, most factoring programs are recourse loans, meaning if a supplier has received payment against an invoice that the buyer subsequently does not pay, the lender has recourse to claw back the funds.
From Mckinsey on Payments
From Financial Supply Chain Management
From Best Practices in Cash Flow Management and Reporting
From STANDARD DEFINITIONS FOR TECHNIQUES OF SUPPLY CHAIN FINANCE
From Financing GPNs through inter-firm collaboration? Insights from the automotive industry in Germany and Brazil
Intra Firm F-SCM
Working Capital Management
Cash Flow Management
Cash to Cash Conversion Cycle Management (C2C Cycle/CCC)
Financial Supply Chain Management (F-SCM) in Manufacturing companies
Financial Supply chain management in financial institutions
Supply Chain Finance
Accounts Payable Optimization
Accounts Receivable Optimization
Operations and Finance Interfaces
Current Asset Management (Current Ratio Analysis)
This is not a new subject. Corporate Finance, Financial Controls, and working capital management have been active business issues. Benefits of Supply chain management include increase in inventory turnover and decline in current assets.
There are many world class companies who manage their supply chains well and work with minimal working capital. Lean Manufacturing, Agile Manufacturing, JIT manufacturing are related concepts. Just-In-Time manufacturing developed in Toyota Corp. reduces inventory portion of C2C cycle. Other examples include
Currently, most of the Supply Chain analytics efforts unfortunately do not integrate analysis of financial benefits of operating decisions.
There are many studies recently which suggest that Cash to Cash Conversion Cycle is a better determinant of corporate liquidity. C2C Cycle is a dynamic liquidity indicator and Current Assets is a static indicator of liquidity. I would like to point out that none of the studies relate C2C cycle with Current Ratio. Current Ratio is based on balance sheet positions of current assets and current liabilities. C2C cycle is based on flows in supply chains. Accumulation of flow results in Current assets (Stock). To make it Stock-Flow Consistent, more work is required.
From Supply Chain Finance: some conceptual insights.
From Financial Supply Chain Management
From The Interface of Operations and Finance in Global Supply Chains
From SUPPLY CHAIN-ORIENTED APPROACH OF WORKING CAPITAL MANAGEMENT
From IMPROVING FIRM PERFORMANCE THROUGH VALUE-DRIVEN SUPPLY CHAIN MANAGEMENT: A CASH CONVERSION CYCLE APPROACH
From IMPROVING FIRM PERFORMANCE THROUGH VALUE-DRIVEN SUPPLY CHAIN MANAGEMENT: A CASH CONVERSION CYCLE APPROACH
From THE CYCLE TIMES OF WORKING CAPITAL: FINANCIAL VALUE CHAIN ANALYSIS METHOD
Call for papers: Supply Chain Finance
Call for papers for Special Topic Forum in Journal of Purchasing and Supply Management (Manuscript Submission: March 31, 2017)
Supply chain finance is a concept that lacks definition and conceptual foundation. However, the recent economic downturn forced corporates to face a series of financial and economic difficulties that strongly increased supply chain financial risk, including bankruptcy or over-leveraging of debt. The mitigation and management of supply chain financial risk is becoming an increasingly important topic for both practitioners and academics leading to a developing area of study known as supply chain finance. There are two major perspectives related to the idea of managing finance across the supply chain. The first is a relatively short-term solution that serves as more of a “bridge” and that is provided by financial institutions, focused on accounts payables and receivables. The second is more of a supply chain oriented perspective – which may or may not involve a financial institution, focused on working capital optimization in terms of accounts payable, receivable, inventory, and asset management. These longer-term solutions focus on strategically managing financial implications across the supply chain.
Recent years have seen a considerable reduction in the granting of new loans, with a significant increase in the cost of corporate borrowing (Ivashina and Scharfstein, 2010). Such collapse of the asset and mortgage-backed markets dried up liquidity from industries (Cornett et al., 2011). In such difficult times, firms (especially those with stronger bargaining power) forced suppliers to extend trade credit in order to supplement the reduction in other forms of financing (Coulibaly et al., 2013; Garcia-Appendini and Montoriol-Garriga, 2013). The general lack of liquidity, in particular for SMEs, has directly affected companies’ ability to stay in the market, reflecting on the stability of entire supply chains. There are many other factors influencing liquidity and financial health that are critical to assess.
These trends and the continued growth of outsourced spend have contributed considerably to the need for and spread of solutions and programs that help to mitigate and better manage financial risk within and across the supply chain. One of the most important approaches is what is being termed Supply Chain Finance (SCF) (Gelsomino et al., 2016; Pfohl and Gomm, 2009; Wuttke et al., 2013a). SCF is an approach for two or more organizations in a supply chain, including external service provides, to jointly create value through means of planning, steering, and controlling the flow of financial resources on an inter-organizational level (Hofmann, 2005; Wuttke et al., 2013b). It involves the inter-company optimization of financial flows with customers, suppliers and service providers to increase the value of the supply chain members (Pfohl and Gomm, 2009). According to Lamoureux and Evans (2011) supply chain financial solutions, processes, methods are designed to improve the effectiveness of financial supply chains by preventing detrimental cost shifting and improving the visibility, availability, delivery and cost of cash for all global value chain partners. The benefits of the SCF approach include reduction of working capital, access to more funding at lower costs, risk reduction, as well as increase of trust, commitment, and profitability through the chain (Randall and Farris II, 2009).
Literature on SCF is still underdeveloped and a multidisciplinary approach to research is needed in this area. In order to better harmonize contributions of a more financial nature with ones coming from the perspective of purchasing & supply chain, there is a need of developing theory on SCF, starting with a comprehensive definition of those instruments or solutions that constitute the SCF landscape. SCF has been neglected in the Purchasing & Supply Management (PSM) literature, although PSM plays a critical role in managing finance within the supply chain. PSM uses many of the processes and tools that are part of a comprehensive supply chain financial program to better manage the supply base, in terms of relationships, total cost of ownership, cost strategies and pricing volatility (see for example Shank and Govindarajan 1992). Reverse factoring is a technique which is also widely used to manage the supply base (Wuttke et al, 2013a) as is supplier development and investment in suppliers.
Research on SCF from a PSM perspective needs further development. In particular, empirical evidence would prove useful for testing existing models and hypotheses, addressing the more innovative schemes and investigating the adoption level and the state of the art of different solutions. Research is also needed for the development of a general theory of supply chain finance. There is also limited research that focuses on the link between supply chain financial tools and supply chain financial performance. Finally, considering the plurality of solutions that shape the SCF landscape, literature should move towards the definition of holistic instruments to choose the best SCF strategy for a supply chain, considering its financial performance and the contextual variables (e.g. structure, bargaining power) that characterize it.
The purpose of this special topic forum is to publish high-quality, theoretical and empirical papers addressing advances on Supply Chain Finance. Original, high quality contributions that are neither published nor currently under review by any other journals are sought. Potential topics include, but are not limited to:
Theory development, concept and definition of SCF
Taxonomy of SCF solutions
Strategic cost management across the supply chain
Total cost of ownership
Life cycle assessment and analysis
Commodity risk and pricing volatility
Supply chain financial metrics and measures
Relationship implications of supply chain finance
Tax and transfer pricing in the supply chain
Foreign exchange and global currency and financing risk
Financial network design and financial supply chain flows
The organizational perspective on SCF and the implementation process
Role of innovative technologies to support SCF ( (e.g. block chain, internet of things)
Supply chain collaboration for improved supply chain financial solutions
SCF adoption models, enablers and barriers
SCF from different party perspectives (especially suppliers and providers)
SCF and risk mitigation and management
Manuscript preparation and submission
Before submission, authors should carefully read the Journal’s “Instructions for Authors”. The review process will follow the Journal’s normal practice. Prospective authors should submit an electronic copy of their complete manuscript via Elsevier’s manuscript submission system (https://ees.elsevier.com/jpsm) selecting “STF Supply Chain Finance” as submission category and specifying the Supply Chain Finance topic in the accompanying letter. Manuscripts are due March 31, 2017 with expected publication in June of 2018.
FOR COMMENTS OR QUESTIONS PLEASE CONTACT THE GUEST EDITORS:
Federico Caniato, Politecnico di Milano, School of Management, firstname.lastname@example.org
Michael Henke, TU Dortmund and Fraunhofer IML, Michael.Henke@iml.fraunhofer.de
George A. Zsidisin, Virginia Commonwealth University, email@example.com
Cornett, M.M., McNutt, J.J., Strahan, P.E., Tehranian, H., 2011. Liquidity risk management and credit supply in the financial crisis. J. financ. econ. 101, 297–312.
Coulibaly, B., Sapriza, H., Zlate, A., 2013. Financial frictions, trade credit, and the 2008–09 global financial crisis. Int. Rev. Econ. Financ. 26, 25–38.
Garcia-Appendini, E., Montoriol-Garriga, J., 2013. Firms as liquidity providers: Evidence from the 2007–2008 financial crisis. J. financ. econ. 109, 272–291.
Gelsomino, L.M., Mangiaracina, R., Perego, A., Tumino, A., 2016. Supply Chain Finance: a literature review. Int. J. Phys. Distrib. Logist. Manag. 46, 1–19.
Govindarajan, Vijay, and John K. Shank. “Strategic cost management: tailoring controls to strategies.” Journal of Cost Management 6.3 (1992): 14-25.
Wuttke, D. A., Blome, C., Foerstl, K., & Henke, M. (2013a). Managing the innovation adoption of supply chain finance—Empirical evidence from six European case studies. Journal of Business Logistics, 34(2), 148-166.
Wuttke, D. A., Blome, C., & Henke, M. (2013b). Focusing the financial flow of supply chains: An empirical investigation of financial supply chain management. International journal of production economics, 145(2), 773-789.
Hofmann, E., 2005. Supply Chain Finance: some conceptual insights. Logistik Manag. Innov. Logistikkonzepte. Wiesbad. Dtsch. Univ. 203–214.
Ivashina, V., Scharfstein, D., 2010. Bank lending during the financial crisis of 2008. J. financ. econ. 97, 319–338.
Lamoureux, J.-F., Evans, T.A., 2011. Supply Chain Finance: A New Means to Support the Competitiveness and Resilience of Global Value Chains. Social Science Research Network, Rochester, NY.
Lekkakos, S.D., Serrano, A., 2016. Supply chain finance for small and medium sized enterprises: the case of reverse factoring. Int. J. Phys. Distrib. Logist. Manag.
Pfohl, H.C., Gomm, M., 2009. Supply chain finance: optimizing financial flows in supply chains. Logist. Res. 1, 149–161.
Randall, W., Farris II, T., 2009. Supply chain financing: using cash-to-cash variables to strengthen the supply chain. Int. J. Phys. Distrib. Logist. Manag. 39, 669–689.
Production Chain Length and Boundary Crossings in Global Value Chains
From Structure and length of value chains
In a value chain, value is added in sequential production stages and is carried forward from one producer to the next in the form of intermediate inputs. Value chains driven by the fragmentation of production are not an entirely new economic phenomenon, but the increasing reliance on imported intermediate inputs makes value chains global.
According to a 2013 report by the OECD, WTO and UNCTAD for the G-20 Leaders Summit, “Value chains have become a dominant feature of the world economy” (OECD et al., 2013).
Obviously, this dominant feature of the world economy needs measuring and analyzing. Policy-relevant questions include, but are not limited to:
what is the contribution of global value chains to economy GDP and employment? how long and complex are value chains?
what is the involvement and position of individual industries in global value chains? do multiple border crossings in global value chains really matter?
These and related questions generated a considerable amount of investigations proposing new measures of exports and production to account for global value chains. Some of those were designed to re-calculate trade flows in value added terms, whereas other provided an approximation of the average length of production process.
A relatively new stream of research focuses on a deep decomposition of value added or final demand ( rather than exports or imports ) into components with varied paths along global value chains and measurements of the length of the related production processes. Consider, for example, a petrochemical plant that generates some value added equal to its output less all intermediate inputs used. We would be interested to know which part of this value added, embodied in the petrochemicals, is used entirely within the domestic economy and which part is exported.
We would also inquire how much of the latter satisfies final demand in partner countries and how much is further used in production and, perhaps, in exports to third countries and so on. We would be interested, in particular, in counting the number of production stages the value added in these petrochemicals passes along the chain before reaching its final user.
From Structure and length of value chains
From Structure and length of value chains
A natural question is whether this method can be applied to the real economy with myriads of products, industries and dozens of partner countries? It can surely be applied if the data on inter-industry transactions are organized in the form of input-output accounts, and the computations are performed in block matrix environment. In fact, the measurement of the number of production stages or the length of production chains has attracted the interest of many input-output economists. The idea of simultaneously counting and weighting the number of inter-industry transactions was formalized by Dietzenbacher et al. (2005). Their “average propagation length” (APL) is the average number of steps it takes an exogenous change in one industry to affect the value of production in another industry. It is the APL concept on which we build the count of the number of production stages from the petrochemical plant to its consumers in our simplified example above. The only difference is that Dietzenbacher et al. (2005), and many authors in the follow-up studies, neglect the completion stage. First applications of the APL concept to measure the length of cross-border production chains appear in Dietzenbacher and Romero (2007) and Inomata (2008), though Oosterhaven and Bouwmeester (2013) warn that the APL should only be used to compare pure interindustry linkages and not to compare different economies or different industries.
Fally (2011, 2012) proposes the recursive definitions of two indices that quantify the “average number of embodied production stages” and the “distance to final demand”. Miller and Temurshoev (2015), by analogy with Antras et al. (2012), use the logic of the APL and derive the measures of “output upstreamness” and “input downstreamness” that indicate industry relative position with respect to the nal users of outputs and initial producers of inputs. They show that their measures are mathematically equivalent to those of Fally and the well known indicators of, respectively, total forward linkages and total backward linkages. Fally (2012) indicates that the average number of embodied production stages may be split to account for the stages taking place within the domestic economy and abroad. This approach was implemented in OECD (2012), De Backer and Miroudot (2013) and elaborated in Miroudot and Nordstrom (2015).
Ye et al. (2015) generalize previous length and distance indices and propose a consistent accounting system to measure the distance in production networks between producers and consumers at the country, industry and product levels from different economic perspectives. Their “value added propagation length” may be shown to be equal to Fally’s embodied production stages and Miller & Temurshoev’s input downstreamness when aggregated across producing industries.
Finally, Wang et al. (2016) develop a technique of additive decomposition of the average production length. Therefore, they are able to break the value chain into various components and measure the length of production along each component. Their production length index system includes indicators of the average number of domestic, cross-border and foreign production stages. They also propose new participation and production line position indices to clearly identify where a country or industry is in global value chains. Importantly, Wang et al. (2016) clearly distinguish between average production length and average propagation length, and between shallow and deep global value chains.
This paper builds on the technique and ideas of Wang et al. (2016) and the derivation of the weighted average number of border crossings by Muradov (2016). It re-invents a holistic system of analytical indicators of structure and length of value chains. As in Wang et al. (2016), global value chains are treated here within a wider economy context and are juxtaposed with domestic value chains. This enables developing new indices of orientation towards global value chains. The novel deliverables of this paper are believed to include the following. First, all measurements are developed with respect to output rather than value added or final product flows. This is superior for interpretation and visualization purposes because a directly observable economic variable ( output ) is decomposed in both directions, forwards to the destination and backwards to the origin of value chain. It is also shown that at a disaggregate country-industry level, the measurement of production length is equivalent with respect to value added and output. Second, the decomposition of output builds on a factorization of the Leontief and Ghosh inverse matrices that allows for an explicit count of production stages within each detailed component. Third, the system builds on a refined classication of production stages, including final and primary production stages that are often neglected in similar studies. Fourth, the paper re-designs the average production line position index and proposes new indices of orientation towards global value chains that, hopefully, avoid overemphasizing the length of some unimportant cross-border value chains. Fifth, a new chart is proposed for the visualization of both structure and length of value chains. The chart provides an intuitive graphical interpretation of the GVC participation, orientation and position indices.
It is also worth noting that both Wang et al. (2016) and this paper propose similar methods to estimate the intensity of GVC-related production in partner countries and across borders. This is not possible with previous decomposition systems without explicitly counting the average number of production stages and border crossings.
Average Propagation Length
Structure of Chains
Fragmentation of Production
World Input Output Chains
Counting Boundary Crossings
Slicing Up Value Chains
Mapping Value Chains
Geography of Value Chains
Key Sources of Research:
Characterizing Global Value Chains
Xinding Yu and Kunfu Zhu
GLOBAL VALUE CHAIN DEVELOPMENT REPORT 2016
Background Paper Conference
Beijing, 17-18 March 2016
The Great Trade Collapse: Shock Amplifiers and Absorbers in Global Value Chains
CHARACTERIZING GLOBAL VALUE CHAINS:PRODUCTION LENGTH AND UPSTREAMNESS
Characterizing Global Value Chains
Xinding Yu and Kunfu Zhu
MEASURING AND ANALYZING THE IMPACT OF GVCs ON ECONOMIC DEVELOPMENT
GLOBAL VALUE CHAIN DEVELOPMENT REPORT 2017
International Bank for Reconstruction and Development/The World Bank
Global Value Chains
MAPPING GLOBAL VALUE CHAINS
4-5 December 2012
The OECD Conference Centre, Paris
Normally, production and distribution planning are handled separately in firms. Integrated planning of production and distribution can add significant value to a company, particularly, in strategic decisions.
From Facility Location and Supply Chain Management – A comprehensive review
Since, in the literature, model objectives change as a function of the planning horizon length, we consider it opportune to define the features of each horizon in order to contextualize the parameters chosen for the models’ comparison. According to , the planning horizons of the supply chain can be clustered as follows:
• Strategic planning: this level refers to a long-term horizon (3-5 years) and has the objective of identifying strategic decisions for a production network and defining the optimal configuration of a supply chain. The decisions involved in this kind of
planning include vertical integration policies, capacity sizing, technology selection, sourcing, facility location, production allocation and transfer pricing policies.
• Tactical planning: this level refers to a mid-term horizon (1-2 years) and has the objective of fulfilling demand and managing material flows, with a strong focus on the trade-off between the service level and cost reduction. The main aspects considered in tactical planning include production allocation, supply chain coordination, transportation policies, inventory policies, safety stock sizing and supply chain lead time reduction.
• Operational planning: this level refers to a short term period (1 day to 1 year) and has the objective of determining material/logistic requirement planning. The decisions involved in programming include the allocation of customer demands, vehicle routing, and plant and warehouse scheduling.
From Integrated Location-Production-Distribution Planning in a Multi products Supply Chain Network Design Model
‘supply chain strategic design’,
‘supply chain planning’,
‘supply chain optimization’,
‘supply chain network design’,
‘supply chain production planning’,
‘supply chain delocalization’,
‘logistic network design’,
‘distribution network design’,
‘supply chain linear programming’
‘supply chain mixed-integer programming’.
From From Manufacturing to Distribution: The Evolution of ERP in Our New Global Economy
Over the past fifty years, manufacturing has changed from individual companies producing and distributing their own products, to a global network of suppliers, manufacturers, and distributors. Efficiency, price, and quality are being scrutinized in the production of each product. Because of this global network, manufacturers are competing on a worldwide scale, and they have moved their production to countries where the costs of labor and capital are low in order to gain the advantages they need to compete.
Today, the complex manufacturing environment faces many challenges. Many products are manufactured in environments where supplies come from different parts of the world. The components to be used in supply chain manufacturing are transported across the globe to different manufacturers, distributors, and third party logistics (3PL) providers. The challenges for many manufacturers have become how to track supply chain costs and how to deal with manufacturing costs throughout the production of goods. Software vendors, however, are now addressing these manufacturing challenges by developing new applications.
Global competition has played a key role in industrialized countries shifting from being production-oriented economies to service-based economies. Manufacturers in North America, Western Europe, and other industrialized nations have adapted to the shift by redesigning their manufacturing production into a distribution and logistics industry, and the skills of the labor force have changed to reflect this transition. Developing countries have similarly changed their manufacturing production environments to reflect current demands; they are accommodating the production of goods in industries where manufacturers have chosen to move their production offshore–the textile industry being a prime example of this move.
A report from the US Census Bureau titled Statistics for Industry Groups and Industries: 2005 and another from Statistics Canada titled Wholesale Trade: The Year 2006 in Review indicate that wholesalers are changing their business models to become distributors as opposed to manufacturers. Between 2002 and 2005, overall labor and capital in the manufacturing sectors decreased substantially. US industry data (from about 10 years ago) indicates that the North American manufacturing industry was engaged in 80 percent manufacturing processes and only 20 percent distribution activities. Today, however, these percentages have changed dramatically; the current trend is in the opposite direction. Manufacturing processes account for around 30 percent of the industry processes, and wholesale and distribution activities, approximately 70 percent.
In addition, a report from the National Association of Manufacturers indicates that the US economy imports $1.3 trillion (USD) worth of manufactured goods, but exports only $806 billion (USD) worth of goods manufactured in the US. This negative trade balance is a clear indication of the changing economic trend toward the manufacturing of goods in low-cost labor nations.
The main reason for this huge manufacturing shift is the increasing operating costs of production in industrialized countries. These rising costs are forcing manufacturers to move their production to developing nations because of the low cost of labor in these countries. This includes Asian countries (such as China and Indonesia) as well as Eastern European countries (such as the Czech Republic and Slovakia).
The number of workers (in percentages) in specified industries in G7 countries, and uses 1980 as the base year with 100 percent full employment in each industry. The industries with relatively constant rates of employment are the food and drink and the tobacco industries. Since 1995, all other industries have been maintaining less and less manufacturing employees, as indicated by the declining slopes in the graph. The shift in the textiles and leather, metals, and other manufacturing industries is moving toward production of goods in low-wage, developing countries.
Manufacturing is a global industry, and although a manufacturing company may be based in an industrialized country, it may have the bulk of its manufacturing facilities in a developing country. Producing goods in such a country reduces wage and capital costs for the manufacturer; however, some manufacturing control is lost in offshore production. Shipping, distribution, and rental costs, for example, are often difficult to track and manage, and quality control can be compromised in a production environment that is not local.
Two main outcomes can be seen within the manufacturing industry because of this manufacturing shift: manufacturers have a sense of having relinquished control of their production to low-cost labor nations, and supply chain management (SCM) has now become the answer to manufacturing within industrialized nations.
Suppliers that provide components to manufacturers often have issues with quality. Being part of a large network of suppliers, each supplier tries to offer the lowest prices for its products when bidding to manufacturers. Although a supplier may win the bid, its products may not be up to standard, and this can lead to the production of faulty goods. Therefore, when using offshore suppliers, quality issues, product auditing, and supplier auditing become extremely important.
Because the manufacturing model is changing, manufacturing has become more of a service-based industry than a pure manufacturing industry. Even though the physical process of manufacturing hasn’t changed, the actual locations of where the goods are being produced have. This fact is now compelling industrialized countries to engage in more assembly driven activities–a service-based model. The manufacturing process has transformed into obtaining parts and reassembling them into the final product. The final product is then redistributed throughout the appropriate channel or to the consumer. SCM methods are now reacting to this change as well; they are taking into account final assembly needs, and they are distributing particular products to consumers or manufacturers.
SCM is becoming the norm for manufacturers in the industrialized world. Offshoring is now standard practice, and methods such as SCM have been set up to deal with these economic and logistical business realities.
The economic shift happening in both industrialized and developing countries is dramatic. As the level of management knowledge increases, better methods of constructing offshore products are available in SCM solutions. In both types of economies, the changes in the labor force skill sets and manufacturing environments have consequently led to new software solutions being developed in order to manage this dramatic change.
Within the software industry, many SCM and enterprise resource-planning (ERP) vendors are following the economic shift. They are developing new functionality–ERP-distribution software–to meet the recent demands and needs of the changing manufacturing and distribution industries.
SCM and ERP software are converging to better address these new demands in the manufacturing industry. In the enterprise software market, ERP software vendors have reached a point of saturation; their installs are slowing down and they are seeing a reduction in sales. Therefore, ERP providers are developing new functionality in order to remain competitive with other ERP vendors, in addition to looking for new opportunities. ERP vendors are trying to adapt to the changing market in order to increase their revenues. They are integrating SCM functionality into their ERP offerings, creating ERP-distribution software that can span the entire production process across many continents (if necessary), and that is able to track final goods, components, and materials.
Traditional ERP solutions included some SCM functionality, which was needed to distribute the companies’ produced goods. These systems also allowed components and parts to be imported in order to assemble these goods. But offshore manufacturing and expansion into new markets has required SCM functionality in ERP software to be extended. Some larger vendors have acquired other companies in order to meet these changing demands. For example, Oracle acquired G-Log, a transportation management systems (TMS) vendor, and Agile, a product lifecycle management (PLM) vendor; and Activant acquired Intuit Eclipse.
SCM software vendors, in contrast, have felt encroached upon by ERP vendors. The situation has posed a real threat to SCM providers in the market, forcing them to extend their ERP functionality to compete with ERP vendors and to try to gain new clients in the distribution and logistics industry.
ERP-distribution software has integrated SCM functionality into its existing functionality to navigate through the complex global manufacturing environment. SCM software maps five processes into one solution: planning, sourcing (obtaining materials), producing, delivering, and returning final products if defective. These processes help to track and manage the goods throughout their entire life cycles. In addition, ERP solutions are used to manage the entire operations of an organization, not only a product’s life cycle. This gives users the broad capability to manage operations and use the SCM functionality to manage the movement of goods, whether components or finished product.
With the ability to gain accurate inventory visibility and SCM production, ERP-distribution software is able to see the whole chain of manufacturing and distribution events, from supplier to manufacturer, all the way to the final consumer.
There are three business models.
The first is the SCM model, which includes the manufacturing process.
The second is the retail model, which is the distribution of final products to the consumer, business, or retailer.
The third model is a combination of the first two business models, joined by the ERP-distribution software solution into one seamless process.
Within the SCM process, goods can either be brought in (imported) through foreign manufacturers, or acquired locally. The goods are then given to a distributor, 3PL provider, or wholesaler in order to reach the final client.
Within the retail model, the products are taken from a distributor, 3PL provider, or wholesaler, and are distributed to the appropriate person. Note that there is a “shift” for the consumer. This is to indicate that through the Internet or other forms of technology, consumers are now able to buy directly from distributors. The power of the consumer has changed; where manufacturers once provided products to consumers, consumers are now creating demand, and manufacturers have to meet that demand.
SCM solutions focus on the relationship between the supplier and manufacturer. However, ERP- distribution software has taken functionality from SCM software and combined it with retail software (such as point-of-sale and e-commerce solutions); it is now able to span across the entire supply chain and to track goods along the complete manufacturing process.
This is a simplified view of the complexities of today’s manufacturing processes. These complexities have made it crucial for trading partners to unite with manufacturers in order to help alleviate the frustrations that can occur within this global network. Specifically, trading partners are coming together with manufacturers to unite services, products, and customer experience so that business processes (such as manufacturing and distribution) become more efficient and that goods can move through these processes with minimal problems.
SCM can be thought of as the management of “warehousing processes,” in which the movement of goods occurs through multiple warehouses or manufacturing facilities. Tracking the costs of moving products and components through the maze of warehousing and manufacturing facilities is a tricky process, and many organizations lose money at each warehousing step.
Within the flow of goods in the manufacturing sector, the warehouse is a crucial part of the supply chain. Traditionally, the warehouse has been a source of frustration because the manufacturer or supplier pays for the use of the warehouse (whether owned or rented by the company). This leads to two possible scenarios: 1) the costs of the warehouse are incurred by a 3PL or manufacturing company, or 2) the costs are passed from one warehouse to another warehouse, and the original warehouse charges for these costs.
The typical warehouse process includes the following steps: receiving, put away, picking, kitting, packing, repacking, cross-docking, and shipping. ERP-distribution software is able to track costs across the entire organization and to aid companies in reducing costs that were previously tough to track.
ERP-distribution system encompasses the entire production of the final good. The ERP- distribution system is able to include inventory visibility from points “A to Z” (start to finish) and to track each warehouse cost from supplier to manufacturer to user, whether consumer, business, or retailer.
The Final Word: ERP-distribution software has been developed to meet the growing needs of the manufacturing and distribution industries. The capabilities incorporated into the software work across entire organizations, and even across continents.
Because of the economic shift in the manufacturing industry, the emergence of new software has been vital for businesses to stay competitive, meet the industry demands and emerging shift, and to keep business processes efficient to gain better profit margins.
ERP-distribution software is able to track the processes of manufacturing goods and distributing components, even if the manufacturer has facilities in North America and the Far East. With the SCM component in ERP software, manufacturing and tracking goods becomes manageable. Distributors and manufacturers can now work together in order to better meet customer requirements.
In addition of factors for domestic location selection analysis, other factors in international location selection are:
Taxes and Tariffs
How do companies in Computers, Automotive, Apparel, Electronics, Consumer Goods, Machinery manage their supply chain planning functions? What software do they use for forecasting, planning, and scheduling?
I know of these software solutions for Network Design and Optimization:
Intra-firm trade consist of trade between parent companies of a compiling country with their affiliates abroad and trade of affiliates under foreign control in this compiling country with their foreign parent group.
Intra Industry Trade
Different types of trade are captured in measurements of intra-industry trade:
a) Trade in similar products (“horizontal trade”) with differentiated varieties (e.g. cars of a similar class and price range).
b) Trade in “vertically differentiated” products distinguished by quality and price (e.g. exports of high-quality clothing and imports of lower-quality clothing).
From GLOBALISATION AND INTRA-FIRM TRADE: AN EMPIRICAL NOTE
Products which are traded internationally, but which stay within the ambit of a multinational enterprise (MNE), represent a significant portion of foreign trade for several OECD countries. This type of trade is called intra-firm trade as opposed to international trade among unrelated parties, also called arm’s length trade. Intra-firm trade is an important part of the process of globalisation, by which is meant the increasing interdependence of markets and production in different countries through trade in goods and services, cross-border flows of capital, and exchanges of technology.
The phenomenon of intra-firm trade is of interest to trade policy makers, as well as to competition and tax authorities. The use of transfer pricing in intra-firm trade may introduce an element of uncertainty into the value of a fairly large part of international trade and into customs valuation needed for the application of tariffs or similar measures. Competition and tax issues may also arise from intra-firm trade to the extent that the latter may facilitate the dissimulation of real transaction prices between the parent company and its affiliates.
A surge in foreign direct investment (FDI) during the 1980s’ has been cited as evidence in favour of globalisation; it is argued that MNEs have played a central role in globalisation by extending their corporate networks beyond national boundaries through the establishment of foreign branches and subsidiaries. It is often assumed that intra-firm trade reflects these foreign production activities by MNEs, as they trans- fer their factors of production from one country to another.
Little attention has been paid so far to the phenomenon of intra-firm trade. The literature on the subject is still relatively limited and recent. This is partly because most international trade statistics do not distinguish between intra-firm trade and arm’s length trade.
From GLOBALISATION AND INTRA-FIRM TRADE: AN EMPIRICAL NOTE
In considering the interrelationship between globalisation and international trade, it is conceptually useful to distinguish between four types of international trade:
(A) intra industry, intra-firm trade;
(B) intra-industry, arm’s-length trade;
(C) inter-industry, intra firm trade;
(D) inter-industry, arm’s-length trade.
Intra-industry trade is defined as the mutual exchange of similar goods within the same product category (Grubel and Lloyd, 1975, and Greenaway and Milner, 1986).
Intra-industry trade is generally a function of product differentiation and may or may not involve intra-firm trade. If motor vehicles produced in France are exported to the United States and U.S.-built motor vehicles are exported to France, the two countries are said to be involved in intra-industry trade even though such trade is not necessarily intra-firm trade. Intra-industry trade can be readily calculated for any given product category, as only the traditional bilateral trade statistics for that product category are needed.
Intra firm trade is harder to quantify, since knowledge of the relationship between the firms involved in the transactions is necessary. Data on intra-firm trade are available only. through firm surveys, involving the preparation of questionnaires by national authorities.
Most trade in manufactured goods among OECD countries is of the intra-industry type. Intra-industry trade is particularly important within Europe, and to a lesser extent, in North America, accounting for roughly 60 to 70 per cent of total trade in manufacture. This trade generally concerns differentiated products exchanged between countries that are similar in terms of per capita income and relative factor endowments. It has also been argued that economies of scale play an important role in explaining the industry pattern of intra-industry trade.
On the other hand, trade between developed and developing countries (“North-South”) is mostly of the inter-industry type, reflecting large differences in relative factor endowments between the two groups of countries. Inter-industry trade among unrelated parties (type D) – e.g. international exchange of cotton cloth produced by northern manufacturers for wine produced by southern farmers .- is the type of trade which international trade textbooks traditionally deal with.
Trade in manufactured goods between developed countries is predominantly of the intra-industry type and often takes the form of intra-firm trade. An important example of intra-industry, intra-firm trade (Type A) is United States-Canada-Mexico automobile trade. Intra-firm trade is also the dominant pattern of U.S. exports to Canada and Europe in the case of non-electrical machinery and chemicals. Another example is trade in manufactured goods between Pacific Asian economies. These economies have seen a rapid increase in intra-industry trade as a proportion of their total trade over the last decade. Such increase in intra-industry trade in Pacific Asian economies can be primarily attributed to the globalisation of corporate activities by U.S. and Japanese firms and, more recently, by other Asian firms. This involves assembly-line production based on imported parts and components in different countries in East and South East Asia (Fukasaku, 1992; Gross, 1986).
From An Overview of U.S. Intrafirm-trade Data Sources
There are large differences in BEA data and Census data particularly for Imports. There are some measurement issues. Import data from Mexico and China show big errors.
From An Overview of U.S. Intrafirm-trade Data Sources
From An Overview of U.S. Intrafirm-trade Data Sources
Data sources of Intra Firm Trade
BEA (Intra Firm Trade Data)
US Census Bureau (Related party trade data)
From Intrafirm Trade and Vertical Fragmentation in U.S. Multinational Corporations
First, we show that, although intra-MNC trade represents an important fraction of aggregate U.S. exports and imports, the median manufacturing foreign affiliate ships nothing to — and receives nothing from — its parent in the United States. Intra-MNC trade is concentrated in a small group of large affiliates and large corporations: The largest five percent of affiliates accounts for around half of the total trade to and from the parent, while the largest five percent of corporations accounts for almost two thirds of total intra- MNC trade. This skewness is also observed within the corporation: Intra-MNC trade tends to be concentrated in a small number of an MNC’s largest foreign affiliates.
The lack of intra-MNC cross-border trade that we find for foreign affiliates of U.S. multinationals is more surprising than the similar finding in Atalay et al. (2014) for intrafirm trade within the United States. Factor price differences — the theoretical motivation for vertical fragmentation and the intrafirm trade that accompanies it — are much larger across countries than across U.S. cities. In this regard, Brainard (1993) first documented the weak relationship between factor endowments and intra-MNC trade across borders.
The skewness of intra-MNC trade towards large affiliates and corporations in our first finding is reminiscent of the skewness in the distributions of other international activities. Manufacturing exports are concentrated in large firms (Bernard and Jensen, 1995), and even larger firms own foreign affiliates (Helpman et al., 2004). These patterns are consistent with theories of the firm that are based on economies of scale in production. In Grossman et al. (2006), for example, the production of inputs for the entire multinational corporation is concentrated into a few large affiliates, which exploit the strong economies of scale in production. Affiliates created to supply a foreign market — as an alternative to exporting, in order to avoid transportation costs — are relatively small. The model predicts that a small number of large affiliates ship goods within the corporation, while numerous smaller affiliates serve local markets. The concentration of intra-MNC trade in the largest firms is also consistent with the contract theory of the multinational firm proposed by Antras and Helpman (2004): In their framework with heterogeneous firms, only the largest firms choose to integrate offshore activities.
Our second set of facts relates intra-MNC trade to the upstream and downstream links between the industries of the parent and affiliate, as defined by the U.S. input-output table. As previously shown in Alfaro and Charlton (2009), we find that multinational corporations own affiliates in industries that are vertically linked to the parent’s industry. The input-output coefficient between the affiliate’s and the parent’s industries of operation, however, is not related to the existence and the magnitude of the trade in goods between the two. These findings are similar to those in Atalay et al. (2014), who study multi-establishment firms within the United States: The ownership of vertically linked affiliates is not related to the transfer of goods within the boundaries of the firm.
Key Sources of Research:
GLOBALISATION AND INTRA-FIRM TRADE: AN EMPIRICAL NOTE
Marcos Bonturi and Kiichiro Fukasaku
U.S. Direct Investment Abroad: Trends and Current Issues
James K. Jackson
Specialist in International Trade and Finance
June 29, 2017
Foreign Direct Investment in the United States (FDIUS): Final Results from the 2012 Benchmark Survey
FDI vs Outsourcing: Extending Boundaries or Extending Network Chains of Firms
Foreign Direct Investments of Firms can have three objectives:
Vertical Integration (Control of Supply Chain)
Horizontal Integration (Seeking Market Share)
Diversification ( Market Seeking)
In this post, Focus is on Sourcing of Goods and Services in FDI and Outsourcing Decisions of Firms. That means focusing on supply chain related issues.
From GLOBAL SOURCING
A fi rm that chooses to keep the production of an intermediate input within its boundaries can produce it at home or in a foreign country. When it keeps it at home, it engages in standard vertical integration. And when it makes it abroad, it engages in foreign direct investment (FDI) and intra- firm trade. Alternatively, a firm may choose to outsource an input in the home country or in a foreign country. When it buys the input at home, it engages in domestic outsourcing. And when it buys it abroad, it engages in foreign outsourcing, or arms-length trade.
Intel Corporation provides an example of the FDI strategy; it assembles most of its microchips in wholly-owned subsidiaries in China, Costa Rica, Malaysia, and the Philippines. On the other hand, Nike provides an example of the arms-length import strategy; it subcontracts most of its manufacturing to independent producers in Thailand, Indonesia, Cambodia, and Vietnam.
Intermediate Goods – Make vs. Buy Decisions of Firms
From Integration of Trade and Disintegration of Production in the Global Economy
The rising integration of world markets has brought with it a disintegration of the production process, in which manufacturing or services activities done abroad are combined with those performed at home. Companies are now finding it profitable to outsource increasing amounts of the production process, a process which can happen either domestically or abroad. This represents a breakdown in the vertically-integrated mode of production – the so-called “Fordist” production, exemplified by the automobile industry – on which American manufacturing was built. A number of prominent researchers have referred to the importance of the idea that production occurs internationally: Bhagwati and Dehejia (1994) call this “kaleidoscope comparative advantage,” as firms shift location quickly; Krugman (1996) uses the phrase “slicing the value chain”; Leamer (1996) prefers “delocalization;” while Antweiler and Trefler (1997) introduce “intra-mediate trade.” There is no single measure that captures the full range of these activities, but I shall compare several different measures of foreign outsourcing, and argue that they have all increased since the 1970s.
Types of Supply Chain Relations:
Intra-firm Trade of MNCs
Fragmentation of Production
Global Value Chains
Intermediate Goods Trade
Value Added Tasks
Transaction Cost Economics
Trade in Value Added Tasks
Vertical Production Networks
Key Sources of Research:
PHYSICAL CAPITAL, KNOWLEDGE CAPITAL AND THE CHOICE BETWEEN FDI AND OUTSOURCING
Ignatius J. Horstmann
James R. Markusen
The Distributional Effects of International Fragmentation,
Kohler, Wilhelm (2002)
Working Paper, Department of Economics, Johannes Kepler University of Linz, No. 0201
International Fragmentation of Production and the Intrafirm Trade of U.S. Multinational Companies
Maria Borga and William J. Zeile
January 22, 2004
Paper presented at:
The National Bureau of Economic Research/Conference on Research in Income and Wealth meeting on Firm-level Data, Trade, and Foreign Direct Investment, Cambridge, Massachusetts
August 7-8, 2003,
The OECD Committee on Industry and Business Environment/Working Party on Statistics
Session on Globalization,
November 3-4, 2003.
The governance of global value chains
The economic consequences of increased protectionism
Riksbank of Sweden
Deep integration and production networks: an empirical analysis
World Trade Organization
Manuscript date: July 2011
Measuring success in the global economy: international trade, industrial
upgrading, and business function outsourcing in global value chains
Timothy J. Sturgeon and Gary Gereffi
Topics in International Trade
FOREIGN DIRECT INVESTMENT, TRADE, AND GLOBAL PRODUCTION NETWORKS
IN ASIA AND EUROPE
GPN Working Paper 2
Why has world trade grown faster than world output?
Vertical Specialization, Global Value Chains and the changing Geography of Trade: the Portuguese Rubber and Plastics Industry Case
João Carlos Lopes and Ana Santos
The changing structure of trade linked to global production systems: What are the policy implications?
WHO PRODUCES FOR WHOM IN THE WORLD ECONOMY?
Guillaume Daudin (Lille-I (EQUIPPE) & Sciences Po (OFCE), Christine Rifflart, Danielle
Schweisguth (Sciences Po (OFCE))1
This version: July 2009
THE NATURE AND GROWTH OF VERTICAL SPECIALIZATION IN WORLD TRADE