Identifying hot spots in global supply chains using frequency measurement

The quest for optimal resource allocation across borders has often led to the clustering and concentration of critical production capacities within specific countries. However, in an increasingly uncertain global economy, these production hubs can swiftly transform into "choke points", disrupting the entire economic system.

The OECD's Trade in Value-Added (TiVA) indicators reveal the origins of value added in gross exports and final demand, but what about the stages in between? The pass-through frequency (PTF) indicator addresses questions such as: How many countries and industries do intermediate goods and services traverse, and how frequently, before being used in final production? Where, along the supply chains, are potential vulnerabilities to concentration risk?

Prd_cou: Country of end producer

Prd_ind: Industry of end producer

Sup_cou: Country of supplier, with which the transaction frequency of end producer’s supply chain is measured

Sup_ind: Industry of supplier, with which the transaction frequency of end producer’s supply chain is measured

For example, we can measure the exposure risk of the US car industry to the supply bottleneck of Japanese electronic components (as was the case with the great east Japan earthquakes) with the following setup:

Prd_cou = United States (USA)

Prd_ind = “Motor vehicles, trailers and semi-trailers” (29)

Sup_cou = Japan (JPN)

Sup_ind = “Computer, electronic and optical equipment” (26)

CSV

• Data for 1995-2000-2005
• Data for 2010-2015-2020

RData

• Data for 1995-2000-2005
• Data for 2010-2015-2020

Let Country C be the analytical target, and suppose that the supply chain connecting Country A and Country E has six production paths as shown in the diagram. The first path on top does not pass through Country C, the second passes through it twice, the third path once, the fourth path twice, the fifth path once, and the last one does not pass through. Therefore, the frequency (average number of times) that the supply chain passes through the industries of Country C is 1.

In the real world, however, supply chains have countless production paths, and it is not possible to look at them one by one as in this example. Accordingly, the PTF indicator capitalises on the production linkage information in the OECD Inter-country Input-Output tables, which allows full-scale analyses with respect to any combinaton of supply chains covered by the database.

Note: In the current dataset, PTFs of supply chains that lead to the same end producer (prd_cou/ind) via the same targeted supplier (sup_cou/ind) are aggregated for the sake of simplicity, regardless of the value-added sources from which they originate.

By focusing on a specific combination of variables, we can see a supply chain's geographic concentration frequency from different perspectives.

Case 1: With which country does the supply chain of the US automotive industry (USA_29) engage most frequently for the procurement of electronic components (26)?

Case 2: With which industry in Japan does the supply chain of the US automotive industry (USA_29) engage most frequently for intermediate supplies?

Case 3: Which country's automotive industry (29) supply chain engages most frequently with the Japanese electronics industry (JPN_26)?

Case 4: Which US industry's supply chain engages most frequently with the Japanese electronics industry (JPN_26) in producing its own product?

Please cite as: OECD (2024), Trade in Value Added, Pass-through Frequency Indicator.

These graphs show analytical results for the supply chain of the "computer, electronic and optical equipment" sector, aggregated across all OECD economies.

Each panel illustrates the top 10 economies to which the OECD aggregate supply chain is most externally exposed, both in terms of the sourcing amount of foreign value added embodied in the product (Panel A) and the supply chain's pass-through frequency abroad (Panel B).

Large economies, such as the People's Republic of China, the United States and Germany, naturally form dominant sources of foreign value added. However, in the pass-through frequency supply chain concentration, some middle-scale economies appear as potential choke points; this is notably the case for Viet Nam, the Netherlands and Poland, which serve as "intermediaries" of cross-border production processes.

Inomata, S. and T. Hanaka (2024), "Measuring exposure to network concentration risk in global supply chains: Volume versus frequency", Structural Change and Economic Dynamics, Vol.68, Elsevier, pp.177-193.

Inomata, S. and T. Hanaka (2021), "A Risk Analysis on Geographical Concentration of Global Supply Chains", IDE-JETRO Discussion Paper Series No.828, Institute of Developing Economies, JETRO.