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This chapter discusses how to measure cross-border outsourcing based on statistical data. We refer to the advantages and limitations of each measurement, primarily Japanese official statistics, in capturing and quantifying cross-border outsourcing. Statistics covered in this chapter include Input–Output (I-O) tables, custom-clearance statistics, Balance of Payment (BoP), and FDI statistics. Japanese unique statistics, through which we derive micro-data for our original research, are not included in the discussion of this chapter, but will be separately explained in detail in the next chapter. Although our research depends solely on statistical data, we briefly refer to business cases that motivate our research.
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Donaghu and Barff (1990) documents Nike’s “flexibly specialized manufacturing.” On the other hand, Tokatli (2008) describes the case of fashion apparel production by Zara.
The share of firms outsourcing all manufacturing activities tends to be high not only in traditional labor-intensive sectors such as toys and apparel but also in electronics and computers industries in the U.S., according to Kamal et al. (2015).
The detailed decomposition of values by Kraemer et al. (2011), for instance, reveals that the value attributed to labor in China is merely 2% of the total value of the iPhone or iPad. Dedrick et al. (2009) confirm a similar finding concerning iPod and notebook computers. Ali-Yrkko et al. (2011) also report that the final assembly occupies 2% of the retail price of the Nokia phone.
Japanese manufacturing firms, especially automobile manufacturers as a prime example, have a long tradition of actively outsourcing the production of parts and components to independent domestic suppliers without ownership relationships within Japan. See Nishiguchi (1994) for its historical development.
Arndt and Kierzkowski (2001) present an early collection of studies of the input trade. Campa and Goldberg (1997) provide one of the early applications of this measure for international comparison.
Using input–output data of the importer country is also problematic as they differ from those of the producing country.
The ratio is calculated by dividing the imports of all inputs by the producer price of each sector in the input table. The ratio of all the industries combined is that of the total of endogenous sectors in the I-O table (the code number 700,000).
The industries with import shares equal to zero and those with irregularly exceeding one are omitted from the graph. All industries in agriculture, fishery, forestry, mining, and manufacturing are included in the graph, although the imported input ratio is calculated for all the industries including the non-tradable services.
They contrast spiders with snakes while characterizing the input trade. The former corresponds to the case with inputs from multiple countries assembled in one country, while the latter corresponds to the case with same inputs traded from one country to another along production stages.
Particularly, the market clearing condition should be satisfied on quantities, while available data from I-O data and trade data are in values. We will refer to the problem of price adjustment in the final section of this chapter.
Their calculations are based on data of 42 OECD countries and major emerging countries, such as Brazil, China, India, South Africa, and Russia, and the following four sectors: agriculture, manufacturing, non-manufacturing industrial products, and services. Remaining countries are merged with the rest of the world.
As another related project, Timmer et al. (2014) focus on value-added content of final demand, not of gross trade as we discuss here, based on their World Input-Output Database (WIOD).
Even the concept of inputs is ambiguous in some instances. For example, imported food is usually treated as goods for final consumption but can be used as inputs in the catering service industry.
As a more elaborated concept, Antràs et al. (2012) proposed “upstreamness,” which represents the relative position of a good in the value chain based on input–output table.
The revised classification includes the following two new categories in service trade, replacing “goods for processing” and “maintenance of goods” in goods trade: “manufacturing services on physical inputs owned by others” and “maintenance and repair services.”
The reason behind separating processing trade from normal trade in Chinese trade statistics may be attributed to the prevalence of processing trade in early years just after the opening policy.
In 2007 and 2008, net imports of manufacturing service reached 10,158 million yen and 9759 million yen, respectively. Contrary to the fall after this peak, net imports of repair and maintenance services increased. We need to carefully examine the impact of the classification revision, before discussing the expansion of manufacturing services as a long-run trend.
The shares must be evaluated cautiously, because the survey covers wholesales, retails, and ICT services, but only selective segments of other services.
As additional related evidence from the same statistics, Japanese firms that are classified as wholesalers in textile and apparel industries export sizably larger values of goods than firms classified as manufacturers in these industries. We note that these trade values are about trade in goods, not trade in services. While we cannot exactly separate trade by intermediaries without direct data on indirect exports, we can assume that production by firms classified as wholesalers would be partially contributing toward such large exports of textiles and apparel goods.
Crozet and Milet (2017) term the active service trade by manufacturers as “servitization.”
However, we must note that exports of services by manufacturing firms in Japan concentrate on the automobile manufacturing industry, as shown in Table 3.1, especially on intra-firm trade by automobile manufacturers (2213 billion yen).
As per the definition provided by the General Agreement on Trade in Services (GATS), service trade is classified into the four modes. Cross-border outsourcing is primarily related to Mode 1 (cross-border provision of services), while FDI is more related with Mode 3 (commercial presence in a foreign country). Trade in service in BoP statistics includes Mode 2 (consumption abroad) and Mode 4 (temporary movement of natural persons).
According to the footnote to this WTO statistics, India excludes computer-enabled services and business process outsourcing from this category. Certain major trading countries may not be included in this ranking if they do not report data on computer services.
Although slightly more disaggregated classifications are disclosed in some cases, the basic classifications always available are listed here. The classification name is abbreviated in some cases.
Japan turned to a net importer of maintenance and repair services. Japan witnessed a trade surplus of 237 million yen in 1997, but has recorded a trade deficit every year since 1999. The net deficit from service trade in this category reached 5475 million yen in 2014.
In addition to this annual survey, METI also conducts a quarterly survey on overseas subsidiaries. The quarterly survey, however, does not collect data on intra-firm trade, though it collects data on exports/imports between overseas subsidiaries and Japan (no further disaggregation to trade with parent firms).
While this book does not intend to propose a remedy for this major problem, we should notice the limitation of accounting practice in separately reporting the performance of each affiliate. Based on consolidated records of an enterprise group, we can attribute income according to some index of activities such as the employment size. This apportionment would be more suitable to measure values of contributions of shared inputs or corresponding trade in such services.
Some of the private databases are useful, but none of them have direct data on cross-border outsourcing to the best knowledge of the author. FDI-related ownership data of the firms around the world are found in several commercial databases, such as Orbis provided by the Bureau van Dijk.
The prime example is a questionnaire on management organization added to the U.S. manufacturing census.
In other countries, for instance, Görg and Hanley (2005), and Görg et al. (2008) use Irish plant-level data on international outsourcing and separate materials and services in inputs, but treat expenditures on inputs, not necessarily customized, as a measure of outsourcing.
A similar survey on contract manufacturing was also conducted in Italy, although small firms are not well sampled. Federico (2010), for example, uses firm-level data from this Italian survey. Cusmano et al. (2010) use data of firms in a region in Italy, Lombardy.
While we discuss the extent of cross-border versus within-border outsourcing, the problem of price adjustment also has a serious implication to the correct measurement of real productivity growth, which is beyond the scope of our research. As imports of inexpensive inputs expand, we will underestimate real inputs and subsequently overestimate real TFP growth if we fail to adjust price changes appropriately, especially falling prices of imported inputs.
- Measures of Cross-Border Outsourcing
- Springer Singapore
- Chapter 3
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