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This chapter investigates innovation by outsourcing firms. The motivations for this topic choice are twofold. First, innovation is a critical factor determining productivity, specifically Total Factor Productivity. As innovative firms tend to be productive, they are likely to have ample capacity to be active in global activities, including foreign outsourcing. Second, innovativeness of firms is likely to determine barriers to foreign outsourcing. Firms may find it unsafe or difficult to outsource technologically complex tasks across national borders. The study of R&D by Japanese firms is useful even from a global perspective, as Japan is the third largest country in the world in terms of R&D expenditure following the U.S. and China, and records large surplus of trade in technology only next to the U.S., according to the annual report of the Survey of Research and Development by Statistics Bureau, Japan’s Ministry of Internal Affairs. Bearing in mind these issues, we discuss how foreign outsourcing firms differ in their innovativeness from non-outsourcing and domestic outsourcing firms. Third, as firms in developed countries normally outsource production to developing countries, cross-border outsourcing is intrinsically connected to development stage differences across countries in the world. The main issue in the third context includes whether innovative firms outsource their production to developing countries. While firms can outsource any activities including R&D activities, outsourcing of R&D is rare possibly due to the fact that output of R&D is inherently hard to measure or be verifiable. Therefore, we do not discuss the outsourcing of innovation itself, as less than 4% of the firms in the RIETI survey outsource R&D across national borders.
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Bustos (2011) instead analyzed the impact of globalization on technology upgrading.
From the same firm-level data from the MITI survey, Tomiura (2005b) also confirmed that R&D-sales ratio of FDI firms (2.60%) is similar to that of non-FDI firms (2.73%) among positive-R&D firms, though the share of positive-R&D firms is much higher among FDI firms. Although firms may produce multiple products with different R&D intensities, no data on product-specific R&D spending within each firm are available in almost any data sources, including the MITI survey.
The size of outsourcers’ production may be smaller even when they are equal in the size of sales with non-outsourcers. This induces us to interpret Q, not as a production scale, but as a proxy for the headquarter function of the firm.
The firms without K data are excluded from regressions.
Computer networks in the MITI survey for 1998 includes inter-firm, open, and local area networks (LAN).
We include not only majority-owned subsidiaries, minority-owned affiliates, but also branch offices/plants ( jigyousho in Japanese) within the firms captured by the MITI survey in order to widely cover firms’ potential contact channels.
If we include exactly the same set of explanatory variables into all choices without exclusion restrictions, only through the functional form assumption can we identify whether the variable directly affects the choice as specified in the model or indirectly through correlations among error terms. We have, however, confirmed the robustness of our main results even if we include all the explanatory variables. See Table 2 of Tomiura et al. (2009).
To include firms without R&D data or reporting zero R&D expenditures, we add a negligible 10 −8 before taking logarithm.
Firms outsourcing overseas without outsourcing to domestic firms (FO without DO) are exceptional in any sample including ours from the MITI survey.
Feenstra and Spencer (2005) considered DO versus FO in their theoretical model, but did not empirically analyze DO in their dataset on Chinese processing trade.
Fink et al. (2005) found that communication costs significantly affect bilateral trade between countries by using international calling prices as a proxy for communication costs.
Our result is in line with Rangan and Sengul (2009) in that they detected a negative effect of ICT investment on intra-firm trade in U.S. multinationals. Chen and Kamal (2016), on the other hand, reported that Internet-based computer connections positively relate to in-house production, but also found that this effect is muted in industries where production specifications are easily codified into electronic formats.
We also find printing and publishing industry in this last category, but the preference of DO over FO in this industry is likely to be dictated by language barriers rather than R&D intensity.
From a different angle, Glass and Saggi (2001) theoretically argued that FO increases the firm’s profit, and thus, raises its incentives for innovation, though they do not consider DO or incomplete contracts.
As we explained in Chap. 4, the RIETI survey does not distinguish intra-firm sourcing and outsourcing among domestic sourcing.
The negative test statistics could be considered as evidence for the support for IIA assumption in our case.
While number counts, even if economic values of patents cannot be evaluated, of a firm’s patents may be a more precise measure of the firm’s technological knowledge, around two-thirds of surveyed firms do not hold any patent at all. We, therefore, use the binary dummy variable for patent holders.
The input purchase is used as a proxy variable of productivity shock. Labor share and capital share are set at 0.76 and 0.23, respectively. We also use investment as an alternative proxy as proposed by Olley and Pakes (1996), but the results were almost the same. To include firms with zero investment, we choose the Levinsohn–Petrin procedure.
Value-added is defined as the total sales minus the cost of goods sold and the sales general and administrative expenses, plus wage payments, rental payments, capital depreciation, and tax payments. The data on values are deflated by input and output deflator at the three-digit industry level provided by the Japan Industry Productivity (JIP) Database 2008 constructed by RIETI.
While firms report the book value of tangible fixed assets, this is transformed into real values using the ratio of the real value of fixed tangible assets to their book value at the three-digit industry level. The investment goods deflator used for deflating the value of investment flows and the depreciation rate have been taken from the JIP Database 2008. The real capital stock is calculated by the perpetual inventory method.
CF approach treats the unobserved factor as an omitted variable. In the first stage, we gain OLS residuals from the regression of the endogenous variable on IV and covariates of the second stage equation. In the second stage, we estimate the choice model including the OLS residuals as an additional explanatory variable.
Our derivation of knowledge capital flow follows Fors (1996).
Their survey labels it as international sourcing, but their coverage is quite close to our definition of outsourcing, as they exclude “buying or selling directly from a catalogue” (p. 172).
The product cycle theory considers these three stages, but our regression additionally includes the stage before exporting (firms producing and selling all their outputs in the home country). Although it deviates from the original theory, the addition of this non-exporting stage is necessary to cover a large number of non-exporters in the real world.
Exporters with zero R&D spending and zero patents are excluded, as they are inappropriate for our analysis of product cycles.
We concentrate on majority-owned subsidiaries in order to exclude sales offices or portfolio-motivated investment in our discussion of production cycles.
Even if a firm outsources production to foreign firms, the firm could keep producing some fraction of its products in Japan. Outsourcers do not coincide with factoryless goods producers in the U.S. case.
In the sample from the MITI survey, in terms of a continuous measure R&D-sales ratio, FDI firms and non-FDI firms are not different, possibly due to the presence of R&D-intensive small firms concentrating on domestic operations.
The HQ intensity is approximated by sales and general administrative expenses relative to firm size (sales). As no further geographical disaggregation beyond Asia versus ROW is available in the MITI survey, we cannot control for host country factors.
As a rare example of research taking account of selectivity, Belderbos and Sleuwaegen (1996) included 65 non-FDI firms within their sample of 204 firms.
Barbosa and Louri (2002) found that R&D significantly increases FDI with full ownership, while the relation is insignificant for FDI with less-than-full majority ownership in Portugal. Although they depend on the industry-level data, Kogut and Chang (1991) also reported that the effect of domestic R&D on FDI is significant for FDI into new plants, not for FDI in joint ventures in the case of Japanese FDI into the U.S.
We define FDI firms in the North if the number of affiliates with ownership more than 20% in ROW exceeds that in Asia to handle firms simultaneously investing in both Asia and ROW. The average FDI firm in the MITI survey owns 9.387 overseas affiliates, of which 6.468 are in Asia.
- Innovation, Development, and Outsourcing Across National Borders
- Springer Singapore
- Chapter 7
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