*Notes*: (a) The regression is based on full sample. (b) Period dummies are included. (c) \*\*\*, \*\*, and \* denote significance at the 1%, 5%, and 10% levels, respectively. (d) The values in the parentheses are standard errors

negative in the case of the JPO. In contrast, some of the estimated coefficients of OWT*X* are comparable to those for HIIT and VIIT. These results imply that the effect of OWT on technology spillovers is much weaker than that of IIT if the country is an importer, whereas that effect may be comparable to that of IIT if the country is an exporter.

# **5.5 Conclusion**

In this chapter, we have examined how technology spillovers across countries would differ according to bilateral trade patterns. We first developed a two-country model of monopolistic competition with quality differentiation by extending the model of Melitz and Ottaviano (2008). In our model, the quality of each product in the manufacturing sector is differentiated and stochastically determined by firms' engaging in product R&D. The structure of our model is similar to that of Melitz and Ottaviano (2008), except for the fact that firms are heterogeneous in product quality rather than in productivity. We then introduced technology spillovers in our model as the process of expanding the technology frontier of the industry. We assumed that, in a given sector, all firms in the same country equally have access to the "general knowledge" without paying any cost. However, technology spillovers are imperfect across countries. In particular, the degree of international technology spillovers falls as the technology gap between the two countries increases. We then showed that in our model the trade pattern is intra-industry when the technology gap between the two countries is small, while it is inter-industry when the technology gap is sufficiently large. Since products are differentiated in quality in our model, both horizontal and vertical intra-industry trade patterns also emerge endogenously.

From the model we derived three testable hypotheses for empirical analysis. The first hypothesis (Result 5.1) was that technology spillovers are larger when the trade pattern between the two countries is HIIT than when it is VIIT. The second hypothesis (Result 5.2) was that when the trade pattern is VIIT, the relative size of technology spillovers from the country exporting high quality products on average to the country exporting low quality products on average and in the opposite direction is ambiguous. The third hypothesis (Result 5.3) was that technology spillovers are lower when the trade pattern is IIT or OWT than when it is VIIT.

We then empirically tested these hypotheses by using bilateral trade data among 44 countries at six-digit level and patent citations data at the EPO and JPO. Following Jaffe et al. (1993) and other recent studies on technology spillovers, we measure international technology spillovers by patent citations among countries.

Our estimation results basically confirmed the predictions of our model. That is, we found that an increase in the shares of HIIT and VIIT has a significantly positive effect on international technology spillovers. Our estimation results showed that HIIT has a larger effect on spillovers than VIIT does. On the other hand, the relative magnitudes of technology spillovers between the country exporting high quality products and the country exporting low quality products on average under VIIT are generally ambiguous. We also found that the effect of OWT on technology spillovers tends to be much weaker than that of other trade patterns. These results from Japanese and European patents are generally consistent with the finding by Jinji et al. (2015) from US patents. Therefore, we conclude that intra-industry trade plays a significant role in technology spillovers.

In this chapter, we primarily focused on technology spillovers through international trade but did not take the effects of FDI into account. As argued in the introduction, however, a number of existing studies have empirically confirmed that FDI is also a major channel of international technology spillovers. In our estimations, we found that an increase in the share of OWT has a significantly positive effect on technology spillovers in some cases, in particular in the cases of the JPO and EPO. The positive effect of OWT with exporting the good in question even exceeds those of HIIT and/or VIIT in some cases in Table 5.7, which contradicts the predictions by our theoretical model. This may be due to FDI. Thus, in the next chapter, we analyze the effects of FDI on technology spillovers.

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# **Chapter 6 Vertical Versus Horizontal Foreign Direct Investment and Technology Spillovers**

# **6.1 Introduction**

Foreign direct investment (FDI) and international trade are two major channels of international diffusion of technological knowledge (Keller 2004,2010). While a number of empirical studies confirm significant spillover effects of knowledge through imports, the empirical findings on technology spillover effects through FDI are conflicting. In particular, there is relatively little evidence of spillovers of knowledge from inward FDI to the host country's firms in the same industry. For example, Haskel et al. (2007) examine the situation in the United Kingdom and find significantly positive productivity spillovers from FDI. In addition, Keller and Yeaple (2009) show similar results for the United States. By contrast, Aitken and Harrison (1999) and Haddad and Harrison (1993) find no significant or weak productivity spillovers from FDI for developing countries hosting FDI (the former and the latter obtain results on Venezuela and Morocco, respectively).1 Addressing the endogenous nature of the FDI decision, Lu et al. (2017) examine the spillover effects of FDI in China and find that the presence of FDI in the same industry has a significantly negative effect on the productivity levels of domestic firms due to the competition effect, while it has no significant effect on the exporting performance and research and development (R&D) investment of domestic firms. Todo (2006) and Todo and Miyamoto (2006) derive that R&D activities play an important role in productivity spillovers from FDI to local firms in the same industry: for example, a positive, statistically significant spillover effect is observed only for R&D-performing foreign firms (in Indonesia) or foreign firms' R&D stock (in Japan). In contrast, a number of studies find significant productivity spillovers from inward FDI to the host country's upstream firms through backward linkages. These studies include Javorcik (2004) for the case of Lithuania, Javorcik and Spatareanu (2008) for that of Romania, Blalock and Gertler (2008) for that of Indonesia, and Newman et al. (2015) for that of Viet-

99

1 For example, Aitken and Harrison (1999) find negative spillover effects of FDI on the productivity of domestically owned plants in Venezuela.

© The Author(s) 2022

N. Jinji et al., *Deep Integration, Global Firms, and Technology Spillovers*, Advances in Japanese Business and Economics 26,

https://doi.org/10.1007/978-981-16-5210-3\_6

nam.2 Moreover, Branstetter (2006) and Singh (2007) find evidence of technology spillovers from outward FDI. That is, firms investing in foreign countries acquire technological knowledge from other firms in the host countries.

When a firm establishes business enterprises in two or more countries through FDI, it becomes a multinational enterprise (MNE). Canonical FDI theory defines two types, namely horizontal and vertical, of FDI and an MNE's activities.3 Horizontal FDI (HFDI) replicates a subset of the production process in foreign countries to serve local markets (Brainard, 1993, 1997; Helpman et al., 2004; Markusen, 1984, 1995; Markusen and Venables, 1998, 2000). It is often motivated by an intention to reduce transportation costs. In contrast, vertical FDI (VFDI) involves geographical fragmentation of the production process and is often motivated by an intention to take advantage of factor cost differentials (Helpman, 1984, 1985; Helpman and Krugman, 1985; Venables, 1999). However, the actual patterns of FDI and an MNE's activities are much more complex than the simple dichotomy of "horizontal" and "vertical". Yeaple (2003a) constructs a model in which horizontal, vertical, and complex (i.e., both horizontal and vertical) FDI arises endogenously. Grossman et al. (2006) also analyze MNEs' integration strategies that may simultaneously involve horizontal and vertical FDI. Ekholm et al. (2007) propose the type of "export-platform FDI" as another type of FDI. Moreover, utilizing the information on sales and sourcing patterns of foreign affiliates of MNEs, Baldwin and Okubo (2014) support the importance of "networked FDI."

A number of empirical studies provide support to the predictions for HFDI. For example, Brainard (1993, 1997) obtains evidence of HFDI but little evidence of VFDI. Markusen and Maskus (2002) argue that a large proportion of FDI takes place among developed countries and is characterized by the horizontal type of FDI. Looking at the location decisions of MNEs, however, Yeaple (2003b) and Hanson et al. (2005) find evidence consistent with comparative advantage. Moreover, Alfaro and Charlton (2009) point out that the share of VFDI is much higher than previously thought even within developed countries. They argue that a significant amount of VFDI has been misclassified as horizontal in the previous studies and find that a substantial amount of VFDI between developed countries emerges in high-skill sectors, because parent firms own the stages of production proximate to their final production and source raw materials and inputs in low-skill production stages from outside of the firms.

2 However, Keller (2010) argues that some issues such as a measurement problem in contractual payment between theMNEs and local firms may lead to estimation bias. Besides, Barrios et al. (2011) argue that the measures of backward linkages used in recent studies on spillovers are potentially problematic. Using the standard measures employed in the literature, they fail to find robust evidence for spillovers through backward linkages. On the other hand, they obtain robust evidence for positive FDI backward spillover effects.

3 See Markusen (1995), Markusen (2002), Markusen and Maskus (2003), and Helpman (2006) for the survey of the literature.

A literature that is separated from studies on FDI has shown the importance of international fragmentation of production and the involvement of firms in international production networks (or global value chains (GVC)).4

Depending on the type of FDI or the degree of involvement in GVC, affiliates of MNEs conduct different activities in their host economies, affecting the extent of technology spillovers between MNEs and domestic firms in the host economies. For example, when MNEs conduct VFDI, foreign affiliates engage in intra-firm trade with their parent firms in the source country. On the other hand, in the case of networked FDI, foreign affiliates are involved in GVC and are likely to trade with other affiliates in the same region. Ramondo et al. (2016) document that intra-firm trade is concentrated on a small group of large foreign affiliates of MNEs headquartered in the United States. They find that the median manufacturing foreign affiliate of the US MNEs has no transaction of goods with its parent in the United States. This finding suggests that VFDI is concentrated on large firms and large affiliates.5 To the best of our knowledge, no existing studies have investigated how the type of FDI or the degree of involvement in GVC affects technology spillovers between MNEs and their host economies.

An empirical work by Branstetter (2006) is closely related to the analysis in this chapter. He defines the term "technology spillovers" as "the process by which one inventor learns from the research outcomes of others' research projects and is able to enhance her own research productivity with this knowledge without fully compensating the other inventors for the value of this learning" (pp. 327–328). In this sense, technology spillovers must be distinguished from the term "productivity spillovers" which measures how productivity of a firm is affected by other firms' one or R&D activities. Then, in the literature with respect to spillovers, patent citation data have been used as a proxy of technology spillovers in the above definition (Jaffe et al. 1993).6 Branstetter (2006) analyzes firm-level data on Japanese MNEs in the United States and patent citations at the United States Patent and Trademark Office (USPTO) and obtains evidence that FDI facilitates technology spillovers both from investing firms to local firms in the host country and from the local firms to the investing firms. Although he examines whether different types of FDI, such as acquisition, greenfield investment, and R&D facilities, have different effects on spillovers, he does not distinguish various production activities of foreign affiliates.

4 See Antràs and Chor (2013), Baldwin and Venables (2013), and Costinot et al. (2013) for theoretical work and Alfaro et al. (2019) and Timmer et al. (2014) for empirical evidence. See Amador and Cabral (2016) for the survey of the literature.

5 The finding by Ramondo et al. (2016) is related to the observation by Atalay et al. (2014) for production chains within the United States. In particular, they show that the ownership of vertically linked affiliates is not related to the transfer of goods within the boundaries of the firm. Ramondo et al. (2016)) argue that the vertical ownership promotes efficient intra-firm transfers of intangible inputs.

6 There are a number of existing empirical studies on technology spillovers based on patent citations (Cappelli and Montobbio, 2020; Haruna et al., 2010; Hu and Jaffe, 2003; Jaffe and Trajtenberg, 1999; Jinji et al., 2013, 2015, 2019a; Li, 2014; MacGarvie, 2006; Mancusi, 2008; Murata et al., 2014; Peri, 2005).

In this chapter, we attempt to identify how the structure of MNEs' activities in terms of horizontal and vertical FDI affects technology spillovers between MNEs and host countries. Then, we combine a comprehensive firm-level dataset of the business activities of Japanese MNEs' foreign affiliates and information on the patent citations betweenMNEs and their host countries. Following Branstetter (2006), we define "technology spillovers" as the effects on the research productivity from the outcomes of others' research activities without full compensation for the value of research productivity enhancement.7 Alternatively, we use firm-level data on Japanese firms' FDI and patent citations at the USPTO.8 Our firm-level dataset includes information on the sales and purchases of the foreign affiliates, classified according to the destination and source countries. We exploit this information to construct new measures of horizontal and vertical FDI based on the shares of the host and home countries in their transactions. Now we define a measure of "pure horizontal FDI" as the extent to which affiliates' purchases of intermediate inputs and sales of final goods are concentrated in the local market. We also define a measure of "pure vertical FDI" as the extent to which their affiliates' purchases of intermediate inputs and sales of final goods are linked to the home country. We, furthermore, define measures of "partially horizontal" and "partially vertical" FDI. We then estimate how different types of FDI affect technology spillovers from Japanese MNEs to the host country and from the host country to Japanese MNEs. As for the empirical methodology, we follow Branstetter (2006). Since the dependent variable (i.e., patent citations) is the count data, we utilize a negative binomial model developed by Hausman et al. (1984). Moreover, to deal with a potential endogeneity problem we employ an endogenous switching model discussed by Miranda and Rabe-Hesketh (2006).

Our main findings are as follows. We find that an increase in the degree of pure VFDI has significantly positive effects on technology spillovers captured by patent citations when technologically advanced economies host Japanese MNEs. Technology spillovers occur in both directions between the MNEs and their host countries. These positive effects of pure VFDI on technology spillovers are robust for different specifications as well. Partially VFDI (i.e., FDI with a higher share of purchase of intermediate inputs in the local market and a higher share of sales of outputs to the home country) also has significantly positive effects on technology spillovers from the (high-income) host countries to the MNEs.9 By contrast, an increase in the degree of pure HFDI has no significant effect or significantly negative effects on technology spillovers between the MNEs and their host countries. Partially HFDI (i.e., FDI with a higher share of purchases from the home country and a higher share of sales to

7 Therefore, our definition of technology spillovers is narrower than that used in studies on the productivity change due to FDI or trade. However, it seems that our definition is useful, because it focuses on direct effects and can still capture an important part of the effects in terms of the contribution to the expansion of the world's technology frontier.

8 We acknowledge that the range of technology spillovers measured in the data may be narrowed, particularly for developing countries, by using patent citations, because many indigenous firms in developing countries are not so active in the application of patents.

9 The positive effects of partially VFDI are not robust when we employ different specifications, although we do not report the details of the estimated results in this chapter.

the local market) has significantly positive effects on technology spillovers from the MNEs to the (high-income) host countries, but the result is not robust for different estimations. From these results, we conclude that pure VFDI plays a dominant role in technology spillovers in both directions between Japanese MNEs and the high-income host countries.

To explain the mechanism for the observed relationship between the structure of FDI and technology spillovers, we develop a simple partial-equilibrium model of FDI and technology spillovers among developed countries. A differentiated good is produced in three stages.10 The product market is characterized by monopolistic competition. Depending on parameter values, firms may have an incentive to engage in horizontal or vertical FDI. Assuming that factor costs are the same in the two countries, there is no possibility of VFDI in the usual sense. Nevertheless, VFDI *does* occur if there are technology gaps in some production stages between the two countries and/or if firms can take advantage of the superior technology by fragmenting their production process abroad. Technological differences in some production stages are considered to be the source of technology spillovers through FDI. We show that technology spillovers occur in one way or two ways if firms engage in VFDI, depending on how the three production stages are located in the two countries. Besides, we show that HFDI does not necessarily induce technology spillovers, because it is mainly motivated by saving transportation costs and hence appears even in the absence of technological differences.

The rest of this chapter is organized as follows. Section 6.2 describes the data employed in our empirical analysis. Section 6.3 introduces estimation methods. Section 6.4 provides empirical results. Section 6.5 develops a simple theoretical model of FDI and technology spillovers to explain the observed relationship in our empirical study. Section 6.6 concludes this chapter.

# **6.2 Data**

In this section we describe the data employed in our empirical analysis.

# *6.2.1 Data on Patent Citations and Japanese Firms' FDI*

Following Jaffe et al. (1993), Jaffe and Trajtenberg (1999), and other studies, we use patent citation data as a proxy for technology spillovers. The patent citations are collected from the dataset compiled by the National Bureau of Economic Research

10 As the models of vertical production structure with multiple stages there are, for example, Bridgman (2012), Dixit and Grossman (1982), Kohler (2004), and Yi (2003). None of these papers, however, consider the possibility of VFDI driven by cross-country technology gaps in production stages. Baldwin and Venables (2013) consider more general structure of the GVC in production processes.

(NBER) patent database for patents at the USPTO.11 The dataset includes information on the application date, the country name of the assignee, the main US patent class, and citations made and received for each patent. From the dataset we extract information on the patent applications and citations by Japanese MNEs and their host countries. Because of the truncated problems of citations in the NBER dataset, we concentrate our analysis on the period before 2003, though the NBER patent dataset includes data until 2006.

Our data on Japanese MNEs' activities abroad are obtained from the Basic Survey on Overseas Business Activities (BSOBA) or *Kaigai Jigyo Katsudo Kihon Chosa* conducted by the Japanese Ministry of Economic, Trade and Industry (METI). This data source provides detailed data on affiliate-level FDI activities such as the sales and purchases of affiliates of Japanese MNEs, classified by their destinations and sources, i.e., sales to (or purchases from) the local market or exports to (or imports from) the home country and a third country.

The foreign affiliates listed in the BSOBA are either foreign affiliates with at least 10% of their capital held by a Japanese parent company or those with at least 50% of their capital held by a foreign subsidiary, which in turn has at least 50% of its capital held by a Japanese parent company. These affiliates exclude those that run businesses in the financial and insurance industry or the real estate industry in host countries. According to the METI, there were approximately 15,000 foreign affiliates that responded to the survey in 2000.12 Table 6.1 shows the top 30 host economies for Japanese MNEs in 2000, based on the number of affiliates that had completed the BSOBA.13 As shown in the table, the United States attracts the largest number of Japanese firms' affiliates, followed by China. Asian economies as well as developed countries are popular host economies for Japanese MNEs.

Our sample covers the period between 1995 and 2003. All countries in which Japanese MNEs have at least one affiliate are included in our sample. Since the number of countries varies greatly depending on that of patent applications made, we divide our sample of the countries into two groups, Groups I and II, according to the number of USPTO patent applications made by the sample countries during the period of 1995–2003. We call Group I countries/economies "Technologically Advanced Economies," which mainly include high income countries/economies. In contrast, we call Group II countries/economies "Technologically Less Advanced Economies," which mainly include middle and low income countries. The number of patent applications in Group I is larger than 1,000, while it is less than that in Group II.14 All countries that are categorized into each group are listed in Table 6.2.

11 See the Bronwyn Hall's website (www.nber.org/people/bronwyn\_hall?page=1&perPage=50) for the NBER patent database.

12 See the METI website (www.meti.go.jp/english/statistics/tyo/kaigaizi/index.html) for the details of the BSOBA.

13 About 10,100 affiliates reported full or partial information on their sales and purchases classified by the destinations and the sources in 2000.

14 Although the number of USPTO patent applications made by China, India, Russia, and Singapore is more than 1,000 during the period, a large jump in the applications is observed after 2000,

**Table 6.1** Top 30 FDI host countries/economies in the year 2000

*Note*: The number of affiliates is those reported by respondents of the BSOBA in 2000

**Table 6.2** List of countries/economies

We use the Nikkei company code system to link the two data sources and collected the data on 1,445 parent companies that run at least one affiliate during the sample period. 279 parent companies out of 1,445 ones made at least one citation to USPTO patent applications from 93 countries and, on the other hand, 301 ones received at least one USPTO patent citation.

# *6.2.2 Types of FDI*

In the literature, FDI and MNEs' activities are usually categorized into horizontal and vertical cases. In the empirical studies, there are a number of ways to measure horizontal and vertical FDI. Hummels et al. (2001) and Alfaro and Charlton (2009) use the industrial classifications to define the types of FDI. Hanson et al. (2001, 2005) utilize the firm-level database of the United States Bureau of Economic Analysis (BEA) to characterize VFDI as intra-firm flows of inputs that they observed flowing from parent companies in the United States to affiliates abroad. The method of Hanson et al. (2001, 2005) enables them to measure one-way US bilateral intra-firm trade. Using the same BSOBA dataset as in this chapter, Fukao and Wei (2008) employ the local sales ratio of the affiliates to classify vertical and horizontal FDI. In particular, if a local sales ratio of a foreign affiliate is less than the average ratio in the sample, then FDI to the affiliate is classified into VFDI. By contrast, if a local sales ratio is larger than the average ratio, FDI to the affiliate is classified into HFDI.

An advantage of the BSOBA dataset is that it allows us to measure vertical and horizontal FDI by using information on the sale of outputs and the purchase of inputs by foreign affiliates.15 The local sales and local purchases ratios of foreign affiliates of Japanese MNEs are denoted by *ShSaH FDI* and *ShPuH FDI*, respectively. Similarly, the sales and purchases ratios to and from Japan for foreign affiliates of Japanese MNEs are denoted by *ShSaV FDI* and *ShPuV FDI*, respectively. Table 6.3 shows the average values of both ratios during the sample period for the subsamples of technologically advanced and less advanced economies. Looking at the ratios over the years, no evident trend is observed during the sample period. Interestingly, the table also shows that the values of *ShSaV FDI*, which indicates the vertical structure of sales from foreign affiliates, are around 10–12% in technologically advanced economies and around 20–22% in technologically less advanced economies. If we focus on *ShPuV FDI* (i.e., the vertical structure of purchases by foreign affiliates), then the values climb up to around 40–42% in technologically advanced economies and 37–40% in technologically less advanced economies. With respect to the pur-

compared with a very limited number in the early years for these countries. We therefore categorize these four countries into the second group.

15 One limitation of the BSOBA dataset is, however, that it does not track transactions between foreign affiliates or between foreign affiliates and the parent companies. As a result, there may exist some biases for measuring the types of FDI by using information on sale and purchase because we cannot examine flows within the boundary of a firm from our dataset.

**Table 6.3** Sales and purchases ratios of affiliates abroad

*Source*: Authors' calculation from the BSOBA data from 1995 to 2003

chase of inputs, Japanese MNEs engage in VFDI more actively in technologically advanced host countries than in technologically less advanced host countries. This evidence is consistent with Alfaro and Charlton (2009), who showed that VFDI emerges as far more prevalent between developed countries.

By exploiting information on the horizontal and vertical structures in sales and purchases of foreign affiliates, we construct new indexes of horizontal and vertical FDI, i.e., *HFDI*, *VFDI*, *PHFDI*, and *PVFDI*, in the following way:

> *HFDI* = *ShSaH FDI* × *ShPuH FDI VFDI* = *ShSaV FDI* × *ShPuV FDI PHFDI* = *ShSaH FDI* × *ShPuV FDI PVFDI* = *ShSaV FDI* × *ShPuH FDI*

As is evident from the definition of the index, *HFDI* measures the extent to which affiliates' purchases of intermediate inputs and sales of final goods are concentrated in the local market. Now, *HFDI* captures the degree of "pure" HFDI. If *HFDI* = 1, a foreign affiliate makes all purchases and sales in the local market, whereas, if *HFDI* = 0, either or both of purchases and sales of the foreign affiliate become zero in the local market. Note that *HFDI* = 0 does not necessarily mean that the foreign affiliate engages in vertical activities because there is a possibility of transactions with third countries. Next *VFDI* measures the extent to which affiliates' purchases of intermediate inputs and sales of final goods are linked to the home country (i.e., Japan), so that *VFDI* captures the degree of "pure" VFDI. On the other hand, *PHFDI* and *PVFDI* capture "partially" horizontal and "partially" vertical FDI, respectively. The value of *PHFDI* rises if an affiliate buys more intermediate goods from the home country and sells more final goods to the local market. Since the structure of sales is more important to distinguish the type of FDI than the structure of purchases, we consider that *PHFDI* measures the degree of "partially" horizontal FDI in terms of its horizontal sales structure. Similarly, the value of *PVFDI* becomes large if an affiliate buys more intermediate goods from the local market and sells more final goods to the home country. Since the structure of sales is vertical in *PVFDI*, we consider that it measures the degree of "partially" vertical FDI.

We then test whether there are any differences in the effects on technology spillovers among these types of FDI.

# **6.3 The Empirical Model**

In this section, let us explain our empirical model. Although the BSOBA is conducted every year, there are many blanks in the data on a particular firm because in some years certain respondents did not report to the METI. For this reason, we use only a pooled data in our estimation. Consequently, we run the following specification as in Branstetter (2006),

$$C\_i = \beta\_1 + \alpha\_1 LPHost\_i + \alpha\_2 LPParent\_i + \beta\_2 FDI\_i + u\_i,\tag{6.1}$$

where *i* refers to the affiliate *i*, and *Ci* is the number of citations made (or received) by USPTO patents of the Japanese parent company that owns affiliate *i*. Note that *Ci* = *Ci* holds for affiliate *i* and affiliate *i* if the same parent company owns affiliates *i* and *i*- . We expect that citations made by Japanese parent companies capture the technology spillovers flowing from the host countries to Japanese companies, while the citations received by them reflect the flows from Japanese companies to host countries. *FDIi* in Eq. (6.1) is one of the alternative measures of the FDI types, i.e., *HFDI*, *VFDI*, *PHFDI*, and *PVFDI* for affiliate *i*. In Eq. (6.1), β1 is a constant term and *ui* is an error term.

As indicated in Branstetter (2006), patent citations may rise as the "citable" host invention increases. At the same time, the higher absorptive capacity in the home country may be associated with a higher ability to understand and exploit external knowledge, and cite more external patents (Mancusi, 2008). Thus, for elucidation of the assertions we use *L P Parenti* and *L P Hosti* , which refer to the logarithm of the count of the USPTO patent applications made by affiliate *i*'s Japanese parent company and the host country where the affiliate *i* runs its business, respectively, to proxy the home absorptive capacity and "citable" host invention. Note that *L P Parenti* is the same across affiliate *i* for the same parent company, and *L P Hosti* is the same across affiliate *i* for the same host country.

The focus of interest in Eq. (6.1) will be the coefficient β2. Hence, we examine if the FDI types of Japanese firms in host countries have an influence on patent citations made and received by the firm. We also investigate if there is a difference in the magnitude and sign of the coefficients between the citations made and received by the home and host countries and across the types of FDI that Japanese firms implemented.

#### 6.3 The Empirical Model 109

Since the observations of a dependent variable (i.e., patent citations) are the count data, we utilize a standard estimation technique, namely, a negative binomial model discussed in Cameron and Trivedi (1998), where the data are Poisson process, but more flexible modeling of the variance to account for overdispersion than the Poisson is allowed. We use this estimation technique to acquire our basic findings and alternative estimation results.

The other challenge of estimating the effects of each of the FDI types on technology spillovers arises from the fact that patent citations may be endogenous, because unobservables in determining the types of FDI may be correlated with those in determining the citations. Besides, certain geographic factors such as distance and language may influence the citations as well as the types of FDI. Neglecting these unobserved or endogenous factors may cause biased and inconsistent estimators.16 To address this issue, we employ an endogenous switching model.17 In that model, the citation *Ci* follows a Poisson distribution, and the probability distribution for count data is given by

$$Pr(C\_i, \mu\_i) = \frac{\mu\_i^{C\_i} \exp(-\mu\_i)}{C\_i!},\tag{6.2}$$

so that a log-linear model for the mean of *Ci* , μ*i* , can be specified as

$$\log(\mu\_i) = \beta\_1 + \alpha\_1 LPHost\_i + \alpha\_2 LPParent\_i + \alpha\_3 LDist\_i + \alpha\_4 LGDP\_i$$

$$+ \alpha\_5 LCost\_i + \beta\_2 D\_i + \epsilon\_i,\tag{6.3}$$

where *LDisti* is the logarithm of the distance (measured as kilometers) between Japan and the host economy of affiliate *i*, *LGDPi* is the logarithm of GDP of affiliate *i*'s host economy, *LCosti* is the logarithm of salary per employee of affiliate *i*, and *i* is an unobserved heterogeneity term. *LGDPi* measures the market size of the host economy, and *LCosti* measures the labor cost of the affiliate. Instead of *FDIi* in Eq. (6.1), here we use a dummy *Di* (*DHFDIi* , *DVFDIi* , *DPHFDIi* , or *DPVFDIi*) for the types of FDI, which equals one for a particular type, and zero otherwise. Following Fukao and Wei (2008), we construct the dummy for a particular type of FDI such that it equals one when the value of an FDI type's index (*HFDI*, *VFDI*, *PHFDI*, or *PVFDI*) for affiliate *i* is greater than the average value of the particular FDI type's index in the full sample, and zero otherwise. We then use a probit model to examine how a parent firm determines its FDI type. The logic we use is that the type decision on FDI depends on factors that favor a particular type of FDI or not. The probit model can be formulated as

$$D\_i^\* = z\_i' \nu + \lambda \epsilon\_i + u\_i,\tag{6.4}$$

16 See Wooldridge (2010) for dealing with endogenous problems.

17 An estimation method of count data regression with endogenous switching is proposed by Terza (1998). Kenkel and Terza (2001) discuss an application of the method suggested by Terza (1998). See Miranda and Rabe-Hesketh (2006) for technical details of the endogenous switching model.

and

$$D\_i = \begin{cases} 1, & \text{if} \quad D\_i^\* > 0, \\ 0, & \text{otherwise}, \end{cases}$$

where *D*∗ *i* is an auxiliary random variable,*zi* is a vector of factors which may influence the particular type of FDI. As usual, we have *ui* ∼ *N*(0, 1), and *ui* is independent of *i* . In the so-called endogenous switching model, *V ar*(*i*) = σ2, and total variance is λ2σ2 + 1. If λ = 0, then *Di* is considered to be exogenous. Although a Poisson distribution is used, the variance of *Ci* is not necessarily equal to the conditional mean, and overdispersion is allowed in this model. Using the normality assumption for *i* , we have

$$Var(C\_i|\mathbf{x}\_i, D\_i) = E(C\_i|\epsilon\_i, \mathbf{x}\_i, D\_i)[1 + E(C\_i|\epsilon\_i, \mathbf{x}\_i, D\_i)(\exp(\sigma^2) - 1)],$$

where *xi* is a vector of explanatory variables in Eq. (6.3) (i.e., *L P Hosti* , *L P Parenti* , *LDisti* , *LGDPi* , *LCosti* , and the constant term), which implies that if σ = 0, then the model exhibits overdispersion, as we would expect from the negative binomial model in Eq. (6.1).18

In the estimation, following Fukao and Wei (2008), we include *LDisti* (distance), *LGDPi* (market size), and *LCosti* (labor cost) in *zi* in Eq. (6.4). Among those variables, the data on salaries and the number of employees of foreign affiliates are obtained from the BSOBA. The data on distance are collected from the database of the CEPII Research Center and the data on GDP in host countries are obtained from the Penn World Table.19

# **6.4 Empirical Results**

In this section, we report our estimation results. We first show the basic findings obtained by the negative binomial model. We then report the results by the endogenous switching model and discuss whether the endogeneity issue matters in our analysis. Finally, we discuss the robustness of our findings by showing the results of alternative estimations with additional explanatory variables.

# *6.4.1 Basic Findings*

We first estimate Eq. (6.1) by the negative binomial model, and the results are reported in Table 6.4. The upper panel of Table 6.4 shows the estimated results for the sub-

18 See Miranda and Rabe-Hesketh (2006) for more details.

19 CEPII: www.cepii.fr/CEPII/en/bdd\_modele/presentation.asp?id=6. Penn World table: www.rug. nl/ggdc/productivity/pwt/.

**Table 6.4** Negative binomial estimates for patent citations

*Notes*: (a) \*\*\*, \*\*, and \* indicate significance at the 1%, 5%, and 10% levels, respectively

(b) The values in the parentheses are standard errors

(c) A constant term is included in the estimations

sample of technologically advanced economies. We observe from the results that the estimates of *HFDI* are significant and negative, whereas they are significantly positive for *VFDI* both for the citing and cited. As for *PHFDI*, the estimated coefficient is insignificant for the citing, but significantly positive for the cited, whereas the coefficient of *PVFDI* are significant and positive for the citing and insignificant for the cited.

The lower panel of Table 6.4 presents the estimated results for the subsample of technologically less advanced economies. Unlike the case of the technologically advanced economies, only the coefficients on *PVFDI* show significantly positive, whereas the coefficients of the other types of FDI reveal negative or insignificant effects on the citing as well as the cited.

These results show that an increase in the degree of the "pure" VFDI has a significantly positive effect on technology spillovers in both directions between Japanese parent companies and their host countries if Japanese MNEs invest in high-income countries. This implies that VFDI plays a dominant role in technology spillovers with mutual effects in technologically advanced economies. When middle- and lowincome countries host Japanese MNEs, an increase in the "partially" vertical FDI has a significantly positive effect on the number of patent citations in both directions between the Japanese parent companies and firms in their host countries.

# *6.4.2 Estimating with an Endogenous Switching Model*

To deal with endogeneity issues, we simultaneously estimate both an endogenous switching model described by Eqs. (6.2) and (6.3) for technology spillovers and a probit model based on Eq. (6.4) for the decision on FDI types. We focus on the subsample of technologically advanced economies. The estimated results are summarized in Table 6.5. We first observe that the coefficient of *LCost* is significantly positive for both "pure" VFDI (*DVFDI* ) and "partially" vertical FDI (*DPVFDI* ). This implies that VFDI to technologically advanced economies is not motivated by wage cost saving.

In terms of the endogeneity between technology spillovers and the decision on FDI types, the estimates of ρ in Table 6.5 show strong significance against the null hypothesis in two cases out of eight estimations for technologically advanced economies and in all cases for technologically less advanced economies.20 Consequently, neglecting the endogenous issues may cause biased and inconsistent estimators (Miranda and Rabe-Hesketh, 2006). The estimations in Table 6.5 reveal that *DVFDI* based on the endogenous switching model for both the citing and cited cases provides results similar to those based on the negative binomial model. For *DPVFDI* , the two models also provide similar results, suggesting that more local purchases and more sales in Japan may favor Japanese parent companies with more technology spillovers from

20 Now ρ stands for the correlation between *i* and λ*i* + *ui* in Eq. (6.3), and ρ = λ/ 2(λ2 + 1) where ρ is identified by λ.

**Table6.5**Endogenousswitchingestimatesfortechnologicallyadvancedeconomies

(b) The values in the parentheses

(c) A constant term is included in the estimations

(d)

*DHFDI* , *DVFDI* ,

*DPHFDI* and

*DPVFDI* are dummy variables for the four types of FDI

 are standard errors the host economies. As observed for *DHFDI* and *DPHFDI* , the estimates become insignificant and significantly negative for citing, respectively.

Our findings imply that, for technologically advanced economies, "pure" VFDI is associated with significant technology spillovers even if we control for endogenous issues.

# *6.4.3 Alternative Estimations*

To check the robustness of the basic findings in Sect. 6.4.1, we conduct alternative estimations for Group I countries by adding explanatory variables. We include *PROX* (technological proximity), *CapRatio* (capital ratio), and *Close* (a dummy for industrial classification, which is one for the same sector and zero otherwise). We also include *LDist* (the logarithm of the distance between Japan and host countries) and *Y ear* which captures the changes in citations.

Japanese parent companies and firms in their host countries may increase their citations of each other just because Japanese parent companies and firms in their host countries change the focus of their research activities in ways that bring them "closer" to each other in the technology space (Branstetter, 2006). To control for this issue, we include a measure of technological proximity (*PROX*) in the regression. As suggested by Jaffe (1986) and Branstetter (2006), *PROX* is constructed by

$$PRO\,X\_i = \frac{F\_i F'\_{host,i}}{(F\_i F'\_i)(F\_{host,i} F'\_{host,i})},$$

where *Fi* = ( *f*1*i*, ··· , *fki*) is a vector of the cumulative count of patents obtained by affiliate *i*'s parent firm in *k*th technical area21 and *Fhost*,*i* is a vector of the aggregate count of patens obtained by all firms in the host country in which affiliate *i* is located.

The literature on the role of affiliate ownership in technology spillovers is limited. There are a few studies focused on the correlation between productivity and the ownership of affiliates. Javorcik (2004) and Javorcik and Spatareanu (2008) found that the correlation of productivity with FDI is stronger if the affiliate is only partially, and not fully foreign owned, because joint ownership generates more technology transfer, and wholly owned affiliates employ more sophisticated technology that is out of reach of the average domestic supplier. As indicated by Keller (2010), however, the technology gap may be a key reason for differential effects for wholly versus partially owned affiliates. *CapRatio*, which is the share of affiliate capital owned by Japanese parent companies, is included to test the effects of ownership of affiliates on technology spillovers.

As in Sect. 6.4.1, we use patent citations at the USPTO as the dependent variable and employ the negative binomial model for our estimation. The estimated results

21 We aggregate the US patent classes into 44 fields derived by Schmoch et al.(2003).

are presented in Table 6.6. 22 The coefficients of *PROX* are significantly positive in all cases of citing and cited. These results confirm the findings in Branstetter (2006). The coefficients of *Close* are significantly positive in technology spillovers from host economies to Japanese MNEs, which implies that Japanese parent companies cite more patents of host economies when their affiliates run a business that is the same as or close to that of the parents. However, this is not the case for technology spillovers from Japanese MNEs to host economies, since the coefficients of*Close* are significantly negative. The ownership variable, *CapRatio*, has positive coefficients that are mostly significant, which implies that a higher share of ownership of foreign affiliates by Japanese parent companies tends to facilitate technology spillovers in both directions between Japanese MNEs and their host economies.

The estimates of *HFDI*, *VFDI*, *PHFDI*, and *PVFDI* for the USPTO give similar results to those we observed in Table 6.4, except for *PHFDI* and *PVFDI* in the cited. Specifically, the significantly positive effect of *PVFDI* is not robust for some combinations of explanatory variables.

# **6.5 A Simple Model of FDI and Technology Spillovers**

To explain the observed relationship between the structure of FDI and technology spillovers in the previous section, we develop a partial-equilibrium model of FDI and technology spillovers among developed economies. We consider a world of two countries, home and foreign. Foreign variables are denoted by an asterisk.

We focus on the market for a differentiated good *x*. Consumers in the two countries share the same preference. The preference of the representative consumer takes the standard Dixit-Stiglitz form (Dixit and Stiglitz, 1977):

$$U = \left(\sum\_{j=1}^{N} x\_j^{\alpha}\right)^{1/\alpha}.$$

where *x j* is the consumption of a variety *j* of good *x*, α = 1 − 1/σ, σ > 1 is the elasticity of substitution across varieties, and *N* is the total number of varieties supplied in the home market. We also assume that the market size is the same in the two countries.

The demand for a variety *j* of good *x* in the home country is given by

$$
\alpha\_j = (p\_j^c)^{-\sigma} P^{\sigma - 1} E,
$$

22 Here, we only report the estimated results with full sets of explanatory variables. The results with various combinations of the explanatory variables are available from the corresponding author upon request.

**6.6**Alternativeestimationsfortechnologicallyadvancedeconomies

(b) The values in the parentheses (c) A constant term is included in the estimations

 are standard errors where *E* is the total expenditure on good *x* in the home country (which is the same for the foreign country), *pc j* is the CIF price of variety *j* produced by a home firm and *P* is the price index for the *x* sector goods, which is defined as

$$P = \left[\sum\_{j=1}^{n} (p\_j^c)^{1-\sigma} + \sum\_{k=1}^{n^\*} (p\_k^{\*c})^{1-\sigma}\right]^{1/(1-\sigma)},$$

where *n* and *n*∗ are the numbers of varieties produced by the home firms and the foreign firms, respectively, with *n* + *n*∗ = *N*, and *p*∗*c k* is the CIF price of variety *k* produced by a foreign firm.

In each country, there is one primary factor of production, labor, denoted by *l*. The wage rate, w, is the same in the two countries, i.e., w = w∗ ≡ ω. Labor is immobile across countries.

# *6.5.1 Production and Supply*

Good *x* is differentiated by variety and is supplied by monopolistically competitive firms. Each firm produces one variety. The nationality of a firm is identified by the location of its headquarters.

Good *x* is produced in three sequential stages.23 Intermediate inputs for good *x* are specific to varieties, and hence there is no market for its intermediate inputs. For simplicity we assume away the possibility of outsourcing production of intermediate inputs. All of the three production stages must be conducted in house, although firms can offshore some or all of the production stages by establishing affiliates in the other country.

The first stage of production is simply conducted by using only labor, so that one unit of an intermediate input *m*1 is produced by one unit of labor: *m*1 = *l*1. The quality of the intermediate input may be differentiated. It is θ1 if the first stage is performed in the home country and θ ∗ 1 if it is in the foreign country, where 0 < θ1 ≤ 1 and 0 < θ ∗ 1 ≤ 1. The quality of *m*1 matters when it is used in the second stage of production. The production function of an intermediate product is given by *m*2 = (θ1*m*1) γ *l* 1−γ 2 , where *m*2 stands for an intermediate product in the second stage and γ ∈ (0, 1). In the production function of *m*2, θ1 is replaced by θ ∗ 1 if *m*1 produced in the foreign country is used. Moreover, in the final stage of production, *m*2 and labor are used to produce a variety *j* of good *x*: *x j* = (θ2*m*2) γ *l* 1−γ 3 , where the quality of *m*2 is θ2 (θ ∗ 2 ) if the second stage of production is performed in the home (foreign) country, where 0 < θ2 ≤ 1 and 0 < θ ∗ 2 ≤ 1. A difference in the quality of intermediate inputs *m*1 and *m*2 reflects the technology gap for the particular production stage between the two

23 This means that we consider the "snake" type in the terminology of Baldwin and Venables (2013).

countries.24 Note that the location of the production stage rather than the nationality of firms determines the quality of intermediate inputs. This may be because the information on the technology of producing intermediate inputs is locally spilled over, while it is not spilled over across countries.25 Since the final good *x* is *not* differentiated in quality, the location of the final production stage does not affect characteristics of varieties.

Iceberg transportation costs apply to cross-country shipment of both intermediate inputs and final goods. For one unit of an intermediate input and a final good to arrive at a foreign destination, τ ≥ 1 units of an intermediate input and *t* ≥ 1 units of the final good must be sent, respectively.

When a firm sets up a production stage in the country different from the country in which its headquarters is located, it engages in FDI and incurs an extra fixed cost of Φ/3 units of labor per stage, where Φ > 0 is exogenously given and constant.

Each monopolistically competitive firm chooses both the location of the three production stages and the price for its own variety in each market, taking the price index as given. As is well known, a monopolistically competitive firm charges a constant mark-up over the unit cost of the final good, which is given by *pj* = *Cj* /α, where *pj* is the FOB price of variety *j* and *Cj* is the unit cost of producing variety *j*, which will be shown in detail below.

# *6.5.2 Technology Spillovers*

We introduce the possibility of technology spillovers. Suppose that the quality of intermediate inputs can be upgraded by R&D. We suppose that the outcome of R&D is stochastic. Consequently, the difference in the quality of intermediate inputs arises, depending on whether R&D was successful or not.26

We take up the effects of spillovers from one firm's R&D outcome to other firms' product stage. As is well known, technology spillovers occur from a firm with higher technology to a firm with lower technology. We assume that when a firm uses a better quality of an *i*th-stage intermediate input for the production of the (*i* + 1)th stage, the information on the better quality of an *i*th-stage intermediate input is spilled over to the (*i* + 1)th stage and improves the productivity of R&D for the (*i* + 1)th-stage intermediate input.

Under the above assumptions, the following lemma on spillovers holds.

24 For our purpose of this chapter, we do not need to specify the cause of the technology gap between the two countries. Then, we assume that there is a technology gap between the two countries.

25 This is different from technology spillovers that we will discuss below.

26 The purpose of our analysis in this section is to investigate the relationship between types of FDI and technology spillovers. For this, we want to show how the technology gap between the two countries is related to FDI types and how it is also related to technology spillovers. But, we do not need to specify the details of R&D and the production in the next period after spillovers occur.

**Lemma 6.1** *Technology spillovers occur only if the ith and the* (*i* + 1)*th stages are located in different countries.*

The reason is straightforward. If the *i*th and the (*i* + 1)th stages are located in the same country, then all firms produce the same quality of the *i*th-stage intermediate inputs and use them at the (*i* + 1)th stage. Thus, there is no scope for technology spillovers.

One may think that technology spillovers in our model are associated with imports of intermediate inputs with better quality. However, since each intermediate input is specialized to each variety in our model, inter-firm trade of intermediate inputs does not occur. Moreover, we can argue that technology spillovers through FDI are stronger than those through imports of intermediate inputs, because FDI involves various activities more than just the transaction of intermediate inputs.

# *6.5.3 FDI and Technology Spillovers: HFDI*

We next analyze how firms locate production stages and how international technology spillovers are associated with FDI.

We define horizontal and vertical FDI in the following way. If a firm conducts the final stage in both the home and the foreign countries, it engages in HFDI. On the other hand, if a firm conducts either or both of the first two stages in the country that is different from the country in which its headquarters is located without conducting the final stage in that country, it engages in VFDI.

We focus on the representative firm whose headquarters is located in the home country. We call it "the home firm." All firms with the same nationality behave in the same way.

We first look at HFDI.We denote the combination of locations for three production stages by three capital letters. For example, if the first stage is located in the home country, the second stage is in the foreign country, and the final stage is in the home country, then we denote this combination by *HFH*. Then, the possible patterns of HFDI for the home firm are {*FFF*, *HFF*, *HHF*, *FHF*}.

The unit cost function to serve the foreign market by the pattern of *FFF* is given by

$$C\_{FFF}(\omega; \theta\_1^\*, \theta\_2^\*) = B\left( (\theta\_1^\*)^{-\mathcal{V}} (\theta\_2^\*)^{-1} \right)^{\mathcal{V}} \omega,\tag{6.5}$$

where *B* ≡ {γ γ (1 − γ )1−γ }−(1+γ ) is a constant. Similarly, the unit cost functions to serve the foreign market by the patterns of *HFF* and *HHF* are given by, respectively,

$$C\_{HFF}(\omega, \mathfrak{r}; \theta\_1, \theta\_2^\*) = B \left(\mathfrak{r}^\mathcal{V} \theta\_1^{-\mathcal{V}} (\theta\_2^\*)^{-1}\right)^\mathcal{V} \omega,\tag{6.6}$$

$$\mathcal{C}\_{HHF}(\omega,\,\pi;\theta\_1,\theta\_2) = B(\pi \theta\_1^{-\gamma} \theta\_2^{-1})^\vee \omega. \tag{6.7}$$

Moreover, the unit cost function to serve the foreign market by exporting from the home country is

$$C\_{HHH}(\omega;\theta\_1,\theta\_2) = B\left((\theta\_1)^{-\nu}(\theta\_2)^{-1}\right)^{\nu}\omega,\tag{6.8}$$

where the transport costs for the shipment of the final good are not included.

In the analysis of the optimal configuration of production for the foreign market, it turns out that the configuration for the home market does not matter. Thus, we just focus on the profits from supplying to the foreign market. Given the demand for a variety and pricing policy under monopolistic competition, profits of the home firm from supplying a variety to the foreign market by the patterns *FFF*, *HFF*, *HHF*, and *HHH* are given by, respectively,

$$
\pi\_{FFF} = \frac{\alpha^{\sigma - 1} E (P^\*)^{\sigma - 1}}{\sigma} (C\_{FFF})^{1 - \sigma} - \Phi \omega,\tag{6.9}
$$

$$
\pi\_{HFF} = \frac{\alpha^{\sigma - 1} E (P^\*)^{\sigma - 1}}{\sigma} (C\_{HFF})^{1 - \sigma} - \frac{2}{3} \Phi \omega,\tag{6.10}
$$

$$
\pi\_{HHF} = \frac{\alpha^{\sigma - 1} E (P^\*)^{\sigma - 1}}{\sigma} (C\_{HHF})^{1 - \sigma} - \frac{1}{3} \Phi \omega,\tag{6.11}
$$

$$
\pi\_{HHH} = \frac{\alpha^{\sigma - 1} E (P^\*)^{\sigma - 1}}{\sigma} (tC\_{HHH})^{1 - \sigma}. \tag{6.12}
$$

Note that since *FHF* is dominated by *HHF* and *HFF* for θ*i* = θ ∗ *i* = 1 for *i* = 1, 2, we can exclude *FHF* from our analysis. Then, the following lemma is obtained.

**Lemma 6.2** *Suppose that* θ*i* = θ ∗ *i* = 1*, i* = 1, 2*. The home firm still has an incentive to engage in HFDI if t* > τ γ *and* Φ *is sufficiently low.*

**Proof** It is sufficient to prove that

$$\pi\_{HHH} \le \max\left\{ \pi\_{FFF}, \ \pi\_{HFF}, \ \pi\_{HHF} \right\} \tag{6.13}$$

holds when θ*i* = θ ∗ *i* = 1, *i* = 1, 2. Since we focus on the incentive for one firm to choose a particular production configuration and since an individual firm takes the price index as given, we treat the price index as fixed. Then, define *A* ≡ ασ−1*E*(*P*∗)σ−1*B*1−σ /σ and φ ≡ Φω/*A*. Substitute Eqs. (6.9)–(6.12) and Eqs. (6.5)– (6.8) into Eq. (6.13) and rearrange terms to yield

$$
\phi \phi^{\sigma^{-1}} \le \max \left\{ \left( 1 - t^{1-\sigma} \right), \left( \tau^{\nu^2 (1-\sigma)} - t^{1-\sigma} + \frac{1}{3} \phi \omega^{\sigma^{-1}} \right),
$$

$$
\left( \tau^{\mathcal{V} (1-\sigma)} - t^{1-\sigma} + \frac{2}{3} \phi \omega^{\sigma^{-1}} \right) \right\}, \tag{6.14}
$$

defining the upper bound of Φ. Equation (6.14) is satisfied if

$$\begin{aligned} \phi \omega^{\sigma - 1} &\le 1 - t^{1 - \sigma}, \\ \phi \omega^{\sigma - 1} &\le \frac{3}{2} (\tau^{\nu^2 (1 - \sigma)} - t^{1 - \sigma}), \quad \text{or} \\ \phi \omega^{\sigma - 1} &\le 3 (\tau^{\nu (1 - \sigma)} - t^{1 - \sigma}) \end{aligned}$$

holds, which requires that Φ = φ*A*/ω is sufficiently low for the above inequalities to hold. Since γ ∈ (0, 1) and σ > 1, it holds that τ γ (1−σ ) < τ γ 2(1−σ ) < 1 for τ > 1. Thus, we need τ γ (1−σ ) > *t* 1−σ or *t* > τ γ . *Q.E.D.*

This lemma implies that even without technology differences, firms have an incentive to engage in HFDI if transportation costs for the cross-border shipment of the final goods are sufficiently high and the additional fixed costs for FDI are sufficiently low. This is just the standard motive for the HFDI. Lemma 6.2 yields the following proposition.

#### **Proposition 6.1** *HFDI does not necessarily induce technology spillovers.*

**Proof** From Lemma 6.2, HFDI occurs even in the case of no technology difference. If the technology level is the same across countries, then there is no possibility of technology spillovers. *Q.E.D.*

When the production configuration is *HFF*, the first and the second stages are located in different countries. Thus, Lemma 6.1 suggests that technology spillovers may occur. However, the proof of Lemma 6.2 shows that *HFF* can be the optimal configuration even in the absence of a technology gap. If this is the case, technology spillovers do not occur in the case of *HFF*.

# *6.5.4 FDI and Technology Spillovers: VFDI*

We turn to the case of VFDI. In this case, the home firm conducts the final stage only in the home country, so that the possible patterns of VFDI are {*FHH*, *FFH*, *HFH*}. We obtain the following lemma.

**Lemma 6.3** *Suppose that* θ*i* = θ ∗ *i* = 1 *for i* = 1, 2*. Then, the home firm has no incentive to engage in VFDI as long as* τ *and* Φ *are positive.*

**Proof** By definition, the home firm conducts the final stage in the home country for any type of VFDI. We need to examine the supply to the home market. Then, compared to the national integration (i.e., the pattern of *HHH*), FDI in either or both of the first two stages incurs additional transportation costs and fixed costs of FDI. Thus, in the absence of technology advantage in the foreign production, VFDI always increases production costs as long as τ and Φ are positive. *Q.E.D.*

This lemma implies that in the absence of a factor cost differential, firms have no incentive to engage in VFDI without a technology difference as long as there are positive transportation costs for intermediate inputs and FDI requires additional fixed costs.

Now, the unit cost functions for the home firm in VFDI are given by

$$\mathcal{C}\_{HFH}(\omega,\tau;\theta\_1,\theta\_2^\*) = \mathcal{B}\left(\tau^{1+\gamma}\theta\_1^{-\gamma}(\theta\_2^\*)^{-1}\right)^{\mathcal{V}}\omega,\tag{6.15}$$

$$C\_{FFH}(\omega,\,\tau;\theta\_1^\*,\,\theta\_2^\*) = B\left(\tau(\theta\_1^\*)^{-\gamma}(\theta\_2^\*)^{-1}\right)^{\gamma}\omega,\tag{6.16}$$

$$\mathcal{C}\_{FHH}(\omega,\mathfrak{r};\theta\_1^\*,\theta\_2) = B(\mathfrak{r}^\mathcal{V}(\theta\_1^\*)^{-\mathcal{V}}\theta\_2^{-1})^\mathcal{V}\omega.\tag{6.17}$$

In order to simplify the analysis, we assume that *t* = 1, so that firms have no incentive to locate the final stage of production for the foreign market separately from that for the home market. It is then shown that as long as the final stage is performed in a single location, firms have no incentive to conduct either or both of the first two stages at more than one location. Given this, profits of the home firm in production configurations *k* = *HFH*, *FFH*, *FHH*, and *HHH* are given by, respectively.

$$
\Pi\_{HFH} = \frac{\alpha^{\sigma - 1} E\{ (P)^{\sigma - 1} + (P^\*)^{\sigma - 1} \}}{\sigma} (C\_{HFH})^{1 - \sigma} - \frac{1}{3} \Phi \omega,\tag{6.18}
$$

$$
\Pi\_{FFH} = \frac{\alpha^{\sigma - 1} E\{ (P)^{\sigma - 1} + (P^\*)^{\sigma - 1} \}}{\sigma} (C\_{FFH})^{1 - \sigma} - \frac{2}{3} \Phi \omega,\tag{6.19}
$$

$$\Pi\_{FHH} = \frac{\alpha^{\sigma - 1} E\{ (P)^{\sigma - 1} + (P^\*)^{\sigma - 1} \}}{\sigma} (C\_{FHH})^{1 - \sigma} - \frac{1}{3} \Phi \omega,\tag{6.20}$$

$$
\Pi\_{HHH} = \frac{\alpha^{\sigma - 1} E\{ (P)^{\sigma - 1} + (P^\*)^{\sigma - 1} \}}{\sigma} (\mathcal{C}\_{HHH})^{1 - \sigma}, \tag{6.21}
$$

where Π*k* denotes the home firm's total profits when it engages in VFDI with configuration *k*. Then, we can prove the following proposition on the relationship between VFDI and technology spillovers.27

**Proposition 6.2** *VFDI of the configuration H F H by the home firm may induce technology spillovers in both directions. Other types of VFDI can induce technology spillovers in one direction.*

**Proof** First, we derive the conditions for *HFH* to be chosen. Define *G* ≡ ασ−1*E*{(*P*)σ−1 + (*P*∗)σ−1}*B*1−σ /σ and ψ ≡ Φω/*G*. It is shown from Eqs. (6.8), (6.15), (6.18), and (6.21) that Π*HFH* ≥ Π*HHH* holds if and only if

$$(\theta\_2^\*/\pi^{1+\gamma})^{\chi(\sigma-1)} - (\theta\_2)^{\chi(\sigma-1)} \ge \frac{\alpha^{\sigma-1}\psi}{\Im(\theta\_1)^{\chi^2(\sigma-1)}}.\tag{6.22}$$

It is also shown from Eqs. (6.15), (6.16), (6.18), and (6.19) that Π*HFH* ≥ Π*FFH* holds if and only if

27 To simplify the analysis, we assume that firms treat technology spillovers as pure externality and do not take the effects of technology spillovers into account in their decision on production locations. Including technology spillovers in firms' profits will not alter the qualitative results.

$$(\theta\_1^\*)^{\boldsymbol{\uprho}^2(\sigma-1)} - (\theta\_1/\mathfrak{r})^{\boldsymbol{\uprho}^2(\sigma-1)} \le \frac{\boldsymbol{\uprho}^{\sigma-1} \boldsymbol{\uprho}}{\mathfrak{Z}(\theta\_2^\*/\mathfrak{r})^{\boldsymbol{\uprho}(\sigma-1)}}\tag{6.23}$$

and from Eqs. (6.15), (6.17), (6.18), and (6.20) that Π*HFH* ≥ Π*FHH* holds if and only if

$$(\theta\_1)^{\mathcal{V}}(\theta\_2^\*/\mathfrak{r}) \ge (\theta\_1^\*)^{\mathcal{V}} \theta\_2. \tag{6.24}$$

Since θ2 < θ ∗ 2 is necessary for Eq. (6.22) to hold, Lemma 6.1 implies that technology spillovers in R&D occur for *m*2 from the foreign country to the home country. On the other hand, θ1 < θ ∗ 1 can be consistent with the inequality Eq. (6.23) if the gap between θ1 and θ ∗ 1 is sufficiently small, τ is sufficiently low, and ψ is sufficiently high. Thus, the technology spillovers in R&D for *m*1 from the home country to the foreign country may not occur. On the other hand, if θ1 > θ ∗ 1 holds, however, they do occur. It is easy to prove that θ1 > θ ∗ 1 and θ2 < θ ∗ 2 satisfy Eq. (6.24) and that θ1 < θ ∗ 1 and θ2 < θ ∗ 2 can satisfy Eq. (6.24) if θ ∗ 1 ≤ (θ ∗ 2 /τ θ2)1/γ θ1 holds. Moreover, in order for Eqs. (6.22) and (6.23) to hold simultaneously, we need

$$\left( (\theta\_1^\*)^\vee (\theta\_2^\*/\mathfrak{r}) \right)^{\chi(\sigma - 1)} + ((\theta\_1)^\vee \theta\_2)^{\chi(\sigma - 1)} \le 2 \left( (\theta\_1/\mathfrak{r})^\chi (\theta\_2^\*/\mathfrak{r}) \right)^{\chi(\sigma - 1)},$$

which can be consistent with Eq. (6.24).

We next derive the conditions for *FHH* to be chosen. It is shown from Eqs. (6.8), (6.17), (6.20), and (6.21) that Π*FHH* ≥ Π*HHH* holds if and only if

$$(\theta\_1^\*/\tau)^{\chi^2(\sigma-1)} - (\theta\_1)^{\chi^2(\sigma-1)} \ge \frac{\alpha^{\sigma-1} \psi}{\Im(\theta\_2)^{\chi(\sigma-1)}}.\tag{6.25}$$

It is also shown from Eqs. (6.16), (6.17), (6.19), and (6.20) that Π*FHH* ≥ Π*FFH* holds if and only if

$$(\theta\_2^\*/\mathfrak{r})^{\mathbb{Y}^{(\sigma-1)}} - (\theta\_2/\mathfrak{r}^\mathbb{Y})^{\mathbb{Y}^{(\sigma-1)}} \le \frac{\omega^{\sigma-1} \mathbb{V}}{\mathfrak{Z}(\theta\_1^\*)^{\mathbb{Y}^2(\sigma-1)}}\tag{6.26}$$

and from Eqs. (6.15), (6.17), (6.18), and (6.20) that Π*FHH* ≥ Π*HFH* holds if and only if

$$(\theta\_1)^{\mathcal{V}}(\theta\_2^\*/\mathfrak{r}) \le (\theta\_1^\*)^{\mathcal{V}} \theta\_2. \tag{6.27}$$

In order for Eqs. (6.25) and (6.26) to hold simultaneously, we need

$$2\left( (\theta\_1^\*)^\vee(\theta\_2^\*/\mathfrak{r}) \right)^{\chi(\sigma-1)} + \left( (\theta\_1)^\mathcal{V}\theta\_2 \right)^{\mathcal{V}(\sigma-1)} \le 2\left( (\theta\_1^\*/\mathfrak{r})^\mathcal{V}\theta\_2 \right)^{\mathcal{V}(\sigma-1)},$$

which can be consistent with Eq. (6.27). Since θ1 < θ ∗ 1 is necessary for Eq. (6.25) to hold and satisfies Eq. (6.27), this implies from Lemma 6.1 that technology spillovers in R&D occur for *m*1 from the foreign country to the home country.

We finally derive the conditions for *FFH* to be chosen. It yields from Eqs. (6.8), (6.16), (6.19), and (6.21) that Π*FFH* ≥ Π*HHH* holds if and only if

$$\left( (\theta\_1^\*)^\vee (\theta\_2^\*/\tau) \right)^{\nu(\sigma - 1)} - (\theta\_1^\vee \theta\_2)^{\nu(\sigma - 1)} \ge \frac{2\alpha^{\sigma - 1} \psi}{3}. \tag{6.28}$$

Also, from Eqs. (6.16), (6.17), (6.19), and (6.20) it holds that Π*FFH* ≥ Π*FHH* if and only if

$$(\theta\_2^\*/\tau)^{\mathbb{Y}^{(\sigma-1)}} - (\theta\_2/\tau^{\mathbb{Y}})^{\mathbb{Y}^{(\sigma-1)}} \ge \frac{\alpha^{\sigma-1} \psi}{\Im(\theta\_1^\*)^{\mathbb{Y}^2(\sigma-1)}}\tag{6.29}$$

and from Eqs. (6.15), (6.16), (6.18), and (6.19) it holds that Π*FFH* ≥ Π*HFH* if and only if

$$(\theta\_1^\*)^{\mathcal{Y}^2(\sigma - 1)} - (\theta\_1/\mathfrak{r})^{\mathcal{Y}^2(\sigma - 1)} \ge \frac{\omega^{\sigma - 1} \psi}{\Im(\theta\_2^\*/\mathfrak{r})^{\mathcal{Y}(\sigma - 1)}}.\tag{6.30}$$

Since θ2 < θ ∗ 2 is necessary for Eq. (6.29) to hold and satisfies Eq. (6.28), this implies from Lemma 6.1 that technology spillovers occur in R&D for *m*2 from the foreign country to the home country. *Q.E.D.*

This proposition shows that if home firms engage in VFDI in the configuration of *HFH*, technology spillovers occur from the foreign country to the home country. Technology spillovers in the opposite direction may also occur but do not necessarily. For other types of VFDI, we expect that technology spillovers occur only in one direction from the foreign country to the home country.

Note that if the wage rates between the two countries are sufficiently different, then firms may have an incentive to engage in VFDI even in the absence of a technology difference, but VFDI does not induce international technology spillovers. This situation corresponds to the typical North-South VFDI rather than the North-North VFDI. Then, this never undermines our argument on the relationship between VFDI and technology spillovers. That is, North-South VFDI does not necessarily involve international technology spillovers.

# **6.6 Conclusion**

In this chapter, we have investigated how the structure of MNEs' activities affects technology spillovers between MNEs and their host countries by using detailed firmlevel data on Japanese MNEs and patent citation data. We propose new specifications of FDI by information on sales and purchases of foreign affiliates of MNEs. We define pure HFDI as FDI with a high share of both purchases of intermediate inputs and sales of outputs in the local market and pure VFDI as FDI with a high share of transactions (i.e., both purchases of intermediate inputs and sales of outputs) with the home country. In addition, partially horizontal and partially vertical FDI are defined.

Our estimation results reveal that when a technologically advanced country hosts Japanese MNEs, an increase in the degree of pure VFDI has a significantly positive effect on technology spillovers as measured by patent citations in both directions between the host country and Japanese MNEs. In contrast, pure HFDI has no significant effect or significantly negative effects on technology spillovers in either direction. We also find that VFDI by Japanese firms to technologically advanced countries is not based on factor price differentials.

To explain the mechanism for the observed relationship between the structure of FDI and technology spillovers, we have developed a simple model of FDI and technology spillovers, in which a good is produced in multiple stages. Our model reveals that VFDI among technologically advanced economies would be associated with international technology spillovers, while HFDI does not necessarily induce technology spillovers.

The results indicate that technology spillovers from FDI occur among technologically advanced economies. In particular, VFDI plays an important role in technology spillovers. We come to the conclusion that technologically advanced countries can gain knowledge flow from MNEs' activities both as the home country and as the host country when FDI involves the geographical fragmentation of the production process.

Another finding of this chapter is that when the host country is a technologically less advanced country, any types of FDI do not have positive effects on technology spillovers. One possible explanation for this finding is that we have focused on technology spillovers measured by patent citations. Since patent applications are made to be counted as "spillovers," indigenous firms in technologically less advanced countries do not largely benefit from technology spillovers in our definition. Another possible explanation for the finding is that it reflects the stringency of intellectual property right (IPR) protection in host countries. Branstetter et al. (2006) and Wakasugi and Ito (2009) find that the stronger protection of IPR in host countries has a positive effect on technology transfer from parent firms to their foreign affiliates. Nagaoka (2009) also finds a positive effect of stronger patent protection on expanding the scope of the recipients of technology transfer. Taking these empirical findings into account, we notice that the weaker protection of IPR in developing countries generally hinders technology spillovers from FDI in our measurement.

Since our findings are based on Japanese MNEs' data, we suggest testing whether our findings could be applicable to other countries' MNEs through examining detailed data on MNEs in other countries.

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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# **Chapter 7 Do Deep Regional Trade Agreements Enhance International Technology Spillovers? Depth, Breadth, and Heterogeneity**

# **7.1 Introduction**

In Chap. 5, we examine the relationship between bilateral trade patterns and international technology spillovers. In Chap. 6, we analyze how horizontal and vertical foreign direct investment (FDI) of multinational enterprises (MNEs) affects technology spillovers between themselves and firms in host countries. Both chapters analyze the issues from theoretical and empirical points of view. Each chapter shows that international trade or FDI is an important channel of international technology spillovers, but the effect on them is heterogeneous, depending on the type of trade patterns or the structure of FDI. In both chapters we measure technology spillovers using patent citation data.

In this chapter, we shed light on the role of deep regional trade agreements (RTAs) in international technology spillovers. As is well known in the field of international economics, trade creation and trade diversion are the most popular effects of signing an RTA (Viner 1950). The dynamic effects of RTAs such as the effects on technology adoption and technology diffusion have been examined by relatively recent studies (e.g., Bustos 2011; Das and Andriamananjara 2006; Ederington andMcCalman 2008; Schiff and Wang 2003).

As Jinji et al. (2019a) argue, RTAs may facilitate cross-border technology spillovers both directly and indirectly. The indirect effects of RTAs on cross-border technology spillovers are through international trade in goods and FDI. Since RTAs, particularly deep RTAs with investment provisions, stimulate international trade and FDI, they can facilitate cross-border technology spillovers through an increase in bilateral trade and FDI. A more direct link between RTAs and cross-border technology spillovers may arise from the deep nature of recent RTAs. As examined in Chap. 2, recent RTAs tend to be deeper than old ones. Many RTAs include provisions related to technology, such as trade-related aspects of intellectual property rights (TRIPS), intellectual property right (IPR), innovation policy, and research and development (R&D). Moreover, some RTAs explicitly include provisions that stimulate

N. Jinji et al., *Deep Integration, Global Firms, and Technology Spillovers*, Advances in Japanese Business and Economics 26, https://doi.org/10.1007/978-981-16-5210-3\_7

technology spillovers. For example, the United States-Peru Free Trade Agreement includes a provision on the promotion of innovation and technological development (Article 16.12), which encourages the engagement in collaborative scientific research projects and transfer of technology.

Using patent citations as a proxy for technology spillovers, Jinji et al. (2019a) empirically examine whether RTAs actually enhance international technology spillovers. They use panel data on patent citations at the USPTO for 114 countries/economies for the period 1991–2007. The focus of their study is on whether the depth of RTAs that is measured by the legal enforceability information on the policy areas in the WTO-X group matters for technology spillovers among member countries/economies of RTAs.1 They show that the depth of integration actually influences technology spillovers. Interestingly, they find that deeper integration in a broad sense has a greater impact on technology spillovers than do technology-related policy areas. They classify 26 out of 38 policy areas in the WTO-X group into three subsets by utilizing the technique of the factor analysis. Then, they construct a new measure of the nature of deep RTAs by calculating the ratio of the sum of the points of legally enforceable WTO-X policy areas included in each subset that are covered by RTAs to the total points of all legally enforceable WTO-X policy areas covered by the same RTAs. They interpret the first subset as the set of policy areas representing a healthy economic environment in a general sense and the second subset as the set of policy areas representing basic social and economic conditions. The WTO-X policy areas included in these two subsets are not directly related to technology spillovers. On the other hand, the third subset is interpreted as the set of policy areas directly related to competition and technology. Their finding is that the estimated coefficients of the indexes for the first two subsets tend to be larger than those for the third subset.

The analysis in this chapter extends and enriches the study by Jinji et al. (2019a) in several ways. First, as in Jinji et al. (2019a) and Chaps. 5 and 6 of this book, we use patent citations as a proxy of technology spillovers.2 We extend the observation period until 2015. Thus, our observation period is 25 years from 1991 to 2015. The eight-year extension of the observation period from Jinji et al. (2019a) is important because more recent RTAs tend to be much deeper. Second, when we estimate the model, we include directional country-pair fixed effects, which are not included in the analysis in Jinji et al. (2019a), as well as time-varying citing and cited countries fixed effects. As we discuss below, the inclusion of country-pair fixed effects is particularly important to address the endogeneity issue (Baier and Bergstrand 2007). Third, we include intra-national citation data in the analysis, which are not included in the estimations of Jinji et al. (2019a). This is essential to measure the effect of RTAs, as argued by Dai et al. (2014). Finally, although Jinji et al. (2019a) restrict the measurement of deep RTAs to the WTO-X policy areas, we include policy areas in

1 See Chap. 2 for the details of the policy areas in WTO-extra (WTO-X) and WTO-plus (WTO+) groups.

2 This approach was pioneered by Jaffe et al. (1993) and has been employed by a number of studies (e.g., Branstetter 2006; Cappelli and Montobbio 2020; Hall et al. 2001; Haruna et al. 2010; Jaffe and Trajtenberg 1999; Jinji et al. 2013, 2015; Li 2014; MacGarvie 2006; Maurseth and Verspagen 2002; Murata et al. 2014; Peri 2005).

both WTO+ and WTO-X groups and employ the classification of the policy areas in terms of the depth and breadth of RTAs proposed by Limão (2016).3

In this chapter, we first estimate the effects of the depth and breadth indexes of RTAs on bilateral patent citations. With regard to the depth of RTAs, we use the indexes in the fields of tariffs, non-tariff barriers (NTBs), the behind-the-border policies (BBPs), and other policies (OPs). On the other hand, as for the breadth of RTAs, we construct indexes for services, technology, investment/capital, labor, and noneconomic policies (NEPs) fields. Throughout this chapter, we employ the Poisson pseudo-maximum likelihood (PPML) estimator with time-varying citing country fixed effects, time-varying cited country fixed effects, and citing-cited-pair fixed effects, which is recommended in the gravity literature (Anderson and van Wincoop 2003; Head and Mayer 2014; Santos Silva and Tenreyro 2006, 2011; Yotov et al. 2016).

We next analyze the heterogeneous effects of individual RTAs. In the trade literature, a number of previous studies find the heterogeneous effects of RTAs on trade in goods by the type of agreements (Roy 2010; Vicard 2009) and by the characteristics of country-pairs and particular countries (Baier et al. 2019; Behar and Cirera-i Crivillé 2013; Cheong et al. 2015; Vicard 2011). Although Jinji et al. (2019a) investigate the heterogeneous effects of RTAs on technology spillovers by the type of agreements (i.e., customs union versus free trade agreement) and by the characteristics of country-pairs (i.e., North and South combinations), the heterogeneous effects of individual RTAs on technology spillovers have not been investigated. As major sources of international technology spillovers, we focus on RTAs signed by the United States and European countries, such as the North American Free Trade Agreement (NAFTA), the European Community (EC)/European Union (EU), RTAs with the United States, and FTAs with the EC/EU.

The main findings of this chapter are as follows. First, we find that the estimated coefficient on the RTA dummy is positive and highly significant, which confirms the finding by Jinji et al. (2019a) and even strengthens their finding by showing that the significantly positive effect of the RTA dummy is kept even if directional countrypair fixed effects are included. Second, we find that the coefficients on the depth indexes other than the OP index are positive and highly significant, suggesting that technology spillovers among signatories of RTAs are stronger as RTAs are deeper. However, when each of the depth indexes is estimated together with the RTA dummy, the coefficient on the depth index becomes insignificant, which suggests that the additional effect of including the depth policy areas may be small or even negligible. A possible reason for the insignificant additional effect of the depth policy areas is that since most of the recent RTAs tend to become deeper and deeper, the estimated average effect of the RTA dummy partly captures the impact of the depth policy areas on technology spillovers. The problem of multicollinearity may affect the result.

Third, the estimated coefficients on the breadth indexes are all positive and statistically significant, suggesting that technology spillovers among members of RTAs become stronger as RTAs cover more policy areas with legal enforceability in each of

3 See Chap. 2 for the details of the depth and breadth of RTAs by Limão (2016).

the breadth fields. However, similar to the depth indexes, when the breadth indexes are estimated with the RTA dummy, all of them lose their significance. Therefore, the additional effects of including legally enforceable provisions in services, technology, investment/capital, labor, or NEP fields on cross-border technology spillovers may be small or insignificant. Since some of the specific provisions in these fields actually strengthen regulations, this result implies that the same level of the index in each breadth field may include both the positive and negative effects on technology spillovers.

Finally, with regard to the heterogeneous effects of the RTAs signed by the United States and the EU, we find that the impact of NAFTA on bilateral technology spillovers is particularly strong. By contrast, the impacts of the EU and EU enlargement on technology spillovers are weak or not positive. On the other hand, both the RTAs with the United States and the FTAs with the EU enhance technology spillovers. Thus, the results suggest that signing RTAs with technologically advanced countries, such as the United States and major European countries, is effective in stimulating cross-border technology spillovers.

The remainder of the chapter is organized as follows. In Sect. 7.2, we explain the empirical framework. In Sect. 7.3, we describe the data employed in our empirical analysis. In Sect. 7.4, we present our empirical results. Section 7.5 concludes the chapter.

# **7.2 Empirical Framework**

In this section, we explain our empirical framework to examine the effects of deep RTAs on international technology spillovers.

# *7.2.1 The Depth and Breadth Indexes*

We first explain how we measure the contents of deep RTAs. As explained in Chap. 2, Horn et al. (2010) identify 52 policy areas covered by RTAs and classify them into two groups: WTO-plus (WTO+) and WTO-extra (WTO-X) (See Table 2.1 in Chap. 2 for the details of the WTO+ and WTO-X policy areas). Then, Limão (2016) proposes recategorizing the WTO+ and WTO-X policy areas from the viewpoints of the depth and breadth of RTAs. The depth of RTAs is measured by four fields: (a) import tariffs, (b) NTBs, (c) BBPs, and (d) OPs. On the other hand, the breadth of RTAs is measured by five fields: (a) services, (b) technology, (c) investment/capital, (d) labor, and (e) NEPs. Which policy areas are classified into each field of the depth and breadth measures is shown in Table 2.2 in Chap. 2.

For each field of the depth measure, we construct the following index:

$$RTA\\_Depth\\_d\\_index\_{ijt} = \frac{\sum\_{p \in \Theta^d} Max\\_LE^p\_{ijt}}{2K^d},\tag{7.1}$$

where *Depth*\_*d* ∈ {*T ari f f*, *NTB*, *BBP*, *O P*} indicates the fields of the depth measure, *d* is the set of policy areas that consist of the field *d* ∈ {*T ari f f*, *NTB*, *BBP*, *O P*}, *Max*\_*L E p ijt* ∈ {0, 1, 2} is the maximum point of the LE index of policy area *p* in all RTAs of which countries *i* and *j* are members in year *t* if they sign any common RTAs, and *Kd* is the number of policy areas in field *d*. Note that 2*Kd* in the denominator of Eq. (7.1) is the total points of the LE index of policy areas in field *d* if all LE indexes are equal to two in field *d*. Thus, *RT A*\_*Depth*\_*d*\_*indexijt* takes the value between zero and one.

Similar to the depth measure, we construct the following index for each field of the breadth measure:

$$RTA\\_Breadh\\_c\\_index\_{ijt} = \frac{\sum\_{p \in \Phi^c} Max\\_LE\_{ijt}^p}{2K^c},\tag{7.2}$$

where *Breadth*\_*c* ∈ {*GAT S*, *T ech*, *Cap*, *Lab*, *non*−*E P*} indicates the fields of the breadth measure, *c* is the set of policy areas that consist of the field *c* ∈ {*GAT S*, *T ech*, *Cap*, *Lab*, *non*−*E P*}, *Max*\_*L E p ijt* ∈ {0, 1, 2} is the maximum point of the LE index of policy area *p* in all RTAs of which countries *i* and *j* are members in year *t* if they sign any common RTAs, and *Kc* is the number of policy areas in field *c*.

# *7.2.2 RTA Dummies*

Let us next explain our RTA dummies. In addition to the usual RTA dummy, we use various dummies to capture the heterogeneous effects of individual RTAs by decomposing the RTA dummy.

First of all, *RT A dummyijt* is a dummy variable that takes the value of unity if countries *i* and *j* both belong to the same RTA in *t* and zero otherwise. Next, we decompose the *RT A dummyijt* in two ways. The first decomposition is to take into account the NAFTA and the EC/EU. That is, we set *N AFT Aijt* , which is a dummy variable that takes the value of unity if countries *i* and *j* both belong to the NAFTA in *t* and zero otherwise. Similarly, *EUijt* is a dummy variable that takes the value of unity if they both belong to the EC/EU in *t* and zero otherwise. Furthermore, we set *Other*\_*RT Aijt* , which is a dummy variable that takes the value of unity if they both belong to the same RTA other than the NAFTA and the EC/EU in *t* and zero otherwise.

Next, our second decomposition of the RTA dummy is to take into account RTAs signed by the United States other than the NAFTA and EU enlargement from 1995. We construct *RT A*\_w*ith*\_*U Sijt* , which is a dummy variable that takes the value of unity if either of countries *i* and *j* is the United States and the two countries belong to the same RTA other than the NAFTA in *t* and zero otherwise. *EU*\_*Enlargeijt* is a dummy variable that takes the value of unity if at least one of the two countries (*i* and *j*) becomes a member of the EU in or after 1995 and both they are the members of the EU in year *t* and zero otherwise. Moreover, *FT A*\_w*ith*\_*EUijt* is a dummy variable that takes the value of unity if either of countries *i* and *j* is a member of the EC/EU in *t* and the other country is not a member of the EC/EU in *t* but countries *i* and *j* belong to the same FTA in *t* and zero otherwise. Finally, *non*\_*U S*\_*EU*\_*RT Aijt* is a dummy variable that takes the value of unity if they belong to the same RTA in *t* and *N AFT Aijt* = *EUijt* = *RT A*\_w*ith*\_*U Sijt* = *EU*\_*Enlargeijt* = *FT A*\_w*ith*\_*EUijt* = 0 holds for them in *t* and zero otherwise.4

In addition, we use the GATT/WTO dummy. That is, *WT O dummyijt* is a dummy variable that takes the value of unity if both of countries *i* and *j* are GATT/WTO members in *t* and zero otherwise.

# *7.2.3 Empirical Model and Strategy*

We next specify a model of knowledge flows between countries and discuss our empirical strategy to estimate the model.

We first measure technology spillovers from country *j* to country *i* at time *t* by extending the framework proposed by Jaffe et al. (1993), Jaffe and Trajtenberg (1999), and Peri (2005). Let Φ*ijt* be the actual flow of knowledge from country *j* to country *i* at time *t* in terms of the actual effects on the research output in country *i*. Then, we assume that Φ*ijt* depends on both the knowledge stock in country *j* at *t*, *K jt* , and the research ability of firms in country *i* at *t*, *Qit* , as follows:

$$\Phi\_{ijt} = (\mathcal{Q}\_{it})^{\alpha\_1} (\tilde{\phi}\_{ijt} K\_{jt})^{\alpha\_2},\tag{7.3}$$

where φ˜*ijt* ∈ [0, 1] is the degree of accessibility for firms in country *i* to the knowledge stock in country *j* at *t*. Thus, (φ˜*ijt K jt*) is the effective unit of country *j*'s knowledge stock from the perspective of firms in country *i*. Parameters α1 and α2 are both positive. For notational simplicity, we relabel φ˜*ijt* as φ*ijt* ≡ (φ˜*ijt*)α2 .

The degree of accessibility for firms in country *i* to the knowledge stock in country *j* at *t*, φ*ijt* , depends on the *economic distance* between countries *i* and *j*, which is affected by not only bilateral geographical distance but also other potential*resistance factors* (Peri 2005, p. 310). The resistance factors include both time-invariant and time-varying country-pair specific characteristics. The time-invariant country-pair specific characteristics include bilateral geographical distance, the use of a common

4 Note that in the second decomposition we do not set a dummy variable for the country-pairs of which both countries were the members of the EC/EU before 1995. However, with the use of country-pair fixed effects in our estimations, this omission of the dummy variable for the old members of the EC/EU does not affect the estimations, because this dummy always takes the value of unity during our observation period.

language, and so on. On the other hand, the time-varying country-pair specific characteristics, which affect the economic distance between two countries, are typically represented by the membership of the same RTA and that of GATT/WTO.

Let *xijt* be a set of bilateral country characteristics. Then, we have

$$\begin{split} \phi\_{ijt} &= \phi(\mathbf{x}\_{ijt}) \\ &= e^{\xi\_{ij}} e^{\mathcal{V}\_{1}(RTA \text{ } \textit{dummy}\_{ijt})} e^{\mathcal{V}\_{2}(DRTA \text{ } \textit{index}\_{ijt})} \\ &\times e^{\mathcal{V}\_{3}(WTO \text{ } \textit{dummy}\_{ijt})}, \end{split} \tag{7.4}$$

where ξ*i j* denotes the time-invariant country-pair specific characteristics and *DRT A*\_*indexijt* denotes various indexes of the depth and breadth of RTAs.

Since Φ*ijt* , *Qit* , and *K jt* in Eq. (7.3) are not directly observable, we need to use some proxies for those variables in our analysis. We use *Cijt* , the number of patent citations made by the patents of country *i* to those of country *j* at time *t*, as a proxy for Φ*ijt* . Moreover, following the gravity literature (Head and Mayer 2014; Yotov et al. 2016), we capture *Qit* by the time-varying citing country fixed effects, μ˜*it* , and *K jt* by the time-varying cited country fixed effects, ν˜*jt* . According to Peri (2005), we assume the following relationship between *Cijt* and Φ*ijt* :

$$C\_{ijt} = \tilde{\lambda}\_{ij} \Phi\_{ijt} e^{\epsilon\_{ijt}},\tag{7.5}$$

where λ˜*i j* denotes the time-invariant individual effect associated with patent citations between the two countries and *e ijt* is an error term with zero-mean distribution.

Substitute Eqs. (7.3) and (7.4), μ˜*it* and ν˜*jt* into Eq. (7.5) to obtain

$$C\_{ijt} = \tilde{\lambda}\_{ij} (\tilde{\mu}\_{it})^{\alpha\_1} (\tilde{v}\_{jt})^{\alpha\_2} e^{\xi\_{lj}} e^{\mathcal{V}^1(RTA \text{ }dummyi\_{lijt})} e^{\mathcal{V}^2(DRTA \text{ }i\_index\_{lijt})} \times \\ \times e^{\mathcal{V}^3(WTO \text{ }dummyi\_{lijt})} e^{\epsilon\_{lijt}} \,. \tag{7.6}$$

This equation is quite similar to the standard gravity equation that specifies the relationship between the volume of bilateral trade and the market sizes of the two countries with bilateral geographical distance (Anderson and van Wincoop 2003; Head and Mayer 2014; Yotov et al. 2016). By redefining the fixed effects variables, Eq. (7.6) can be rewritten as

$$C\_{ijt} = \exp\left(\wp\_1 RTA \,\,dummy\_{ijt} + \wp\_2 DRTA\\_index\_{ijt} + \wp\_3 WTO\\_dommy\_{ijt}\right)$$

$$+\xi\_{ij} + \mu\_{it} + \nu\_{jt} + \epsilon\_{ijt}\Big). \tag{7.7}$$

Equation (7.7) is our estimation equation for the analysis in Sect. 7.4. In this equation, we use the RTA dummy and the decomposed RTA dummies that we construct in Sect. 7.2.2. We also use the depth and breadth indexes that we construct in Sect. 7.2.1 for *DRT A*\_*indexijt* . In the estimations, we make use of one-year lagged variables for *RT A dummiesijt* , *DRT A*\_*indexijt* , and *WT O dummyijt* .

In the estimations, we employ the PPML estimator with time-varying citing country fixed effects, time-varying cited country fixed effects, and directional countrypair fixed effects, recommended in the gravity literature (Anderson and van Wincoop 2003; Head and Mayer 2014; Santos Silva and Tenreyro 2006, 2011; Yotov et al. 2016). As is popularly argued in the gravity literature, the use of PPML estimator with time-varying country fixed effects can address the issues of many observations with zero value for the dependent variable and unobservable multilateral resistance terms. Now there is one important issue that we should address the potential endogeneity of RTAs. Possible sources of such endogeneity are the existence of an omitted variable bias and reverse causality (Baier and Bergstrand 2007). Baier and Bergstrand (2007) and Yotov et al. (2016) recommend the use of country-pair fixed effects to take "the unobservable linkages between the endogenous trade policy covariate and the error term in gravity regressions" (Yotov et al. 2016, p.21) into account. We follow their recommendation and include citing-cited-country-pair fixed effects in our estimations to address the endogeneity issue.

# **7.3 Description of the Data**

The data on patent citations are taken from the Global 2019 edition of the EPO Worldwide Patent Statistical Database (PATSTAT). We extract the patent statistics of the USPTO from the PATSTAT.5 This dataset includes information on the application date, the country name of the assignee, the main US patent class, and citations made and received for each patent. We use patent citation data from 1991 to 2015 for our analysis.

The sample includes all countries and economies that are included in the USPTO data during the observation period. Our sample covers 243 countries and economies, which are listed in Table 7.1. We then construct a panel of 11,667 pairs of the citing and cited countries/economies for 25 years from 1991 to 2015.

Data on RTAs are taken from the database provided by Mario Larch (Egger and Larch 2008),6 and data on the content of RTAs are taken from the World Bank's website.7

Table 7.2 shows the descriptive statistics of the variables.

# **7.4 Empirical Results**

In this section, we report our estimation results.

5 We also extract the patent statistics of the EPO and use them for a robustness check.

6 www.ewf.uni-bayreuth.de/en/research/RTA-data/index.html.

7 This dataset was originally provided by Horn et al. (2010) and extended by Hofmann et al. (2019) (datacatalog.worldbank.org/dataset/content-deep-trade-agreements).

**Table 7.1** Sampled countries/economies

(continued)

**Table 7.1** (continued)

# *7.4.1 The Depth of RTAs*

First, we estimate the impact of the depth of RTAs on bilateral technology spillovers. The results obtained are reported in Table 7.3. Column (1) shows that the estimated coefficient on the RTA dummy is positive and highly significant. This result confirms the finding by Jinji et al. (2019a) and strengthens their finding by showing that the significantly positive effect of the RTA dummy remains even when directional country-pair fixed effects are included. Signing an RTA increases bilateral citation of patents by 4.8% on average.8

The effects of the four depth indexes are shown in columns (2)–(5). The coefficients on the first three depth indexes are positive and highly significant. The estimated coefficient on *RT A*\_*T ari f f* \_*index* is 0.042. Since this index consists of two policy areas with four points in total, an increase in one policy area from zero points to two points (i.e., a change from no legal enforceability to legally enforceable

8 (*e*0.047 − 1) × 100 ≈ 4.8.

**Table 7.2** Descriptive statistics

policy area) raises bilateral citation of patents by 2.1% on average.9 Similarly, the estimated coefficients of the *RT A*\_*NTB*\_*index* and the *RT A*\_*BBP*\_*index* are 0.059 and 0.066, where *RT A*\_*NTB*\_*index* and *RT A*\_*BBP*\_*index* consist of five policy areas and six policy areas, respectively. Thus, an increase in one policy area from zero points to two points raises bilateral citation of patents by 1.0% for the NTB index and 1.4% for the BBP index, respectively.10 Only the estimated coefficient on the *RT A*\_*O P*\_*index* is insignificant.

Columns (6) and (8) show the results on estimations when the RTA dummy and each of *RT A*\_*NTB*\_*index*, *RT A*\_*BBP*\_*index*, or *RT A*\_*O P*\_*index* are jointly used as explanatory variables. The results may suffer from the problem of multicollinearity as the correlations among the RTA dummy and the depth indexes are high. In column (6), both the RTA dummy and the *RT A*\_*NTB*\_*index* become insignificant, whereas in columns (7) and (8) the coefficient on the RTA dummy is positive and significant but that on the *RT A*\_*BBP*\_*index* or the *RT A*\_*O P*\_*index* is insignificant. These results imply that the additional effect of including the depth policy areas in the fields of NTB, BBP, and OP may be small or even insignificant. A possible reason for the insignificant additional effect of the depth policy areas is

9 (*e*0.042 − 1) × (1/4) × 2 × 100 ≈ 2.1.

10 (*e*0.059 − 1) × (1/10) × 2 × 100 ≈ 1.0 and (*e*0.066 − 1) × (1/12) × 2 × 100 ≈ 1.4.

(c) Standard errors clustered by

(d) The regressions

 include a constant term

country-pair

 are in parentheses

**Table 7.3** The impact of the depth indexes on technology spillovers

that most of the RTAs that have recently been signed tend to be increasingly deeper. Thus, we can conclude that the estimated average effect of the RTA dummy partly captures the impact of the depth policy areas on technology spillovers.

By contrast, the estimated coefficient on the *WTO* dummy is positive and highly significant in all columns, and its magnitude is large. If both countries are members of GATT/WTO, then bilateral citations of patents are on average 30.2% higher than those in other types of country-pairs.11

# *7.4.2 The Breadth of RTAs*

We next estimate the impact of the breadth of RTAs on bilateral technology spillovers. Estimated results are shown in Table 7.4. Columns (1)–(5) indicate the individual effect of the five breadth indexes: *RT A*\_*GAT S*\_*index*, *RT A*\_*T ech*\_*index*, *RT A*\_*Cap*\_*index*, *RT A*\_*Lab*\_*index*, and *RT A*\_*nonE P*\_*index*. The estimated coefficients are positive and statistically significant for all of them. For example, the estimated coefficient on the *RT A*\_*GAT S*\_*index* is 0.045 and the GATS field consists of just one policy area, so that an increase in this index from zero points to two points raises cross-border citations of patents by 4.6%.12 Since the coefficient on the *RT A*\_*T ech*\_*index* is 0.084 and the technology field consists of six policy areas, an increase in one technology-related policy area from zero points to two points raises bilateral citations of patents by 1.5%.13 Similarly, since the coefficient on the *RT A*\_*Cap*\_*index* is 0.045 and the investment/capital field is comprised of three policy areas, an increase in one investment/capital policy area from zero points to two points raises bilateral citations of patents by 1.5%.14 On the other hand, the impact of the labor-related policy fields becomes much larger. The estimated coefficient on the *RT A*\_*Lab*\_*index* is 0.133. The labor field consists of four policy areas, so that a rise in one labor-related policy area from zero point to two points causes bilateral citations of patents by 3.6% to increase.15 The larger impact of the RTAs with legally enforceable labor-related policy areas than those with technology and investment/capital policy areas implies that the movement of workers across countries stimulated by signing RTAs is an important channel of technology spillovers. In column (5), the coefficient on the *RT A*\_*nonE P*\_*index* is 0.189, where the NEP field consists of nine policy areas, so an increase in one NEP policy area from zero point to two points raises bilateral citations of patents by 2.3%.16

Compared with the results above, the results presented in columns (6)–(10) indicate that all of them lose their statistical significance when the breadth indexes are

11 (*e*0.264 − 1) × 100 ≈ 30.2.

12 (*e*0.045 − 1) × (1/2) × 2 × 100 ≈ 4.6.

**Table7.4**Theimpactofthebreadthindexesontechnologyspillovers \*\*\*, \*\*, and \* indicate statistical significance at the 1%, 5%, and 10% levels, respectively

(c) Standard errors clustered by country-pair are in parentheses

(d) The regressions include a constant term

estimated with the *RT A* dummy. This means that the additional effect of including legally enforceable policy areas in services, technology, investment/capital, labor, or NEP field on bilateral technology spillovers may be negligible. However, it should be noted that the problem of multicollinearity may distort the estimated coefficients of the breadth indexes. Moreover, the inclusion of legally enforceable policy areas in services, technology, investment/capital, labor, or NEP field does not necessarily increase bilateral citations of patents because some of the policy areas in these fields actually strengthen regulations. The same level of the index in each breadth field may include not only the positive effect but also the negative effect on technology spillovers. Consequently, the positive impact of the breadth index may be weakened.

Note that the estimated coefficient on the *WTO* dummy in Table 7.4 is almost the same as that in Table 7.3.

# *7.4.3 Heterogeneous Effects of RTAs by the United States and the EU*

Our next focus is on the heterogeneous effects of the RTAs signed by the United States and the EU. Table 7.5 shows the estimated results. Column (1) shows the effects of the NAFTA and the EU. Interestingly, while the estimated coefficient on the *N AFT A* dummy is positive and highly significant, that of the *EU* dummy is negative and significant. NAFTA increases bilateral citations of patents among Canada, Mexico, and the United States by 28.5% on average.17 The NAFTA membership is fairly comparable in magnitude to the GATT/WTO membership.

Column (2) reports the effects of RTAs with the United States, EU enlargement, and FTAs with the EU. RTAs with the United States, other than NAFTA, have on average a positive and significant effect on bilateral patent citations. Moreover, although the impact of EU enlargement is insignificant, FTAs with the EU affect positively bilateral patent citations on average. These results suggest that the impact of NAFTA is much larger than that of other RTAs.

# *7.4.4 Robustness Check*

The large impact of NAFTA and the small one of the EU on bilateral citations of patents shown in the previous subsection may be due to the use of the USPTO data. In other words, one may suspect that the home bias may cause the heterogeneous effects between NAFTA and the EU.

To check the robustness of the finding in the previous subsection, we employ the EPO data and construct the dataset, so that the same country-pairs are included in both the USPTO and the EPO citations. Then, we estimate the same specifications

17 (*e*0.251 − 1) × 100 ≈ 28.5.

**Table 7.5** Heterogeneous effects of RTAs by the United States and the EU

*Notes*: (a) Estimations were implemented using Stata command ppmlhdfe

(b) \*\*\*, \*\*, and \* indicate statistical significance at the 1%, 5%, and 10% levels respectively

(c) Standard errors clustered by country-pair are in parentheses

(d) The regressions include a constant term

with those in Table 7.5 for both the USPTO and the EPO citations. The results are reported in Table 7.6. Columns (1) and (2) show the results of the USPTO citations, and columns (3) and (4) show those of the EPO citations.

Comparing the estimated coefficients on the *N AFT A* dummy in columns (1)– (2) and columns (3)–(4), the coefficient is positive in both columns (3)–(4) and significant in column (4), although the magnitude becomes smaller in columns (3)– (4). By contrast, the coefficient on the *EU* dummy is negative and significant in both columns (1) and (3). The estimated coefficient on the EU enlargement dummy is insignificant in both columns (2) and (4). Moreover, whereas the coefficient on the *FT A*\_w*ith*\_*EU* dummy is positive and significant in column (2), it is negative and insignificant in column (4).

We conclude that the findings in Table 7.5 are not due to the home bias and that these findings are robust.

**Table 7.6** Robustness check: USPTO citations versus EPO citations

*Notes*: (1) Estimations were implemented using Stata command ppmlhdfe

(2) \*\*\*, \*\*, and \* indicate statistical significance at the 1%, 5%, and 10% levels, respectively

(3) Standard errors clustered by country-pair are in parentheses

(4) The regressions include a constant term

# **7.5 Conclusion**

In this chapter, we investigated the impact of deep RTAs on international technology spillovers, employing patent citations as a proxy of technology spillovers. We extended and enriched the research by Jinji et al. (2019a) in several ways. The focus of our research is on the impacts of the depth and breadth of RTAs and the heterogeneous effects of individual RTAs on international technology spillovers.

Our main findings are as follows. First, we confirmed the finding by Jinji et al. (2019a) that RTAs significantly enhance international technology spillovers measured by bilateral patent citations. We actually strengthened their finding by deriving the one that the significantly positive coefficient on the RTA dummy is kept even when the model is estimated with directional country-pair fixed effects.

Second, we found that deep RTAs with higher coverage of policy areas in the depth fields such as tariffs, NTBs, and BBPs, and those with higher coverage of policy areas in the breadth fields such as services, technology, investment/capital, labor, and NEPs at the legally enforceable level have positive effects on international technology spillovers. The additional effects of including the depth or breadth policy areas may be small or even negligible. This may be because the estimated average effect of the RTA dummy partly captures the impact of the depth policy areas on technology spillovers as recent RTAs tend to be increasingly deeper. Moreover, with regard to the breadth fields, the same level of the breadth index may include both the positive and negative effects on technology spillovers, as some policy areas actually strengthen rather than relax regulations. Finally, we found that the NAFTA has a strongly positive impact on international spillovers, whereas the impacts of the EU and EU enlargement on technology spillovers are weak or not positive. However, the RTAs with the United States and the FTAs with the EU both positively affect technology spillovers.

Our empirical results imply that signing deep RTAs with higher coverage of the depth or breadth policy areas is quite effective to enhance bilateral technology spillovers. In addition, signing RTAs with technologically advanced countries, such as the United States and major European countries, is also effective in stimulating cross-border technology spillovers.

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# **Chapter 8 Conclusion and Policy Implications**

After about a quarter century of countries having pursued deep regional integration through negotiating on deep regional trade agreements (RTAs), we observed a number of historical events that symbolize the curbing of the trend of globalization in 2016. On June 24, 2016, the people of the United Kingdom voted to leave the European Union (EU) in a referendum. On November 8, 2016, Mr. Donald Trump, who proposed the "America First" policy and a number of protectionist policies, such as the withdrawal from the Trans-Pacific Partnership Agreement and the construction of a substantial wall on the United States–Mexico border, during his presidential campaign, won the US presidential election.

Since then, it seems that the world has been taking "a momentary pause" (Baier et al. 2019), at least until 2020, in the trend toward deep integration. For example, after the Brexit referendum in 2016, the United Kingdom struggled for more than three years to decide whether and when it would actually leave the EU. Finally, it withdrew from the EU on January 31, 2020. On the other hand, the Trump administration of the United States implemented various unprecedented protectionist policies. It increased tariffs on imports from China, which was retaliated by China and resulted in a trade war. As pledged in Mr. Trump's election campaign, the United States withdrew from the Trans-Pacific Partnership Agreement on January 23, 2017, and suspended negotiations with the EU for the Transatlantic Trade and Investment Partnership Agreement. Moreover, the Trump administration re-negotiated over the NAFTA with Canada and Mexico and signed a new agreement called the United States–Mexico–Canada Agreement, which entered into force on July 1, 2020. With regard to trade negotiations, the Trump administration emphasized bilateralism rather than multilateralism or pluralism.

In 2020, due to the spread of COVID-19, major countries closed national borders and implemented lockdown policies to limit the movement of people both across and within countries. Shortages of masks and other medical materials made it difficult to maintain free trade in those goods. Moreover, there has been a surge in protectionism for trade in COVID-19 vaccines. Like all other countries, the countries producing the vaccines want to vaccinate their people against COVID-19 as quickly as possible. For example, then US President Donald Trump signed an executive order to use USmade vaccines to meet their domestic demand first in December 2020. The European Commission announced in January 2021 that the EU was to introduce tighter rules over the exports of COVID-19 vaccines to non-EU countries. In March 2021, those tighter export measures were extended to June 2021.

In contrast to the waves of protectionism due to COVID-19, moves toward deep integration were partially regained in 2020. For example, the Regional Comprehensive Economic Partnership (RCEP), which has been negotiated by 10 Association of South-East Asian Nations (ASEAN) countries and six Asia-Pacific countries (Australia, China, India, Japan, New Zealand, and South Korea) since 2012, was signed by 15 countries excluding India on November 15, 2020. It is expected that RCEP will enter into force by the end of 2021. Moreover, in a foreign policy speech in February 2021, US President Joe Biden stated that the United States would work with the international community to tackle global challenges, such as the COVID-19 pandemic and climate change, and to advance freedom and dignity for people in the world. In spite of this US departure from unilateralism, the so-called "economic decoupling" between the United States and China may be accelerated.

So, the world economy seems to be currently in the mixed situation of slowly regaining the momentum of globalization and protectionism/nationalism. The main topics in this book, namely, deep integration, global firms, and technology spillovers, will continue to be important issues in economic research. In this chapter, we summarize the findings from the analyses in the book and discuss the issues for future research.

This final chapter of the book is organized as follows. In Sect. 8.1, we summarize the main findings in this book. In Sect. 8.2, we discuss policy implications that are obtained from our findings. Finally, the issues for future research are discussed in Sect. 8.3.

# **8.1 Summary of the Main Findings**

In this section, we summarize the main findings of the research outlined in Chaps. 2–7.

# *8.1.1 The Trend of Deep Regional Integration in the Asia-Pacific Region*

In Chap. 2, we investigated the trend of deep regional integration in the Asia-Pacific region from the perspective of the depth and breadth of regional integration. The depth measure consists of four fields: import tariffs; non-tariff barriers (NTBs); behind the border policies (BBPs); and other policies (OPs). Each of those fields includes a number of policy areas. For example, the BBPs field includes five policy areas: state trading enterprises; state aid; public procurement; anti-corruption; and competition policy. On the other hand, the breadth measure consists of five fields: services; technology; investment/capital; labor; and non-economic policies (NEPs), each of which includes a number of policy areas. For example, the field of technology includes six policy areas: trade-related aspects of intellectual property rights (TRIPS); intellectual property right (IPR); innovation policies; economic policy dialogue; information society; and research and technology.

We found that the depth and breadth of RTAs signed by ASEAN countries, China, and Japan are quite heterogeneous. Specifically, the heterogeneity is significant in the fields of the NTBs and the BBPs in the depth measure and in the fields of investment/capital and labor in the breadth measure. Although RTAs signed by ASEAN countries include more NTB policy areas in the 2000s, an improvement in the inclusion of the BBP policy areas is small. Moreover, RTAs signed by China indicate a large improvement in the inclusion of both NTBs and BBPs in the 2000s, but the coverage of the legally enforceable BBP index is still low even in 2010–2015, compared with RTAs signed by Japan, the United States, and European countries. By contrast, RTAs signed by Japan in 2010–2015 show almost the same level of the coverage of the NTB and BBP policy areas as those by the United States and European countries. As for the breadth measure, on the other hand, the coverage of the investment/capital policy areas is much narrower for RTAs signed by China than those signed by ASEAN countries and Japan even in 2010–2015. Moreover, the coverage of the labor policy areas is almost the same among ASEAN countries, China, and Japan in 2000–2009, whereas the coverage becomes much wider for RTAs signed by Japan than those signed by ASEAN countries and China in 2010–2015.

# *8.1.2 Firm Performance and the Choice of Globalization Mode*

In Chaps. 3 and 4, we examined how a firm's choice of globalization mode, such as export, foreign outsourcing (FO), and foreign direct investment (FDI), is associated with firm performance. We use various measures of firm performance, which include labor productivity (LP), total factor productivity (TFP), Tobin's *q*, and the ratio of patent stock to tangible fixed capital. The last measure is a proxy of the ratio of intangible to tangible assets. With regard to the measure of globalization activities, we employ the ratio of the size of a firm's globalization activity to the size of its domestic sales. The size of its activity is measured by the value of exports, sales of foreign affiliates, and costs of FO. This measure can capture the relative importance of a particular type of globalization activity (i.e., export, FDI, or FO) for a firm in relation to the size of its domestic activity. In addition, we construct indexes to measure the relative choice of globalization modes, such as the ratio of export sales to foreign affiliate sales and the ratio of outsourcing costs to foreign affiliate sales.

Our main findings in Chap. 3 are as follows. First, we found that an increase in LP or Tobin's *q* enhances the size of export or FDI relative to its domestic sales, whereas it does not necessarily induce a firm to expand its FO relative to its domestic sales. These results imply that exporters and multinational enterprises (MNEs) are likely to be more productive and have higher Tobin's *q* than non-globalized firms, but firms that engage in FO are not necessarily superior in LP or Tobin's *q* to non-globalized firms. It seemed that our result does not clearly indicate a difference between LP and Tobin's *q* in terms of the effects on firms' globalization. Second, we found important differences between LP and Tobin's *q* on a firm's relative choice among modes of globalization. Specifically, an increase in LP tends to motivate a firm to choose more FDI and less exporting, but does not affect the choice between FDI and FO. By contrast, an increase in Tobin's *q* tends to motivate a firm to choose more FDI and less FO, but does not affect the choice between exporting and FDI. Third, we also found that Tobin's *q* and intangible asset intensity work in a different way when one tries to capture the importance of the knowledge capital in the choice of globalization mode. Headquarters companies with relatively higher intangible assets tend to favor FDI over exporting and outsourcing. In other words, a difference in the intangible asset intensity is important to the choice between exporting and FDI as well as to the choice between FDI and FO. In this way, we revealed the differences among various measures of firm performance in the effects of globalization activity.

In Chap. 4, we further investigated the effects of Tobin's *q* on a firm's choice of globalization mode. We distinguished between total FDI and horizontal FDI (HFDI) through taking advantage of the feature of our dataset that allows us to observe detailed information on sales of foreign affiliates of Japanese MNEs. We measure total FDI by total sales of foreign affiliates and HFDI by sales of foreign affiliates excluding exports to Japan. Then, employing a number of different estimation techniques, we confirmed that Tobin's *q* is negatively and significantly correlated with the ratio of FO to the total FDI. This strongly supports the prediction by Chen et al. (2012) that a higher Tobin's *q* is associated with a higher FDI engagement relative to FO by MNEs. In contrast, little evidence was found on a definite relationship between Tobin's *q* and the ratio of exports to HFDI. It implies that the imperfect contractibility of knowledge capital and a higher technology transfer cost actually matter for knowledge-capital intensive firms to choose between exports and FDI, because these factors weaken the positive relationship between Tobin's *q* and the ratio of FDI to exports.

We also confirmed that the relationship between Tobin's *q* and the firm's choice of globalization mode fairly differs from that of TFP. We found that TFP is negatively and significantly correlated with the ratio of exports to HFDI, whereas no significant relationship existed between TFP and the ratio of FO to the total FDI. The former result is consistent with the theoretical prediction of Helpman et al. (2004), but the latter result differs from the prediction of Antràs and Helpman (2004).

# *8.1.3 The Impact of Trade/FDI on Technology Spillovers*

We explored the impact of international trade and FDI on international technology spillovers in Chaps. 5 and 6. To measure international technology spillovers we used patent citation data throughout this book. Patent citations are references to existing patents included in patent documents. The advantage of using patent citations as a proxy of technology spillovers is that it is a direct measure of knowledge flows (Hall et al. 2001).

In Chap. 5, we focused on the relationship between the bilateral trade structure and technology spillovers. We first conducted a theoretical analysis using a twocountry model of monopolistic competition with quality differentiation, in which inter- and intra-industry trade patterns endogenously arise. Our model predicted that the bilateral trade pattern is horizontal intra-industry trade (HIIT) when the two countries have access to a similar level of technology, while it is vertical intraindustry trade (VIIT) when there is a technological difference between them. If the technological difference is sufficiently large, then the bilateral trade pattern becomes one-way (or inter-industry) trade. As for the relationship between trade patterns and international technology spillovers, our model predicted that technology spillovers are highest when the bilateral trade pattern is HIIT, followed by VIIT and one-way trade. We tested these theoretical predictions using European and Japanese patent data and found that the predictions are supported by empirical results. Specifically, an increase in the share of intra-industry trade in the bilateral trade has a positive effect on the number of patent citations between the two countries. HIIT has a larger effect on spillovers than VIIT. On the other hand, the effects of one-way trade on the number of citations are much weaker than those of IIT.