Abstract
This paper contributes to the literature on vertical specialization in R&D (or ‘R&D fragmentation’) and trade in intermediate services by examining the role of multinational enterprises (MNE) activities associated with bilateral foreign direct investment (FDI). Prior work in production networks (or global value chains [GVCs]) and R&D fragmentation suggests a complementarity relationship between FDI in R&D and technological knowledge flows. The paper examines this proposition empirically for R&D services trade by extending the gravity framework of supply-chain trade for intermediate services with bilateral MNE operations as economic mass variables. The results are partially consistent with the hypothesized complementarity. The econometric strategy accounts for zero trade observations. The latter addresses possible selection and consistency issues of traditional gravity trade specifications, and allows exploring extensive vs. intensive margin of trade. Understanding the role of MNEs in these transactions may be useful for policies aimed at increasing participation and upgrading in MNE-driven GVCs.
Notes
Trade in business services includes accounting, managerial, legal, computer, and other services. Trade in intangibles is the subset of knowledge-based business services such as R&D services and charges for the acquisition or use or intellectual property (IP) (e.g., patent licensing), among other technical services. International patent licensing has been studied extensively elsewhere and will not be covered in this paper (see OECD 2009 and references therein). A separate literature studies international flows of technological knowledge in the form of spillovers: uncompensated flows of knowledge as by product of other activities such as merchandise or capital goods trade or FDI financial flows. For a review of this literature see Keller (2010).
See Ibarra-Caton (2015), and references therein, for selected firm-level studies on business services.
In this paper, FDI in R&D is proxied by MNE R&D investments (R&D stocks by affiliates). The latter is an indicator of early-stage (pre-patenting) innovation activities by MNE and GVCs networks, and it is considered here as an example of VFDI and R&D fragmentation.
Further, these transactions reflect heterogeneity in technological capabilities. For example, among MNE R&D performers, those that report transactions in intangibles have been associated with higher intensity of knowledge activities compared with R&D performers that do not report such transactions (Moris and Zeile 2016).
The U.S. is of course a key source and destination for trade and FDI flows, the largest source of global technology, and a high-income market for innovative products, making it an ideal reference country to understand the interplay between MNEs and flows of intangibles. As well, data on bilateral trade in services related to intangibles used in this paper are not widely available from other countries.
Gravity models focused on distance factors have been applied beyond trade in goods to explain total services trade (Head et al., 2009), FDI, and other international flows. For studies on the role of different forms of ‘distance’ in the location of MNE R&D and technology diffusion see Castellani et al. (2013) and Feldman and Massard (2002).
Affiliated (intra-MNE) vs. unaffiliated trade for R&D services, discussed shortly, are available only for total R&D services exports and imports, not by bilateral trading partner. Further, trade in R&D services is not available by industry, technology area, or by ‘Research’ vs. ‘Development’. Lastly, data in R&D services included testing services from 2006 to 2011, though the latter component is not separately available. See also footnote 4.
Unreported exploratory regressions used GDP, GDP per capita, and openness (total trade as share of GDP) from the World Bank’s 2015 World Development Indicators.
R&D services accounted for 4.7% and 6.9% of U.S. private services exports and imports, respectively. By comparison, in 2014 charges for the use of industrial processes IP (licensing and sales/purchases) accounted for 6.9% and 5.0% of U.S. private services exports and imports, respectively, based on BEA data.
Full panel used in this paper ends in 2013 given unavailable data for key explanatory variables for 2014.
R&D stocks are cumulative expenditures of R&D performed, which in turn are based on current cost (OECD 2015). The perpetual inventory formula used by the author to compute R&D stocks (K) was: Kt = (1-δ) Kt-1 + It-1, with δ = 0.05 (depreciation rate), using 1999 as starting values.
Exporter and importer country FE and country pair fixed effects (Cheng and Wall 2005) have collinearity issues in our data since we have a bilateral panel for a single reference country. Country-year fixed effects are similarly not feasible. The regional dummies are defined over Asia (2), Europe (3), North America (4), and the omitted ROW region (1).
Missing values were treated as zeros for purposes of this paper. As discussed below, zero and missing values may not be true zeros (e.g., unreported data) or may be true corner solutions for firms. Available information does not allow distinguishing these cases.
Panel estimation accounts for unobserved heterogeneity or individual (country) effects by specifying individual-specific intercepts. The latter may be estimated via random effects, fixed effects, or mixed effects. A Hausman test for fixed vs. random effects did not reject the null hypothesis that that the individual-specific intercepts are uncorrelated with the regressors (at 5% for the export equation and strongly rejected for the imports equation), favoring RE effects estimation. For an application of mixed effects for trade in R&D services see Moris (2015). In the present paper, panel RE was estimated using Stata xtreg with and cluster robust standard errors (26 countries/clusters with positive trade). Statistical output for all estimations use Stata/IC 12.1 for Windows.
See Helpman et al. (2008) for more on Heckman estimation in international trade research. Results shown in the present paper are based on full maximum likelihood estimation where all parameters are estimated jointly. A two-step procedure, where the first step is a probit selection equation, yielded similar results.
Due to convergence issues, the selection equation in Heckman specifications has fewer variables than the outcome equation.
A full identification of the extensive margin of trade requires firm-level models (Helpman et al. 2008).
Indeed, the first order condition in Poisson MLE only requires nonnegative y (Cameron and Trivedi 2013: 72). The Poisson distribution, whether applied to discrete or continuous variables, assumes that the mean equals the variance σ2 y = μ y, known as equi-dispersion. This is relaxed by the related negative binomial distribution used later in the paper. Without any independent variables, Poisson distribution assumes a single mean for all observations. By pairing a Poisson distribution for the dependent variable with an exponential mean of the form μ = exp.(XB), Poisson regression allows for heteroscedasticity (and more generally observed heterogeneity) (see Long and Freese 2014: 487, 507). The negative binomial distribution keeps μ = exp.(XB) but allows for unobserved heterogeneity by introducing a variance parameter to account for over or under-dispersion (ibid). See also Cameron & Trivedi (2013: 74-76). For reviews of Poisson family estimation in recent empirical research on gravity trade see De Benedictis and Taglioni (2011), Head and Mayer (2014), and Martin and Pham (2015).
Using “ppml” command in Stata, developed by Santos Silva & Tenreyro based on their 2006 paper and subsequent research.
An alternative (unreported) dispersion assumption, quadratic variance σ2 y = μ y (1+ delta * μ y), was tested. The latter yielded a lower log likelihood for both equations.
Results are shown for logit.
Voung z test statistic results: ZIP: z = 2.44, p = .0073 for exports; z = 2.53, p = 0057 for imports; ZINB: z = 3.61, p = 0.0002 for exports and z = 3.21, p = 0.0007 for imports
Panel versions of Poisson estimation did not converge for our dataset. This paper accounted for panel level heterogeneity in all Poisson family estimations by using cluster robust standard errors.
Due to convergence issues, the outlier test for ZINB imports equation only excluded Bermuda.
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Acknowledgments
Thanks to Prof. Kaye Husbands Fealing, Georgia Institute of Technology USA, Prof. Kathryn E. Newcomer and Prof. Joseph J. Cordes (George Washington University) and to Dr. Carol Robbins (National Science Foundation) for comments on several versions of this paper. I also want to thank participants at the 2015 Meeting of the Academy of International Business in Bengaluru, India and the 2015 International Knowledge Sourcing Workshop, University of Catania, Italy. None of them are responsible for any errors in this paper. Views expressed are those of the author and do not necessarily reflect those of the National Science Foundation or other organizations.
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Moris, F. Intangibles Trade and MNEs: Supply-Chain Trade in R&D Services and Innovative Subsidiaries. J Ind Compet Trade 18, 349–371 (2018). https://doi.org/10.1007/s10842-017-0265-0
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DOI: https://doi.org/10.1007/s10842-017-0265-0
Keywords
- Trade in intermediate services
- R&D fragmentation
- Gravity model
- Global value chains
- Multinational enterprises
- Knowledge flows