Abstract
This paper shows that value creation by multinational enterprises (MNEs) is the result of activities where geographic distance effects can be overcome. We submit that geographic distance has a relatively low impact on international research and development (R&D) investments, owing to the spiky nature of innovation, and to the unique ability of MNEs to absorb and transfer knowledge on a global scale. On the one hand, MNEs need to set up their labs as close as possible to specialized technology clusters where valuable knowledge is concentrated, largely regardless of distance from their home base. On the other, MNEs have historically developed technical and organizational competencies that enable them to transfer knowledge within their internal networks and across technology clusters at relatively low cost. Using data on R&D and manufacturing investments of 6320 firms in 59 countries, we find that geographic distance has a lower negative impact on the probability of setting up R&D than manufacturing plants. Furthermore, once measures of institutional proximity are accounted for, MNEs are equally likely to set up R&D labs in nearby or in more remote locations. This result is driven by MNEs based in Triad countries, whereas for non-Triad MNEs the effect of geographic distance on cross-border R&D is negative and significant.
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Other authors have used similar concepts and denominations, such as asset-exploiting vs asset-seeking FDI (Dunning & Narula, 1995), or home-based exploiting vs home-based augmenting (Kuemmerle, 1997) FDI.
See Beugelsdijk and Frijns (2010), Slangen and Beugelsdijk (2010), Slangen, Beugelsdijk, and Hennart (2011) and Schmitt and Van Biesebroeck (2013) for recent applications of cultural distance to the analysis of exports, affiliate sales, cross-border asset allocation and outsourcing relations.
Some authors claim that distance should be examined in terms of managers’ perceptions rather than exogenous differences between countries. Cognitive maps have been used for this purpose (Ellis, 2008; Evans & Mavondo, 2002; Sousa & Bradley, 2006). However, this approach leads to a problem of causality (Dow & Ferencikova, 2010; Dow & Karunaratna, 2006). Since it is unusual to be able to survey a decision maker’s perceptions immediately prior to a critical decision, most researchers rely on ex post perception. Unfortunately, it is very difficult to distinguish whether ex ante perceptions lead to the decision, or whether the ex post experience influences the perception itself. Håkanson and Ambos (2010) have analyzed how managers’ perceptions are affected by objective measures of cultural, social, economic and institutional, and geographical separation, and found that the latter explain a large share of variance.
Although there are methodological differences in how to measure relevant distance factors, “when one disaggregates the concept of institutional distance, the overlap between psychic distance and institutional distance becomes quite substantial” (Dow & Ferencikova, 2010: 48).
It has been noted that firms locate R&D resources in new geographic areas in order to gain access to “locally embedded sectoral specialists” (Cantwell & Santangelo, 1999: 120), and some of the most productive R&D-intensive firms are co-located near research universities that employ “star scientists” (Zucker, Darby, & Brewer, 1998).
Although it should be noted that foreign production might also be associated with the transfer of some intangible assets, such as patents and designs.
In fact, the costs of transporting material goods across borders impose a relatively stronger limitation on the spatial distribution of supply chains in manufacturing activities. The historical reduction of the cost of moving goods on a global scale, which has been especially dramatic between the second half of the 19th century and the first half of the 20th century (Bairoch, 1990; Dollar, 2001; Findlay & O’Rourke, 2003), is not only affecting some sectors more than others (Hummels & Skiba, 2004), but also appears to have reached a limit over the past two decades. As argued by Levinson (2008: 140): “Slower, costlier and less certain transportation of goods will not put an end to the growth of international trade. But on the margin, where business decisions are made, the strategies of manufacturers and retailers will change. As transportation eats up a greater share of the total costs of an imported product, supply chains will shorten and production will move closer to home”. In sum, the alleged end of a long cycle of falling transportation costs is likely to negatively affect the geographic dispersion of the fragmentation of production and of MNE’s manufacturing networks much more than the internationalization of R&D facilities. In a similar vein, Baldwin (2003: 368) suggests that: “As we move on to the second wave of globalization, we presume that the cost of transporting goods asymptotes to some natural limit, but additionally, and importantly, we assume that the cost of trading ideas decreases”.
More information is available at http://www.fdimarkets.com/.
To clarify what is intended for R&D investments, here are two examples that fDi Markets reports with specific reference to IBM as an investor. Example 1: a nanotech research center in Egypt is intended to be a world-class facility for both local engineers and scientists, and IBM’s own researchers, to develop nanotechnology programs. The center will work in coordination with other IBM research efforts in the field in Switzerland and the United States. Example 2: a business solution center to promote new technologies that help save energy used to run computer equipment and reduce hardware management costs. Teaming up with automakers and electronics manufacturers, the center will study how to make the best use of advanced technologies. IBM Japan intends to use the results of these efforts to win system development projects.
One could be tempted to consider DDT as competence exploiting, and R&D as competence creating, but we believe the classification was not designed with this taxonomy in mind.
For the sake of clarity we report only data for EU15- and US-based firms (which account for more than 60% of the sample). The extended table with data on firms from other areas is available upon request.
Since our geographic and institutional differences are mostly time invariant (at least in the short term), there is not much to be gained by estimating our regressions as a panel, so we collapse all the information for investments in the period 2003–2008 into a single data point for each firm-destination country pair.
See http://www.cepii.fr/anglaisgraph/bdd/distances.htm for more details.
The role of BIT as a determinant of FDI has been highlighted in recent studies, such as Egger and Pfaffermayr (2004b) and Egger and Merlo (2007). Data on bilateral investment treaties have been compiled by the authors from information on UNCTAD’s website (http://www.unctad.org).
The three components are: (a) the distance between the two closest major religions for each pair of countries (R1); (b) the incidence of country i’s major religion(s) in country j (R2); and the incidence of country j’s major religion(s) in country i. More details are provided at https://sites.google.com/site/ddowresearch/home/scales/religion.
The number of observations may be lower than the theoretical 361,340, owing to missing values for language and religious similarity for Tunisia, and because some countries or sectors have no investments in R&D. In these cases, sector and country dummies perfectly predict these observations, which are thus dropped from the estimation sample.
Results for the individual country and sector dummies have not been shown, but are available from the authors upon request. Suffice it to say that the fixed effects are jointly significant, and a casual inspection of the coefficients reveals that, as expected, the highest destination country fixed effects for R&D investments are found for China, India, the United States, Japan, the major EU countries (such as Germany, France and the United Kingdom) and for some countries in South Asia (Taiwan, Singapore, South Korea and Malaysia). In the case of manufacturing investments, the largest fixed effects are for China, India, Brazil, Mexico, Malaysia, Thailand and Vietnam. As far as the sectors are concerned, we register remarkably higher fixed effects for software and IT, biotech and pharmaceutical for R&D FDI, whereas automotive, beverages and business machines and equipment stand out when manufacturing investments are considered.
In the next section we perform further robustness tests to ensure that the large number of zeros does not drive our results.
The estimated elasticity implies that, ceteris paribus, a country that is 10% further away from the home country has an 11.35% lower probability of receiving manufacturing investments. These estimates are in line with those obtained in gravity models from trade and FDI (De Benedictis & Taglioni, 2011).
Results are available upon request.
We could not obtain marginal effects for bivariate probit regressions, so the magnitude of the coefficients cannot be compared with the baseline probit regressions.
We are aware that endogeneity problems may arise in estimation of the effect of these two variables, and may partially explain their large coefficients. We have avoided introducing a further complexity in the analysis by estimating our model with instrumental variables, since the purpose of this test is not to evaluate the extent of co-location of R&D and manufacturing, but to assess the robustness of the effect in the distance-related variables.
Ideally, one would like to control for the stock of R&D and manufacturing activities of each firm in a given country. Unfortunately, our data source provides only information on the period 2003–2008, so we do not have any information on the previous activity of our sample firms in the selected countries.
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Acknowledgements
The authors thank John Cantwell, Mark Lorenzen, Vasyl Taras, three anonymous reviewers and the Guest Editors (Sjoerd Beugelsdijk and Ram Mudambi) for their helpful comments. This paper also benefited from comments received at various workshops, conferences and seminars, including those held at the Fox School of Business (Temple University, Philadelphia, USA), Henley Business School (University of Reading, UK), Rutgers Business School (Newark, NJ, USA), UNU-MERIT and School of Economics and Business (Maastricht University, the Netherlands), University of Lausanne (Switzerland) and Nanzan University (Nagoya, Japan). Castellani and Zanfei gratefully acknowledge financial support from the Italian Ministry of University and Research (Prin 2009 project on “Production, R&D and knowledge offshoring: Economic analyses and policy implications”). Jimenez gratefully acknowledges financial support from Caja de Burgos. Research for this paper was partly carried out while Jimenez was a visiting scholar at the University of Perugia, and Castellani was visiting at Rutgers Business School. The authors thank those institutions for their hospitality.
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Accepted by Sjoerd Beugelsdijk and Ram Mudambi, Guest Editors, 7 May 2013. This paper has been with the authors for three revisions.
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Castellani, D., Jimenez, A. & Zanfei, A. How remote are R&D labs? Distance factors and international innovative activities. J Int Bus Stud 44, 649–675 (2013). https://doi.org/10.1057/jibs.2013.30
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DOI: https://doi.org/10.1057/jibs.2013.30