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ABSTRACT
This paper explores the location strategies of multinational enterprises (MNEs) from emerging countries (EMNEs) in search for regional strategic assets. The analysis is based on a systematic comparison between EMNEs and multinationals from advanced countries (AMNEs) in order to unveil similarities and differences between these two major sources of foreign investment into the regions of the European Union. The empirical results suggest that EMNEs follow a distinctive logic in their location strategies. They are attracted by the availability of technological competences only when their subsidiaries pursue more sophisticated and technology-intensive activities. Conversely, EMNEs share some behavioural similarities with AMNEs in their response to the spatial agglomeration of investments.
Acknowledgements
The authors wish to thank participants at the 2014 EUROLIO Geography of Innovation workshop in Utrecht, at the North American Regional Science Conference in Washington, DC, and at the 2015 Reading-UNCTAD International Business Conference for their helpful comments. Rabellotti thanks the Riskbankens Jubileumsfond for financial support All errors and omissions are our own.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
† The opinions expressed in this paper are those of the authors and do not necessarily reflect the views of the Inter-American Development Bank, its Board of Directors or the countries they represent.
1. Asset-augmenting activities have the generation of new knowledge as their primary purpose, for augmenting existing competences, whether this is through their own (formal) R&D activities, or through other non-hierarchical means in partnership with other economic actors (Narula, Citation2010).
2. The accuracy and robustness of the information reported in fDi Markets have been checked using different methodologies: (1) comparison with UNCTAD information on FDI flows at the country level; (2) comparison of regional-level distribution of investments with Euromonitor database, which provides information about greenfield investments in Europe based on a completely independent source. All these checks confirm the reliability of the fDi Markets database on the spatial distribution of greenfield investment.
3. With regard to emerging countries, there is not an official definition, but there are several alternative classifications utilized by different research institutions. Different classifications are available at http://en.wikipedia.org/wiki/Emerging_markets (accessed 19 June 2013). In order to check the robustness of our definition of emerging in countries in the empirical analysis, we have also tested an enlarged group including Argentina, Malaysia and Ukraine obtaining very similar results.
4. There is an additional reason for this choice: even if the database provides information on the value of the investment, in most of the cases this is estimated.
6. The Nomenclature of Units for Territorial Statistics (NUTS) is a geocode standard for referencing the EU countries for statistical purposes. The NUTS regions are based on the existing national administrative subdivisions. Countries without equivalent subnational regions (Cyprus, Estonia, Denmark, Ireland, Latvia, Lithuania, Luxembourg and Malta) are necessarily excluded from the econometric analysis. Sweden is also excluded due to the lack of regional data for some of its regions.
7. It would have been interesting to adopt a more fine-grained disaggregation of activities as in Crescenzi et al. (Citation2014), but the number of observations does not make this approach feasible in this paper.
8. The structural variables for each dimension () are combined by means of PCA on the basis of the scores presented in .
9. Investments are classified in 39 sectors by fDi Markets.
10. As explained in Section 3.2, the data set provides a classification of the investments in 18 activities.
11. EU 10 includes Cyprus, Czech Republic, Estonia, Hungry, Lithuania, Latvia, Malta, Poland, Slovenia and Slovakia. EU15 includes Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Spain, Sweden and the UK. For the sake of brevity, these results are not included in the paper, but they are available from the authors.
12. The results of this test are not included and they are available upon request.
13. In our robustness check - where broader EU15 vs. EU 10 nests replace national ‘controls’ – intra-EU investments favour locations where the supply of labour is more abundant and potentially cheaper (i.e. those with a higher unemployment rate), while North American investments prefer ‘core’ low unemployment locations. In other words, if NA MNEs decide to invest in the EU, they rather seek strategic assets than higher efficiency (lower costs) locations. The same does not apply to EU MNEs that, when investing within the EU, look for ‘cheaper’ locations. For EMNEs this variable is never significant.
14. The Random Utility model restricts dissimilarity parameters to a range between 0 and 1 and values outside this range mean that while the model is mathematically correct, the fitted model is inconsistent with the random utility theory (Cameron & Trivedi, Citation2008).
15. See for instance a recent article in the Financial Times (http://www.ft.com/cms/s/2/53b7a268-44a6-11e4-ab0c-00144feabdc0.html, accessed 17 February 2015).
