Abstract
The aim of this paper is to assess the impact of “third country effects” and trade policies on the outward stocks of FDI of the EU. We estimate a model based on the knowledge-capital theory of the multinational enterprise over the period 1995–2008 by using a sample of five EU countries and 24 partner countries. Explanatory variables include an index of applied bilateral tariffs, a dummy to capture the presence of bilateral investment treaties (BITs) and a variable to take into account the impact of the participation of host countries to free trade agreements (FTA) with other than EU countries. The paper checks the third country effects by testing whether there is spatial lag dependence in bilateral FDI. The results show that trade costs play a key role in explaining the pattern of FDI in the manufacturing sector as a whole and in four out of six disaggregated industries. The impact of tariffs varies across industries, suggesting the predominance of horizontal FDI in some industries, and the existence of export-platform FDI in others. BITs and the participation of the host country in other FTAs positively affect the outward stock of EU FDI, while we find no empirical evidence to support the hypothesis of spatial lag dependence in bilateral FDI.
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Notes
These are: Australia, Argentina, Brazil, Bulgaria, Canada, Chile, Czech Republic, Egypt, Estonia, Hungary, Israel, Japan, Latvia, Lithuania, Mexico, Morocco, Norway, Poland, Romania, Slovakia, Slovenia, Switzerland, USA and Uruguay. Countries that joined the EU have been excluded from the database starting from their date of entry.
Details on the data and the methodology used to compute the average tariffs are provided in Sect. 4.
In more detail, we have computed the 3-year period mean value of FDI, GDP, skilled labour endowments and tariffs, where data in value were in constant 2000 dollars, while the dummies BIT and FTA are equal to one if there is a BIT or a FTA in at least 1 year over each 3-year period. Five periods are here considered: 1995–1997, 1998–2000, 2001–2003, 2004–2006 and 2007–2008.
Even though there could be some unobservable heterogeneity due to specific effects for each host country, we have not included host country fixed effects, because their inclusion raises multicollinearity problems and the coefficients of most regressors become not significant.
For robustness check, estimations have been run by considering an alternative FTA variable, based on the count of the number of FTA in force for each host country during the considered period. Results, which are available upon request, do not substantially change.
The relative GDP is here in level rather than in logarithm. The reason is that the correlation between the log of the relative GDP and the log of the sum of GDPs in the six industries is, in absolute value, higher than 0.7. Following Baltagi et al. (2008), also the differences in the skilled labour endowments are included in level. Finally, because of the zero values, in our final specification, we have not considered tariffs in log; indeed, this would determine the loss in the estimations of 19 observations for total manufacturing and 184 observations for the six industries. The inclusion of tariffs in log, however, does not substantially change the results.
Equation (1) has been also estimated with the inclusion of interaction terms between tariffs and the (squared) differences in skilled labour endowment, but the coefficients are mostly not significant.
We have performed the Verbeek and Nijman (1992) test for attrition bias applied to cross-sectional estimates, similarly to that performed by Nese and O’Higgins (2007) for several waves of survey firm data. Test results reject the hypothesis of attrition bias for both the manufacturing sector as a whole and data disaggregated for the six industries.
For robustness check, we estimated the spatial model also with the maximum likelihood estimator using the STATA command spatreg provided by Pisati (2001); results do not substantially vary from those obtained by using 2SLS.
As already mentioned, we checked the robustness of our results by using different specifications. We estimated the model (1) also with the inclusion of tariffs in logarithm rather than in level and interacted tariffs with the (squared) difference of skills. This result seems to be robust for all the different models.
However, the effect of host tariffs on EU FDI in developed countries is not significant.
In Table 6, ind1 indicates “Food products”, ind2 is “Total textiles and wood activities”; ind3 refers to “Total petroleum, chemical, rubber, plastic products”; ind4 stands for “Total metal and mechanical products”, ind5 is “Total office machinery, computers, RTV, communication equipment” and ind6 indicates “Total vehicles and other transport equipment”.
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Acknowledgments
The authors thank Giovanni Anania, Maurizio Zanardi, an anonymous referee and participants to the 14th ETSG Conference held in Leuven (Belgium), 13–15 September 2012, for their valuable comments on an earlier draft of this manuscript. The authors wish to thank also Rafal Raciborski for his useful advice on some STATA commands for spatial analyses.
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Cardamone, P., Scoppola, M. The pattern of EU FDI in the manufacturing industry: What role do third country effects and trade policies play?. Ann Reg Sci 54, 511–532 (2015). https://doi.org/10.1007/s00168-015-0664-2
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DOI: https://doi.org/10.1007/s00168-015-0664-2