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Is there a euro effect in the drivers of US FDI? New evidence using Bayesian model averaging techniques

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Abstract

In this paper the authors analyze the potential determinants of US outward FDI stock with a particular focus on the euro effect during the period 1985–2017. To this aim, they consider a large set of candidate variables suggested both by theory and previous empirical analysis. They select the covariates using Bayesian model averaging, a data-driven methodology. Their sample includes a total of 56 host countries, that represent around the 70% of US outward FDI stock. They study the role of the euro on American FDI both in Europe and the rest of the world. In Europe, they consider various country groups: the European Union (EU), the euro area (EA), as well as core and periphery within this last group. They conclude that many variables studied by previous FDI literature cannot be considered robust determinants. Moreover, US OFDI is explained by both horizontal and vertical motives. However, HFDI strategies predominate in EA core countries, whereas VFDI prevails in the periphery. As for the euro effect, the common currency seems to have played an important role encouraging US FDI, being a crucial element in the convergence of EA periphery to its core. In addition, the results indicate that the adoption of the euro has favoured VFDI to the detriment of HFDI.

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Notes

  1. As reported in Carril-Caccia and Pavlova (2018) the estimated increase in FDI due to the EU membership ranges between 28 and 83 percentage points, while the incremental effect of euro area membership ranges between 21 and 44 percentage points. However, these studies consider different periods and different sets of countries, so they are not fully comparable. See, i.e. Baldwin et al. (2008), Neary (2009) and Stojkov and Warin (2018).

  2. See also Coeurdacier et al. (2018).

  3. See Helpman (2006).

  4. See, also, Dhingra et al. (2016), Bruno et al. (2016) or Treasury (2016).

  5. See Bureau of Economic analysis (BEA) statistics.

  6. While Markusen and Maskus (2002) knowledge-capital model is about foreign affiliate sales (FAS), Bergstrand and Egger (2007) is about both, FAS and proper FDI.

  7. Ownership, Location and Internalization.

  8. See Dunning (1980).

  9. See, for example, Anderson and Wincoop (2003), Chaney (2008), Disdier and Head (2008), Head and Mayer (2014) for surveys of the trade gravity literature.

  10. Obviously, Bayesian statistical techniques have not only been applied to FDI, but also to other fields of economics. These are the cases of export market shares (Benkovskis et al. 2019), the current account balance (Desbordes et al. 2018), the relationship between energy consumption and economic growth (Camarero et al. 2015) and growth models (Fernández et al. 2001). In the present research, we apply a robust probabilistic approach to select the explanatory variables from a large set of potential candidates. For that objective, we use the R-package BayesVarSel (García-Donato and Forte 2015), and apply Bayesian Variable Selection techniques for linear regression models using Gibbs sampling.

  11. If the missing data are unevenly distributed,they may create a selection bias problem that can question the accuracy of the coefficient estimates. This problem is, notwithstanding, relevant in this literature and has been solved using different approaches. For example, Eicher et al. (2012) who introduced Heckit BMA. They use a sample of 46 countries (25 OECD countries) from 1988 to 2000, and FDI flows as the dependent variable. The results show only mixed support for horizontal or export platform FDI theories, whereas the evidence of vertical FDI was quite weak. Jordan and Lenkoski (2018) use a Tobit Bayesian Model Averaging (TBMA) technique to improve the estimation of the inclusion probabilities of Eicher et al. (2012) and develop a full Bayesian model. Such method gives support for roughly the same determinants as the Heckit BMA when modeling the magnitude of FDI flows.

  12. For more information about fixed effects estimation in panel data, see Fernández-Val and Weidner (2016), Fernández-Val and Weidner (2018) and Weidner and Zylkin (2019).

  13. Germany, the Netherlands, France and Belgium not only founded the ERM in 1979 but were also members of the European Snake since 1972.

  14. This dummy captures the different levels of integration, from trade agreements to a common market

  15. This is, by far, the largest group of countries we analyze (a total of 56), and even if we have removed the unobservable time-invariant country heterogeneity from our estimation (see Sect. 3.2), they remain very diverse. A large sample increases the power of the BMA analysis, being able to detect very small size effects, and then, a large number of variables can be considered relevant.

  16. Its posterior mean is positive for the core (HFDI) and negative for the other two groups, that would imply resource seeking FDI or VFDI.

  17. Defined as the sum of US FDI in the host’s neighboring countries wieighted by the distances (see Table 2 for more details).

  18. See Table 2 for the complete list, definition and sources of candidates.

  19. The reason explaining the negative sign of population density is that it could attract a higher concentration of firms looking for abundant and cheaper labour. Consequently, the competition effect could offset the positive spillovers arising from a common pool of resources, deterring the entry of new firms. For more information about competition forces and FDI location, see Crozet et al. (2004).

  20. In the whole sample an important proportion of countries are from Central and Latin America, East Asia, East Europe and Africa. Moreover, the EU group contains the available Central and Eastern European countries.

  21. On the one hand, the positive sign of trade openness of the host country for the whole sample and EA core countries, as well as the negative sign of revenue from trade taxes for the whole sample, would imply that FDI and trade have been complements during the period considered (consistent with VFDI). Similar results were found by Helpman (1984), Helpman and Krugman (1985), Brainard (1997) and Camarero and Tamarit (2004). On the other hand, the mean tariff rate of the host country for the EA group and its periphery, and that one of the revenue from trade taxes for EU countries, would indicate a substitution pattern between trade and FDI and, thus, HFDI (Markusen 1984; Markusen and Venables 1999, 2000; Blonigen 2001).

  22. These variables are corruption, democratic accountability, law and order, bureaucracy quality, protection of property rights, and integrity of the legal system.

  23. See, for example, (Wei 2000; Chiappini 2014; Kinoshita and Campos 2003; Hyun 2006)

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Acknowledgements

The authors are grateful to Astrit Sulstarova for providing us with the extended version of FDI stock from the UNCTAD’s Bilateral FDI Statistics database. Furthermore, we are indebted to Anabel Forte for her help using the BayesVarSel package. This paper has benefited from the comments of the participants in the KOF Euro20 workshop and, in particular from S. Maiani, J. de Haan, D. Furceri and J.E. Sturm. The authors acknowledge the public funding from the EU (ERDF, ESF) and the AEI-Spanish Ministry of Economy and Competitiveness ECO2017-83255-C3-3-P project, as well as the Valencian regional government (Generalitat Valenciana-PROMETEO 2018/102 project). Cecilio Tamarit and Mariam Camarero also acknowledge the funding from European Commission project 611032-EPP-1-2019-1-ES-EPPJMO-CoE. The European Commission support does not constitute an endorsement of the contents which reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein. All remaining errors are ours.

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Authors and Affiliations

  1. Department of Economics, University Jaume I and INTECO, Campus de Riu Sec, 12080, Castellón, Spain

    Mariam Camarero & Sergi Moliner

  2. Department of Applied Economics II, University of València and INTECO, Av. dels Tarongers, s/n Eastern Department Building, 46022, Valencia, Spain

    Cecilio Tamarit

Authors
  1. Mariam Camarero
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  2. Sergi Moliner
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  3. Cecilio Tamarit
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Correspondence to Cecilio Tamarit.

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Appendices

Appendix 1: Trace of posterior inclusion probabilities

The following trace plots are obtained from 20000 iterations, the maximum that the R-function GibbsBvs allows to elaborate such plots. The PIPs are very close to converge with such number of iterations (Figs. 1, 2, 3, 4, 5).

Fig. 1
figure 1

Whole sample trace estimation

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Fig. 2
figure 2

EU countries trace estimation

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Fig. 3
figure 3

EA countries trace estimation

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Fig. 4
figure 4

EA core countries trace estimation

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Fig. 5
figure 5

EA peripheral countries trace estimation

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Appendix 2: Posterior inclusion probabilities

Figures 6, 7, 8, 9, 10

Fig. 6
figure 6

PIPs for whole sample

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Fig. 7
figure 7

PIPs for EU countries

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Fig. 8
figure 8

PIPs for EA countries

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Fig. 9
figure 9

PIPs for EA core countries

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Fig. 10
figure 10

PIPs for EA peripheral countries

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Camarero, M., Moliner, S. & Tamarit, C. Is there a euro effect in the drivers of US FDI? New evidence using Bayesian model averaging techniques. Rev World Econ 157, 881–926 (2021). https://doi.org/10.1007/s10290-020-00405-y

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