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Determinants of FDI for Spanish regions: evidence using stock data

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Abstract

After decades of academic debate on the factors that determine FDI, the discussion still remains open. This article empirically investigates the determinants of FDI activity using Spanish regional data (NUTS 2) for the period 2004–2013. We apply the Poisson pseudo-maximum-likelihood estimator with country-origin fixed effects in a gravity framework and implement an exploratory factor analysis to avoid collinearity problems. The empirical analysis revealed the following allocation patterns: FDI locational strategies in the Spanish regions are determined significantly by the economic potential, competitiveness and agglomeration effects of the regions, and to a lesser extent, by the productive capacity. We also confirm the adequacy of using the stock of FDI in the empirical analysis and obtain an improved specification of the model compared to previous literature based on FDI flows. The results allow us to draw some policy implications about the prospective promotion of incoming FDI at the subnational level.

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Fig. 1

Source: UNCTAD database

Fig. 2

Source: DataInvex

Fig. 3

Source: DataInvex

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Notes

  1. According to Bertola et al. (2013), there were two primary and interrelated causes explaining these imbalances: First, the monetary union created optimistic expectations regarding the rapid convergence of the peripheral countries with the core ones in the Eurozone. Second, the introduction of the euro eliminated the exchange rate risk and induced investors to disregard country-specific bankruptcy risks. Both causes generated an investment and credit boom in the periphery and implied a large-scale reallocation of capital from the core to the periphery that materialized as current-account deficits.

  2. Note that data on inward FDI stock are only available since 1980.

  3. The empirical literature at the country level has focused mainly in OECD countries, since traditionally they have represented a prominent share of world’s FDI flows (Bénassy-Quéré et al. 2007; Talamo 2007).

  4. We do not account for Ceuta and Melilla due to data availability. DataInvex provides data for these autonomous cities only for 3 out of 40 of the source countries included in our sample and not for the whole time span. Furthermore, FDI data for these cities are not provided separately what could be problematic for the analysis when including specific host region characteristics.

  5. The Spanish Ministry of Economy, Industry and Competitiveness through its investment registry (DataInvex database) provides data on FDI flows disaggregated by regions and sectors, whereas FDI stock data are available at national level since 2007. Indeed, we are indebted to Isaac Barbero and Emilio Carmona from the Spanish Ministry of Economy, for providing us with the extended and disaggregated version of FDI stock data from the DataInvex database that allows us to conduct the study at the regional level.

  6. According to international recommendations, DataInvex defines FDI as those transactions through which a direct investor acquires or increases its participation in a company resident in another country so that it can exert an effective influence in its management. In practice, it is considered that the investor has the ability to influence the management of a company when he has at least 10% of the capital or voting rights. Specifically, we use in our study the investment position which represents the value of the assets that direct investors hold in companies, resident in countries other than their own, with direct investment. The position data are established from the perspective of the country that presents them (reporting country). For the purposes of this study, the position of foreign investment in Spain would be the value of the shares of non-resident investors in companies domiciled in Spain. The participations are valued on the basis of the book value of the equity of the direct investment company.

  7. See, for instance, Russ (2009), Russ (2012) or Cavallari and D’Addona (2013).

  8. Markusen and Maskus (2002).

  9. While Markusen and Maskus (2002) knowledge–capital model is about FAS, Bergstrand and Egger (2007) is about both, FAS and proper FDI.

  10. Recently, theory has been directed, as well as empirical work, to FAS, starting with Brainard (1997) and continuing to Ramondo et al. (2015).

  11. The factor–proportions model is based on Venables (1999).

  12. Some examples of these studies are Bajo-Rubio and Sosvilla-Rivero (1994), Pelegrín (2003) and Martínez-Martín (2011), among others.

  13. We do not include FDI information of ETVE (Empresas de Tenencia de Valores Extranjeros-brokers) firms because they are considered instrumental companies whose existence obeys to fiscal optimization strategies within a business group and in many cases their investments lack direct economic effects.

  14. Exceptions are Wacker (2013) and Blonigen and Piger (2014).

  15. Bénassy-Quéré et al. (2007), p.769.

  16. This last point can be tricky. Following the advice of one referee we elaborate on this point to justify our position. According to UNCTAD (2018): “FDI flows are presented on a net basis, i.e. as credits less debits. Thus, in cases of reverse investment or disinvestment, FDI may be negative.” Thereby, negative FDI flows have real economic meaning. On the contrary, although analytically a negative sign on FDI stocks indicates that at least one of the three components of FDI flows (i.e. equity capital, reinvested earnings or intra-company loans) is negative and has not been offset by positive amounts of other components, from an economic perspective this negative sign lacks real economic meaning and are usually considered the consequences of accounting methods (see Gouel et al. 2012; Bae and Jang 2014; Baronchelli and Uberti 2018; Petkova et al. 2018). Thereby, replacing the negative FDI stocks with a zero have become a common practice among some recent empirical studies (see, for instance Bae and Jang 2014; Petkova et al. 2018).

  17. It should be noted that the choice of variables was somewhat restricted by the availability of disaggregated Spanish data.

  18. Notice that the results are interpreted considering these three new dimensions: region’s economic potential, productive capacity and competitiveness and agglomeration effects.

  19. We have also repeated the exercise of all the before-mentioned estimations applying OLS instead of PPML as a robustness check and the results obtained point to a superior performance of the PPML method compared to OLS. All these results are not reported in the paper but are available from the authors upon request.

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Acknowledgements

The authors are grateful to Matilde Mas Ivars for kindly providing data of the Valencian Institute of Economic Research (Ivie). We are also indebted to Isaac Barbero and Emilio Carmona from the Spanish Ministry of Economy, for providing us with the extended version of FDI stock data from the DATAINVEX database. Furthermore, we would like to thank Jeffrey Bergstrand, Estrella Gómez, Rafael Llorca and María Rochina for kindly providing advice with technical doubts as well as the comments and suggestions gathered from the XIII and XIV Inteco Workshops (Valencia), the ETSG 2017 19th Annual Conference (Florence) , the XLIII International Conference on Regional Science (Seville), 42nd Simposium of Economic Analysis (SAE-Barcelona) and the 5th International Symposium in Computational Economics and Finance (ISCEF-Paris). Finally, comments and suggestions by the Editor, R.M. Kunst and two anonymous referees are also acknowledged. All remaining errors are ours.

Funding

The authors acknowledge the public funding from FEDER and the AEI-Spanish Ministry of Economy and Competitiveness (BES-2015-072395 and ECO2014-58991-C3-2-R and ECO2017-83255-C3-3-P projects), the European Social Fund and the Valencian regional government (Generalitat Valenciana-PROMETEO 2018/102 project).

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Correspondence to Mariam Camarero.

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Appendix

Appendix

See Tables 7, 8, 9, 10 and 11.

Table 7 Data description and source
Table 8 Descriptive statistics
Table 9 Cross-correlation table
Table 10 Countries included in the study
Table 11 Summary of Spanish regional FDI determinants

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Camarero, M., Montolio, L. & Tamarit, C. Determinants of FDI for Spanish regions: evidence using stock data. Empir Econ 59, 2779–2820 (2020). https://doi.org/10.1007/s00181-019-01748-8

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