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The role of public policy in closing foreign direct investment gaps: an empirical analysis

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Abstract

Using public policy instruments to attract Foreign Direct Investment (FDI) has become standard in most countries, irrespective of their level of development, geographical location or industrial structure. Against this background the paper analyses the suitability of various public policies to attract inward FDI based on a sample of 11 countries and 10 industries from the manufacturing sector over 10 years. For this aim we derive an empirical baseline model of the determinants of inward FDI-stock. From this baseline model FDI-gaps—measured as the difference between the “estimated actual” inward FDI-stock and the “potential” FDI-stock, which could be realized if a certain “best practice policy” were carried out—are derived. Thereby the analysis focuses on business taxation, public research and development expenditures, the information and communication infrastructure endowment, labor costs as well as institutional and skill-related policies. The analysis inter alia reveals the share of each of these location factors in the total industry- and country-level FDI-gap. Moreover, the analysis explores how policy advice depends on the definition of the “best practice policy”.

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Notes

  1. The EU countries included are: Austria (AUT), Finland (FIN), France (FRA), Great Britain (GBR), The Netherlands (NLD), Germany (GER), the Czech Republic (CZE), Hungary (HUN), Slovenia (SVN) and Slovakia (SVK).

  2. These variables are titled “policy variables” throughout the paper.

  3. However, Helpman (2006) argues, “… the traditional classification of FDI into vertical and horizontal forms has become less meaningful in practice. Large multinationals invest in low-cost countries to create export platforms from which they serve other countries around the world ….” (p. 590) Thus, in this paper, we do not classify the determinants into market- and efficiency-related factors.

  4. The level of political risk decreases with an increase in the value of the risk variable chosen.

  5. We do not present detailed results of the general-to-specific-approach to limit the length of the paper. These are however made available upon request.

  6. For comparison the “Appendix” (Tables 9, 10, 11, 12) contains FDI-gaps calculated on the results from the random effects estimator with two-years-lagged variables.

  7. Table four also shows that the AR(1) and AR(2) tests as well as the Hansen-J-test for the AB estimator are in favor of the validity of the GMM estimates.

  8. Note, that the labor costs variable is insignificant in the fixed effects specification. Yet, its coefficient is comparable to that of the random effects specification. The insignificance, thus, can be seen as an indication of the inefficiency of the fixed effects estimator.

  9. Using minimum/maximum values in our study leads to a range of total FDI-gaps between 9 and 33%. Results can be received upon request.

  10. An exception is the textile industry in the US.

  11. For example, industries may be clustered by important characteristics like R&D intensity or educational intensity, export propensity, scale intensity etc.

Abbreviations

FDI:

Foreign Direct Investment

R&D:

Research and Development

CEECs:

Central and East European Countries

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Acknowledgments

We would like to thank the anonymous referee for valuable comments. This contribution originates from the study “Policies to Attract Foreign Direct Investment: An Industry-Level Analysis” commissioned by the Austrian Federal Ministry of Economics and Labour (BMWA) within the scope of the Research Centre International Economics (FIW), funded by the Internationalisation Program “go International”.

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Correspondence to C. Bellak.

Appendix

Appendix

See Tables 7, 8, 9, 10, 11, and 12.

Table 7 Classification of industries
Table 8 Shares of the policy variables in industry-specific gap by country, industry and policy variable in % (2004)
Table 9 Total country gap and industry-specific gaps (2004) for random effects model with two-years-lagged variables (overall sample mean value as “best practice policy”)
Table 10 Total country gap and shares of policy variables in total country gap in percent (2004) for random effects model with two-years-lagged variables (overall sample mean value as “best practice policy”)
Table 11 Total country gap and industry-specific gaps (2004) for random effects model with two-years-lagged variables (country group-specific mean value as “best practice policy”)
Table 12 Total country gap and shares of policy variables in total country gap in percent (2004) for random effects model with two-years-lagged variables (country group-specific mean value as “best practice policy”)

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Bellak, C., Leibrecht, M. & Stehrer, R. The role of public policy in closing foreign direct investment gaps: an empirical analysis. Empirica 37, 19–46 (2010). https://doi.org/10.1007/s10663-009-9107-6

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