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Does foreign direct investment stimulate new firm creation? In search of spillovers through industrial and geographical linkages

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Abstract

This paper examines the spillover effects of inward foreign direct investment (FDI) on the entrepreneurial activities of new firm creation through both industrial and geographical linkages. Using a dataset of 44,434 newly created small firms in 234 regions of South Korea in 2000–2004, this study finds that while the spillover impacts of FDI in the low-tech industry are positive and significant across almost all four possible combinations of the intra-/inter-regional and intra-/inter-sectoral channels, the impacts in the high-tech industry are largely intra-sectoral within the host region and across neighboring regions. Moreover, all statistically significant spillover effects follow an inverted ‘U’-shaped curvilinear trend.

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Fig. 1

Notes

  1. In monetary term, global FDI inflows grew from US$ 208 billion in 1990 to US$ 1.3 trillion in 2012; the total stock rose from US$ 2 trillion (equivalent to 7 % of world GDP) in 1990 to US$ 23 trillion (equivalent to 32 % of world GDP) in 2012 (UNCTAD FDI Statistics; UNCTAD World Investment Report 2013).

  2. Assessing the joint sectoral and spatial spillover effects is challenging. For example, catching the upstream to downstream supplier-customer relationship across regions typically involves multi-regional input–output tables. Measuring and analyzing labor similarity and technical similarity across industries (Jofre-Monseny et al. 2011; Marshall 1890) and regions would involve multi-industrial and multi-regional matrices more complicated than multi-regional input–output tables. To avoid complicated big matrices without compromising our research mission, we focus on the high-tech and low-tech sub-sectors in the manufacturing industry. Such a high-tech versus low-tech dichotomy would make the issue of spillover from labor similarity and technology similarity largely an intra-industrial affair within the focal region and across neighboring regions.

  3. The high-tech sub-sample includes new firms operating in the following industries: biotechnology; the environment; alternative energy; semi-conductor equipment and electronic components; audio and video; telecommunication equipment, computers and auxiliary devices; medical equipment; precise mechanics; optical; and sophisticated parts and materials. The low-tech sub-sample includes firms in all other remaining manufacturing industries.

  4. The adoption of a 1-year lag is a natural accommodation to the fact that FDI spillovers would take time and furthermore, such adoption of a time-lag for independent variables is also popular in the literature (e.g., among others, Fritsch and Falck 2007; Sutaria and Hicks 2004).

  5. Because the performance of these two variables in our major 3SLS and system GMM regressions is insignificant, we exclude them in the major regressions.

  6. It is worth mentioning that for the least squares estimators, the correlations between location control variables have no impact on the unbiasedness of estimates although the correlations may affect the variances of the estimates (Greene 2008). In addition, the VIF statistics for the full set of explanatory variables is 8.97 for the high-tech equation and 8.95 for the low-tech equation, respectively, which are less than the popularly accepted critical value of 10. Therefore, multicollinearity in general or among locational control variables is not a serious problem in our empirical analysis.

  7. There may be a potential endogeneity issue between new firm creation activities and inward FDI. Foreign firms may be attracted to certain geographic regions or industrial sectors because these regions or sectors offer the same degree and type of opportunities that attract local entrepreneurial firms such as a cluster of established firms, an abundance of cheap workforce, affordable land, etc. In addition, foreign capital injections may improve the economic performance of regions, thus further enhancing new opportunities for prospective entrepreneurs (Aitken and Harrison 1999). A lack of control for such potential endogeneity may generate biased and inconsistent empirical results.

  8. The robustness test based on a second-order contiguity spatial weight matrix produces statistically equivalent results and therefore we do not report them here.

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Authors and Affiliations

  1. Department of Geographical Sciences, University of Maryland, 2181 Samuel J. LeFrak Hall, College Park, MD, 20742, USA

    Laixiang Sun

  2. Department of Management, Quinlan School of Business, Loyola University Chicago, 820 N. Michigan Ave., Chicago, IL, 60611, USA

    In Hyeock Lee

  3. School of Finance and Management, SOAS University of London, Thornhaugh Street, Russell Square, London, WC1H 0XG, UK

    Laixiang Sun & Eunsuk Hong

  4. International Institute for Applied System Analysis (IIASA), Schlossplatz 1, 2361, Laxenburg, Austria

    Laixiang Sun

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  1. Laixiang Sun
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Correspondence to Eunsuk Hong.

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Table 6.

Table 6 Results of the first-stage estimation of the full system
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Sun, L., Lee, I.H. & Hong, E. Does foreign direct investment stimulate new firm creation? In search of spillovers through industrial and geographical linkages. Small Bus Econ 48, 613–631 (2017). https://doi.org/10.1007/s11187-016-9803-0

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