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What Causes the Regional Disparity of FDI in Russia? A Spatial Analysis

  • Russia Transition Papers
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Transition Studies Review

Abstract

We analyse the determinants of the regional disparity of FDI inflows to Russia. The spatial distribution of FDI is attributed to regional and/or trans-regional factors. Region specific characteristics such as wage, education level, transportation as well as gross regional product, which accounts for market size, in host and alternative regions are considered to analyze the spatial interaction between regions employing spatial econometrics. We find that shocks to FDI levels in proximate regions have no effect on FDI inflows to hosts. However, FDI in a region depends on spatial market size and endowment of natural resources in alternative host regions.

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Notes

  1. See Blonigen (2005) for a survey of the empirical literature on FDI determinants.

  2. Blonigen et al. (2007) mention resource-seeking FDI as part of vertical-complex type.

  3. Krai is mostly translated as province or territory and oblast means region.

  4. Tinbergen (1962), Pöyhönen (1963) and Linneman (1966) introduced this model to international trade analysis; Brenton et al. (1999), Buch et al. (2003), Carstensen and Toubal (2004) and Cieslik and Ryan (2004) have used it in FDI studies.

  5. These models originate from the seminal paper of Harris (1954) and have been used by Krugman (1992) and others (such as Hanson, 2005) in analysing geographic concentration of economic activity at inter- and intra-national levels such as Head and Mayer (2004), Cieslik and Ryan (2004) and Carstensen and Toubal (2004), Altomonte (2008) investigate the determinants of FDI employing market potential models.

  6. Statistical data sometimes include portfolio investment and business loans as well as direct investment in the foreign investment item. Portfolio investment indicates that although the foreigners have some shares in the firm they have no control over its operations. FDI, on the other hand, means that foreign investors have control over the operations of the company.

  7. It was 1.286 trillion dollars in 2007.

  8. See Bayulgen (2005) for details.

  9. According to Yudaeva et al. (2000) as the share of foreigners grow in a company, the firm becomes more productive and as the foreign share increases in an industry, productivity of the medium-sized firms increases. FDI is observed to force local firms to reorganize and restructure their activities.

  10. The region administered by the city council.

  11. Moscow city and the surrounding region are treated separately.

  12. She estimates the model for three different periods, i.e. 1995–1998, 1999–2002 and 2003–2005. The variables indicate averages in each period.

  13. Coughlin and Segev (2000), who look at the geographic distribution of FDI within China, find that a shock to FDI in one province has a positive effect on FDI in a nearby province. Baltagi et al. (2007) analyse the third country effects on US outward FDI in different industries to various host countries and find evidence for spatial correlation in independent variables and error terms. Emphasizing that FDI into a particular host country is not independent of FDI into alternative host countries, Blonigen et al. (2007) estimate a model, which differentiates between types of US based FDI to OECD countries and find a significant interdependence between the FDI a host country receives and the FDI inflows to its neighbours.

  14. They suggest to use the spatial error model in case that the ‘groups’ are not defined by “specifically observable characteristics but by ‘likeness’ in a way that is best captured by geographic proximity”.

  15. The details of this approach can be found in various works of Anselin (1999) and LeSage (1998, 1999, 2006).

  16. The elements of the weighting matrix are the inverse distances between regions. Following the general practise, rows are normalised to 1. An extensive evaluation of weights can be found in Anselin (1988)

  17. Since the natural logarithms are used in the estimations, substituting such a small number for zero outward FDI observations does not affect the estimation results, however allows us to keep the regions without any FDI at a given period in the data set. Razin et al. (2004) and Eichengreen and Tong (2007) have done a similar fill-in without any change in estimation results.

  18. We could have used GRP per capita to reflect final demand and industry output to capture intermediate demand but these variables are closely correlated therefore we opted for the GRP variable on its own.

  19. Apart from these variables we used domestic investments, profitability, railway and road density as alternative explanatory variables however they were statistically insignificant.

  20. We have calculated the Moran’s I statistics on annual basis. For all years, but only 1999, the statistics show significant spatial autocorrelation.

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Authors and Affiliations

  1. Istanbul Technical University, Faculty of Management and the Economic and Social Research Centre (ITU-ESRC), Istanbul, Turkey

    Saime Suna Kayam & Mehtap Hisarciklilar

  2. Istanbul Technical University, Institute of Social Sciences, Istanbul, Turkey

    Alexandr Yabrukov

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  1. Saime Suna Kayam
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  2. Alexandr Yabrukov
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Correspondence to Saime Suna Kayam.

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Kayam, S.S., Yabrukov, A. & Hisarciklilar, M. What Causes the Regional Disparity of FDI in Russia? A Spatial Analysis. Transit Stud Rev 20, 63–78 (2013). https://doi.org/10.1007/s11300-013-0272-8

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