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FDI and productivity convergence in Central and Eastern Europe: an industry-level investigation

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Abstract

This paper presents empirical evidence of the effect of FDI inflows on productivity convergence in Central and Eastern Europe, using a new and harmonized industry-level data set. Four conclusions stand out. First, there is a strong convergence effect in productivity, both at the country and at the industry level. Second, FDI inflow plays an important role in accounting for productivity growth. Third, the impact of FDI on productivity critically depends on the absorptive capacity of recipient countries and industries. Fourth, there is important heterogeneity across countries, industries and time with respect to some of the main findings.

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Notes

  1. Weighted average of the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia and Slovenia. In this paper, Central and Eastern Europe refers to these eight EU countries.

  2. See a meta-analysis by Görg and Greenaway (2004) or an investigation in a cross-country setup by Damijan et al. (2003). More recent contributions to the spillover literature focusing on Central and Eastern Europe include Gersl et al. (2007), Gorodnichenko et al. (2007) and Kolasa (2008).

  3. Only some very recent studies have used the EU KLEMS database for research on transition economies. See, for example, Baas et al. (2009), Persyn (2008) or Polgar and Wörz (2009).

  4. For presentational reasons, the individual industries for which data are available have been lumped together in this section into four broadly defined sectors. Industry, in the first panel, mainly consists of manufacturing, together with mining and quarrying and electricity, water and gas supply (NACE categories D, C and E, respectively). The second sector is construction (NACE F). The third and fourth sectors are (market) services, with the former covering the more “traditional” services, such as trade and repairs, hotels and restaurants as well as transport and communication (NACE G, H and I), while the latter comprises financial and business-related services (NACE J and K). These four sectors together cover all economic activities except agriculture (and related branches) and non-market services.

  5. In transition economies FDI inflows may also play an important role in the process of restructuring of formerly state-owned companies (see, e.g. Blanchard 1997).

  6. See also Kokko (1994).

  7. See also Benhabib and Spiegel (2005). A confirmation of the Nelson-Phelps hypothesis, using a panel of OECD countries, can be found in Griffith et al. (2004).

  8. In principle, this kind of problems can be mitigated by using instrumental variable techniques. However, lack of good instruments makes this option rather impractical or can even do more harm than good (see Nelson and Startz 1990 or Bound et al. 1995).

  9. We use the xtabond2 procedure for Stata. See Roodman (2006).

  10. We treat all lagged explanatory variables as predetermined, which means that they are assumed to be uncorrelated with present and future errors. This assumption might be violated, e.g. if FDI inflow is motivated by expectations of future shocks, which seems rather unlikely.

  11. This means that our cross-section approach also exploits some time series variation in the data, although to a much lesser extent than the system GMM technique applied to yearly data.

  12. EU KLEMS stands for EU analysis of capital (K), labour (L), energy (E), materials (M) and service (S) inputs. The database is downloadable at http://www.euklems.net. See also Koszerek et al. (2007) for its extensive overview.

  13. These adjustments were done by the EU KLEMS consortium on the basis of agreed procedures to ensure harmonisation of the data and to generate growth accounts in a consistent and uniform way. Harmonisation focused, among others, on industrial classifications, aggregation levels, reference years for volume measures, price concepts and methods for solving breaks.

  14. Bulgaria and Romania are not covered in the EU KLEMS database.

  15. While data on mining and quarrying (NACE C), electricity, gas and water supply (NACE E) and manufacture of leather and leather products (DC) are generally available, these sections are excluded from our sample. The reason for doing so is their high regulation (C and E) or very small share in total economy’s output (DC). It has to be noted that adding these industries to our sample keeps the main results qualitatively unchanged.

  16. Ideally, we would want to measure productivity as total factor productivity. Unfortunately, this and related measures are not available (or are hard to estimate in a consistent way) for the group of countries we focus on, particularly at this level of disaggregation.

  17. Whenever possible, data on labour productivity and nominal value added are extrapolated to 2005 using official Eurostat sources.

  18. Similarly to all value-added shares defined below, this ratio was calculated by converting the relevant variables to a common currency using market exchange rates.

  19. This means that our measure of FDI inflow captures not only flow of funds, but also the revaluation effect. Unfortunately, the availability of direct data on FDI inflows is very limited, so relying on them would dramatically truncate our sample.

  20. In the OLS estimation all yearly observations are pooled without imposing any cross-section structure. This implies that the first intercept is identical across all observations in the OLS specification.

  21. In the OLS version there is only one intercept, common across all observations of a given 5-year subperiod.

  22. This becomes apparent once one realises that our specification can be viewed as a special case of an error-correction model. By definition, \( \ln {\text{RLP}} = \ln {\text{LP}} - \ln {\text{LP}}^{*} \), where an asterisk indexes the euro area. By re-arranging the terms in Eq. 1 we obtain the long-run semi-elasticity of LP with respect to FDI equal to −γ/β.

  23. Detailed results are available from the authors upon request.

  24. This hypothesis seems to be confirmed by the unrestricted variant of our SUR estimations: if we allow the coefficients in regression 3 from Table 4 to vary across the two subperiods, we get a positive and significant estimate of the interaction term only in the first equation, covering the period 1995–2000 (see Table 7).

  25. The estimation results described in this section are available from the authors upon request.

  26. This is the approach pursued by Cameron et al. (2005) in a similar setup covering UK manufacturing industries.

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Acknowledgments

This paper was written while Marcin Kolasa was working in DG Economics of the ECB. The authors would like to thank the participants to the internal ECB seminar and the INFER Workshop in Cluj-Napoca for useful comments. Special thanks are owed to: Hans-Joachim Klöckers, Reiner Martin, Monica Pop-Silaghi and three anonymous referees.

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Authors and Affiliations

  1. European Central Bank, DG Economics, Kaiserstr. 29, 60311, Frankfurt, Germany

    Martin Bijsterbosch

  2. National Bank of Poland, Economic Institute, ul. Swietokrzyska 11/21, 00-919, Warsaw, Poland

    Marcin Kolasa

  3. Warsaw School of Economics, Department of Applied and Theoretical Economics, Al. Niepodleglosci 162, 02-554, Warsaw, Poland

    Marcin Kolasa

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  1. Martin Bijsterbosch
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Correspondence to Marcin Kolasa.

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Bijsterbosch, M., Kolasa, M. FDI and productivity convergence in Central and Eastern Europe: an industry-level investigation. Rev World Econ 145, 689–712 (2010). https://doi.org/10.1007/s10290-009-0036-z

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