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Foreign Direct Investment, Technology Transfer and Productivity Growth. Empirical Evidence for Hungary, Poland, Romania, Bulgaria and the Czech Republic

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  • Torlak, Elvisa

Abstract

Many governments offer significant inducements to attract inward investment, motivated by the expectation of spillover benefits. Foreign direct investment (FDI) is generally perceived as the best channel for technology transfer, not only across national boundaries but also between firms – in particular, between foreign and domestic companies. This paper tests this hypothesis for five transition countries in Eastern Europe using panel data on more than 8000 plants in the Czech Republic, Poland, Hungary, Romania and Bulgaria. In a log-linear model, the Cobb-Douglas production function is estimated to examine the productivity effect of: (a) foreign ownership in firms, and (b) foreign presence in industries and regions. In the first case, regression coefficients indicate a positive correlation between foreign equity participation and plant productivity. In the second case, the impact of foreign investment on productivity of domestically owned firms turns out to be either negative or insignificant. Thus, the study corroborates the hypothesis that technology is transferred internationally through multinational companies, but provides no evidence of diffusion of technology from foreign to domestic firms.

Suggested Citation

  • Torlak, Elvisa, 2004. "Foreign Direct Investment, Technology Transfer and Productivity Growth. Empirical Evidence for Hungary, Poland, Romania, Bulgaria and the Czech Republic," Conference papers 331189, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
  • Handle: RePEc:ags:pugtwp:331189
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    1. Weale, Martin, 1985. "Testing Linear Hypotheses on National Account Data," The Review of Economics and Statistics, MIT Press, vol. 67(4), pages 685-689, November.
    2. G. Günlük‐Şenesen & J. M. Bates, 1988. "Some Experiments with Methods of Adjusting Unbalanced Data Matrices," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 151(3), pages 473-490, May.
    3. H. Theil & Guido Rey, 1966. "A Quadratic Programming Approach to the Estimation of Transition Probabilities," Management Science, INFORMS, vol. 12(9), pages 714-721, May.
    4. Richard Stone & D. G. Champernowne & J. E. Meade, 1942. "The Precision of National Income Estimates," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 9(2), pages 111-125.
    5. Golan, Amos & Judge, George & Robinson, Sherman, 1994. "Recovering Information from Incomplete or Partial Multisectoral Economic Data," The Review of Economics and Statistics, MIT Press, vol. 76(3), pages 541-549, August.
    6. Michael H. Schneider & Stavros A. Zenios, 1990. "A Comparative Study of Algorithms for Matrix Balancing," Operations Research, INFORMS, vol. 38(3), pages 439-455, June.
    7. van der Ploeg, Frederick, 1985. "Econometrics and inconsistencies in the national accounts," Economic Modelling, Elsevier, vol. 2(1), pages 8-16, January.
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