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Spillovers from foreign direct investment in Central and Eastern Europe

An index for measuring a country’s potential to benefit from technology spillovers

Society and Economy
Authors: Balázs Szent-Iványi and Gábor Vigvári

In the paper, we construct a composite indicator to estimate the potential of four Central and Eastern European countries (the Czech Republic, Hungary, Poland and Slovakia) to benefit from productivity spillovers from foreign direct investment (FDI) in the manufacturing sector. Such transfers of technology are one of the main benefits of FDI for the host country, and should also be one of the main determinants of FDI incentives offered to investing multinationals by governments, but they are difficult to assess ex ante. For our composite index, we use six components to proxy the main channels and determinants of these spillovers. We have tried several weighting and aggregation methods, and we consider our results robust. According to the analysis of our results, between 2003 and 2007 all four countries were able to increase their potential to benefit from such spillovers, although there are large differences between them. The Czech Republic clearly has the most potential to benefit from productivity spillovers, while Poland has the least. The relative positions of Hungary and Slovakia depend to some extent on the exact weighting and aggregation method of the individual components of the index, but the differences are not large. These conclusions have important implications both the investment strategies of multinationals and government FDI policies.

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. Although there are some papers that have proposed the use of a composite index, here we follow the approach pioneered by Han – Goetz (2015) because their indicator captures the two aforementioned dimensions of resilience jointly. 11 This indicator

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Acta Oeconomica
Authors: Ivan Vujačić, Jelica Petrović-Vujačić, Svetozar Tanasković, and Marko Miljković

is a composite index, observing the change in the separate dimensions of the index could shed light on which dimension is the main driver of the convergence process between the former Yugoslav states. As shown in Figure 3 , in the observed period the

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://, accessed 02/04/2018. Antony , G. – Visweswara Rao, K. ( 2007 ): A Composite Index to Explain Variations in Poverty, Health, Nutritional Status and Standard of Living: Use of Multivariate Statistical Methods . Public Health 121 ( 8 ): 578 – 587

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ESRI (2013): Guide for Using Diffusion Indexes and Composite Indexes . ESRI. Guide for Using Diffusion Indexes and Composite Indexes 2013

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