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Institutional Quality in Central and East European Countries and Its Impact on FDI Inflow

Abstract

Objective: To verify the quality of institutional environment and its role in attracting foreign direct investment (FDI). The article attempts to provide a quantitative description of the impact of institutional quality (IQ) on FDI inflow to 17 countries of Central and Eastern Europe (CEECs) in 2007-2017.

Research Design & Methods: Firstly, we reviewed studies dedicated to the relationship between institutional operations and investment attractiveness. Then, we selected 17 CEECs and ranked them for the value of FDI in absolute terms and as a share of GDP. The third stage focused on building an original set of indicators. We used selected categories of Global Competitiveness Index (GCI). Based on rankings, in the fifth stage, we divided 17 CEECs into groups representing similar IQ using hierarchical cluster analysis. The final stage consisted in examining the impact of IQ on the inflow of FDI within
a selected group of countries by estimating dynamic panel data models.

Findings: The study demonstrated that CEECs differ with respect to IQ; an aspect that exerts a statistically significant impact on FDI inward stock as % of GDP.

Implications & Recommendations: The study has implications for research and practice. The results may be interesting for policymakers and may have an application value for institutions. An efficient and effective institutional system may importantly contribute to the boosting of investment attractiveness of countries and impact FDI flows.

Contribution & Value Added: The article sheds more light on the discussion about the relevance of IQ as a factor determining FDI inflow. The added value of this article consists in grouping 17 CEECs based on their similarity with respect to IQ and demonstrating that it impacts the size of FDI inflow. We proposed an original set of indicators for these countries that help in identifying their IQ.

       

Keywords

FDI, institutions, institutional quality, hierarchical cluster analysis, panel models,

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