Stable political environment and prominent development of political institutions increase foreign direct investment flows by providing lower risks for investors. However, this impact can vary according to the development of the country. This study aims to investigate the impact of various indicators of political stability on foreign direct investment attraction for different economies distinguished by their development level. Our database includes 66 FDI-recipient countries and 98 FDI-investing countries for the period from 2001 to 2018. By applying the gravity approach and Poisson Pseudo Maximum Likelihood method with instrumental variables (IV PPML), we model bilateral FDI flows, incorporating variables reflecting various aspects of political stability formed by the principal components analysis. Interestingly, we found mixed results regarding the impact of political stability on FDI flows. In particular, political stability indicators were found to be insignificant, when analysing the bilateral FDI flows for the group of developed economies. We obtained similar result for the group of developing economies. However, political stability variables significantly influence FDI flows for countries with different development level, confirming the hypothesis that countries’ development affects bilateral FDI flows. Besides, we discover the significant difference between developed and developing countries referring to FDI-investors. Based on the obtained results, we highlight a few policy implications for developing and developed economies.

Given the importance of stock market synchronization for international portfolio diversification, we estimate the degrees of co-movements among US, Chinese and Russian markets. By applying the TVP-VAR approach, we measure total and bivariate synchronization indices utilizing daily data from 1998 to 2021. Our analysis demonstrates that the total connectedness index (TCI) is 26.15% among the three markets. We find that the US market is the highest volatility contributor, whereas the Russian market is the highest receiver. Since stock market synchronization is exposed to geopolitical risk, at the second stage, we apply the Quantile-on-Quantile framework to measure the response of total and bilateral connectedness indices to geopolitical risk (GPR). The findings affirm our proposition that GPR impedes TCI when it has a bullish state and a higher quantile of GPR. The response of bilateral connectedness is negative towards GPR concerning US–China and US–Russian pairs. However, the degree of connectedness between Russian and Chinese stock markets is less responsive to GPR.

This study is devoted to the evaluation and scrutiny of political stability as a determinant of foreign direct investment (FDI) inflows to different countries. The primary objective of the research is to estimate the impact and influence of various indicators of political stability on foreign direct investment inflows. The analysis is delivered based on a database on cross-country FDI inflows of 66 FDI-importer countries and 98 FDI-exporter countries, in the period between 2001-2018. This article uses the assumption that the impact of political stability might be different for both the groups of developed and developing countries. As the developed economies have higher political stability, they tend to attract larger amounts of foreign direct investment compared to developing economies, where the political situation can be less stable. Furthermore, the estimation applies the gravity approach, while the main method used for the econometric calculations is the Pseudo Poisson Maximum Likelihood (PPML) regression. The outcome revealed that in most cases the indicators of political stability had a positive impact on the foreign direct investment inflows. However, the results are not constant for all groups of countries. Therefore, if a developed country is an importer of investment, then most of the indicators of political stability become significant and have a positive influence on the foreign direct investment. At the same time, if the importer is a developing country, then for the investor-developed economy, political stability becomes a significant factor. Similarly, if the FDI-exporter is a developing economy, then determinants of political stability are insignificant. Based on these results, possible recommendations for refined government policies can be suggested.

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Foreign direct investment and international movement of commodities are interrelated in the world economy. At the same time, the nature of this relationship and the causality issues are ambiguous and need to be studied from both theoretical and empirical sides. The aim of this paper is to estimate empirically the mutual influence of foreign direct investment and international trade in the modern economy. The econometric model is based on the gravity approach, the estimation is made using the Poisson pseudo maximum likelihood method on the data for 67 host and 109 home FDI countries for the period of 2001–2016. The hypotheses on the positive mutual influence of foreign direct investment and international trade are tested. A positive and significant influence of export and import flows on inward foreign direct investment is observed. The largest impact of export and import on foreign direct investment is observed when a two-year lag is considered. We could not reveal a significant influence of foreign direct investment on export and import flows either within one year, or for the lagged FDI values. The authors argue that pro-trade government policy, aimed at the integration of the country into global value chains is an important factor stimulating the inflow of foreign direct investment to the country. From the practical point of view, understanding the causal linkages between export, import and foreign direct investment helps state authorities better forecast the direct and indirect effects of various trade policy incentives.

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This research paper is devoted to analysis of various institutional factors as determinants of foreign direct investment (further – FDI) inflows to different countries. The objective of the research is to estimate the effect of institutions on FDI inflows. The analysis is provided on a database of cross-country FDI inflows on 72 countries FDI-importers and 112 countries FDI-exporters in the period from 2001 to 2016. It is supposed in the paper that the impact of institutional factors might be different for the groups of developed and developing countries; since developed economies have higher institutional indicators, they tend to attract larger amounts of foreign direct investment compared to developing economies, where institutional development is at the lower level. The estimation is based on the gravity approach, which considers the positive effects of countries’ GDP and the negative effect of the distance between them. The main method used for the econometric estimation is the Pseudo Poisson Maximum Likelihood (PPML) regression, which is considered to be one of the adequate methods for estimating such data. During the research the problems of zero-observations and correlation between institutional indicators are solved. The results have shown that higher quality of institutions tends to attract more foreign direct investment to a country. Thus, institutions in developed countries have positive and significant impact on FDI attraction. At the same time, the analysis of developing countries has shown that some institutions have less significant influence on the FDI inflows. Based on the results of the research, possible recommendations for government policy on institutional improvement can be suggested.

Infrastructure is one of the main determinants of consistent and sustainable development in different countries and regions. Considering the Russian Federation, where there are 85 regions, not counting the federal cities, the problem of regional development and factors that can promote it, is currently of high importance. Different levels of regional development lead to higher economic differentiation between regions and cause serious damage to the Russian economy. Our main hypothesis suggests that a higher level of infrastructure development in a region positively affects economic development in the areas. Therefore, the main aim of our research is to estimate the impact of infrastructure on economic development in Russian regions using econometric analysis. In addition, cluster analysis was implemented to reveal the difference in infrastructural development levels in the regions. To provide a precise estimation, a database was constructed on Russian regions for the period of 2012 to 2016. The main method used in the research is econometric analysis and cluster analysis by using k-means method based on three main indicators: social, industrial and financial. The results of the analysis reveal 5 different clusters with highly differentiated levels of infrastructural development. Econometric analysis has shown that the most significant infrastructural factors are industrial factors and social factors. The results of the research could be taken into consideration as recommendations for development in order to improve government policy towards less developed Russian regions.