The impact of management quality on firms' innovation and productivity in Russia

Innovations are among the most important drivers of firms' productivity improvement. Newly introduced products and processes, as well as organizational and marketing practices, are important for firms' performance and for countries' prosperity. In this paper, we analyze how management processes influence firms' innovation and performance based on Russian data. Our main research question is how the quality of management affects innovative results and thereby, productivity. We employ a survey of enterprise activities, and business climate BEEPS covering the period 2012-2014 and including 1564 firms. Based on the existing literature, we create a framework to study the impact of various factors on firms' innovation and productivity. We study both internal factors such as a firm's quality of management and external factors such as innovation climate in the region and availability of private and public financing. A model applied in our research is a well-known CDM model containing three stages. This model makes it possible to analyze expenditures for research and development, implementation of innovations, and then its impact on the firm's performance. Estimation results demonstrate that enterprises benefit from innovations. The same time, our research shows the importance of management quality in the firms' innovative activities among the other internal and external factors affecting innovations. Results can be applied by the enterprises interested in innovations and by policymakers involved in facilitating innovations at the regional and country level. Implications for Central European audience: A version of a well developed CDM model is used, which makes results reliable; the model can be further applied for the analysis of various economies, including the countries of Central Europe. Our research sheds light on the determinants of innovation activity at all its stages, creating a background for analysis and development of economic policy. A key implication is that management quality deserves attention along with other factors affecting firms' innovation and productivity. The research is based on firm-level BEEPS data for Russia, making the possible comparison with the other countries covered by BEEPS survey. © 2020, Economics - Prague, Faculty of Business Administration.Russian Science Foundation, RSF: 19-18-00262Research was supported by the grant of the Russian Science Foundation No 19-18-00262 "Empirical modelling of balanced technological and socioeconomic development in the Russian regions

Is Russia successful in attracting foreign direct investment? Evidence based on gravity model estimation

The aim of this paper is twofold. First, it is to answer the question of whether Russia is successful in attracting foreign direct investment (FDI). Second, it is to identify partner countries that "overinvest" and "underinvest" in the Russian economy. We do this by calculating potential FDI inflows to Russia and comparing them with actual values. This research is associated with the empirical estimation of factors explaining FDI flows between countries. The methodological foundation used for the research is the gravity model of foreign direct investment. In discussing the pros and cons of different econometric methods of the estimation gravity equation, we conclude that the Poisson pseudo maximum likelihood method with instrumental variables (IV PPML) is one of the best options in our case. Using a database covering about 70% of FDI flows for the period of 2001-2011, we discover the following factors that explain the variance of bilateral FDI flows in the world economy: GDP value of investing country, GDP value of recipient country, distance between countries, remoteness of investor country, remoteness of recipient country, level of institutions development in host country, wage level in host country, membership of two countries in a regional economic union, common official language, common border and colonial relationships between countries in the past. The potential values of FDI inflows are calculated using coefficients of regressors from the econometric model. We discover that the Russian economy performs very well in attracting FDI: the actual FDI inflows exceed potential values by 1.72 times. Large developed countries (France, Germany, UK, Italy) overinvest in the Russian economy, while smaller and less developed countries (Czech Republic, Belarus, Denmark, Ukraine) underinvest in Russia. Countries of Southeast Asia (China, South Korea, Japan) also underinvest in the Russian economy. © 2016 by Oleg Mariev

The impact of externalities on the innovation activity of Russian firms

In this paper, we analyze the impact of externalities on firms’ capacity to develop and implement innovations. We evaluate a Probit model containing both firm level factors and regional factors, such as the institutional environment, state support, and human capital. The dependent variable is a dummy variable reflecting the involvement of a firm in innovation activity. We employ data provided by BEEPS 2012-2014 for firm-level indicators and data provided by the Russian Federal State Statistics Service for region level indicators. The results confirm that at present the most important external factors affecting the innovation activity of Russian firms are state support, both at the firm level and at the regional level, the economic situation in the region, institutions, and quality of human capital. At the same time, we found that several factors such as political stability, tax policy, and investment risks were insignificant. These results require further analysis. We also found that the impact of the factors mentioned above depends on whether a region receives state support. The results imply that a differentiated policy that considers regional characteristics will probably be more effective than a uniform policy on innovation. © 2018, National Research University, Higher School of Econoimics. All rights reserved.The study was supported by the Russian Foundation for Basic Research (project №18-010-01190 “Models for analysing innovation development factors and comparative advantages in the Russian economy”). The authors are grateful to Karina Nagieva, postgraduate student at the Higher School of Economics and Management of the Ural Federal University, for her contributions to this study. The authors would also like to thank participants of the conference hosted by the HSE Centre for Market Studies and Spatial Economics (St. Petersburg, June, 2016) for their valuable comments. Sole responsibility for any possible mistakes lies with the authors

Impact of infrastructure on socio-economic development of Russian regions: methodology and analysis

Relevance. Regional infrastructure development directly affects economic growth, social development and the quality of life. To identify the key areas of infrastructure development in Russian regions, it is necessary to develop a methodological approach to the analysis of the impact of infrastructure on socio-economic development, which determines the relevance of this study.Research objective. This study aims to improve the methodology of assessment of the role infrastructure plays in the socio-economic development of Russian regions.Data and methods. The analysis relies on a system of general and integral, static and dynamic indicators used to assess the current state and dynamics of infrastructure in regions. The analysis takes into account the structural and functional features of infrastructure. The proposed methodology comprises methods for obtaining comparative estimates of regional infrastructure development, which can be applied to compile regional rankings. The study also uses methods of econometric and K-means cluster analysis.Results.  A comparative analysis of the infrastructure development of Russian regions allowed us to assess the infrastructural potential of these regions, the discrepancies in infrastructure development and compare the infrastructure-related characteristics of the leading lagging regions. The results of econometric analysis as well as cluster analysis of regions based on general and integral dynamic indicators are discussed.Conclusions. The methodological approach proposed by the authors has been tested by using the data on Russian regions. The analysis has revealed the most typical problems faced by Russian regions. These problems should be taken into account in strategic decision- and policy-making

Determinants of Foreign Direct Investment in Developed and Developing Countries: Impact of Political Stability

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

Determinants of Foreign Direct Investment in Developed and Developing Countries: Impact of Political Stability

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

Does Income Inequality Matter for CO2 Emissions in Russian Regions?

Research background:Intensive economic growth in Russian regions during recent decades has been associated with numerous environmental issues, particularly increasing CO2 emissions, as well as income inequality. To achieve sustainable development, it is necessary to resolve these issues. Purpose of the article: To shed light on the impact of income inequality on CO2 emissions based on Russian regional data covering the years 2004–2018. Methods: Gini index and decile dispersion ratio are used to measure income inequality. To study the impact of income inequality on CO2 emissions in the Russian regions, we estimate econometric models with fixed and random effects and apply GMM method. We test the hypothesis of the environmental Kuznets curve to determine the impact of economic growth on CO2 emissions. Findings & value added: The results show that CO2 emissions increase in tandem with growth in income inequality between 10% of people with the lowest income and 10% of people with the highest income. Simultaneously, CO2 emissions decrease with growth of Gini coefficient. The hypothesis of the Environmental Kuznets Curve was confirmed based on GMM method. Our findings underscore that the activities of the extraction and manufacturing sectors, as well as energy consumption, increase CO2 emissions. The chief significance of this paper is the finding that large income gap between extremely rich and extremely poor population cohorts increases CO2 emissions. This implies that economic policy aimed at reducing income inequality in Russian regions will also reduce CO2 emissions, especially if accompanied by increased use of environmentally friendly technologies. From the international perspective, our research can be extended to study other countries and regions. © Instytut Badań Gospodarczych.This research was supported by a grant from the Russian Science Foundation № 19-18-00262: “Empirical modelling of balanced technological and socioeconomic development in the Russian regions”

The response of exchange rates to economic policy uncertainty: Evidence from Russia

The Russian economy has encountered substantial exchange rate volatility due to many endogenous and exogenous shocks, including the adoption of different exchange rate systems, the global financial crisis, sanctions, and the COVID pandemic. The economy has long experience with a managed floating exchange rate system, which motivates us to investigate the exchange rate response to domestic economic policy uncertainty, incorporating oil prices and the trade volume under different economic circumstances. We apply quantile-based time-series approaches to deal with extreme values. Our empirical investigation demonstrates that the local currency appreciates in response to increased Russian economic policy uncertainty under different quantiles of the managed floating exchange rate, but it depreciates under most quantiles in a floating exchange rate period. Our findings confirm that the Russian currency appreciates with the rise in international oil prices and trade as Russia is an oil-exporting country. Moreover, the findings are robust under the quantile-on-quantile approach. © 2021 The AuthorsRussian Science Foundation, RSF: 19-18-00262This study was supported by the grant of the Russian Science Foundation , Code: 19-18-00262 . “Empirical modelling of balanced technological and socioeconomic development in the Russian regions”

The impact of spatial concentration on enterprise performance

The objective of this paper is to reveal the impact of spatial concentration of business in the Russian cities on enterprise productivity. The hypotheses are the following: urbanization level and home market potential positively affect enterprise performance; localization economies are positive and start decreasing after some point due to congestion and excessive competition; regional transport infrastructure, business climate and human capital positively affect enterprise performance. We use firm level data augmented with city and regional data. Fixed effects are applied in order to deal with endogeneity. Agglomeration economies are considered in the light of opportunities for knowledge spillovers, input sharing and labor market pooling. Our results confirm that agglomeration economies and home market potential are important for the enterprise performance. We find positive urbanization and diversity economies, while localization economies have an inverted U shape. Results can be used to improve regional policy. For instance, significance of home market potential emphasizes the importance of transport infrastructure. Significance of agglomeration effects implies that if a sufficiently large number of firms work in a city, performance of each firm improves.The authors would like to thank the Russian Science Foundation for its support of the research project. 15-18-10014 "Projection of optimal socio-economic systems in turbulence of external and internal environment". We also express our gratitude to Hubert Jayet, Sergey Kadochnikov, Volodymir Vakhitov, Pavel Vorobyev, Nadezhda Kislyak for their valuable comments and ideas for further research. Any remaining errors are our own