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EN
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.
EN
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.
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