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Income Inequality Factors in Russian Regions from 1991 to 2018

Student: Valeriya Kusmartseva

Supervisor: Valeriya Vladimirovna Lakshina

Faculty: Faculty of Economics

Educational Programme: Economics (Bachelor)

Year of Graduation: 2020

The study examines possible regional income inequality factors in Russia from 1991 to 2018. It is common to think that in contemporary society inequality, including income one, is lower compared to the levels of past century. However, as statistics shows, the opposite is true. Nowadays top 1% and 10% of population posses more than in the 1980s which unavoidably contributes to the decrease in the income ratio of other deciles, primarily middle-class. What is more, Russia takes the first place in terms of regional inequality among developing countries. Therefore, it is reasonable to examine the causes which have led to such results. In order to achieve our aim we made further steps: (i) analyzed the related literature on Russian as well as foreign regional income inequality; (ii) outlined the most frequently identified factors that were found to have a significant impact on the regional income indicators from those studies; (iii) collected and derived with the help of MICE algorithm the needed data; (iv) estimated several static as well as dynamic models and chose the most suitable one; (v) run additional test to make sure that are estimations are correct. In the empirical part, 79 Russian regions have been chosen as sample objects during the time period from 1991 to 2018. As a dependent variable a regional Gini coefficient was used. As for independent variables, VRP per capita measured in constant prices, real investments per capita, the percentage of employed population with higher education, the sum of export and import, the level of unemployment, a binary variable that divides regions on west and east ones and our potential factor, the number of small enterprises per 10 000 people, were selected. First, we demonstrated that the model with fixed-effects is rather better than OLS regression and the model with random-effects. Then, having implemented a dynamic panel approach and Difference GMM together with System GMM methods on the panel data, we have determined that the number of small enterprises per 10 000 people in the region, a variable whose coefficient has been assumed to be significant, coupled with the VRP per capita measured in constant prices and the percentage of employed population with higher education are the key components of regional Gini coefficient indicators, our dependent variable. Moreover, the results have demonstrated the importance of including past levels of income inequality in the models which seems to be the most crucial factor of the present level of regional Gini coefficient. As well as capturing short-term effects of these variables, we have managed to produce estimations for long-run ones. Mostly, the model outcomes appear to be in line with previous studies in this field, though the VRP per capita sign has not met our expectations. The research innovation lies in the newly adopted model as well as methods for Russian regional data.

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