Tuyara Gavrilyeva (p), Anna Naberezhnaya , Sergey Fedulov

Kassoum Ayouba

The Russian Federation, due to its huge area and complex ethnic and confessional composition, is a unique state. Despite unified legislation, most internal social and economic processes are heterogeneous. However, there is a consolidation point - the presidential elections in Russia, where the majority of citizens cast their votes for the main candidate from the state. Vladimir Putin wins the elections with an overwhelming advantage (63.6% of votes in 2012 and 76.69% of votes in 2018). In 2008, the state candidate was Dmitry Medvedev; he also won by a large margin (70.28% of the vote). This reflects a certain long-term pattern of power in Russia where an unconditional person should lead the country.
In these conditions, certain conclusions about the state of society allow us to make an analysis of the voting results for the main opponent, who took 2nd place. The main opponent in the presidential elections in Russia in 2008 was Gennadiy Zyuganov (in Russia - 17.72%, in Yakutia - 20.1%), in 2012 - he again (in Russia - 17.18%, in Yakutia - 14.4 %), and in 2018 - Pavel Grudinin (in Russia - 11.77%, in Yakutia - 27.25%). The share of votes for him in Yakutia was the highest among 85 regions of Russia. This is still an important topic for scientific and public debate in the region and Russia as whole. What could have affected this? Perhaps, the key factor is poverty, one of the highest in Russia. In 2017, 20.3% of the population were poor, and in 2012 - 16.7 %, in 2008 - 19.0%. In addition, it is the result of rapid penetration of information technology in society. A number of experts note that Grudinin as alternative was actively advertised through the WhatsApp is the most popular messenger in Yakutia.
This study is devoted to the analysis of factors of the voting population of Yakutia for the main opponent as a way of social protest reflection. Database of research: open data of state and municipal statistics for 411 settlements of Yakutia for 2006-2018 period. These data will be compared with data on voting for the main state candidate and the main opponent in the presidential elections of Russia in 2008, 2012 and 2018.

Amit Batabyal (p), Seung Yoo

Tuyara Gavrilyeva

Consider an aggregate economy of two cities. We study the impact that the use of utilitarian and Rawlsian policies by these two cities has on their ability to attract members of the the creative class. We first focus on the case in which both cities adopt utilitarian policies. Second, we analyze the case where both cities implement Rawlsian policies. Third, we study the case where one city uses a Rawlsian policy but the other city pursues a utilitarian policy. Fourth, we compare the policy outcomes in the first and the third cases above and show that if one city switches to a Rawlsian or more egalitarian objective when the other city remains utilitarian, the aggregate economy becomes less egalitarian. Finally, we compare the second and the third cases above and demonstrate that if one city switches to a Rawlsian or more egalitarian objective when the other city remains Rawlsian, the aggregate economy becomes more egalitarian.

Kassoum Ayouba (p), Julie Le Gallo, Andrès Vallone

Amit Batabyal

This paper aims to analyze the socioeconomic diversity of European Union (EU-28) regions from a dynamic perspective. For that purpose, we combine a series of exploratory space-time analysis approaches to multiple Factor Analysis (MFA) applied to a large range of indicators collected at the NUTS-2 level for the period 2000-2015 for the EU-28. First, we find that the first factor of MFA, interpreted as economic development (ECO-DEV), is spatially clustered and that a moderate convergence process is at work in European space from 2000 to 2015. Second, when comparing these results with those obtained for GDP per capita, we show that the convergence pattern detected with GDP is more pronounced: ECO-DEV adjusts slower over time compared to GDP per capita. Third, pictures provided by the remaining interesting factors, i.e. factors 2 to 4 are very different.