nep-cis New Economics Papers
on Confederation of Independent States
Issue of 2021‒11‒01
three papers chosen by
Alexander Harin
Modern University for the Humanities

  1. Ways To Succeed At Different Types Of Universities By Anastasia A. Byvaltseva; Anna A. Panova
  2. 푸틴 4기 한ㆍ러 투자 활성화 방안: 고부가가치 산업을 중심으로 (Plans to Activate Investment between Korea and Russia During Putin's Fourth Term-Focusing on High Value-Added Industries) By Park, Joungho; Kim, Seok Hwan; Jeong, Minhyeon; Kang, Boogyun; Kim, Cho Rong; Sutyrin, Sergei; Trofimenko, Olga Y.; Korgun, Irina A.
  3. Bank transactions embeddings help to uncover current macroeconomics By Maria Begicheva; Oleg Travkin; Alexey Zaytsev

  1. By: Anastasia A. Byvaltseva (National Research University Higher School of Economics); Anna A. Panova (National Research University Higher School of Economics)
    Abstract: The Russian academic sector can be characterized by university differentiation, which leads to differentiation of their goals and priorities. Governmental policies have stimulated the formation of a group of leading research universities. Different aims of universities mean there are different incentives for faculty. This paper estimates the “success” of Russian faculty in contemporary conditions. We measure success as the difference between an individual’s wage and the average university wage. We find that research-oriented universities pay great attention to the top journals, while for teaching-oriented universities journal rankings are of less importance – they need journals to be foreign. Time spent on teaching is not significant in teaching-oriented universities, while in research-oriented universities it is. Comparing the success of faculty in case they changed university shows that people from research-oriented universities could be more successful at teaching-oriented universities than their colleagues, while faculty of teaching-oriented universities would not be attractive employees for research-oriented universities.
    Keywords: teaching, research, Russian University Excellence Initiative, 5-100 Project, faculty.
    JEL: I2 I23 I28
    Date: 2021
  2. By: Park, Joungho (KOREA INSTITUTE FOR INTERNATIONAL ECONOMIC POLICY (KIEP)); Kim, Seok Hwan (KOREA INSTITUTE FOR INTERNATIONAL ECONOMIC POLICY (KIEP)); Jeong, Minhyeon (KOREA INSTITUTE FOR INTERNATIONAL ECONOMIC POLICY (KIEP)); Kang, Boogyun (KOREA INSTITUTE FOR INTERNATIONAL ECONOMIC POLICY (KIEP)); Kim, Cho Rong (KOREA INSTITUTE FOR INTERNATIONAL ECONOMIC POLICY (KIEP)); Sutyrin, Sergei (Saint Petersburg State University); Trofimenko, Olga Y. (Saint Petersburg State University); Korgun, Irina A. (Institute of Economics of the Russian Academy of Sciences (RAS))
    Abstract: 이 연구는 푸틴 4기 한국과 러시아 간 투자 협력 활성화 방안을 마련하려는 목적으로 수행되었다. 특히 4차 산업혁명 시기 다른 나라의 다양한 대러시아 투자 협력 사례 연구를 통해 보다 실제적인 한국의 대러시아 투자 증진 방안을 제시하고자 했다. 이 연구는 신북방정책 추진과 한·러 수교 30주년을 계기로 미래지향적 경제협력을 모색해야 하는 시점에서, 러시아의 해외투자 패턴과 투자유치 정책 기조를 종합적으로 이해함으로써 한국의 대러시아 경제협력 정책 수립과 신북방정책의 내실화에 유용한 도움을 줄 수 있을 것이다. The main goal of this study is to identify policy implications for investment cooperation between Korea and Russia in the 4th presidential period of Putin and to seek ways to increase mutual investment. In particular, case studies were conducted of various investment cooperation projects by Russia with other countries during the 4th Industrial Revolution, aiming to suggest a more practical way to increase Korean investment in Russia. (the rest omitted)
    Keywords: Korea; Russia; Putin; high value-added industries; investment cooperation; the 4th Industrial Revolution
    Date: 2020–12–30
  3. By: Maria Begicheva; Oleg Travkin; Alexey Zaytsev
    Abstract: Macroeconomic indexes are of high importance for banks: many risk-control decisions utilize these indexes. A typical workflow of these indexes evaluation is costly and protracted, with a lag between the actual date and available index being a couple of months. Banks predict such indexes now using autoregressive models to make decisions in a rapidly changing environment. However, autoregressive models fail in complex scenarios related to appearances of crises. We propose to use clients' financial transactions data from a large Russian bank to get such indexes. Financial transactions are long, and a number of clients is huge, so we develop an efficient approach that allows fast and accurate estimation of macroeconomic indexes based on a stream of transactions consisting of millions of transactions. The approach uses a neural networks paradigm and a smart sampling scheme. The results show that our neural network approach outperforms the baseline method on hand-crafted features based on transactions. Calculated embeddings show the correlation between the client's transaction activity and bank macroeconomic indexes over time.
    Date: 2021–10

This nep-cis issue is ©2021 by Alexander Harin. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.