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The paper contains a retrospective analysis of macroeconomic policy and macroeconomic reforms in post-Soviet countries in 1992-2021, that is, after obtaining political and economic independence at the end of 1991. A special attention is given to problems of macroeconomic stabilization and economic growth. As a result of structural distortions inherited from the Soviet economy and slow pace of economic and institutional reforms, countries of the former Soviet Union suffered from the long and deep output decline in the 1990s, and their post-transition growth recovery in the 2000s did not last long. Furthermore, they remain vulnerable to both domestic and external economic shocks. Given a limited predictability of post-COVID global economic trends, this vulnerability will continue, most likely, in the next couple of years.
We currently see a large increase in e-commerce sector; it is becoming a central trend in the banking industry. Fraudsters keep up with modern technologies, and use weak points in human psychology and security systems to steal money from regular users. To ensure the required level of security, banks began to apply artificial intelligence in their anti-fraud systems. Fraud detection can be formulated as a classification problem with a case-based reasoning or knowledge extraction task with unbalanced classes. In this paper we present a framework of models based on various approaches of artificial intelligence, such as neural networks, decision trees, copula models and others to recognize payment behavior of fraudster. The considered framework is evaluated with different metrics and implemented in an actual anti-fraud system, which leads to an improvement of the system performance. Finally, the interrelation between the anti-fraud system indicators and banks operational risks is discussed in this paper.
Russia is one of the most important nations in the world, with 145 million inhabitants and 85 regions. The process of structural change in its economic transition has been influenced by its size, distance to markets, climate, natural resource endowments and allocation of industries, producing strong regional differentials. In this paper, we analyse the differences among regions and their dynamics from 2007-2013. For this purpose, we apply a dynamic multivariate method, named STATIS, in order to identify the main socio-economic characteristics of the regions, to find homogeneous clusters, and to examine their temporal dynamics.
An explanation of the Dunning–Kruger effect is provided which does not require any psychological explanation, because it is derived as a statistical artifact. This is achieved by specifying a simple statistical model which explicitly takes the (random) boundary constraints into account. The model fits the data almost perfectly
Internal-Ratings Based (IRB) approach is one of the founding blocks in the modern credit risk management and regulation. Its implementations by the banks world-wide incentivizes researchers, central bankers and investors to evaluate the outcome versus the non-IRB banks, i.e., the treatment effect. However, there are obstacles in such evaluation. From one side, all the banks may transit to IRB for a relatively modest economy (like Greece). From another side, there is no single IRB transition point (like in the EU where the transition is voluntarily; a single point exists for the USA where the transition was mandatory for the largest banks). That is why we discuss the problem of the ‘control’ group depletion (attrition) in the Difference-in-Differences method. We provide two ways to replicate data using python language. The first code is based on the matrix structure (object * time), the second – panel data structure. Comparing both ways of the data replication (resampling) we choose the second algorithm due to it versality and faster computing results, than the first one.
Many datasets used in the social sciences have a hierarchical structure, where lower units of aggregation are ‘nested’ in higher units. In many disciplines, such data are analyzed using multilevel modeling (MLM, also known as hierarchical linear modeling). However, MLM as a framework is relatively unknown in economics. Instead, economists use a range of separate econometric methods, including cluster-robust standard errors, fixed effects models, models with cross-level interactions, and estimated dependent variable models. Relying on an extensive literature review, this paper describes this methodological divide and provides a detailed comparison between MLM and ‘economic methods’ in their abilities to deal with three methodological challenges inherent in multilevel data ‒ clustering, omitted variables, and coefficients' heterogeneity across groups. We unfold the comparative advantages of these two methodological approaches and provide practical recommendations about which of them should be used, why, and in what settings.
The aim of this paper is to analyse the convergence of housing prices in German regions using spatial regional data. We provide empirical analysis on the panel data set of 397 German regions for the period 2004–2020 taking into account their relative geographical location and prices. The main contribution of our paper is the analysis of convergence in housing prices, considering the historical aspects of the divergence of German regions. We discover if the housing prices become more homogenous over the years or not and also study the effect of various factors on the housing market.
We build spatial econometric models for both selling and rental price, taking into account such demand factors as unemployment level, pendulum migration ratio, wages, number of employees, gross regional product, migration flow for regions, emigration and immigration for Bundesländer. Additionally, we consider the effect of price and determinants of neighboring regions. As the result of the analysis, we can conclude that factors which lead to personal income growth affect the price growth rate positively and vice versa. Emigration lowers the demand together with the price growth rate. Immigration contrary rises the demand and price growth rate. In the paper, we show that convergence among German regions exists over past years, mostly for rental prices, as the speed for them is higher. The practical significance of the current work is its applicability to regional economic and migration policy formation. Moreover, the analysis can be extended to the housing policy of other countries in order to allow cross-country comparisons.
This paper investigates how corporate governance of unlisted firms in an emerging market economy affects financing constraints, measured by the sensitivity of investment to cash flow. In order to evaluate the quality of corporate governance, we develop two corporate governance indices based on a large-scale survey of Russian enterprises – one for shareholder protection and one for transparency. We estimate standard investment regressions where the cash flow variable is interacted with our corporate governance indices and variables capturing the ownership structure. The central result is that better shareholder protection diminishes the cash flow sensitivity of investment, particularly in firms with an outside controlling owner. In contrast, we do not find such an effect for transparency, which can be partially explained by the threat of hostile takeovers. We address the problem of the endogeneity of corporate governance by using fixed-effects regressions and a novel instrumental variable based on particular legal provisions for corporate governance in Russia depending on the number of shareholders.
This paper examined a set of over two thousand crypto-coins observed between 2015 and 2020 to estimate their credit risk by computing their probability of death. We employed different definitions of dead coins, ranging from academic literature to professional practice; alternative forecasting models, ranging from credit scoring models to machine learning and time-series-based models; and different forecasting horizons. We found that the choice of the coin-death definition affected the set of the best forecasting models to compute the probability of death. However, this choice was not critical, and the best models turned out to be the same in most cases. In general, we found that the cauchit and the zero-price-probability (ZPP) based on the random walk or the Markov Switching-GARCH(1,1) were the best models for newly established coins, whereas credit-scoring models and machine-learning methods using lagged trading volumes and online searches were better choices for older coins. These results also held after a set of robustness checks that considered different time samples and the coins’ market capitalization.
Among the factors of court performance—a crucial element of the institutional environment for a well-functioning market economy—productivity (disposition time) and adjudicatory quality (minimum legal errors) are significant. This paper investigates presumed quantity-quality tradeoff in Russian commercial courts when considering claims to annul administrative infringement decisions, on the example of antitrust cases. Using a dataset of the first instance court decisions regarding claims to annul decisions of Russian competition authority during 2008–2015, we explore the influence of extra efforts by a judge to assess the evidence on the probability of appealing and annulling her decision. The effect is not found to be statistically significant which means the absence of adjudicatory quantity-quality tradeoff. We discuss then the implications of the finding to the rules for additional evidence presented in the courts when considering a case. We conclude, first, that in Russia the rules on reasonable disposition time and the motivation of judges to prevent backlog do not increase the probability of legal errors. Second, new evidence acquired during judicial review does not statistically improve the legal quality of court decisions. The policy implication is that the recent initiative of the Supreme Court of the Russian Federation to limit additional evidence when considering claims to annul administrative antitrust decisions is reasonable.
The paper focuses on trends in the convergence of labor and multifactor productivity in Russia. Using firm-level data for the period 2011–2016, we show that firms with low-productivity grow faster than those with high-productivity. This result is, however, mostly driven by new entrants. The catch-up momentum fades after the first few years of a firm’s life, so it is not capable of closing the gap between the most and least productive firms in the Russian economy. We show that the gap widened over the period 2011–2016, suggesting major divergence in productivity levels of Russian firms. We also use stochastic frontier analysis to verify the divergence within narrowly defined industries. Our estimates confirm divergence in most industries.
The aim of the research is to estimate the level of the early career gender wage gap in Russia, its evolution during the early stages of a career, gender segregation and discrimination among university graduates, and to identify factors which explain early career gender differences in pay. Special emphasis is placed on assessing the contribution of horizontal segregation (inequal gender distribution in fields of studies and industries of employment) to early-career gender inequality.
The study is based on a comprehensive and nationally representative survey of university graduates, carried out by Russian Federal State Statistics Service in 2016 (VTR Rosstat). The authors use Mincer OLS regressions for the analysis of the determinants of gender differences in pay. To explain the factors which form the gender gap, the authors use the Oaxaca-Blinder and Neumark gender gap decompositions, including detailed wage gap decompositions and decompositions by fields of study. For the analysis of differences in gender gap across wage distribution, quantile regressions and quantile decompositions based on recentered influence functions (RIFs) are used.
The study found significant gender differences in the early-career salaries of university graduates. Regression analysis confirms the presence of a 20% early-career gender wage gap. This gender wage gap is to a great extent can be explained by horizontal segregation: women are concentrated in fields of study and industries which are relatively low paid. More than half of the gender gap remains unexplained. The analysis of the evolution of the gender wage gap shows that it appears right after graduation and increases over time. A quantile decomposition reveals that, in low paid jobs, females experience less gender inequality than in better paid jobs.
The analysis has some important policy implications. Previously, gender equality policies were mainly related to the elimination of gender discrimination at work, including positive discrimination programs in a selection of candidates to job openings and programs of promotion; programs which ease women labour force participation through flexible jobs; programs of human capital accumulation, which implied gender equality in access to higher education and encouraged women to get higher education, which was especially relevant for many developing countries. The analysis of Russia, a country with gender equality in access to higher education, shows that the early career gender gap exists right after graduation, and the main explanatory factor is gender segregation by field of study and industry, in other words, the gender wage gap to a high extent is related to self-selection of women in low-paid fields of study. To address this, new policies related to gender inequality in choice of fields of studies are needed.
It has been frequently stated that gender inequality appears either due to inequality in access to higher education or after maternity leave. Using large nationally representative dataset on university graduates, we show that gender equality in education does not necessarily lead to gender equality in the labour market. Unlike many studies, we show that the gender gap in Russia appears not after maternity leave and due to marital decisions of women, but in the earliest stages of their career, right after graduation, due to horizontal segregation (selection of women in relatively low-paid fields of study and consequently industries).
This special issue presents a selection from the papers presented at the conference on “Experiences and Challenges in Measuring Income and Wealth in CIS Countries and Eastern Europe”, jointly organized by the International Association for Research in Income and Wealth (IARIW) and National Research University Higher School of Economics (HSE University) in Moscow on 17–18, September 2019. This conference marks a significant event being the first IARIW conference held on the territory of the former Soviet Union.
The IARIW has historical roots in Eastern Europe. The first IARIW chairman, Simon Kuznets (1901-1985), was born in Pinsk (Belarus today). He started his academic training here in Eastern Europe, at the Kharkov Commercial Institute (Ukraine) and published his first paper “Money wages of factory employees in Kharkov in 1920” (in Russian), just before his emigration to the United States. The debates on Soviet industrialization in the early 1920s; his teachers in the institute and early experience of the analysis of the Soviet economy in the turmoil years of the Russian Revolution and the Civil War; as well as professional contacts and reading of economic literature in Russian in the 1920s and early 1930s contributed to his professional outlook (Syrquin, 2021). These topics and debates are echoed in this IARIW Conference Program. Two major themes, the risk of the middle-income trap, and the causes and consequences of inequality run through the papers in this issue.
National journals represent an important part of the landscape in almost any academic system. Their role may vary from being mere outlets for publishing country-specific studies in local languages to hosting global research. With the process of globalization in recent decades, such journals (as well as their authors) have increasingly gained opportunities to become internationally visible and to be read worldwide. Suggesting the definition of a national journal as being exogenous and with unaltered characteristics with respect to any changes in the journal’s content and policy, we provide the first up-to-date analysis of national output in post-Soviet countries, at the levels of both journal and article, for the period 2010–2019. In general, publications in local journals (associated in Scopus with the countries under consideration) constitute a substantial proportion of the national research output, with the most numerous representations of national journals found in Russia, Ukraine, Lithuania, and Estonia. The journals in the countries under consideration differ in their disciplinary composition, quality, language policy and visibility, reflecting the divergence of the countries in the decades since their independence. Although analysis of these journals suggests they are not true vehicles for communication with a global community providing international visibility for their authors, the demand for publication in internationally indexed, national journals is high and the number of journals (and articles published) has grown substantially in the last decade.
Global value chains (GVCs) generate significant effects on participating firms. But can GVCs affect other companies in the host economies? We propose a conceptual framework for GVC spillovers and test it using data for Russian manufacturing firms in 2009–2015. Using a panel estimation technique with random and fixed effects, we find that firms in industries that are intensively integrated into GVCs, on average, have higher total factor productivity (TFP), controlling for firm heterogeneity, industry and region fixed effects. TFP gains in GVCs are unequally distributed and depend on (i) the industry’s position in the GVC, (ii) the industry’s technological intensity and (iii) the firm’s TFP level. We relate the findings to the evidence of the “optimal” technological gap that maximizes productivity spillovers for national companies. The results are highly relevant for policymakers as they prove that trade policy and foreign direct investment attraction policy should not go hand in hand but should be incorporated into GVC-oriented policy to encourage the full range of TFP improvements in local (non-GVC-included) firms. To fully benefit from GVC-oriented policy, State policy should encourage the development of inter-firm links. In addition, our results support the importance of evolutionary structural changes in economic upgrading in GVCs and the strength of the role of policies oriented towards medium-technology industries as drivers of technological development.
This study evaluates the effect of a compulsory military service reform conducted in 2007–2008 on the demand for higher education in Russia. The reform shortened the conscription term (from two years to one year), abolished several deferments, and significantly reduced the number of military departments in Russian universities, which provided an opportunity to avoid being conscripted as a private. The difference between the Russian reform and the armed forces reforms carried out in several European countries in the 1990s–2000s lies in the fact that compulsory military service was not abolished completely. Based on data compiled from the Russian Longitudinal Monitoring Survey, we find that the men affected by the reform are, in general, approximately 12% less likely to graduate from higher education. The effect is more pronounced for men from cities and more advantaged family backgrounds. Army veterans exhibit steadily lower demand for higher education irrespective the reform.
Governments promote pro-environmental behavior explicitly, through regulatory provisions, or implicitly, by setting general environmental objectives without explicit requirements. Shared values and commitment to government objectives supposedly help towards greener behavior. We argue that the lack of explicit guidance counteracts, especially if green options are perceived as conflicting with strict regulatory requirements on other issues. In Russian public procurement, organizations are subject to either a rigid procurement law, or a flexible law, or both; neither law formalizes environmental priorities or approaches. We design a survey on practices of green procurement, collecting 223 responses from the whole range of organizations subject to public procurement regulation. Results from probit regressions, robustified on further 800 responses from an additional survey and 250 000 official procurement records, show that regulatory rigidity hinders green practices. Federal authorities are more likely to apply environmental criteria than local governments, but this is rather due to the expertise of their staff than to their commitment to governmental objectives. Publicly funded institutions are less likely to adopt green procurement than state corporations. Caution and avoidance of unintended contraventions seem to impede adoption of green procurement. Provision of information, guidance and improved expertise can help overcome this effect.