2019. vol. 23. No. 4

Microfinance organizations have become widespread in the crisis years, issuing microloans (up to 100000 rubles) at high interest rates almost without documents. Today, the Central Bank of Russia actively regulates this market, more and more tightening requirements, limiting rates and pennies on loans. This necessitates the formation of a new strategy for assessing the risk of non-repayment of a loan or loan, based on the prevention of delinquency on the part of customers. To do this, first, it is necessary to obtain more informative data about customers, without complicating the relationship with them. Secondly, it is necessary to have a good understanding of the possibilities of certain methods of classification in solving various problems of evaluating potential customers. The authors of this study analyze the importance for the clients classification quality of those indicators that are traditionally collected by MFIs, as well as the importance of some new indicators based on data from social networks. In this case, the importance of indicators is interpreted in the context of specific classification algorithms (methods).To model credit default (delay of more than 30 days), the authors use several algorithms for constructing classification trees – CART and C 4.5 algorithms, logistic regression and Random Forest algorithm. Modeling is carried out based on a sample of customer profiles of real MFI. Ambiguous results were obtained. Depending on the formulation of the problem of classification of customers have advantage different algorithms descriptive analysis (CART, C4.5, Logit). At the same time, as you might expect, the non-interpreted predictive algorithm “Random Forest” provides the best quality of forecasts. According to the results of the analysis, it was revealed that the credit history of the borrower, as well as his age, is of great importance for the classification of MFI clients. Gender had no impact on the classification results. In addition, data from social networks turned out to be unimportant.

Based on a demand-side approach, Thirlwall’s law claims that, in the long-run, economic growth is constrained by the balance of payments. Income elasticity of demand for exports should be greater than income elasticity of demand for imports in order to grow faster than the limit imposed by export demand. According­ly, accurate estimations of income elasticities are necessary to identify these bounds. This research estimates export and import functions using bilateral trade data between Russia and 53 of its major partners. Then, we empirically test the validity of Thirlwall’s law over the years 1996–2016, generalizing Thirlwall’s model in a bilateral panel framework. In addition, export and import functions are estimated taking into account export and import composition, controlling for key sectoral effects on aggregate elasticities. Using dynamic panel data models, the findings suggest that, on average, the Russian economy has been growing faster than what Thirlwall’s law predicts. The sectoral composition of the Russian external sector has eased the external constraint to growth. Russian exports still significantly consist of oil and gas, price inelastic goods, with positive effects on trade balance over the period of study. However, in a transition toward green energies, the allocation and investment of exports revenues is a key factor to address future scenarios where carbon-based resources lose relevance.

The level of private investment has traditionally been in the focus of government attention, especially in the conditions of currently low economic growth observed in Russia. Potentially, tax policy affects the investment decisions of companies, firstly, due to changes in investment costs, and secondly, due to sources of financing: for some companies access to loans is limited. At the same time, studies show that, depending on the institutional conditions and the level of development of the financial sector, the importance of the state tax policy as a determinant of investment varies from a sufficiently significant impact to its complete absence. It is also emphasized in empirical research that the influence of tax policy on the investment activity of a company highly depends on the individual characteristics. The impact of corporate tax rate on the level of investment is investigated on a sample of Russian companies for 2006–2018 period. A censored model with fixed individual effects is estimated on the panel data. The results indicate that an increase in corporate income tax rate has a significant negative effect on the investments of companies. At the same time, the scale of this effect differs significantly for companies depending on their characteristics. The sensitivity to the income tax rate is maximum for small companies with little amount of own funds for investment. As a rule, this is due to the limited access of such companies to loans, and therefore a lower corporate income tax rate increases the resources available for investment. We conclude that for small companies setting lower tax rates contributes to the growth of investment activity in the region.

The article analyzes the current natural gas pricing system in Russia and suggests directions for its transformation, taking into account the situation in the economy and the energy sector. The methodology is based on the methods of system analysis using economic and mathematical optimization modeling of the energy sector and the economy. The authors show that the current gas pricing system in the country limits the development of competition, does not allow to increase the efficiency of economic sectors and to modernize the energy sector. Mechanisms of gas exchange trading, which were created in the country, do not reflect the state of the market. Under these conditions, it is necessary to implement a set of measures that will allow creating a transparent pricing system based on market principles and reflecting the real situation in the consuming sectors. The gas industry can become a tool to stimulate economic development. The increase in gas prices would allow to create conditions for modernization, leading to GDP growth due to the expansion of orders for the Russian industry from the energy and consuming sectors. At the same time, increased efficiency helps to contain the growth of consu­mer spending, while increased tax revenues from the gas industry would make it possible to avoid the growth of other taxes on consumers, which could be inevitable due to the expected decline in revenues from the oil industry. In parallel, objective conditions would be created for the development of inter-fuel competition, and the gas industry itself will become a more attractive segment for investment.

The cryptocurrency market is young and booming. The growth of this market is to a large extent associated with an increase in the number of initial coin offerings, also known as ICOs, as well as the volume of funds raised during the placement of new cryptocurrencies. In this article, we consider the relationship between the returns of cryptocurrencies emerged through ICOs and traded on specialized exchanges and various market factors as well as we examine the risk factors associated with the failure of cryptocurrency projects expressed in the delisting of their tokens or in a critical drop in their value. As part of the regression analysis, we showed that, in full accordance with the available literature, the returns of cryptocurrencies are not related to traditional factors of the financial market. However, sentiments in the cryptocurrency market itself, proxied by Bitcoin's returns, have a significant impact on long-term profitability, as well as sentiment in the market of Internet companies stocks. Moreover, the latter play an important role in relation to new tokens entering the market. We also showed that sentiment in the cryptocurrency market is important for the survival of cryptocurrencies. The token entering the market during periods of rising Bitcoin and increasing number of ICOs significantly reduces the chances of its survival. The results of this article are also relevant for the Russian market, since Russian companies together occupy the fourth place in the world in the number of ICOs conducted.

Since 2014, the Russian food market has undergone a series of simultaneous shocks, including restrictions on imports from a few countries, a sharp increase in prices and a rise in the cost of raw materials due to the depreciation of the ruble. This paper presents a methodology for assessing changes in consumer welfare on the micro-data of RLMS household surveys, as well as the decomposition of these changes into income and substitution effects, that is, to reactions associated with higher prices to the real income of buyers and with a change in the supplied set of products. For calculations, an econometric model was used, which is a combination of traditional models that take into account economic and socio-demographic determinants. The evaluations show an increase in costs compared to the expected level with a decrease in the actual volume of purchases, that is, even an increase in spending did not allow the residents of Russia to maintain the required level of consumption, but the effects are different for the food groups under consideration. The monetary estimate of the losses from the transformation of the food market is approximately 900 rubles per month per family, and the reduction in consumption in 2013 prices was estimated at 560 rubles, which is a significant amount, especially if we consider that the analyzed RLMS base has a shift towards poor households.