Abstract

Digital transformation is taken in the article as changes in business models inspired by “new technologies” (cloud, artificial intelligence, big data, distributed ledgers etc.), by threats of disruption from new competitors (FinTech startups and big techs), and by growing demand for individualized and integrated services distributed via digital channels. In this sense, digital transformation means platformification, i.e. when business is developed through digital information systems connecting buyers with sellers, including third-party service providers. Financial institutes, while selling their native services on platforms and/or orchestrating platforms, are able to position their proposals as technological, thus making the modern financial mantra (“Work like Google”) true. The article outlines the dis- tinctions between the platform business model and the traditional pipeline model; compares the definitions of “platform”, “marketplace” and “ecosystem”; summarizes domestic and international experience of platformification in capital market infrastructure (exchanges/trading venues, CCP clearing houses, central securities depositories etc.); differentiates the models of such platformification according to where they lead away from business as usual: onto other markets (mono- and multi-product platforms), to other services (“universal platforms” and digital asset platforms) or to other interactions (platforms for non-core services, “at the top of exchange” platforms, “apps warehouses”); and describes the place of platformification in growth strategies—both real ones, including the Moscow Exchange Group case, and hypothetical ones in line with the Ansoff Matrix.

Keywords: digital transformation, digital platforms, marketplaces, capital market, infrastructure.
JEL: D02, G20, O16, O32.

Sergei I. MAYOROV, Cand. Sci. (Econ.). Strategy Department, Moscow Exchange (13, Bol’shoy Kislovskiy per., Moscow, 125009, Russian Federation).
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Abstract

The article studies price rigidity for food, non-food products and services based on daily data of online retailers in Moscow collected for the period from February 2019 to May 2020. It was found that the average price invariance period was 19.5 days. In addition, there is significant heterogeneity in the frequency of price changes: food prices change more often than non-food ones, and the cost of services changes the least often. The observed price behavior confirms the implications of time- and state-dependent models. The dependence on time is indicated, for example, by the fact that the probability of price changes sharply increases by days 335 and 353 of their invariability. This fact reinforces the conclusions on the presence of seasonality in price changes found in a number of foreign studies. The analysis also suggests that, in March–May 2020, growth in uncertainty caused by the coronavirus pandemic led to firms beginning to change prices more often and by a smaller magnitude. As a result, the share of price increases went up from 50.2% in March–May 2019 to 62.7% in March–May 2020. The number of days in which prices for any goods and services increased rose from 65 to 84 (out of 92 days in March–May). Thus, the results of this paper confirm that pricing behavior on the e-commerce market can vary significantly depending on economic conditions.

Keywords: price rigidity, e-commerce market, menu costs, hazard function, price changes.
JEL: E30, E31, D40, D21.

Alexandra V. BOZHECHKOVA, Cand. Sci. (Econ.). Russian Presidential Academy of National Economy and Public Administration (82, Vernadskogo pr., Moscow, 119571, Russian Federation); Gaidar Institute for Economic Policy (3–5, Gazetnyy per., 125009, Moscow, Russian Federation). E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Alexey S. EVSEEV. Russian Presidential Academy of National Economy and Public Administration (82, Vernadskogo pr., Moscow, 119571, Russian Federation).E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Abstract

This article uses the gravity model to explain the trade flows between the regions of Russia. The data used contains information on all rail freight flows in tonnes for the period 2012–2016. The authors discuss the assumptions that make the gravity model applicable for trade by one mode of transport. For the purpose of correct estimation of the gravity equation, the special method of obtaining trade values in rubles was developed. The coefficients of distance and gross regional products are close to the results of previous studies on international and domestic trade. Along with the distance factor, the average rail tariff variable was included in the model. The significance of the coefficients of both variables has confirmed that trade flows are determined not only by transport costs, but by other trade costs, too. The results suggest that the export volume of the sending region proportional to its output is positively related to the volume of bilateral domestic trade flows. Accounting for regional fixed effects has not changed the coefficients of distance and average tariffs significantly, indicating the robustness of the estimates. No proof has been found for the impact of infrastructure on trade. An institutional factor that can negatively affect the trade volume is the level of corruption in the recipient region.

Keywords: domestic trade, freight flows, railway transport, gravity equation, institutional quality, transport infrastructure.
JEL: F14, C23, R41

Alexander O. TOMAEV. Russian Presidential Academy of National Economy and Public Administration (82, Vernadskogo pr., Moscow, 119571, Russian Federation). E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Andrey S. KAUKIN, Cand. Sci. (Econ.). Russian Presidential Academy of National Economy and Public Administration (82, Vernadskogo pr., Moscow, 119571, Russian Federation); Gaidar Institute for Economic Policy (3–5, Gazetnyy per., Moscow, 125009, Russian Federation). E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Pavel N. PAVLOV. Russian Presidential Academy of National Economy and Public Administration (82, Vernadskogo pr., Moscow, 119571, Russian Federation).
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Abstract

The article examines the evolution of regulation of the conditions for granting subsidies to equalize the budget sufficiency of Russian regions as well as the practice of concluding agreements between the Russian Ministry of Finance and Russian regions on the provision of such subsidies. The author states that there is a tendency to tighten the conditions for granting subsidies to regions, and substantiates the opinion that concluding agreements with the Ministry of Finance being required as a condition for granting subsidies to regions that are not among the highly subsidized ones does not correspond to the principle of budget independence. According to the author, in a situation where most of the regions’ expenses on subjects of joint management are predetermined by federal law, grants cannot be considered as non-targeted financial support, the provision of which may depend on the discretion of federal authorities. Among the shortcomings of the institution of agreements with the Ministry of Finance as a condition for granting subsidies, the author also considers the lack of legislative regulation of the terms of such agreements, unrealistic demands to regional authorities, and the objective impossibility of establishing effective mechanisms for enforcement of such agreements. Based on the results of the analysis, the author comes to the conclusion that it is advisable to abandon the practice of concluding agreements with the Ministry of Finance as a condition for granting subsidies to all regions, or to significantly soften the terms of such agreements. The author also offers alternative tools for the Federation’s influence on the financial policy of regions in order to improve their budgets.

Keywords: intergovernmental transfers, subsidies, subsidies for equalization of bud­ get sufficiency levels, conditions for granting subsidies, agreements on granting sub­sidies.
JEL: Н7, Н77.

Anna B. ZOLOTAREVA, Cand. Sci. (Law). Institute of Applied Economic Research, Russian Presidential Academy of National Economy and Public Administration (84, pr. Vernadskogo, Moscow, 119571, Russian Federation); Gaidar Institute for Economic Policy (str. 1, 3–5, Gazetnyy per., Moscow, 125009, Russian Federation). Е-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Abstract

In Russia, means testing is used to identify low-income households and measure poverty headcount as well as to establish the eligibility of the applicants to targeted social assistance. The current means-testing formula, however, is restricted to comparing the per capita income with the cost of the so-called minimum consumer basket or the standard subsistence income. The authors propose an improved means-testing formula which is claimed to measure the wealth and consumption needs of a household more accurately and more equitably, as it includes a revised equivalence scale and а filter for the possession of certain valuable assets. Based on a representative household survey, which covers three subfederal jurisdictions and has been specifically designed to test the performance of the new formula, the authors measure the contribution of each of the proposed formula modifications and the combined effect of all modifications upon the overall poverty headcount and the total income gap as well as the effects upon the poverty status of selected categories of households. Even though during the modeling phase the poverty threshold had to be raised by 12–16% against the official poverty line effective in the respective jurisdictions in order to eliminate the influence of the proposed equivalence scale on the poverty headcount, the ultimate effect of the new formula, which combines the new equivalence scale and several property filters, is a 25% reduction of poverty. This reduction is mainly due to sorting out the households that own excessive property or cars from the low-income category.

Keywords: poverty rate, targeted social assistance, neediness criteria, low-income families, means testing, household survey.
JEL: I32, I38, I39, H53, H75, C83.

Elena I. ANDREEVA. Center of Social Sphere Finances, Financial Research Institute of the Ministry of Finance of the Russian Federation (3, str. 2, Nastas’inskiy per., Moscow, 127006, Russian Federation).
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Dmitry G. BYCHKOV, Cand. Sci. (Sociol.). Center of Social Sphere Finances, Financial Research Institute of the Ministry of Finance of the Russian Federation (3, str. 2, Nastas’inskiy per., Moscow, 127006, Russian Federation).
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Olesya A. FEOKTISTOVA, Cand. Sci. (Econ.). Center of Social Sphere Finances, Financial Research Institute of the Ministry of Finance of the Russian Federation (3, str. 2, Nastas’inskiy per., Moscow, 127006, Russian Federation). E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Abstract

This paper provides a joint analysis of business and credit cycles with a focus on unobservable factors affecting both cycles, at the cross-country level. Using quarterly data for 19 developed countries and Russia for the period from 1994 to 2018, we build a system of two dynamic probit models, which includes a cross-correlation between the errors of the equations governing the probability of a recession and the probability of credit crisis. The results show that, first, our system allows us to correctly predict 91% of episodes of joint realization of macroeconomic and credit crises and 89% of non-crisis periods in the training sample, and 92% and 95% respectively in the testing sample. Second, switching from two independent regression models to a system of correlated equations significantly (by 16 percent- age points) increases the share of correctly predicted crisis episodes while only slightly (by 7 percentage points) reducing the proportion of correctly predicted non-crisis episodes. Third, our system can predict an approaching crisis earlier, by 1–4 quarters, in comparison with similar single models. Our results complement the literature on forecasting recessions and credit crises. Fourth, it is revealed that the models which have been constructed on developed countries allow one to predict crisis events for Russia. The model we have constructed correctly predicts 100% of joint crisis episodes and 92% of joint non-crisis episodes in the training sample as well as 86% of joint crisis and 90% of joint non-crisis episodes in the testing sample for Russia.

Keywords: cross-country analysis, business cycles, credit cycles, bivariate dynamic probit, crisis prediction, in-sample forecasting, out-of-sample forecasting.
JEL: C34, G21, G33.

Mikhail E. MAMONOV, Cand. Sci. (Econ.). MGIMO University (76, Vernadskogo pr., Moscow, 119454, Russian Federation); CERGE-EI, Charles University, Economics Institute of the Czech Academy of Sciences (Politických vězňů 7, 111 21, Prague 1, Czech Republic). E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Anna A. PESTOVA, Cand. Sci. (Econ.). MGIMO University (76, Ver- nadskogo pr., Moscow, 119454, Russian Federation); CERGE-EI, Charles University, Economics Institute of the Czech Academy of Sciences (Politických vězňů 7, 111 21, Prague 1, Czech Republic). E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Vera A. PANKOVA. Center for Macroeconomic Analysis and Short-Term Forecasting (47, Nakhimovsky pr., Moscow, 117418, Russian Federation); National Research University Higher School of Economics (20, Myasnitskaya ul., Moscow, 101000, Russian Federation). E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Renat R. AKHMETOV. Center for Macroeconomic Analysis and Short- Term Forecasting (47, Nakhimovsky pr., Moscow, 117418, Russian Federation); National Research University Higher School of Economics (20, Myasnitskaya ul., Moscow, 101000, Russian Federation). E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it