A mandatory savings pillar was added to the Russian pension system in 2002, but by 2022 the Ministry of Finance terminated it and transformed it into voluntary pension savings. Because it was in effect for much less time than the typical life-cycle horizon, mandatory pension savings never had a chance to show its potential for increasing the pension benefits of future pensioners. The successful implementation of a mandatory savings pillar requires that general rules and regulation remain stable over at least a 40-year time horizon for the accumulation phase and a 20-year time horizon for the decumulation or payout phase. In addition to the brevity of its existence, the mandatory savings pillar also faced several other obstacles. The state prioritized the welfare of existing pensioners, and this bias eventually led to the introduction of more and more restrictions on the pension savings pillar and its coverage. Many problems arose in the administration of pension savings and in coordinating the actions of various government departments involved in its regulation. Low investment efficiency for pension savings, irrational asset allocation in non-state pension funds’ portfolios, and poor active management decisions were among other issues. Based on the problems outlined, the authors have formulated basic proposals for the future of the pension savings system.

The energy crises during 2021 and 2022 in the USA and EU have shown that technological transformation can have negative consequences for the electric power industry by causing power outages and sharp fluctuations in market prices. This prompted the authors to determine the role of institutions in overcoming problems in intra-sectoral coordination. After clustering the electric power industry’s alternatives for institutional organization in different countries using OECD data and Ward's method, the conclusion is that, in addition to economic and technological factors, the characteristics of social orders (as outlined by D. North et al.) influence the choice of a regulatory model. Moreover, after comparing the results of clustering with the dynamics of investment in the industry and of electricity prices, the authors maintain that these factors are significantly correlated with institutional organization. Therefore, management alternatives for ensuring stable electricity prices and incentives for investment in the industry should take into account not only economic and technological factors but also the social order established in the country.

The sanctions imposed on Russia in February 2022 have affected the current and future revenues of the domestic energy sector as well as the tax revenues derived from it, while they have also made public welfare losses due to accumulated imbalances in the fuel and energy sector more sensitive to the Russian economy (for example, through subsidies for oil refining). Developing recommendations for adjusting the way the Russian oil refining sector is subsidized is now an urgent matter. This paper estimates the factor payment for the use of oil rent, considers the structure of its distribution in the Russian economy, and provides a scenario analysis of the consequences of imposing sanctions, which include a “price ceiling,” changes in tax regulation, and an increase in the processing depth of refineries. Based on this analysis, reforms in the taxation of the Russian oil refining sector are proposed. The results of the scenario analysis show that, under the current conditions, it is extremely important to continue modernizing oil refineries. A potential reduction in the production of petroleum products would result in the smallest losses industry-wide, provided that it is achieved by suspending the least efficient refineries (those with low GVA in the absence of subsidies) and by ending subsidies for friendly economies.

This paper examines the quality of nowcasts and forecasts for Russian GDP and its components (in constant and current prices) using a mixed-frequency Bayesian vector autoregression model (MFBVAR) which is currently one of the most advanced time series forecasting models. It enables use of quarterly and monthly frequency data within a single monthly frequency VAR model in a statespace form while taking into account the intra-quarter dynamics of monthly indicators; this approach improves forecasting accuracy when new monthly data is published. The MFBVAR model’s resistance to the jagged edge problem is especially important for real-time forecasting, and it can incorporate a large number of predictors because of its Bayesian estimation with a Minnesota-type prior distribution. The paper sets up three experiments with differing availability of monthly data in order to test pseudo out-of-sample nowcasting and forecasting. The MFBVAR model exhibits statistically significant outperformance compared to a naïve benchmark, as well as to ARIMA and quarterly BVAR models, in nowcasting and forecasting a few steps ahead for GDP, consumption and foreign trade variables. The test sample is also quite representative and covers two crisis periods, specifically 2015 and 2020. In both crises, the model accurately estimates the scale of the recession and recovery of economic activity. Nevertheless, there was no significant improvement in the quality of forecasts when new available monthly data was introduced.

Russia’s experiment with legalization of self-employment has gone on for more than three years. The number of persons officially registered as self-employed exceeded five million by mid-2022, and they have been included in the employment statistics for small and medium-sized enterprises (SMEs), which are a target indicator for one of the national goals defined by the President of the Russian Federation. However, the increase in self-employment may not indicate a qualitative advance in the SME sector but may instead partially conceal a decline in the main indicators. The purpose of this article is to trace the principal trends and factors in the development of self-employment in Russia’s various regions, including a possible exodus of workers from SMEs to self-employment. Based on an econometric model, the article analyses the main factors that determine the development of self-employment in Russia’s regions: GRP per capita, average salary, unemployment, the economic structure, and human capital. The results indicate that self-employment in the regions is correlated with such negative economic factors as low salaries and GRP per capita, high unemployment, low-quality human capital, and less industrial productivity in the regional economy. The growth of self-employment is not accompanied by a qualitative advance in the SME sector. Analysis of current development trends for Russia’s SME sector has shown that the growth in selfemployment is due mostly to legalization of micro-businesses. During a crisis, self-employment becomes a way for entrepreneurs to maintain their income. The article proposes measures for supporting formal employment in the SME sector in order to help the self-employed create promising businesses.