The purpose of this paper is to determine whether the transparency of monetary policy affects the likelihood of achieving the inflation target under the inflation targeting regime. Transparency is the degree of completeness of information that the central bank provides to the public—information about its own strategy, forecasts and decision-making procedures. The transparency index by Nergiz Dincer and Barry Eichengreen is used for econometric calculations. The study is based on cross-country panel data for 32 countries, each of which adhered to an inflation targeting policy at the time of the study. Calculations are based on binary choice logit models with random and fixed effects, as well as on instrumental variables. The paper reveals a steady influence of policy transparency on the success of inflation targeting implementation. As the openness of monetary authorities’ actions increases, the probability of achieving the inflation target also becomes higher. The revealed empirical evidence is consistent with the theoretical literature: transparent monetary policy strengthens the reputation of the central bank, and helps it manage inflation expectations. This influence, however, is non-monotonous: when a certain threshold level is reached, the positive effect of additional policy transparency is exhausted and its further increase may even reduce the effectiveness of inflation targeting. The study obtains estimates of this threshold level, which are then compared with the current level of transparency of the Bank of Russia monetary policy. At the moment the degree of transparency of the Russian monetary authorities’ actions is optimal. The results obtained can be useful for central banks when choosing the level of openness of monetary policy implementation procedures.

The article assesses the long-term trends of stock market development measured as the share of capitalization in GDP in 18 developed countries over the time horizon of 1880–2020. We show that there were three periods of stable capitalization growth in 1880–1913, 1950–1969 and 1980–1999 between long transitional periods of stagnation and even decline in this indicator. Over the long-term horizon, capitalization depends on changes in economies and the level of state involvement. Fundamental changes in the models of capitalism are caused by the progress of technologies and methods of organizing business; the accumulation of disproportions and contradictions in the economy; and geopolitical and other factors. The growth of capitalization during periods of sustainable development of capitalism is largely due to the opportunities for companies to attract new capital through share issuance and capital gain. This being the case, the influence of capital gain becomes predominant. It is shown that, over time, the role of country differences in explaining the level of capitalization decreases with an increase in the importance of a unified set of macroeconomic, demographic and financial variables. The factors that have a positive effect on capitalization at all historical stages are the size of GDP per capita and the real return on stocks. The share of rural residents in the total population, as well as the yield of government bonds and the level of budget expenditures, which characterize the degree of state involvement in the economy, had a negative impact on capitalization. Factors such as openness of the economy, demographic burden on the population, inflation and level of public debt had a different effect on capitalization in different historical periods, depending on the combination of certain conditions.

The paper is devoted to the quantitative assessment and analysis of changes in income and poverty in the Russian Federation during the first wave of the COVID-19 pandemic. In order to mitigate a certain decline in the financial conditions of households derived from the lockdown shock on the labor market, the federal government adopted a set of income support measures mainly addressed to families with children and officially registered unemployed. The study aims to quantify the impact of these new long-term and short-term cash transfers on average income and poverty rate for the entire population as well as across different categories of households. The two-stage microsimulation modeling is based on the data of the Statistical Survey of Income and Participation in Social Programs, annually conducted by Rosstat. The estimates show that the additional cash transfers accounted for about 15% and 35% mitigation of the decrease in average income and the increase of the poverty rate, respectively, compared to the levels at the start of 2020. It is found that families with children aged between 3 and 7 were likely to benefit most from the support measures, which almost totally prevented the growth of poverty in this category of households. Conversely, the positive impact was minimal for families with children over 7 years and negligibly small for families without children. Informal workers also fell outside the scope of federal anti-crisis social policy tools. So, for these three population categories, the aggravated problems of income reduction and rising poverty remained unresolved. The findings of the study also confirm that the universal and temporary cash transfers are much less effective in income support and curbing the growth of poverty even among recipients.

Despite the fact that the life of a particular person does not have a direct cost, the issues of economic feasibility of financing programs aimed at reducing risks to the life and health of individuals, as well as regulation of markets to ensure the safety of citizens, are closely related to the concept of the value of statistical life. This value reflects the willingness of individuals to pay for reducing risks to their own life and health, and is used by economists when analyzing the costs and benefits of policymakers’ decisions. The systematic methodology for calculating the value of statistical life is especially important due to the current transition to risk-oriented regulation, one of the most fundamental principles of which is to take into account potential damage both when establishing mandatory legal requirements aimed at protecting life and health of citizens, and when assessing compensation for damage caused in judicial practice. The article provides an overview of the existing methods of statistical life evaluation. Using an econometric analysis of Rosstat data on wages and fatal injuries, the value of statistical life in Russia was evaluated at 15.8 and 26.3 million rubles, depending on the specification used. The alternative approaches to obtaining the monetary equivalent of human life in Russia are presented: the value of life is calculated using the lost income method; also, information on the monetary compensations for relatives of the victims in Russian legislation is analyzed. The limitations of the above methods of life evaluation are discussed, and so are problematic issues associated with the implementation of the study results in public policy, especially in the field of control and supervisory regulation.

In the Russian economy, the living wage indicator has traditionally played an important role, since it serves as the basis for calculating the minimum wage, pensions, and social benefits. For a quarter of a century, the cost of living was calculated as the cost of a consumer basket in current prices and was adjusted once every three months. At the end of last year, a law was unexpectedly passed that equates the cost of living with a fixed share of the median income in the region. At the same time, the poverty line was set significantly below the generally accepted level of half the median income. It is shown that the new measuring instrument of the subsistence minimum characterizes inequality in the low-income half of the population but is unable to measure the level of poverty. Therefore, as a result of the application of the new method, the subsistence minimum will significantly decrease in poor regions and increase in rich regions—consequently, inequality in society will increase. It is also shown that the actual rise in inequality will be accompanied by a formal reduction in poverty in the backward regions due to a decrease in the poverty threshold. Another negative consequence of the new methodology will involve growth in income inequality between regions, since the previous method of calculation guaranteed equal real wages for ordinary workers in different regions.