The role of social protection in supporting people’s well‑being, reducing poverty and inequality is difficult to overestimate in stable times, but its importance increases especially in times of crisis, as confirmed by the global shock of the COVID‑19 pandemic. Under the conditions of increasing uncertainty, a social protection system turns into a “safety cushion” — a macroeconomic and sociopolitical stabilizer. The purpose of the article is to show, based on the analysis of the main trends in the development of the Russian social protection system and considering the challenges of the current moment, possible alternative choices in its development in the mid‑term period. Authors use a broad definition of the social protection system, which includes non‑contributory measures of social protection (social assistance), contributory and non‑contributory pensions, minimum wages, and social services (long‑term care). The article contributes to the literature on economics and public administration, which focuses on social policy in times of economic crises. The article discusses the turn in social protection associated with the adoption of the national development goals in 2018. The authors analyze to what extent has the coronavirus pandemic affected the main challenges and problems facing Russian social protection; what were the key decisions in the field of anticrisis support for the population, and how they affected indicators of poverty and inequality. The article also presents recent research results on changes in public attitudes toward social protection as well as issues of using the time‑budget as a component of a comprehensive assessment of the population’s quality of life. In conclusion, the article discusses lessons which can be learned from the experience of the pandemic‑related crisis for the future development of the Russian social protection system, including in the conditions of turbulence that have arisen in 2022.

The subject of the article is a discussion of the grounds for qualifying the system of compulsory health insurance (CHI) created in Russia as an insurance system, which is questioned by many experts. The article sys tematizes theoretical and methodological approaches to the description of social health insurance systems. A comparative analysis of the evolution of national systems of compulsory health insurance and systems of budgetary financing of health care was carried out from the standpoint of considering institutional changes as strengthening or weakening three types of regulation (state, societal, private), which ensure the performance of the functions of revenue collection, pooling of funds, purchasing of services. On this basis, general characteristics of modern models of social health insurance, which distinguish them from budgetary systems, are highlighted. The Russian CHI system can be qualified as a social insurance system in terms of the formation and pooling of financial resources, but as a hybrid, combining elements of the insurance and budgetary systems, in terms of the purchase of medical care.

The article examines the influence of economic factors on life satisfaction in Russia. To conduct this study, we took RLMS­HSE data from 1994 to 2019. We applied the panel data ordered logit model on the samples of men and women. It has been confirmed that the level of life satisfaction is the higher, the better the financial situation of an individual in absolute and relative terms. Maintaining a balance in the distribution of time between work and leisure also has a significantly positive effect on the level of life satisfaction. However, the impact of objective economic factors on life satisfaction in Russia is mediated by their subjective perception, which plays a decisive role. Since when it includes in the model, the objective parameters become insignificant. The results are consistent with similar works conducted on other countries’ data and spatial data. They deepen the understanding of the impact of the economic component on the subjective well­being of Russians. The conclusion about the significant influence of the subjective perception of economic reality on happiness in Russia is of practical importance for the guidelines of economic and social policy.

The study explores routine and non-routine content of job tasks across occupations in the Russian labor market. Each occupation contains a bundle of job tasks, which can be routine or non-routine, cognitive or manual. Occupations, in which most job tasks are routine, are under the risk of automation, and therefore their future is of concern. Our empirical analysis uses three main data sources that contain information in the detailed occupational breakdown: on employment, on the extent of routinization, and on annual labor incomes. This data set allows to estimate task indices for disaggregated occupations and different socio-economic groups and also present empirical estimates of wage penalties and premiums for workers with different job content. The calculations suggest that the proportion of jobs in which routine tasks tend to prevail is not large and hardly exceeds 10%. From this follows that a massive substitution of labor by machines or AI in foreseeable future does not look like a plausible option. Content of job tasks is expectedly associated with the level of pay: workers with non-routine cognitive tasks are better rewarded, and non-routine manual tasks are the most penalized. A polarization scenario feared by some observers does not seem a likely option in Russia for years to come.

The article examines the impact of changes in the number of employees on wages and housing prices in Russian regions. According to the local labor market model higher nominal wages in a particular region can attract workers from other regions. Due to the limited supply of housing an increase in the number of people employed in a certain area leads to an increase in demand for housing and an increase in prices for it, which can cause workers to lose in terms of real wages. In the presented work we tried to answer the question of whether the change in the number of employees in the region affects wages and housing prices in the subjects of the Russian Federation. The estimates were carried out on aggregated regional Rosstat data using panel models with fixed and spatial effects (SEM and SAR models). The study showed that there are spatial correlations both between regional labor markets and between local housing markets. Changes in the number of employees in the region make a significant positive impact on housing prices and a significant negative impact on real wages. In terms of significance and direction of impact all the evaluated models are similar to each other. The results of the analysis can be used to carry out social, regional and migration policies.

The article presents the results of a large­scale study devoted to the formation of forecast indicators of recruitment needs of the Russian Arctic economy as the most important geostrategic territory of the country. Information about them is presented in regional, industry and professional terms. The basic and additional sources to ensure these needs of the Russian Arctic economy have been identified. It is concluded that there is a serious shortage of the internal labor resources of the Arctic territories, hence the need to attract additional labor force, including for the implementation of investment projects. It is shown that the current situation in the system of training qualified specialists in the Arctic territories contradicts the long­term plans for the Russian Arctic development. In the context of the implementation of the state’s new Arctic strategy, the importance of building a well­thought­out state policy for staffing the Arctic territories, development of human capital sectors, and diversification of the Arctic economy is noted.

The article developed a methodology for nowcasting and short-term forecasting key Russian macroeconomic aggregates: real GDP, consumption, investment, export, import, using machine learning methods: boosting, elastic net, and random forest. The set of predictors included indicators of the stock market, money market, surveys, world prices for resources, price indices, and other statistical indicators of different frequency, from daily to quarterly. Our approach makes available a detailed examination of the changes in forecasts with the flow of new information. For most of the considered variables, a monotonic non-deterioration of the forecast quality was obtained with an expansion of available information. Furthermore, machine learning methods have shown significant superiority in predictive performance over naive prediction. The considered methods within the framework of the pseudo-experiment quickly showed a strong drop in real GDP, household consumption, and other variables in the context of the spread of the COVID-19 pandemic in the 2nd and 3rd quarters of 2020.