The article covers the issues of primary data sources’ transformation for the production of official statistics. This topic is in sight of the UN Statistical Commission and its committees and working groups. The global statistical community and national statistical systems have faced a new challenge – the data revolution. This phenomenon is associated with the ever-expanding digitalization of all aspects of modern life and the emergence of new sources of information that are not the result of classical statistical observations. In some countries, administrative statistics have replaced the collection of primary data. They have practically abandoned surveys and reporting.

In recent decades, the phenomenon of Big Data has declared itself as a source of operational and detailed information on many phenomena in society, economy and environment. National statistical systems are faced with a choice of how to organize data collection in the modern world most effectively. For the international statistical community, the results of this discussion are of decisive importance in working out a development strategy, including ensuring conditions for the integration of information resources and the creation of tools for managing data obtained from various sources. The author considers the change of data sources, given the need to reduce the statistical burden on society by reducing budget expenditures and the time respondents spend preparing and providing information.

The article addresses improving the methodology for distributing the annual volume of unobserved agricultural production in Russia by month.

The study used methods for calculating averages and variances, dispersion analysis and the index method.

The study aims to explain the algorithm for distributing the annual volume of agricultural production by month to improve the quality of official statistical information. The authors analyzed existing approaches to the distribution of agricultural output, reviewed the experience of other countries in the subject area of the study, and formulated methodological recommendations for distributing the volume estimates of the output of unobserved types of agricultural production by month.

Official statistical methodology, published results of scientific research by Russian and foreign scientists, current regulatory legal acts of the Russian Federation and other countries in the subject area, and agricultural calendars for the period under review served as a theoretical foundation for this work.

The scientific novelty of the conducted study lies in developing scientific and methodological recommendations for the distribution of agricultural production volumes in actual prices by month to improve the objectivity of official statistics. The relevance of the study stems from the need to refine the existing approaches to the distribution of the annual volume of agricultural production.

The study analyses the dynamics of economic inequality in Russia in 2006–2022 and the contribution of the social transfer system to income distribution in society. The study examines the distribution of recipients of social benefits by decile income groups, the amount of social transfers and its share in household incomes with different income levels. To identify the relationship between household income and the probability of receiving social transfers and the size of benefits, regressions with random effects were estimated using panel logistic regression and generalized OLS.

The study determined that the decrease in economic inequality in Russia was accompanied by increased social support to the population, especially pronounced in 2020–2022. The likelihood of receiving social benefits increased as real per capita household incomes declined, but the sum of social transfers was unrelated to household income.

The author found that recipients of social benefits were present in all decile groups, and the value of social transfers was higher in house-holds with middle-and high-income levels. At the same time, benefits for families with children and unemployment benefits were most often provided to families from lower income groups; recipients of tax breaks predominated in the upper-income groups; subsidies for housing and communal services were common among households in middle decile groups, and surcharges for fuel were evenly distributed among different income groups. On average, during the study period, about 50% of households from the bottom decile remained without any social transfers, and during the crisis of 2020–2021, only a third of households in the bottom decile received benefits.

The study results suggest that there is potential to reduce income inequality by improving the targeting of social benefits and better identifying the need for social assistance from the poorest households during an economic crisis.

The article presents a method for constructing an age-related fertility model based on the modern system of statistical accounting in Russia. In analyzing the birth rate, it is advisable to use indicators for both calendar years and real generations. The first ones include age-related fertility rates and the structure of their contributions to the total fertility, the average age of the mother at the birth of children. The use of annual age-related fertility rates allows us to make an estimated calculation of fertility rates for real generations, including those characterizing the age model, which significantly depends on the structure of fertility: the more children are born second, third, and subsequent in the order of birth, the more the age model of fertility will be shifted to older ages.

It is established that to characterize the age model of fertility, fertility indicators (both for conditional and real generations) should be used, taking into account the order of birth. It is noted that changes in the level and age model of fertility for calendar years may be due to the influence of timing shifts associated with the birth of children at a younger age or, conversely, the postponement of births in many families.

The study of indicators for calendar years allowed us to hypothesize the presence of such timing shifts, and the results of the analysis of fertility rates in age ranges for real generations confirmed the hypotheses. All the indicators used in the article are calculated using the data of the All-Russian Population Census of the 2020 round.

The article presents some results of a study on the impact of fertility on women's employment using the example of the Samarkand region of the Republic of Uzbekistan based on data for 2014–2023. The author emphasizes the growing relevance of these issues due to the continuing key role of women in the country's economic development and raising the living standards.

Issues of gender equality in the labor market are considered with an emphasis on the impact of fertility on women's employment. It is noted that ensuring gender equality is embedded in the UN Sustainable Development Goals and is studied by many international organizations.

The study used methods of trend analysis, correlation and regression analysis, and a projection of possible scenarios based on special and total fertility rates. The described methodology includes several complementary methods of data analysis that allow a comprehensive assessment of the impact of fertility on female employment and modelling potential changes in employment levels with changes in fertility.

The study showed a close direct relationship between the female employment rate and the overall employment rate in the region, as well as an inverse relationship between the fertility rate and female employment, which hinders their active participation in labor activity. The author concluded that a high fertility rate hurts the female employment rate since women are often forced to leave work to take care of their children. In this regard, the need to develop public policies aimed at supporting women in combining work and family responsibilities, improving access to preschool educational institutions and increasing the efficiency of their functioning is emphasized. Addressing the problems associated with women's return to the labor market after the birth of children can help reduce gender differences in employment.