Аналитический доклад подготовлен научным коллективом Института демографических исследований ФНИСЦ РАН. В докладе рассматриваются тенденции демографического развития стран бывшего СССР в 1991–2021 гг. В докладе дан комплексный анализ семейно-демографических и миграционных процессов, а также результативности семейной, демографической и миграционной политик стран бывшего СССР за тридцать лет, даны некоторые прогнозные оценки динамики численности населения в регионе на среднесрочную перспективу. При подготовке аналитического доклада были использованы данные Межгосударственного статистического комитета СНГ, национальных служб государственной статистики, Евростата, международных организаций системы ООН. Доклад адресован государственным служащим, научным сотрудникам, преподавателям университетов, аспирантам и студентам.
The relevance of the study of the impact of living standards on the reproductive behavior of men and women is due to the complexity of the demographic situation at the national level, the inconsistency of the reproductive behavior of Russians, the need to develop predictive hypotheses of fertility, as well as identify institutional solutions that can be used to develop new measures in the framework of state programs to increase the birth rate in the Russian Federation. The empirical basis of the study is data from representative sample surveys: sample observations of the population’s reproductive plans, which were conducted by Rosstat in 2012 and 2017, as well as the results of the first wave of the all-Russian sociological study “Demographic well-being of Russiaˮ, conducted in late 2019 — early 2020 under the guidance of Doctor of Sociological Sciences, Professor T. K. Rostovskaya. The results of the study will be used in monitoring and implementing family policy measures at the micro- , meso- and macrolevels: forming the conceptual and legislative framework for family and demographic policy; evaluating the effectiveness of family and demographic policy.
The article analyzes age-specific fertility rates and the average age of mothers at birth in Moscow in comparison with Russia as a whole and St. Petersburg. Special attention is paid to these indicators differentiated by the order of birth (first, second, third), as they do not depend on the birth rate. In Moscow, the average age of mothers at birth is significantly higher than in Russia as a whole, but lower than in Saint Petersburg. Lack of data on the number of population by sex and age in Moscow's municipal districts prevents a correct comparative analysis of the age pattern of fertility for them. But based on the distribution of the numbers of births by maternal age groups, the average age at birth for 2021 is calculated.
Demographic indicators are important functions of state programs for the development of Russia, operational monitoring of demographic development is the key to the successful implementation of programs. Very often, government statistics data are published with a delay, which does not allow their use for operational monitoring and planning. In this work, the approach allows for the rapid assessment of demographic processes in the field of formation and forecasting of demographic trends in the short term based on data from query statistics from Google Trends. The relationships between the search queries and demographics are analyzed using Pearson's correlation. The analysis uses annual (total fertility rate, abortions per 100 births, abortions per 1000 women, marriages and divorces per 1000 population) and monthly data (number of births, number of marriages and divorces) by birth, marriages and abortions with and without lags. The analysis is carried out on data for Russia as a whole and for the eight most populated regions: Moscow, Moscow Region, Krasnodar Territory, St. Petersburg, Rostov Region, Sverdlovsk Region, Republic of Tatarstan, Republic of Bashkortostan. Using the temporal metrics available in Google Trends since 2004, some demographics can be predicted based on data from related queries to the Google search algorithm using the ARIMA model. Thus, it is possible to use query data as a supplement to demographic data, when building multiple regression models for demographic calculations, or use it as a proxy variable.
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