26 research outputs found
Parental labor in the Republic of Tuva in the context of the regional cluster structure of Russia's demographic space (1990-2019)
Despite the overall positive dynamics of demographic processes related to the natural reproduction of the population in the Republic of Tuva, the region currently experiences a fairly negative state of several spheres of human capital functioning and development. This may indicate a problematic state of the sphere of parental labor in the region. The article presents the results of identifying the place of the Republic of Tuva in the cluster structures of Russian regions. These structures were formed by the parameters of parental labor during the period from 1990 to 2019. The authors assess the place of the Republic of Tuva in the cluster space of Russian regions according to indicators that demonstrate the conditions, organization and results of parental labor. The research is based on an interdisciplinary concept of parental labor, which implies the labor character of parenthood and its compliance with all the features of work activity. The sources of the study are the data of the Federal State Statistics Service and the data of federal statistical observations on socio-demographic issues. Two key features of the position of the Republic of Tuva in the regional cluster structure have been identified. The first one is that Tuva either does not fit into the structure of the formed regional cluster space and is not included in any of the identified clusters, or it is geometrically located at a very far distance from the center of the cluster to which it is assigned. The second feature is that the profile of Tuva as part of the entire cluster is in most cases characterized by polarity and extreme ambiguity. Β© 2023 New Reaearch of Tuva. All rights reserved.SSβ1327.2022.2The research was conducted as part of the project βRussian Pro-Natalist Policy Support Institutions: Potential and Prospects for Influencing Birth Rate Growthβ supported by the Council for Grants of the President of the Russian Federation for State Support to Leading Scientific Schools of the Russian Federation (SSβ1327.2022.2)
Models of grandparents' labour in the socio-economic space of Russia
ΠΠ·ΡΡΠ΅Π½ΠΈΠ΅ ΡΠ΅Π½ΠΎΠΌΠ΅Π½Π° ΠΏΡΠ°ΡΠΎΠ΄ΠΈΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ ΡΡΡΠ΄Π° ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΎΠ΄Π½ΠΎΠΉ ΠΈΠ· ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ² ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΈ ΡΠΎΠ΄ΠΈΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ ΡΡΡΠ΄Π° - Π½Π°ΡΡΠ½ΠΎΠ³ΠΎ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ, Π°ΠΊΡΠΈΠ²Π½ΠΎ ΡΠ°Π·Π²ΠΈΠ²Π°Π΅ΠΌΠΎΠ³ΠΎ Π·Π°ΡΡΠ±Π΅ΠΆΠ½ΡΠΌΠΈ, Π° Ρ Π½Π°ΡΠ°Π»Π° 2000-Ρ
Π³Π³. - ΠΈ ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΠΌΠΈ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠΌΠΈ. ΠΡΡΠΎΠΊΠΈΠΉ ΡΡΠΎΠ²Π΅Π½Ρ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎ-ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄ΠΈΡΡΠ΅ΡΠ΅Π½ΡΠΈΠ°ΡΠΈΠΈ ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ
ΡΠ΅ΡΡΠΈΡΠΎΡΠΈΠΉ, Π° ΡΠ°ΠΊΠΆΠ΅ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π²Π°ΠΆΠ½ΡΡ
ΡΡΡΠ°ΡΠ΅Π³ΠΈΡΠ΅ΡΠΊΠΈΡ
Π·Π°Π΄Π°Ρ, ΡΡΠΎΡΠΌΡΠ»ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
Π² Π½Π°ΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠΌ ΠΏΡΠΎΠ΅ΠΊΡΠ΅ Β«ΠΠ΅ΠΌΠΎΠ³ΡΠ°ΡΠΈΡΒ» ΠΈ Π‘ΡΡΠ°ΡΠ΅Π³ΠΈΠΈ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠ°Π·Π²ΠΈΡΠΈΡ Π ΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π€Π΅Π΄Π΅ΡΠ°ΡΠΈΠΈ Π½Π° ΠΏΠ΅ΡΠΈΠΎΠ΄ Π΄ΠΎ 2025 Π³ΠΎΠ΄Π°, Π°ΠΊΡΡΠ°Π»ΠΈΠ·ΠΈΡΡΡΡ ΠΈΠ·ΡΡΠ΅Π½ΠΈΠ΅ ΠΏΡΠ°ΡΠΎΠ΄ΠΈΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ ΡΡΡΠ΄Π°. Π¦Π΅Π»Ρ Π½Π°ΡΡΠΎΡΡΠ΅Π³ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠΎΡΡΠΎΠΈΡ Π² Π²ΡΡΠ²Π»Π΅Π½ΠΈΠΈ ΡΠΏΠ΅ΡΠΈΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅Π³ΠΈΠΎΠ½Π°Π»ΡΠ½ΡΡ
ΡΠΈΡΡΠ°ΡΠΈΠΉ - ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΠΏΡΠ°ΡΠΎΠ΄ΠΈΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ ΡΡΡΠ΄Π° Π² ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎ-ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΌ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π΅ ΡΡΡΠ°Π½Ρ. ΠΠΌΠΏΠΈΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΎΡΠ½ΠΎΠ²ΠΎΠΉ Π°Π½Π°Π»ΠΈΠ·Π° ΠΏΠΎΡΠ»ΡΠΆΠΈΠ»ΠΈ Π΄Π°Π½Π½ΡΠ΅ ΠΎΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π ΠΎΡΡΡΠ°ΡΠ° Β«ΠΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠ΅ Π½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΠ΅ ΡΡΠ»ΠΎΠ²ΠΈΠΉ ΠΆΠΈΠ·Π½ΠΈ Π½Π°ΡΠ΅Π»Π΅Π½ΠΈΡΒ», ΠΎΡΠ΄Π΅Π»ΡΠ½ΡΠ΅ Π²ΠΎΠΏΡΠΎΡΡ ΠΊΠΎΡΠΎΡΠΎΠ³ΠΎ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡ Π² ΠΏΠ΅ΡΠ²ΠΎΠΌ ΠΏΡΠΈΠ±Π»ΠΈΠΆΠ΅Π½ΠΈΠΈ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΡ ΠΏΡΠ°ΡΠΎΠ΄ΠΈΡΠ΅Π»ΡΡΠΊΠΈΠΉ ΡΡΡΠ΄. Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΎΡΠ½ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠ΅ΡΠΎΠ΄Π° ΠΏΡΠΈΠΌΠ΅Π½ΡΠ»ΠΈΡΡ ΠΈΠ΅ΡΠ°ΡΡ
ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΊΠ»Π°ΡΡΠ΅ΡΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· (Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΌΠ΅ΡΠΎΠ΄Π° ΠΠ°ΡΠ΄Π°, ΠΊΠ²Π°Π΄ΡΠ°ΡΠ° Π΅Π²ΠΊΠ»ΠΈΠ΄ΠΎΠ²Π° ΡΠ°ΡΡΡΠΎΡΠ½ΠΈΡ ΠΈ Π΄ΡΡΠ³ΠΈΡ
ΠΌΠ΅Ρ) ΠΈ Π½Π΅ΠΈΠ΅ΡΠ°ΡΡ
ΠΈΡΠ΅ΡΠΊΠΈΠΉ ΠΊΠ»Π°ΡΡΠ΅ΡΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· (ΠΌΠ΅ΡΠΎΠ΄ k-ΡΡΠ΅Π΄Π½ΠΈΡ
). Π ΠΏΡΠΎΡΠ΅ΡΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠ»ΡΡΠ΅Π½Ρ ΡΠ΅ΠΎΡΠ΅ΡΠΈΠΊΠΎ-ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈ ΠΏΡΠΈΠΊΠ»Π°Π΄Π½ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ. ΠΡΠ»Π° ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π° ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠ°Ρ ΠΎΡΠ½ΠΎΠ²Π° ΠΈΠ·ΡΡΠ΅Π½ΠΈΡ ΠΏΡΠ°ΡΠΎΠ΄ΠΈΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ ΡΡΡΠ΄Π°, ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½Π½Π°Ρ Π½Π° Π±Π°Π·Π΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ ΡΠΎΠ΄ΠΈΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ ΡΡΡΠ΄Π° ΠΈ Π·Π°ΡΡΠ±Π΅ΠΆΠ½ΡΡ
Π΄Π΅ΠΌΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ ΠΏΡΠ°ΡΠΎΠ΄ΠΈΡΠ΅Π»ΡΡΡΠ²Π°. ΠΠΌΠΏΠΈΡΠΈΡΠ΅ΡΠΊΠΈΠΉ Π°Π½Π°Π»ΠΈΠ· ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ» Π²ΡΡΠ²ΠΈΡΡ ΠΈ ΠΎΡ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΠΎΠ²Π°ΡΡ 6 ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΠΏΡΠ°ΡΠΎΠ΄ΠΈΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ ΡΡΡΠ΄Π° Π±Π°Π±ΡΡΠ΅ΠΊ, ΡΠ»ΠΎΠΆΠΈΠ²ΡΠΈΡ
ΡΡ Π² ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠΌ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎ-ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΌ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π΅. ΠΡΡΠ²Π»Π΅Π½Π½ΡΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΡΠ°Π·Π»ΠΈΡΠ°ΡΡΡΡ ΠΏΠΎ ΡΡΠΎΠ²Π½Ρ ΠΈ ΠΈΠ½ΡΠ΅Π½ΡΠΈΠ²Π½ΠΎΡΡΠΈ Π²ΠΊΠ»ΡΡΠ΅Π½Π½ΠΎΡΡΠΈ ΠΆΠ΅Π½ΡΠΈΠ½ ΡΡΠ°ΡΡΠ΅Π³ΠΎ Π²ΠΎΠ·ΡΠ°ΡΡΠ° Π² ΡΡΠ΅ΡΡ ΠΏΡΠ°ΡΠΎΠ΄ΠΈΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ ΡΡΡΠ΄Π°, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΈΡ
ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»Ρ Π² ΡΡΠΎΠΉ ΡΡΠ΅ΡΠ΅. ΠΡΠ» ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ ΡΠ΅Π³ΠΈΠΎΠ½Π°Π»ΡΠ½ΡΠΉ ΡΠΎΡΡΠ°Π² ΠΊΠ°ΠΆΠ΄ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΏΡΠ°ΡΠΎΠ΄ΠΈΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ ΡΡΡΠ΄Π° - Π²ΡΠ΄Π΅Π»Π΅Π½Ρ ΡΠ΅Π³ΠΈΠΎΠ½Ρ-ΡΠ΄ΡΠ°, ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΡΡΠΈΠ΅ ΡΠΎΠ±ΠΎΠΉ Π³ΡΡΠΏΠΏΡ ΡΠ΅Π³ΠΈΠΎΠ½ΠΎΠ², ΡΡΡΠΎΠΉΡΠΈΠ²ΠΎ ΡΠ²ΡΠ·Π°Π½Π½ΡΡ
Π² ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
Π²Π°ΡΠΈΠ°Π½ΡΠ°Ρ
ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ. ΠΠΎΠΊΠ°Π·Π°Π½Ρ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²Π° Π°ΠΊΡΠΈΠ²ΠΈΠ·Π°ΡΠΈΠΈ ΠΈ ΡΡΠΈΠΌΡΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠΎ ΡΡΠΎΡΠΎΠ½Ρ Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ²Π° ΠΏΡΠ°ΡΠΎΠ΄ΠΈΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ ΡΡΡΠ΄Π° Π² ΡΠ΅Π³ΠΈΠΎΠ½Π°Ρ
ΡΡΡΠ°Π½Ρ. ΠΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ - ΠΈΠ·ΡΡΠ΅Π½ΠΈΠ΅ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠ΅ΠΉ ΠΏΡΠ°ΡΠΎΠ΄ΠΈΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ ΡΡΡΠ΄Π° Π΄Π»Ρ ΡΠ°Π·Π½ΡΡ
ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΠΉ Π΅Π³ΠΎ ΡΡΠ±ΡΠ΅ΠΊΡΠΎΠ² - ΠΌΡΠΆΡΠΈΠ½, ΠΏΡΠ°ΡΠΎΠ΄ΠΈΡΠ΅Π»Π΅ΠΉ, ΠΏΡΠΎΠΆΠΈΠ²Π°ΡΡΠΈΡ
Π²ΠΌΠ΅ΡΡΠ΅ Ρ Π²Π½ΡΠΊΠ°ΠΌΠΈ ΠΈ ΠΎΡΠ΄Π΅Π»ΡΠ½ΠΎ ΠΎΡ Π½ΠΈΡ
, ΠΏΡΠΎΠΆΠΈΠ²Π°ΡΡΠΈΡ
Π² ΠΎΠ΄Π½ΠΎΠΌ Π³ΠΎΡΠΎΠ΄Π΅, ΡΠ΅Π³ΠΈΠΎΠ½Π΅ ΠΈΠ»ΠΈ ΡΠ°Π·Π½ΡΡ
ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ
ΡΠ΅Π³ΠΈΠΎΠ½Π°Ρ
.Foreign researchers consider the phenomenon of grandparentsβ labour in the framework of the economy of parental labour. Since the early 2000s, Russian scientists have been actively studying this problem due to high socio-economic disparity of Russian regions, and important strategic objectives stated in the Demography National Project and the Strategy of Spatial Development of the Russian Federation for the period until 2025. We identified and described specific regional situations, namely, models of grandparentsβ labour in the socio-economic space of the country. To analyse grandparentsβ labour, we examined individual questions of the βComprehensive monitoring of living conditionsβ survey published by the Federal State Statistics Service (Rosstat). The research methodology includes both hierarchical (based on Wardβs method, the square of the Euclidean distance, and other measures) and non-hierarchical cluster analysis (the k-means method). We proposed a method for studying grandparentsβ labour based on research of parental labour and international demographic studies on grandparenthood. The empirical analysis revealed 6 models of grandparentsβ labour in the socio-economic space of Russia. These models differ in the level and intensity of involvement of older women in the sphere of grandparentsβ labour and their potential in this area. For each model of grandparentsβ labour, we identified the core regions (groups of regions) involved in the clustering. We explained why the government should be interested in the activation and stimulation of grandparentsβ labour in Russian regions. Further research should focus on examining the particularities of grandparentsβ labour depending on the actors: men, grandparents living with their grandchildren and apart from them, living in the same city, in the same or different regions of Russia.ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΎ ΠΏΡΠΈ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎΠΉ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠ΅ Π Π€Π€Π Π² ΡΠ°ΠΌΠΊΠ°Ρ
Π½Π°ΡΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ΅ΠΊΡΠ° β20β011β00280.The article has been prepared with the support of Russian Foundation for Basic Research, the project No. 20β011β00280
ΠΠΠΠ‘Π’ΠΠ ΠΠ«Π ΠΠΠΠΠΠ Π ΠΠ‘Π‘ΠΠΠΠΠΠΠΠΠ Π ΠΠΠΠΠΠΠΠ¬ΠΠΠ ΠΠΠ€Π€ΠΠ ΠΠΠ¦ΠΠΠ¦ΠΠ ΠΠ ΠΠ¦ΠΠ‘Π‘ΠΠ ΠΠΠ‘ΠΠ ΠΠΠΠΠΠΠ‘Π’ΠΠ ΠΠΠΠΠΠΠΠ ΠΠΠΠΠΠΠΠΠ― Π Π ΠΠ‘Π‘ΠΠ
Historically, there have been significant differences between the various parts of Russia as regards socio-economic parameters. Clearly, the use of a single set of measures to address demographic problems in different parts of Russia cannot be effective. This situation requires a differentiated approach based on the allocation of groups of regions that are similar in features of the current demographic situation. The purpose of the study was to identify the regional differentiation of the processes of young generation reproduction. The authors performed an agglomerative hierarchical clustering algorithm to identify groups of regions characterised by similar features in the sphere of reproduction. The analysis enabled to reveal five groups of regions where the situation with young population has characteristic features.The study has allowed drawing a number of theoretical and practical conclusions aimed at improving the countryβs state youth policy. In addition, this research has shown that statistical cluster analysis can be considered as an effective tool of development of practical recommendations for prompt solving problems among youth. Therefore, this type of analysis can be integrated as an analytical tool in the research component of the state youth policy.ΠΡΡΠΎΡΠΈΡΠ΅ΡΠΊΠΈ ΡΠ΅Π³ΠΈΠΎΠ½Ρ Π ΠΎΡΡΠΈΠΈ ΡΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎ ΡΠ°Π·Π»ΠΈΡΠ°ΡΡΡΡ ΠΌΠ΅ΠΆΠ΄Ρ ΡΠΎΠ±ΠΎΠΉ ΠΏΠΎ ΡΠ΅Π»ΠΎΠΌΡ ΡΡΠ΄Ρ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎ-ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ. ΠΡΠ΅Π²ΠΈΠ΄Π½ΠΎ, ΡΡΠΎ Π² ΡΡΠΈΡ
ΡΡΠ»ΠΎΠ²ΠΈΡΡ
Π΄Π»Ρ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π΄Π΅ΠΌΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠΎΠ±Π»Π΅ΠΌ ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ
ΡΠ΅ΡΡΠΈΡΠΎΡΠΈΠΉ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Π΅Π΄ΠΈΠ½ΠΎΠ³ΠΎ, ΡΡΠ°Π²Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π° Π½Π΅ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΠΌ. ΠΠ΄Π΅ΡΡ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌ Π΄ΠΈΡΡΠ΅ΡΠ΅Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΡΠΉ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΠΉ Π½Π° Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΠΈ Π³ΡΡΠΏΠΏ ΡΠ΅Π³ΠΈΠΎΠ½ΠΎΠ², ΠΎΡΠ»ΠΈΡΠ°ΡΡΠΈΡ
ΡΡ ΡΡ
ΠΎΠΆΠΈΠΌΠΈ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΡΠΌΠΈ ΡΠ»ΠΎΠΆΠΈΠ²ΡΠ΅ΠΉΡΡ Π΄Π΅ΠΌΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΈΡΡΠ°ΡΠΈΠΈ.Π¦Π΅Π»Ρ Π½Π°ΡΡΠΎΡΡΠ΅Π³ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ - Π²ΡΡΠ²Π»Π΅Π½ΠΈΠ΅ ΡΠ΅Π³ΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠΉ Π΄ΠΈΡΡΠ΅ΡΠ΅Π½ΡΠΈΠ°ΡΠΈΠΈ ΠΏΡΠΎΡΠ΅ΡΡΠΎΠ² Π²ΠΎΡΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π° ΠΌΠΎΠ»ΠΎΠ΄ΠΎΠ³ΠΎ ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΡ Π² Π ΠΎΡΡΠΈΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΊΠ»Π°ΡΡΠ΅ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π°. ΠΠ»Ρ Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡ Π³ΡΡΠΏΠΏ ΡΠ΅Π³ΠΈΠΎΠ½ΠΎΠ², Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΡΡΡΠΈΡ
ΡΡ ΡΡ
ΠΎΠΆΠΈΠΌΠΈ ΡΠ΅Π½Π΄Π΅Π½ΡΠΈΡΠΌΠΈ Π² ΡΡΠΎΠΉ ΡΡΠ΅ΡΠ΅, ΠΏΡΠΈΠΌΠ΅Π½ΡΠ»ΡΡ ΠΌΠ΅ΡΠΎΠ΄ Π°Π³Π»ΠΎΠΌΠ΅ΡΠ°ΡΠΈΠ²Π½ΠΎΠΉ ΠΈΠ΅ΡΠ°ΡΡ
ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ. Π ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ΅ Π°Π½Π°Π»ΠΈΠ·Π° Π±ΡΠ»ΠΈ Π²ΡΠ΄Π΅Π»Π΅Π½Ρ ΠΏΡΡΡ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠΎΠ², ΡΠ°Π·Π»ΠΈΡΠ°ΡΡΠΈΡ
ΡΡ ΡΠΏΠ΅ΡΠΈΡΠΈΠΊΠΎΠΉ Π²ΠΎΡΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π° ΠΌΠΎΠ»ΠΎΠ΄ΠΎΠ³ΠΎ ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΡ.ΠΡΠΎΠ²Π΅Π΄Π΅Π½Π½ΠΎΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΎ ΡΡΠΎΡΠΌΡΠ»ΠΈΡΠΎΠ²Π°ΡΡ ΡΡΠ΄ Π²ΡΠ²ΠΎΠ΄ΠΎΠ² ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΈ ΠΏΡΠΈΠΊΠ»Π°Π΄Π½ΠΎΠ³ΠΎ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠ°, Π½Π°ΡΠ΅Π»Π΅Π½Π½ΡΡ
Π½Π° ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠ΅Π°Π»ΠΈΠ·ΡΠ΅ΠΌΡΡ
Π² ΡΡΡΠ°Π½Π΅ ΠΌΠ΅Ρ Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ²Π΅Π½Π½ΠΎΠΉ ΠΌΠΎΠ»ΠΎΠ΄Π΅ΠΆΠ½ΠΎΠΉ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΈ. ΠΡΠΎΠΌΠ΅ ΡΠΎΠ³ΠΎ, ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΎ, ΡΡΠΎ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΠΉ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠ°ΡΠΈΠΉ ΠΊΠ»Π°ΡΡΠ΅ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° ΠΌΠΎΠΆΠ΅Ρ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°ΡΡΡΡ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΠ° ΡΠΎΠ·Π΄Π°Π½ΠΈΡ ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΠΈΠΉ Π΄Π»Ρ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π²ΠΎΠ·Π½ΠΈΠΊΠ°ΡΡΠΈΡ
Π² ΠΌΠΎΠ»ΠΎΠ΄Π΅ΠΆΠ½ΠΎΠΉ ΡΡΠ΅Π΄Π΅ ΠΏΡΠΎΠ±Π»Π΅ΠΌ ΠΈ ΠΏΠΎΡΡΠΎΠΌΡ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΈΠ½ΡΠ΅Π³ΡΠΈΡΠΎΠ²Π°Π½ Π² ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΡΠΊΡΡ ΡΠΎΡΡΠ°Π²Π»ΡΡΡΡΡ Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ²Π΅Π½Π½ΠΎΠΉ ΠΌΠΎΠ»ΠΎΠ΄Π΅ΠΆΠ½ΠΎΠΉ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΈ
Corporate Family-Friendly Policies: The Possibility of Implementation in Russian Regions
Π ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΡ Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ²Π΅Π½Π½ΠΎΠΉ ΠΏΡΠΎΠ½Π°ΡΠ°Π»ΠΈΡΡΡΠΊΠΎΠΉ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΈ ΠΌΠΎΠΆΠ΅Ρ Π²ΠΊΠ»ΡΡΠΈΡΡΡΡ ΡΠ΅Π»ΡΠΉ ΡΡΠ΄ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΡΡ
ΠΈΠ½ΡΡΠΈΡΡΡΠΎΠ². ΠΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠ΅Π΄ΠΏΡΠΈΡΡΠΈΡΠΌΠΈ ΠΊΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½ΠΎΠΉ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΈ, ΠΎΡΠΈΠ΅Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ Π½Π° ΡΠ΅ΠΌΡΠΈ ΡΠ°Π±ΠΎΡΠ½ΠΈΠΊΠΎΠ², ΠΏΡΠΈ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΠΎΡΡΠΈ Π² ΡΠ΅Π»ΠΎΠΌ ΡΡΠ΄Π΅ Π΄ΡΡΠ³ΠΈΡ
ΡΡΡΠ°Π½ Π² Π ΠΎΡΡΠΈΠΈ Π²ΡΡΡΠ΅ΡΠ°Π΅ΡΡΡ Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎ ΡΠ΅Π΄ΠΊΠΎ. ΠΡΠΈ ΡΡΠΎΠΌ Π² Π ΠΎΡΡΠΈΠΈ Π½Π°Π±Π»ΡΠ΄Π°Π΅ΡΡΡ Π²ΡΡΠΎΠΊΠΈΠΉ ΡΡΠΎΠ²Π΅Π½Ρ Π΄ΠΈΡΡΠ΅ΡΠ΅Π½ΡΠΈΠ°ΡΠΈΠΈ ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ
ΡΠ΅Π³ΠΈΠΎΠ½ΠΎΠ² ΠΏΠΎ ΠΌΠ½ΠΎΠ³ΠΈΠΌ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎ-ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΈΠΌ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΡΠΌ. Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠΎΡΡΠΎΠΈΡ Π² ΡΠΎΠΌ, ΡΡΠΎΠ±Ρ, Π²ΠΎ-ΠΏΠ΅ΡΠ²ΡΡ
, Π²ΡΡΠ²ΠΈΡΡ ΡΠ΅Π³ΠΈΠΎΠ½Π°Π»ΡΠ½ΡΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠ³ΠΎ ΠΊΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΡΠ΅ΠΊΡΠΎΡΠ° ΠΈ, Π²ΠΎ-Π²ΡΠΎΡΡΡ
, ΠΎΠΏΡΠ΅Π΄Π΅Π»ΠΈΡΡ ΡΠ΅ ΠΈΠ· Π½ΠΈΡ
, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΎΠ±Π»Π°Π΄Π°ΡΡ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ Π²ΡΡΠΎΠΊΠΈΠΌ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»ΠΎΠΌ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½ΠΈΡ ΠΊΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½ΠΎΠΉ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΈ, ΠΎΡΠΈΠ΅Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ Π½Π° ΡΠ΅ΠΌΡΠΈ ΡΠ°Π±ΠΎΡΠ½ΠΈΠΊΠΎΠ² ΠΈ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½Π½ΠΎΠΉ Π½Π° ΡΠΎΡΡ ΡΠΎΠΆΠ΄Π°Π΅ΠΌΠΎΡΡΠΈ Π½Π°ΡΠ΅Π»Π΅Π½ΠΈΡ ΡΡΠΈΡ
ΡΠ΅Π³ΠΈΠΎΠ½ΠΎΠ². ΠΠΈΠΏΠΎΡΠ΅Π·Π° ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠΎΡΡΠΎΠΈΡ Π² Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ Π²ΡΡΠ²Π»Π΅Π½ΠΈΡ ΡΠ°ΠΊΠΈΡ
ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ
ΡΠ΅Π³ΠΈΠΎΠ½ΠΎΠ² Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΌΠ½ΠΎΠ³ΠΎΠΌΠ΅ΡΠ½ΠΎΠΉ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎ-ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ. ΠΠ»Ρ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠ³ΠΎ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π° ΠΏΡΠΈΠΌΠ΅Π½ΡΠ»ΡΡ ΠΈΠ΅ΡΠ°ΡΡ
ΠΈΡΠ΅ΡΠΊΠΈΠΉ ΠΊΠ»Π°ΡΡΠ΅ΡΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ·, Π·Π°ΡΠ΅ΠΌ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΎΡΡ ΠΏΡΠΎΡΠΈΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΊΠ»Π°ΡΡΠ΅ΡΠΎΠ² ΠΏΠΎ Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΡΠΌ ΠΏΠ΅ΡΠ΅ΠΌΠ΅Π½Π½ΡΠΌ. ΠΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π»ΠΈΡΡ Π΄Π°Π½Π½ΡΠ΅ ΠΎΡΠΈΡΠΈΠ°Π»ΡΠ½ΠΎΠΉ ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠΈ ΠΏΠΎ ΡΡΠ±ΡΠ΅ΠΊΡΠ°ΠΌ Π Π€, Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΡΡΡΠΈΠ΅ ΡΠ°Π·Π²ΠΈΡΠΈΠ΅ ΠΊΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΡΠ΅ΠΊΡΠΎΡΠ°. ΠΡΠ΄Π΅Π»Π΅Π½Ρ ΠΏΡΡΡ ΠΊΠ»Π°ΡΡΠ΅ΡΠΎΠ² ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ
ΡΠ΅Π³ΠΈΠΎΠ½ΠΎΠ². ΠΠ±ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΎ, ΡΡΠΎ Π΄Π²Π° ΠΈΠ· Π½ΠΈΡ
ΠΌΠΎΠ³ΡΡ ΡΡΠ°ΡΡ ΠΏΠΈΠ»ΠΎΡΠ½ΡΠΌΠΈ Π΄Π»Ρ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½ΠΈΡ ΠΏΡΠ°ΠΊΡΠΈΠΊ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΈ, ΠΎΡΠΈΠ΅Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ Π½Π° ΡΠ΅ΠΌΡΠΈ ΡΠ°Π±ΠΎΡΠ½ΠΈΠΊΠΎΠ², Π·Π°Π½ΡΡΡΡ
Π½Π° ΡΡΠΈΡ
ΠΏΡΠ΅Π΄ΠΏΡΠΈΡΡΠΈΡΡ
. Π Π΄Π°Π½Π½ΡΡ
ΠΊΠ»Π°ΡΡΠ΅ΡΠ°Ρ
Π½Π°Π±Π»ΡΠ΄Π°ΡΡΡΡ ΡΠΏΠ΅ΡΠΈΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ Π΄Π΅ΠΌΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ (ΠΎΡΠΎΠ±ΠΎ Π½ΠΈΠ·ΠΊΠΈΠ΅ ΡΠΎΠΆΠ΄Π°Π΅ΠΌΠΎΡΡΡ ΠΈ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π» Π΅Π΅ ΡΠΎΡΡΠ°) ΠΈ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅ (Π²ΡΡΠΎΠΊΠ°Ρ ΠΈΠ½Π½ΠΎΠ²Π°ΡΠΈΠΎΠ½Π½Π°Ρ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΈ Π½ΠΈΠ·ΠΊΠ°Ρ Π΄ΠΎΠ»Ρ ΡΠ±ΡΡΠΎΡΠ½ΡΡ
ΠΏΡΠ΅Π΄ΠΏΡΠΈΡΡΠΈΠΉ, ΡΠ°ΠΌΡΠ΅ Π²ΡΡΠΎΠΊΠΈΠ΅ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ Π΄Π΅ΠΌΠΎΠ³ΡΠ°ΡΠΈΠΈ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΉ ΠΈ ΡΡΠ΅Π΄Π½ΠΈΠΉ ΡΡΠΎΠ²Π΅Π½Ρ ΠΊΠΎΠ½Π΅ΡΠ½ΠΎΠ³ΠΎ ΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ Π½Π°ΡΠ΅Π»Π΅Π½ΠΈΡ) ΡΡΠ»ΠΎΠ²ΠΈΡ. Π‘Π΄Π΅Π»Π°Π½ Π²ΡΠ²ΠΎΠ΄, ΡΡΠΎ Π² ΠΊΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½ΠΎΠΌ ΡΠ΅ΠΊΡΠΎΡΠ΅ ΡΡΠΈΡ
ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ
ΡΠ΅Π³ΠΈΠΎΠ½ΠΎΠ² ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠ°, ΠΎΡΠΈΠ΅Π½ΡΠΈΡΠΎΠ²Π°Π½Π½Π°Ρ Π½Π° ΡΠ΅ΠΌΡΠΈ ΡΠ°Π±ΠΎΡΠ½ΠΈΠΊΠΎΠ², ΠΌΠΎΠΆΠ΅Ρ ΡΡΠ°ΡΡ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ Π²ΠΎΡΡΡΠ΅Π±ΠΎΠ²Π°Π½Π½ΠΎΠΉ Π΄Π»Ρ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»Π° ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΉ, Π²ΠΏΠΎΠ»Π½Π΅ Π΄ΠΎΡΡΡΠΏΠ½ΠΎΠΉ Π΄Π»Ρ ΠΏΡΠ΅Π΄ΠΏΡΠΈΡΡΠΈΠΉ ΠΈ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΠΉ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ Π½ΠΎΠ²ΠΎΠ³ΠΎ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠ° Π΄Π΅ΠΌΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΈ. ΠΠ°Π»ΡΠ½Π΅ΠΉΡΠΈΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΡΠ²ΡΠ·Π°Π½Ρ Ρ Π°Π½Π°Π»ΠΈΠ·ΠΎΠΌ ΠΊΠ΅ΠΉΡΠΎΠ² ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ
ΠΏΡΠ΅Π΄ΠΏΡΠΈΡΡΠΈΠΉ, ΡΠ΅Π°Π»ΠΈΠ·ΡΡΡΠΈΡ
ΠΏΠΎΠ΄ΠΎΠ±Π½ΡΡ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΡ, Π²ΡΡΠ²Π»Π΅Π½ΠΈΠ΅ΠΌ Π±Π΅Π½ΡΠΌΠ°ΡΠΊΠΎΠ², ΠΎΡΠ΅Π½ΠΊΠΎΠΉ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠ΅ΠΉ ΠΌΠ°ΡΡΡΠ°Π±ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ°ΠΊΠΎΠ³ΠΎ ΠΎΠΏΡΡΠ° ΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ Π΅Π³ΠΎ Π΄Π΅ΠΌΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ².Various social institutions may be involved in the implementation of the stateβs pro-natalist policy. Family-friendly corporate policies, common in many countries, are still quite rare in Russia. At the same time, many socio-economic indicators significantly differ across Russian regions. The study aims to identify regional development models of the Russian corporate sector and determine those models that have the highest potential for dissemination of family-friendly corporate policies aimed at increasing the birth rate of the population in these regions. It is hypothesised that such Russian regions can be identified based on a multidimensional classification of socio-economic indicators. The hierarchical cluster analysis was used to model the Russian economic space. Then, the clusters were further grouped according to additional variables. The article analysed official regional statistics characterising the development of the corporate sector. Five clusters of Russian regions were identified. It is proved that corporate family-friendly policies can be disseminated in two regions in particular. Specific demographic (in particular, low birth rate and growth potential) and economic (high innovative activity, small number of loss-making enterprises, the highest demographic indicators among organisations and the average level of priveate consumption) conditions are observed in these clusters. It is concluded that in the corporate sector of these Russian regions, family-friendly policy may gain popularity among staff. This approach, accessible to enterprises, can act as an effective tool of demographic policy. Further research should focus on the analysis of cases of Russian enterprises implementing family-friendly policies, identification of benchmarks, assessment of the possibilities of scaling such experience and forecasting its demographic results.Π Π°Π±ΠΎΡΠ° Π½Π°Π΄ ΡΡΠ°ΡΡΠ΅ΠΉ Π.Π. Π¨ΡΠ±Π°Ρ ΠΈ Π.Π. ΠΠ°Π³ΠΈΡΠΎΠ²ΠΎΠΉ Π² ΡΠ°ΡΡΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠΈ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ, ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ
ΡΠ΅Π³ΠΈΠΎΠ½ΠΎΠ² ΠΈ ΠΈΠ½ΡΠ΅ΡΠΏΡΠ΅ΡΠ°ΡΠΈΠΈ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠ°Π½Π° Π‘ΠΎΠ²Π΅ΡΠΎΠΌ ΠΏΠΎ Π³ΡΠ°Π½ΡΠ°ΠΌ ΠΡΠ΅Π·ΠΈΠ΄Π΅Π½ΡΠ° Π ΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π€Π΅Π΄Π΅ΡΠ°ΡΠΈΠΈ Π½Π° Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ²Π΅Π½Π½ΡΡ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΡ Π²Π΅Π΄ΡΡΠΈΡ
Π½Π°ΡΡΠ½ΡΡ
ΡΠΊΠΎΠ» Π ΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π€Π΅Π΄Π΅ΡΠ°ΡΠΈΠΈ (ΠΠ¨-1327.2022.2). Π’Π΅ΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΠΎΠ±ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ, Π²ΡΠΏΠΎΠ»Π½Π΅Π½Π½ΠΎΠ΅ Π. Π―Π½Ρ, ΡΠ΅Π°Π»ΠΈΠ·ΠΎΠ²Π°Π½ΠΎ ΠΏΡΠΈ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎΠΉ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠ΅ ΠΠΈΠ½ΠΈΡΡΠ΅ΡΡΡΠ²Π° Π½Π°ΡΠΊΠΈ ΠΈ Π²ΡΡΡΠ΅Π³ΠΎ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ Π ΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π€Π΅Π΄Π΅ΡΠ°ΡΠΈΠΈ Π² ΡΠ°ΠΌΠΊΠ°Ρ
ΠΡΠΎΠ³ΡΠ°ΠΌΠΌΡ ΡΠ°Π·Π²ΠΈΡΠΈΡ Π£ΡΠ°Π»ΡΡΠΊΠΎΠ³ΠΎ ΡΠ΅Π΄Π΅ΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠ½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅ΡΠ° ΠΈΠΌΠ΅Π½ΠΈ ΠΏΠ΅ΡΠ²ΠΎΠ³ΠΎ ΠΡΠ΅Π·ΠΈΠ΄Π΅Π½ΡΠ° Π ΠΎΡΡΠΈΠΈ Π.Π. ΠΠ»ΡΡΠΈΠ½Π° Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΎΠΉ ΡΡΡΠ°ΡΠ΅Π³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π°ΠΊΠ°Π΄Π΅ΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π»ΠΈΠ΄Π΅ΡΡΡΠ²Π° Β«ΠΡΠΈΠΎΡΠΈΡΠ΅Ρ-2030Β».The work of Oksana Shubat and Anna Bagirova in terms of developing a research methodology, clustering Russian regions and interpreting the results was supported by the Council for Grants of the President of the Russian Federation for state support of leading scientific schools of the Russian Federation (NSh-1327.2022.2). Theoretical substantiation of the research by Yan Doudou was supported by the Ministry of Science and Higher Education of the Russian Federation within the framework of the development program of the Ural Federal University as part of the strategic academic leadership program Β«Priority 2030Β»
The IARC Monographs: Updated procedures for modern and transparent evidence synthesis in cancer hazard identification
The Monographs produced by the International Agency for Research on Cancer (IARC) apply rigorous procedures for the scientific review and evaluation of carcinogenic hazards by independent experts. The Preamble to the IARC Monographs, which outlines these procedures, was updated in 2019, following recommendations of a 2018 expert Advisory Group. This article presents the key features of the updated Preamble, a major milestone that will enable IARC to take advantage of recent scientific and procedural advances made during the 12 years since the last Preamble amendments. The updated Preamble formalizes important developments already being pioneered in the Monographs Programme. These developments were taken forward in a clarified and strengthened process for identifying, reviewing, evaluating and integrating evidence to identify causes of human cancer. The advancements adopted include strengthening of systematic review methodologies; greater emphasis on mechanistic evidence, based on key characteristics of carcinogens; greater consideration of quality and informativeness in the critical evaluation of epidemiological studies, including their exposure assessment methods; improved harmonization of evaluation criteria for the different evidence streams; and a single-step process of integrating evidence on cancer in humans, cancer in experimental animals and mechanisms for reaching overall evaluations. In all, the updated Preamble underpins a stronger and more transparent method for the identification of carcinogenic hazards, the essential first step in cancer prevention
Comparing NED and SIMBAD classifications across the contents of nearby galaxies
Cataloguing and classifying celestial objects is one of the fundamental
activities of observational astrophysics. In this work, we compare the contents
of two comprehensive databases, the NASA Extragalactic Database (NED) and Set
of Identifications, Measurements and Bibliography for Astronomical Data
(SIMBAD) in the vicinity of nearby galaxies. These two databases employ
different classification schemes -- one flat and one hierarchical -- and our
goal was to determine the compatibility of classifications for objects in
common. Searching both databases for objects within the respective isophotal
radius of each of the ~1300 individual galaxies in the Local Volume Galaxy
sample, we found that on average, NED contains about ten times as many entries
as SIMBAD and about two thirds of SIMBAD objects are matched by position to a
NED object, at 5 arcsecond tolerance. These quantities do not depend strongly
on the properties of the parent galaxies. We developed an algorithm to compare
individual object classifications between the two databases and found that 88%
of the classifications agree; we conclude that NED and SIMBAD contain
consistent information for sources in common in the vicinity of nearby
galaxies. Because many galaxies have numerous sources contained only in one of
NED or SIMBAD, researchers seeking the most complete picture of an individual
galaxy's contents are best served by using both databases.Comment: 11 pages, 11 figures, 2 tables, to be published in MNRA
A quantitative analysis of fertility dynamics in small cities of Russia
The results of comparing the fertility dynamics across Russia and in small cities of Ural in 2006-2010 are provided. Certain components have been studied using structural decomposition. According to the analysis, the fertility behavior of population in small cities is more susceptible to the influence of different factors than in Russia in general. Special measures are necessary to encourage fertility in small cities. In these measures, the specifics of demographic dynamics, population, and lifestyle in small cities must be considered, as well as the higher potential efficiency of their external effects in comparison with big cities. Β© 2012 Pleiades Publishing, Ltd
Methodology for analyzing the demographic potential of Russian regions using fuzzy clustering
The research is aimed at developing and testing a methodology for analyzing demographic potential of Russian regions. The initial data are the regional official Russian statistics indicators. We proposed an approach for assessing the demographic potential based on a differentiated analysis of its quantitative and qualitative components. The paper presents the developed methodology for estimating the demographic potential, combining multidimensional data classification (fuzzy clustering) and expert assessments. Application of the proposed methodology revealed five specific models in the demographic space of Russia. The first model combines a low level of quantitative components of the demographic potential with a high level of its quality. The second model is characterized by average levels of both components. In the third model, an average level of the quantitative component is accompanied by a rather low level of the demographic potentialβs quality. The fourth model combines a high level of quantitative component of the demographic potential with an imbalance of its quality indicators, and the fifth β a high level of both components. We have obtained estimates for the quantitative and qualitative components of the demographic potential for each region and rated them. This has allowed identifying βanchorβ-regions and βdriverβ-regions, as well as regions with the most and least balanced assessments of the two components. The paper shows the potential application of the developed methodology. In particular, this methodology allows identifying groups of regions, which need the implementation of specific measures for increasing the quantity and improving the quality of the demographic potential. The most significant limitation of the developed methodology is the lack of a complete set of indicators in the official Russian statistics for assessing the demographic potential. Future research will be aimed at applying fuzzy clustering methods to various demographic phenomena, since this approach takes into account the natural uncertainty, which is typical for such processes and, therefore, makes the results of demographic analysis more formalized and valid. Β© 2019 The Linguistic Association of Finland. All Rights Reserved.Council on grants of the President of the Russian FederationThe article has been prepared within the research project βFertility and parenting in Russian regions: models, invigoration strategies, forecastsβ, supported by the President of Russian Federation (the grant No. NSh-3429.2018.6)
Forecasting the Number of Grandparents in Sverdlovsk Oblast
Π£Π²Π΅Π»ΠΈΡΠ΅Π½ΠΈΠ΅ ΡΡΠ΅ΠΏΠ΅Π½ΠΈ ΡΡΠ°ΡΡΠΈΡ ΠΏΡΠ°ΡΠΎΠ΄ΠΈΡΠ΅Π»Π΅ΠΉ Π² ΠΆΠΈΠ·Π½Π΅Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΡΠ²ΠΎΠΈΡ
Π²Π½ΡΠΊΠΎΠ² ΠΈΠΌΠ΅Π΅Ρ ΡΡΠ΄ Π΄Π΅ΠΌΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
, ΡΠΎΡΠΈΠ°Π»ΡΠ½ΡΡ
, ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΡΡΠ΅ΠΊΡΠΎΠ². Π ΡΠ²ΡΠ·ΠΈ Ρ ΡΡΠΈΠΌ ΠΎΠ΄Π½ΠΈΠΌ ΠΈΠ· ΠΎΡΠ½ΠΎΠ²Π½ΡΡ
ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ Π΄Π΅ΠΌΠΎΠ³ΡΠ°ΡΠΈΠΈ ΠΏΡΠ°ΡΠΎΠ΄ΠΈΡΠ΅Π»ΡΡΡΠ²Π° Π²ΡΡΡΡΠΏΠ°Π΅Ρ ΡΠΈΡΠ»Π΅Π½Π½ΠΎΡΡΡ ΠΏΡΠ°ΡΠΎΠ΄ΠΈΡΠ΅Π»Π΅ΠΉ Π² ΠΎΠ±ΡΠ΅ΡΡΠ²Π΅. ΠΠ° ΠΎΡΠ½ΠΎΠ²Π΅ Π°Π²ΡΠΎΡΡΠΊΠΎΠΉ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠΈ, ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΠΎΠΉ Π½Π° Π±Π°Π·Π΅ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠΈΡΠ»Π΅Π½Π½ΠΎΡΡΠΈ ΠΏΡΠ°ΡΠΎΠ΄ΠΈΡΠ΅Π»Π΅ΠΉ Π² ΡΡΡΠ°Π½Π΅ Π² ΡΠ΅Π»ΠΎΠΌ, Π² ΡΡΠ°ΡΡΠ΅ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΡΠ΅ΡΡΡ ΡΠΈΡΠ»Π΅Π½Π½ΠΎΡΡΡ Π±Π°Π±ΡΡΠ΅ΠΊ Π² Π‘Π²Π΅ΡΠ΄Π»ΠΎΠ²ΡΠΊΠΎΠΉ ΠΎΠ±Π»Π°ΡΡΠΈ Π½Π° 2021β2025 Π³Π³. Π Π°ΡΡΠ΅ΡΡ ΡΠ²ΠΈΠ΄Π΅ΡΠ΅Π»ΡΡΡΠ²ΡΡΡ ΠΎ ΡΠΎΠΌ, ΡΡΠΎ Π² Π‘Π²Π΅ΡΠ΄Π»ΠΎΠ²ΡΠΊΠΎΠΉ ΠΎΠ±Π»Π°ΡΡΠΈ Π½Π΅Ρ ΡΠ²Π½ΠΎΠΉ ΡΠ΅Π½Π΄Π΅Π½ΡΠΈΠΈ ΠΊ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΡΠΎΠ³ΠΎ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ Π² 5-Π»Π΅ΡΠ½Π΅ΠΉ ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π΅, Π΄ΠΎΠ»Ρ Π±Π°Π±ΡΡΠ΅ΠΊ Π² Π½Π°ΡΠ΅Π»Π΅Π½ΠΈΠΈ ΡΠ΅Π³ΠΈΠΎΠ½Π° ΡΠ°ΠΊΠΆΠ΅ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΠΉ. ΠΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠ°Ρ ΡΠ΅Π½Π½ΠΎΡΡΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² ΡΠΎΡΡΠΎΠΈΡ Π² ΡΠΎΠΌ, ΡΡΠΎ ΠΏΠΎΠ½ΠΈΠΌΠ°Π½ΠΈΠ΅ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΡΠ΅ΠΌΡΡ
ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΡ
ΡΠ΅Π½Π΄Π΅Π½ΡΠΈΠΉ ΠΌΠΎΠΆΠ΅Ρ ΠΏΠΎΠ΄ΡΠΊΠ°Π·Π°ΡΡ ΠΈΡΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΡΠΌ ΠΎΡΠ³Π°Π½Π°ΠΌ ΡΡΠ±ΡΠ΅ΠΊΡΠ° Π Π€, ΠΎΡΠ²Π΅ΡΠ°ΡΡΠΈΠΌ Π·Π° Π΄Π΅ΠΌΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΡΡ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΡ, ΡΠΏΡΠ°Π²Π»Π΅Π½ΡΠ΅ΡΠΊΠΈΠ΅ ΡΠ΅ΡΠ΅Π½ΠΈΡ, ΠΊΠΎΡΠΎΡΡΠ΅ Π±ΡΠ΄ΡΡ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½Ρ Π½Π° ΡΠ»ΡΡΡΠ΅Π½ΠΈΠ΅ ΠΈ ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΡ
Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ ΠΏΡΠ°ΡΠΎΠ΄ΠΈΡΠ΅Π»ΡΡΡΠ²Π°, Π° ΠΈΠΌΠ΅Π½Π½ΠΎ, ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΠ΅ Π΄ΠΎΠ»ΠΈ ΠΏΡΠ°ΡΠΎΠ΄ΠΈΡΠ΅Π»Π΅ΠΉ, Π°ΠΊΡΠΈΠ²Π½ΠΎ ΡΡΠ°ΡΡΠ²ΡΡΡΠΈΡ
Π² ΠΆΠΈΠ·Π½Π΅Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ Π²Π½ΡΠΊΠΎΠ², Π² ΡΠΈΡΠ»Π΅Π½Π½ΠΎΡΡΠΈ Π²ΡΠ΅Ρ
ΠΏΡΠ°ΡΠΎΠ΄ΠΈΡΠ΅Π»Π΅ΠΉ ΡΠ΅Π³ΠΈΠΎΠ½Π°.An increase in the participation of grandparents in the life of their grandchildren has demographic, social, and economic effects. In this regard, one of the main indicators of the demography of grandparenthood is the number of grandparents in society. Using the authorsβ approach developed based on the methodology for forecasting the number of grandparents in the country as a whole, the present research forecasts the number of grandmothers in Sverdlovsk oblast for 2021β2025. Calculations show that, in Sverdlovsk oblast, this indicator is unlikely to change in the 5-year perspective; the share of grandmothers in the population of the region is also stable. Understanding of the perspective quantitative trends can prompt the executive bodies of Russian regions responsible for demographic policy to make decisions aimed at improving the qualitative characteristics of grandparents, namely, increasing the share of grandparents actively involved in the life of grandchildren in the number of all grandparents in the region.ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΎ ΠΏΡΠΈ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎΠΉ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠ΅ Π Π€Π€Π Π² ΡΠ°ΠΌΠΊΠ°Ρ
Π½Π°ΡΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ΅ΠΊΡΠ° β 20β011β00280.The article has been prepared with the financial support of the Russian Foundation for Basic Research, the scientific project No. 20β011β00280
Simple reproduction of the population: experience of normativ eforecasting
Π ΡΡΠ°ΡΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ Π½ΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΠΎΠ³ΠΎ Π΄Π΅ΠΌΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠ°ΡΡΠ΅ΡΠ° ΡΡΠΌΠΌΠ°ΡΠ½ΠΎΠ³ΠΎ ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΠ° ΡΠΎΠΆΠ΄Π°Π΅ΠΌΠΎΡΡΠΈ, Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΠ³ΠΎ Π΄Π»Ρ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΡ ΠΏΡΠΎΡΡΠΎΠ³ΠΎ Π²ΠΎΡΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π° Π½Π°ΡΠ΅Π»Π΅Π½ΠΈΡ. ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΡΠ΅ ΠΏΡΡΠΈ Π΄ΠΎΡΡΠΈΠΆΠ΅Π½ΠΈΡ ΠΎΠ±ΠΎΠ·Π½Π°ΡΠ΅Π½Π½ΠΎΠΉ ΡΠ΅Π»ΠΈ.The article presents the results of the normative population forecasting by calculating the total fertility rate required for simple reproduction of the population. Possiblewaysofachievingthedesignatedgoalarepresented