Issue |
E3S Web Conf.
Volume 535, 2024
XIII International Scientific and Practical Forum “Environmental Aspects of Sustainability of Construction and Management of Urban Real Estate” (ESCM-2024)
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Article Number | 02013 | |
Number of page(s) | 9 | |
Section | Construction Project Life Cycle Management: Technologies, Economics, Expertise | |
DOI | https://doi.org/10.1051/e3sconf/202453502013 | |
Published online | 11 June 2024 |
Demographic burden and average insurance retirement benefit: Scenario approach
Bauman Moscow State Technical University (National Research University), Department of Finance, Moscow, Russia
* Corresponding author: kostyrinev@bmstu.ru
This research examines the main types of retirement systems for citizens of the Russian Federation. The forecasting of population size and dependency ratio was carried out using the Verhulst equation as a tool for demographic projection. A comparative analysis of the obtained forecasts with the official data of the United Nations (UN) was carried out. It is assessed that the demographic forecast based on the Verhulst equation is accompanied by a more moderate decrease in the dependency ratio compared to the UN forecasts. Therefore, a scenario analysis of the impact of a negative demographic forecast based on UN data and a positive one according to the results obtained by the Verhulst equation on the size of the average insurance retirement is proposed. It is concluded that under the negative scenario of the demographic situation the size of the insurance retirement benefit increases by 11 .69%, while under a positive scenario it increases by 58.54%, which exceeds the growth rate of retirement provision for citizens of the Russian Federation by more than 5 times compared to the negative scenario of the demographic situation in the country.
Key words: retirement system / Verhulst equation / comparative analysis / insurance retirement benefit / forecast
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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