Fertility Prediction Models: Example of the Republic of Tuva





Numerous Russian demographers and statisticians have considered the issues of predicting fertility. In recent years, the Federal State Statistics Service (Rosstat) has been publishing demographic forecasts, including data on the total fertility rate. However, despite extensive research, insufficient attention is paid to the analysis of the possibilities of using adaptive forecasting methods to assess the future dynamics of fertility. In this regard, the present study aims to build fertility prediction models for regions based on adaptive methods. The Republic of Tuva was chosen for testing as one of the unique constituent entities of the Russian Federation. During the implementation of the Concept of demographic policy, in particular maternity capital, the total fertility rate in Tuva did not fall below the replacement level fertility (2.14). Adaptive forecasting methods, such as ARIMA, Holt’s and Brown’s models, were utilised. In order to select the best prediction model, the study conducted a formal-logical analysis with a comparison of the main characteristics of the forecast accuracy and quality. The obtained results revealed promising development scenarios: moderately optimistic and regressive. The moderately optimistic scenario scientifically substantiated the feasibility of achieving fertility growth in the Republic of Tuva by 2025, focusing on the higher values of the average total fertility rate — 3.10 children per woman of reproductive age — that meets the goals of the demographic policy.

Author Biographies

Tamara K. Rostovskaya , Institute for Demographic Research FCTAS RAS

Dr. Sci. (Soc.), Professor, Deputy Director for Research, Institute for Demographic Research FCTAS RAS; Chief Research Associate, Institute of Modern Languages, Intercultural Communication and Migration, RUDN University; Scopus Author ID: 57192987864; RSCI Author ID: 767943; https://orcid.org/0000-0002-1629-7780 (6/1, Fotievoy St., Moscow, 119333; 6, Miklukho-Maklaya St., Moscow, 117198, Russian Federation; e-mail: rostovskaya.tamara@mail.ru).

Olga A. Zolotareva , Lomonosov Moscow State University

Cand. Sci. (Econ.), Associate Professor; Scopus Author ID: 57220901427; RSCI Author ID: 327777; https://orcid.org/0000-0001-7339-7510 (24/4, Lomonosovsky Ave., Moscow, 119192, Russian Federation; e-mail: OAMahova@yandex.ru).



How to Cite

Rostovskaya Т. К. ., & Zolotareva О. А. . (2023). Fertility Prediction Models: Example of the Republic of Tuva. Economy of Regions, 19(3), 801–812. https://doi.org/10.17059/ekon.reg.2023-3-14



Social Development of Regions