The paper focuses on the relationship between the economic growth and the age structure. It is assumed thatwhile modeling economic growth, age distribution should be addressed to explain a human capital supply. Weapply econometric analysis to identify how the age structure affects economic development. The study relieson the panel data from 2001 to 2016 for 79 regions of Russia. We consider the following age categories: 0–15,16–24, 25–39, 40–54, 55–64 and 65–100 years. Panel data unit root tests and spatial correlation tests havebeen applied. We estimated the linear panel regression model with spatial lag and spatial autocorrelatederror term. The significance of spatial effects for the Russian regions was confirmed by relevant diagnostictests. Although the coefficients for some age groups are significant in the model, their marginal responsesare insignificant due to spillover effects. The research findings show that the greatest positive effects oneconomic growth are from the youngest age categories 16–24 and 25–39. This differs Russia from othercountries for which similar studies were conducted. The significance of the oldest group 65–100 is a matterof interest, since it may confirm a positive effect of investments made by this group on the economic growth.Impact of the significant age categories are estimated.