IDENTIFICATION OF LIFE EXPECTED FACTORS: ANALYSIS OF PANEL DATA

The aim of the study was to assess the factors affecting the expected life expectancy (life expectancy) of the population of the Republic of Bashkortostan (RB). Material and methods. The study was conducted by regression analysis using panel data. The study used official statistical materials of the Territorial Authority of the Federal State Statistics Service for the Republic of Bashkortostan: Table C 51; collections of «Demographic processes in the Republic of Bashkortostan», «Socio-economic situation of municipal districts and urban districts of the Republic of Bashkortostan»; Rosstat data: collections «Regions of Russia. Socioeconomic indicators» (2002-2018). We considered the data consisting of observations on rural municipalities of 54 municipal districts and 21 cities (urban districts and urban settlements) of the Republic of Belarus traced in 16 years (2002-2017). Results. Life expectancy, both in general and among men and women in the republic, tends to increase, but the life expectancy of women is significantly higher than that for men. The effect on the dynamics of life expectancy of the population is provided by territorial differences. The regression models we constructed with fixed effects according to panel data confirmed the existence of a link between the life expectancy of the population and health care resources (medical services), population density, which is explained by a higher standard of living, the provision of social infrastructure, and medical care, which leads to an increase in life expectancy. The relationship between life expectancy and primary adult disability, the level of crime is negative. Discussion. The level of life expectancy of the population for both men and women is affected by the level of infrastructure development and health care system, as well as crime rate. Conclusion. The results of the analysis show that the regression model according to panel data with fixed effects allows you to get a significant and reasonable version of the simulation, which can be used to assess life expectancy depending on indicators of socioeconomic development, development of the health care system.

life expectancy, municipalities, socioeconomic indicators, regression analysis according to panel data