Global Journal of Management and Business Research, B: Economics and Commerce, Volume 21 Issue 5

(1) We take half of the Russian graduates of basic school (grades 8-9) who have only a basic level of education, i.e. can use the knowledge gained at school in simple familiar situations. At the same time, about a fifth of the graduates of the basic school do not reach the threshold level of the formation of functional literacy in accordance with international requirements. Moreover, from the beginning it is taken for granted that the problem of eliminating school failure couldn't be immediately solved, therefore an additional 0.2% annual GDP growth rate due to the reorientation of the teaching technology, an increase in the level of learning and socialization can be observed only from 2022. And before this year, it would be ideal to get additional GDP growth of at least 0.1% per year. In order to compare the described estimates, the forecast data of GDP growth at current prices for the period 2000-2025 were constructed (Fig. 11). To study and predict the dynamics of GDP t , we use the capabilities of the adaptive Brown model (Brown, (1962). The calculated value at a time moment t+ τ is obtained according to the equation 0 1 ( 1 ) ( 1) ( 1) − + = − + × − Br Y t a t a t τ τ , (3) where τ – forecast horizon (lead interval or number of prediction steps). If τ = 1, then the formula (3) takes the form: 0 1 ( ) ( 1) 1 ( 1) = − + × − Br Y t a t a t . (4) This value is compared with the actual level of the time series Y t and resulting forecast error e t =Y t -Y Br (t) is used to adjust the model. Adjustment of parameters is carried out according to the formulas: 2 0 0 1 ( ) ( 1) 1 ( 1) (1 ) t a t a t a t e β = − + × − + − (5) 2 1 1 ( ) ( 1) (1 ) t a t a t e β = − + − , (6) where β — coefficient of data discount rate, reflecting a greater degree of confidence in earlier data. Its value varies from 0 to 1. In the calculations, the value is taken as β = 0.8. Model modification process ( t = 1, 2, ..., N ), depending on current forecast qualities, ensures its adaptation to new patterns of development. The model obtained in the last step is used for forecasting (if t = N, where N – time series). Figure 11 shows the dynamics of GDPt ( in billion roubles ): the corresponding adaptive Brown model GDP BR (t) (in billion roubles ) and point forecast for this model. In order not to overload the graph, the interval forecast is not given. Sources: the authors’ calculations based on the Rosstat' data Figure 11: Dynamics of GDP of Russia in the scenarios: (1) forecast; (2) reduction the students' educational failure; (3) reduction of the students' economic failure for the period 2019-2025 (in billion roubles) The corresponding adaptive Brown model GDP BR (t ) (in billion roubl es) and point forecast for this model serves as a basis for comparing the dynamics of GDP, which is changing due to a factor of the school failure reduction: at first this growth is additional 0.1% per year, and after 2020 - additional 0.2%. In Figure 11, this change is demonstrated by the GDP curve called 10 years horizon. (2) As noted above, the decrease of school failure of students results in a reduction in their economic failure. Economic assessments of the reducing economic failure' effect are also given by Uncertainty of the Post-Covid Future: How will Humanity Solve this Puzzle? © 2021 Global Journals 65 Global Journal of Management and Business Research Volume XXI Issue V Version I Year 2021 ( ) B