MODELING THE IMPACT OF SOCIO-ECONOMIC CRISES ON MIGRATION PROCESS

Анотація

Migration movements, in particular external migration, are common to all countries of the world, and Ukraine is not an exception. The peculiarity of the role of movement between states is the ability to achieve a balance between the quantitative and qualitative composition of the labor force, so it is important to understand the nature of the migration process and be able to predict it for decision-making at the state level. Modelling of the external migration process are taking into account the influence of economic factors, so using of machine learning methods is proposed. This choice of methodology is explained by the fact that machine learning allows to obtain high values of accuracy in forecasting of socio-economic phenomena. Indicators of socio-economic crises, as well as factors characterizing the level of development of the state were used in the selection of factors influencing the external migration process. The migration process was predicted using the "random forest" method. The selection of the model was based on minimizing the deviations of the predicted data from the actual ones. Еhe model was trained on statistical information from 15 countries, covering a period of 20 years. Сountries were selected according to the size of migration flows with Ukraine, taking into account both the flow of departure and the flow of arrival. Net migration rate, counted by dividing the number of immigrants by the number of emigrants, was used as the indicator of migration movement. To predict the importance of influencing factors, time series forecasting methods were used, in particular the ARIMA model, the choice of the best model was based on minimizing the Akaike criterion. The obtained results were used to forecast the external migration process for Ukraine for 2020–2021. In the course of the work the methods of correlation and regression analysis, theories of statistics, ensemble methods of machine learning, methods of forecasting time series and construction of neural networks were used. The model presented in the article can be used to predict the nature of migration between states, taking into account the influence of various factors.

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