Methodology of statistical modeling of causal relationships in demographic trends

Rakhila Rakhmetova, Kulzada Abenova

2026

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DOI 10.52821/2789-4401-2026-2-115-126
Data confidence 70 medium
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Abstract

The purpose of the study is to justify the choice of a mathematical method for conducting an in-depth study of causal relationships in socio-economic processes based on a limited amount of statistical data using Kazakhstan as an example, as well as to develop a methodology for its application in accordance with the principles of interdisciplinary research. The relevance of the study is determined by the presence of methodological errors in scientific publications related to the incorrect use of mathematical methods in socio-economic research. Research methods – given the identified inadequacy of currently widely used regression models, a reasonable choice of mathematical methods in accordance with the research objective is proposed. A methodology for applying statistical dependence equations has been developed and a forecasting procedure using normative calculations based on this method is presented. The value of the research results lies in presenting the complete procedure for applying the statistical dependence equation method using the example of identifying the causal relationship of the trend in the average annual population of the city of Almaty. The study resulted in a statistical analysis of the main demographic indicators of the city of Almaty was conducted, including the average annual population, migration balance, birth rate, and the volume of natural increase. As a result of the constructed equation of statistical dependencies, it was calculated and proven that population growth in the city is primarily driven by internal migration flows within the country, accounting for 78.9% of the total impact. Since the migration factor is regulated among demographic indicators, a simulation forecast was made with limited values for this factor. On this basis, population regulation models were developed using specified planned values of the influencing factor.