Latent Factor Analysis of Regional Growth in Belarus Using Structural Equation Modeling

Objectives. This study provides a quantitative assessment of the impact of latent variables on regional economic activity and examines the interactions among various economic sectors and their contributions to regional growth. The analysis focuses on identifying latent factors of socio-economic development across Belarusian regions during the period 2016-2024, using factor analysis techniques and structural equation modeling (SEM).

Methods. To reduce data dimensionality and identify latent factors, exploratory factor analysis (EFA) was applied using the Principal Component Analysis (PCA) extraction method. A structural equation model was constructed using the SEMOPY library in Python to estimate relationships among the identified factors. To assess the quality of the model, standard fit indices were used:  CFI  – comparative fit index; TLI  – Tucker-Lewis index; RMSEA  – root mean square error of approximation. The values of these indices allow evaluating the degree of consistency between the model and the empirical data. The model is based on a system of  23 socio-economic indicators across 128 administrative districts and cities of regional subordination.

Results. The resulting SEM demonstrates high internal consistency and statistical reliability (CFI = 0,98;  TLI = 0,97; RMSEA = 0,04), revealing significant causal linkages between latent factors. It was established that the financial sector is a key driver of investment activity, while growth in the housing stock directly stimulates consumer demand. Negative relationships were identified between agricultural potential and financial stability, as well as between industrial development and financial sustainability.

Conclusion. The developed model is an effective analytical tool for formulating evidence-based regional policy, optimizing resource allocation, and strategic planning. Promising directions for future research include incorporating time lags, adding indicators of innovation and human potential, and applying spatial econometrics methods.

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