Economic growth of countries in the context of military operations - Olexandr Shapurov; Oleksii Hrechanyi; Volodymyr Stoiev; Anatolii Karpelianskyi; Alina Sosnovska (Challenges and Issues of Modern Science)
Purpose. The purpose of the study is to assess the economic growth of Ukraine, Syria, and Palestine under wartime conditions, compare growth models (Solow, MRW, Romer, and a machine learning model), identify recovery factors, and develop recommendations for 2030. Design / Method / Approach. The study employs a comparative analysis of growth models, modified by a conflict intensity indicator, based on panel data from 1990–2023 (World Bank, UNESCO, IndexMundi). Random Forest, accounting for nonlinear relationships among variables (investments, education, R&D, international aid), was used for forecasting. Forecasts cover three scenarios for 2025–2030. Findings. The Romer model is the most accurate for Ukraine, projecting a GDP per capita of $13,456 (optimistic scenario, 2030). For Syria and Palestine, projections are $1,183 and $3,012, respectively. Random Forest predicts $23,792 for Ukraine, $6,819 for Syria, and $5,764 for Palestine. Key factors include international aid (29.8%), investments (24.6%), and conflict reduction (19.7%). Theoretical Implications. The study adapts growth models to wartime conditions, highlighting the advantages of endogenous models and machine learning for analyzing complex economies. Practical Implications. The findings contribute to developing recovery strategies, allocating international aid, and planning sustainable development in conflict-affected countries. Originality / Value. The originality lies in adapting models to wartime conditions, comparing their effectiveness, and applying Random Forest for forecasting. Research Limitations / Future Research. Limitations include a small sample size (72 observations), missing data, subjective assumptions, and omission of external shocks. Future research should incorporate broader data, climate, and geopolitical factors. Type of Article. Empirical.