Macroeconomic determinants of government revenues under wartime structural shocks: Evidence from Ukraine

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Type of the article: Research Article

Abstract
The study aims to assess changes in the influence of macroeconomic factors on government revenues and the transfor-mation of their interrelationships under wartime structural shocks in Ukraine. Two periods are considered: the first period (2017–2020) serves as the baseline for comparison, while the second period (2022–2025) captures the structural shifts associated with the full-scale war. The methodological framework combines correlation and regression analyses, supplemented by a pooled regression with a wartime dummy variable to assess structural differences between periods, and by logarithmic transformations of variables to enhance economic interpretability. The findings indicate a substantial transformation in the relationships between macroeconomic indicators and State Budget revenues within the relatively short wartime observation period (2022–2025). During the baseline period, the model shows limited explanatory power (R2 = 0.43) and no statistically significant coefficients, reflecting the distributed nature of factor influence. In the wartime period, the explanatory power of the model increases to R2 = 0.83; however, it is accompanied by a sharp rise in multicollinearity (VIF for GDP: 38.5; for imports: 22.3), limiting the identification of the individual contribution of explanatory variables and suggesting stronger co-movement among macroeconomic indicators. The logarithmic model suggests that GDP may serve as an aggregate indicator reflecting broader macroeconomic dynamics associated with State Budget revenues, while the elasticity of State Budget revenues with respect to GDP equals 1.4, indicating heightened fiscal sensitivity under crisis conditions. The results suggest that macroeconomic indicators exhibit a more coordinated pattern of interaction in explaining State Budget revenues, within which their interrelationships intensify and acquire a systemic character.

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    • Table 1. Data set for econometric analysis
    • Table 2. Regression model for the baseline period (2017–2020)
    • Table 3. Correlation analysis for the baseline period
    • Table 4. Regression model for the period of structural disruption (2022–2025)
    • Table 5. Correlation analysis for the period of structural disruption (2022–2025)
    • Conceptualization
      Alina Hrushyna
    • Data curation
      Alina Hrushyna
    • Formal Analysis
      Alina Hrushyna
    • Funding acquisition
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    • Investigation
      Alina Hrushyna
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    • Project administration
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    • Resources
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    • Software
      Alina Hrushyna
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    • Writing – original draft
      Alina Hrushyna
    • Writing – review & editing
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