Relationship between income inequality, social transfers, poverty, and employment in Ukraine

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The impact of social transfers on income inequality and poverty remains a subject of debate, particularly regarding threshold effects, design, and integration with taxation and labor market dynamics. Using a linear regression model, the study analyzes the dependency of the Gini index on the percentage of social transfers in the average household’s monthly resources, the percentage of households with income below the median, and the percentage of the employed populace in Ukraine from 2010 to 2021. The results show that a 1% increase in social transfers in household income reduces income inequality by 0.13%, a 1% increase in employment decreases income inequality by 0.1%, whereas a 1% rise in poverty leads to a 0.34% increase in income inequality. In line with the results from EU and OECD countries, this study confirms that increasing the share of social transfers in household incomes contributes to the mitigation of income inequality in Ukraine. However, this remains valid only if the share of social transfers in households’ total income rises proportionally. The income and expenditure patterns of Ukrainian households, along with the Gini index, reflect poverty, which is partially mitigated by social transfers; however, their effectiveness is constrained by offsetting inflation. The rise in household incomes without a corresponding reduction in poverty suggests that employment is no longer the predominant factor in poverty alleviation in Ukraine.

Acknowledgment
This paper is funded as part of the project “Financial tools for reducing economic inequality in Ukraine” research project (No. 0124U002254), conducted at the State Organization “Institute for Economics and Forecasting of the National Academy of Sciences of Ukraine”.

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    • Figure. 1. Gini index and poverty ratio for Ukraine, 2010-2021
    • Table 1. List of dependent variables
    • Table 2. Descriptive statistics
    • Table 3. Correlation matrix for variables
    • Table 4. Regression outputs
    • Data curation
      Pavlo Kerimov
    • Investigation
      Pavlo Kerimov, Yuliia Shapoval
    • Methodology
      Pavlo Kerimov
    • Resources
      Pavlo Kerimov
    • Software
      Pavlo Kerimov
    • Visualization
      Pavlo Kerimov
    • Writing – original draft
      Pavlo Kerimov, Yuliia Shapoval
    • Conceptualization
      Yuliia Shapoval
    • Formal Analysis
      Yuliia Shapoval
    • Project administration
      Yuliia Shapoval
    • Supervision
      Yuliia Shapoval
    • Writing – review & editing
      Yuliia Shapoval