Relationships between tax burden, incomes, and poverty in rural areas of Kazakhstan: Regional evidence

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Type of the article: Research Article

Abstract
The purpose of this study is to assess the relationship between tax burden, income, and the level of poverty in rural areas of Kazakhstan. Panel regional data are analyzed for the period 2010–2024, obtained from the Bureau of National Statistics of Kazakhstan and the State Revenue Committee. The methodological framework includes regression analysis and econometric modeling. Five models were constructed showing that the growth of the gross regional product has a positive association with the incomes of the rural population (β = 0.927, p < 0.001). On the contrary, the relationship with the tax burden is statistically insignificant (β = 0.001). The study showed that before the 2018 tax reform, the fictitious indicator was β = –0.014; after that, β = 0.251, indicating a structural shift in household incomes of about 25–29%. The regional resource-based model shows that in non-oil regions, the tax burden is positive (β = 0.014), whereas in oil regions, it is statistically significant but negative (–0.021). The results suggest that higher GRP levels are not statistically associated with corresponding improvements in rural income indicators and poverty rates. In these cases, it is necessary to undertake comprehensive actions to improve the social well-being of the rural population, including strengthening the institutional environment, promoting regional economic development, and ensuring equitable distribution of benefits from economic growth.

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    • Figure 1. Interregional differences in income and depth of poverty in rural areas of Kazakhstan (regions = 14, year = 2024)
    • Figure 2. Dynamics of the tax burden for 2010–2024
    • Table 1. Main variables of econometric analysis
    • Table 2. Descriptive statistics (n = 210, 14 regions, 2010–2024)
    • Table 3. Diagnostic test results
    • Table 4. Regional differences in income, tax burden, poverty, and estimated fixed effects
    • Table 5. Results of panel regression with fixed effects
    • Conceptualization
      Guldar Maulenberdieva, Indira Kozhamkulova, Vassiliy Sherstyuk, Indira Baubekova, Gulshat Zhadigerova
    • Data curation
      Guldar Maulenberdieva, Indira Kozhamkulova
    • Formal Analysis
      Guldar Maulenberdieva, Indira Kozhamkulova
    • Funding acquisition
      Guldar Maulenberdieva, Indira Kozhamkulova, Vassiliy Sherstyuk, Indira Baubekova, Gulshat Zhadigerova
    • Investigation
      Guldar Maulenberdieva, Indira Kozhamkulova
    • Methodology
      Guldar Maulenberdieva, Indira Kozhamkulova, Vassiliy Sherstyuk
    • Project administration
      Guldar Maulenberdieva, Indira Kozhamkulova
    • Validation
      Guldar Maulenberdieva, Indira Kozhamkulova, Vassiliy Sherstyuk, Indira Baubekova
    • Writing – review & editing
      Guldar Maulenberdieva, Indira Kozhamkulova, Gulshat Zhadigerova
    • Supervision
      Indira Kozhamkulova
    • Writing – original draft
      Indira Kozhamkulova, Vassiliy Sherstyuk, Indira Baubekova, Gulshat Zhadigerova
    • Resources
      Vassiliy Sherstyuk, Indira Baubekova, Gulshat Zhadigerova
    • Software
      Vassiliy Sherstyuk, Indira Baubekova
    • Visualization
      Vassiliy Sherstyuk, Indira Baubekova, Gulshat Zhadigerova