Assessment of Poverty Levels in Kazakhstan: The Impact of Cash Income Deficiency

Today, poverty remains a significant problem affecting various population groups and economic stability. Understanding the key determinants of poverty is an important prerequisite for developing effective poverty reduction strategies. This study aims to identify the relationship between the poverty level and the population’s monetary income, as well as to assess the regional features of its spread in Kazakhstan. The methodology is based on an analysis of statistical data for 2001-2023 collected from official data from the Bureau of National Statistics of the Republic of Kazakhstan, the World Bank, and the United Nations. The article uses descriptive statistical methods to study the dynamics of poverty and correlation and regression analysis to identify the relationship between poverty and indicators such as average nominal income per capita, Gini coefficient, unemployment rate and household size. The results showed significant regional differences in poverty levels, with the highest poverty rates observed in the Turkestan region (9%) and the Abai region (8%). Regression analysis confirmed a significant impact of the cash income deficit on the poverty rate (R2=0.86, p<0.01). A high correlation between the poverty rate and the Gini coefficient (0.89) was revealed, indicating a significant impact of income inequality. The prospects for further research include an in-depth analysis of the impact of educational attainment on poverty, a study of the impact of digital financial technologies on household incomes, and an assessment of the effectiveness of government programs to reduce poverty.

1. Alloush, M. (2024). Income, Psychological Well-Being, and the Dynamics of Poverty. Economic Development and Cultural Change, 72(4), 1709–1745. https://doi.org/10.1086/725140

2. Amponsah, M., Agbola, F. W., & Mahmood, A. (2023). The relationship between poverty, income inequality and inclusive growth in Sub-Saharan Africa. Economic Modelling, 126, 106415. https://doi.org/10.1016/j.econmod.2023.106415

3. Arranz, J. M., & García-Serrano, C. (2025). Assessing the impact of an increase in the minimum wage on household income and poverty. Social Science Research, 127, 103143. https://doi.org/10.1016/J.SSRESEARCH.2025.103143

4. Bureau of National Statistics. (2024). Bureau of National Statistics of the Republic of Kazakhstan. Retrieved November 30, 2024 from https://stat.gov.kz/en

5. Caamal-Olvera, C. G., Huesca, L., & Llamas, L. (2022). Universal basic income: A feasible alternative to move people out of poverty in Mexico? Journal of Policy Modeling, 44(5), 1077–1093. https://doi.org/10.1016/J.JPOLMOD.2022.07.005

6. Gómez-Méndez, I., & Amornbunchornvej, C. (2024). Income, education, and other poverty-related variables: A journey through Bayesian hierarchical models. Heliyon, 10(6), e27968. https://doi.org/10.1016/j.heliyon.2024.e27968

7. Karki, S. K. (2024). Determinants of community engagement and its role in income poverty reduction: Evidence from Jajarkot, Nepal. Heliyon, 10(13), e33534. https://doi.org/10.1016/j.heliyon.2024.e33534

8. Khan, S., Yahong, W., & Zeeshan, A. (2022). Impact of poverty and income inequality on the ecological footprint in Asian developing economies: Assessment of Sustainable Development Goals. Energy Reports, 8, 670– 679. https://doi.org/10.1016/j.egyr.2021.12.001

9. Koiry, S., Kairi, B., & Pooja, P. (2024). Impact of income diversification on multidimensional poverty: Household level evidence from tea estates in Bangladesh. Heliyon, 10(5), e26509. https://doi.org/10.1016/j.heliyon.2024.e26509

10. Lechman, E., & Popowska, M. (2022). Harnessing digital technologies for poverty reduction. Evidence for low-income and lower-middle income countries. Telecommunications Policy, 46(6), 102313. https://doi.org/10.1016/J.TELPOL.2022.102313

11. Omir A., B. G. (2024). Analiz bednosti v Kazahstane: vyjavlenie i ocenka deficita denezhnyh dohodov naselenija. Statistika, Uchet i Audit, 4(95), 120–131. https://www.doi.org/10.51579/1563-2415.2024.-4.10 (in Russian)

12. Pinilla-Roncancio, M., Cedeño-Ocampo, G., Medina Ch, A. M., Cortés-García, C. M., & Muñoz-Veira, B. (2024). Changing income levels and multidimensional poverty among persons with disabilities in Colombia: A pseudo panel analysis. SSM - Population Health, 25, 101571. https://doi.org/10.1016/j.ssmph.2023.101571

13. Quiggin, J. (2022). Poverty alleviation as a global public good: The case for Basic Income. Economic Analysis and Policy, 75, 464–471. https://doi.org/10.1016/J.EAP.2022.05.018

14. Smith, A. (1989). Of the origin and use of money. In General Equilibrium Models of Monetary Economies, 47–53. https://doi.org/10.1016/B978-0-12-663970-4.50008-6

15. Son, N. H. (2016). Vulnerability and resilience to climate change in the northern mountainous region of Vietnam. https://doi.org/10.25911/5d77886d4eeae

16. Sumaila, U. R., Wabnitz, C. C. C., Teh, L. S. L., Teh, L. C. L., Lam, V. W. Y., Sumaila, H., Cheung, W. W. L., Issifu, I., Hopewell, K., Cinner, J. E., Bennett, N. J., Folke, C., Gulati, S., & Polasky, S. (2024). Utilizing basic income to create a sustainable, poverty-free tomorrow. Cell Reports Sustainability, 1(6), 100104. https://doi.org/10.1016/j.crsus.2024.100104

17. Wan, G., Hu, X., & Liu, W. (2021). China’s poverty reduction miracle and relative poverty: Focusing on the roles of growth and inequality. China Economic Review, 68, 101643. https://doi.org/10.1016/j.chieco.2021.101643

18. Xu, K. (2024). Digital finance, social security expenditures, and rural-urban household income poverty. Evidence is based on an area- and household-level analysis. Finance Research Letters, 60, 104845. https://doi.org/10.1016/J.FRL.2023.104845

19. Zhang, H., & Dai, J. (2023). Poverty improvement policies and household income: Evidence from China. Heliyon, 9(11), e21442. https://doi.org/10.1016/j.heliyon.2023.e21442