Evaluation of Social Policy Programs on Poverty Reduction in Emerging Economies

Abstract

This study examines the long-run determinants of poverty reduction in BRICS economies by integrating social policy expenditure, financial inclusion, digital development, remittances, governance quality, and macroeconomic factors. Using panel data and applying advanced econometric techniques, including panel unit root tests, Pedroni cointegration analysis, FMOLS, DOLS, and Driscoll–Kraay standard errors, the study investigates both equilibrium relationships and robustness across estimators. The empirical results confirm the existence of a stable long-run cointegrating relationship among the variables. Findings consistently reveal that social policy expenditure, financial inclusion, digital development, remittances, and governance quality significantly reduce poverty, while inflation exacerbates it. Among the determinants, financial inclusion emerges as the most influential poverty-reducing factor, followed by governance quality and social policy expenditure. The results remain robust across alternative estimators, reinforcing the reliability of the findings. The study highlights the importance of an integrated policy framework combining fiscal redistribution, financial access, digital transformation, and institutional quality to achieve sustainable poverty reduction in emerging economies. These findings provide important policy guidance for strengthening inclusive growth strategies in BRICS countries.

Received: 2026-04-10 | Revised: 2026-06-02 | Accepted: 2026-06-23 | Published: 2026-06-30

Declarations 

Ethics and Guidelines: Not Applicable.

Consent to participate: Not Applicable.

Consent to publish: The authors have provided consent to publish.

Competing interests: The authors declare no competing interests.

Data availability statement: Data will be made available on reasonable request from the corresponding author.

Funding: This research received no external funding.

Clinical Trial Number: Not Applicable.

Declaration of using generative AI: During the preparation of this work the author(s) used ChatGPT in order to correct the grammatical errors. After using this tool/service, the author(s) reviewed and edited the content as needed and take full responsibility for the content of the published article.

Author Contributions: Conceptualization, M.A.M.T.; methodology, M.A.M.T.; formal analysis, M.A.M.T.; writing—original draft preparation, M.A.M.T.; writing—review and editing, M.A.M.T. All authors have read and agreed to the published version of the manuscript.