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Can Digital Inclusive Finance Safeguard the Middle Class? A Study Based on Chinese Household Data

Author

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  • Jie Weng
  • Wang Li
  • Jie Zhou

Abstract

Expanding and stabilizing the middle class is critical for reducing income disparities, promoting social equality, and fostering sustainable economic growth. However, the middle class faces considerable vulnerability, which challenges achieving these goals. Digital inclusive finance, as a transformative financial innovation, offers potential solutions to mitigate this vulnerability, yet its role remains insufficiently explored in the existing literature. This study aims to examine the impact of digital inclusive finance on reducing middle-class vulnerability, with a specific focus on its mechanisms and inclusiveness. Using data from the China Family Panel Studies, the analysis employs the Vulnerability as Expected Poverty (VEP) model to quantify middle-class vulnerability and explore the protective effects of digital inclusive finance. The findings demonstrate that digital inclusive finance significantly reduces middle-class vulnerability, a result validated through robustness checks. Mechanism analysis reveals that digital inclusive finance enhances entrepreneurial activities, financial participation, and employment opportunities, collectively contributing to its protective effects. Additionally, the study highlights the inclusive nature of digital finance, as it helps bridge the ‘digital divide’ across regions, urban and rural areas, education levels, and age groups. The findings suggest that policymakers and financial institutions should prioritize expanding access to digital inclusive finance by improving digital infrastructure and financial literacy programs. Targeted initiatives, such as subsidizing digital tools or incentivizing financial innovation, could effectively reduce middle-class vulnerability.

Suggested Citation

  • Jie Weng & Wang Li & Jie Zhou, 2025. "Can Digital Inclusive Finance Safeguard the Middle Class? A Study Based on Chinese Household Data," SAGE Open, , vol. 15(2), pages 21582440251, June.
  • Handle: RePEc:sae:sagope:v:15:y:2025:i:2:p:21582440251342087
    DOI: 10.1177/21582440251342087
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