Financial Inclusion via Blockchain: Evidence from a Natural Experiment

• 成果类型: Article; Early Access
• 署名作者: Jiang, Shangrong; Li, Yuze; Wang, Shouyang
• 署名单位: Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; University of Hong Kong; Chinese University of Hong Kong; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; ShanghaiTech University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.02303
• 发表日期: 2026
• 关键词: blockchain; Financial inclusion; microfinance institutions; LENDING MARKET; information authenticity

摘要

The lack of formal and verifiable credit records among borrowers is a significant barrier to financial inclusion in developing countries. In this paper, we assess the effectiveness of blockchain technology in improving credit information authenticity and enhancing financial inclusion. Using a novel data set from the online prosocial lending platform Kiva, we exploit the implementation of a blockchain-based lending protocol change in Kiva Sierra Leone as an exogenous shock to conduct difference-in-differences analyses. Our results show that borrowers attract more guarantors and larger per-guarantor contributions under the blockchain protocol, thereby increasing their likelihood of being funded on the Kiva platform. Microfinance institutions (MFIs) adopting the blockchain protocol also experience lower portfolio risks and extend larger lending volume to borrowers. In addition, the implementation of the blockchain protocol allows MFIs to achieve both growth in financial revenue and reduction in operational expenses, thus enhancing the sustainability of their lending services in developing countries. We further explore the sensitivities of blockchain effects and find that the blockchain protocol has a greater propensity to benefit borrowers from rural areas, those with weaker financial credit records, and those seeking microloans in sectors such as agriculture, textiles, and the food industry. Our findings are robust against robustness checks and alternative explanations.