本文以各国移动支付平台的推出为准自然实验,基于中国参与通信领域国际公共产品供给的视角,考察数字金融对“一带一路”国家小微企业成长的影响。研究发现,数字金融助推“一带一路”国家小微企业成长水平提升9.35%,且该效应随中国通信项目援助增加而增强。原因在于中国通信项目援助通过扩大国际公共产品供给,提高了“一带一路”受援国信息基础设施水平,进而通过“质量效应”与“成本效应”增强小微企业技术可及性,弥合数字金融发展中的数字鸿沟。此外,数字金融通过改善融资环境、优化社会信用环境和提升资金配置效率促进小微企业成长,这些机制在受援国更为显著。

Born-digital firms increasingly pursue virtual involvement, a digital market-entry strategy that prioritizes iterative data collection and algorithmic adaptation over physical expansion. We measure virtual involvement by the number of unique digital advertisements and define it as the intensity of a firm’s digital experimentation to collect user data, train algorithms, and enable continuous learning through a recursive feedback loop. Using a novel panel dataset covering 194 US and Chinese born-digital firms across 33 countries from 2016 to 2022, our empirical analysis finds that stringent host-country digital privacy regulation significantly reduces virtual involvement. This negative influence is weakened in larger digital markets, for firms from home countries with robust digital regulatory regimes, and when firms receive positive public sentiment. These results extend institutional theory and non-traditional entry mode research by introducing virtual involvement as a new dimension of international strategy under regulatory constraints. For policymakers and managers, our study offers actionable guidance: strict privacy rules protect user welfare, but supportive market conditions and regulatory experience are critical to sustaining data-driven innovation. Policymakers should balance user protection with market dynamism, while firms should leverage large markets and positive sentiment to maintain their virtual involvement.

We investigate whether FinTech can mitigate human biases in lending decisions using proprietary loan-level data from a Chinese auto equity lender. The lender first integrated big data credit scoring as an advisory tool to enhance its traditional lending model, subsequently transitioning to algorithmic decision-making with optional human override. Our findings reveal that cognitive biases decrease significantly when loan officers use algorithmic lending decisions, substantially reducing disparities in loan-to-value ratios between local and nonlocal borrowers without exacerbating default differentials. Notably, the discretionary adjustments made by loan officers remain modest. In contrast, advisory credit scores alone exhibit no discernible bias-reducing effects. Our study is among the first to demonstrate that automation and choice architecture – specifically, nudging via algorithmic defaults – is more effective than mere information provision in combating discrimination and promoting financial inclusion.

Using village-level data from Guangdong Provincse’s targeted poverty alleviation campaign (covering 13,802 villages), this study exploits the rollout of the “E-commerce into Villages” program as a quasi-natural experiment and applies a staggered difference-in-differences strategy to assess how improved market accessibility affects the orientation and effectiveness of poverty alleviation. Results show that the program significantly enhances poverty reduction outcomes. Mechanism analysis reveals that enhanced market access shifts policy emphasis toward development-oriented approaches and away from subsistence-based support, thereby stimulating the aspirations and productive behavior of the rural poor. Heterogeneity analysis further indicates stronger effects in villages with more cadres from enterprises or developed cities, and in less-developed areas. Finally, evidence suggests that improving market accessibility—emphasizing capacity building over direct transfers—supports sustained rural development. These findings highlight the importance of e-commerce as a tool for promoting inclusive growth and effective poverty alleviation in developing countries.