电商消费信贷对中国居民杠杆率的影响机理与风险边界研究——基于金融生态视角的理论分析

Study on the Impact Mechanism and Risk Boundary of E-Commerce Consumer Credit on China’s Household Leverage Ratio—A Theoretical Analysis Based on the Perspective of Financial Ecology

摘要: 近年来，随着电子商务的蓬勃发展，以蚂蚁“花呗”、京东“白条”为代表的电商平台消费信贷产品迅速普及，深刻改变了中国居民的消费与信贷行为。电商消费信贷在释放消费潜力的同时，也因其准入门槛低、扩张速度快、与居民日常消费深度绑定等特征，成为影响中国居民杠杆率的重要变量。本文基于金融生态视角，构建了一个非实证的理论分析框架，旨在探究电商消费信贷影响居民部门杠杆率的深层机理，并系统界定其潜在的风险边界。研究发现：电商消费信贷通过流动性约束缓解机制、心理账户与行为扭曲机制以及收入–债务错配加速机制，在微观层面改变了居民家庭的跨期预算约束和消费决策，进而从宏观层面推高了居民部门的整体杠杆率。同时，本文识别了该模式面临的三大核心风险：一是基于大数据的动态信用风险，其顺周期性可能加剧经济波动；二是共债风险在多平台间的隐匿与传导，构成了对传统征信体系的挑战；三是数据滥用与算法歧视引发的公平性风险与监管套利风险。在此基础上，本文探讨了在宏观杠杆率可控范围内的“风险边界”，并从功能监管、数据治理和消费者保护三个维度提出了构建适应性监管框架的政策建议。本研究为理解数字金融时代的居民债务问题提供了新的理论视角，并为防范系统性金融风险提供了参考。

Abstract: In recent years, with the booming development of e-commerce, consumer credit products on e-commerce platforms, represented by Ant’s “Huabei” and JD’s “Baitiao”, have rapidly proliferated, profoundly altering the consumption and credit behavior of Chinese residents. While unleashing consumption potential, e-commerce consumer credit has also become a significant variable affecting the household leverage ratio in China due to its low entry barriers, rapid expansion, and deep integration with daily household consumption. Based on the perspective of financial ecology, this paper constructs a non-empirical theoretical analysis framework aimed at exploring the underlying mechanisms through which e-commerce consumer credit influences the household leverage ratio and systematically defining its potential risk boundaries. The research findings indicate that e-commerce consumer credit, through the liquidity constraint relaxation mechanism, the mental accounting and behavioral distortion mechanism, and the income-debt mismatch acceleration mechanism, alters the intertemporal budget constraints and consumption decisions of micro-level households, thereby pushing up the macro-level household leverage ratio. Concurrently, this paper identifies three core risks associated with this model: first, the dynamic credit risk based on big data, whose pro-cyclicality may exacerbate economic fluctuations; second, the concealment and transmission of co-borrower risk across multiple platforms, challenging the traditional credit investigation system; and third, the fairness risk and regulatory arbitrage risk arising from data misuse and algorithmic discrimination. Building on this analysis, the paper discusses the “risk boundary” within the controllable range of the macro leverage ratio and proposes policy recommendations for constructing an adaptive regulatory framework from three dimensions: functional regulation, data governance, and consumer protection. This study offers a new theoretical perspective for understanding household debt issues in the era of digital finance and provides a reference for preventing systemic financial risks.