扩大信用数据共享可降低借贷成本，缓解贫富差距。
Data for Inclusion: The Redistributive Power of Data Economics
- 模拟三种数据模式：仅负面信息、部分正向数据、合成全量可见。
- 全量数据共享使利率差异缩小，信贷负担基尼系数下降。
- 适合关注金融包容与政策设计的研究者阅读。
本文评估了在金融排斥经济体中扩大正向信用信息共享的再分配效应与效率影响。基于乌拉圭2021年家庭调查的微观数据，我们模拟了三种数据制度：仅负面信息、部分正向（Score+）和合成全量可见，并分析其对信贷可及性、利息负担及不平等的影响。研究发现，扩大数据共享显著降低金融成本，压缩利率差异，降低信贷负担的基尼系数。尽管部分可见性仅惠及特定群体，但合成全量访问能实现最公平高效的成果。分析表明，信用数据是一种非竞争性公共资产，对金融包容与减贫具有变革性意义。
原文摘要 · Abstract (English)
This paper evaluates the redistributive and efficiency impacts of expanding access to positive credit information in a financially excluded economy. Using microdata from Uruguay's 2021 household survey, we simulate three data regimes negative only, partial positive (Score+), and synthetic full visibility and assess their effects on access to credit, interest burden, and inequality. Our findings reveal that enabling broader data sharing substantially reduces financial costs, compresses interest rate dispersion, and lowers the Gini coefficient of credit burden. While partial visibility benefits a subset of the population, full synthetic access delivers the most equitable and efficient outcomes. The analysis positions credit data as a non-rival public asset with transformative implications for financial inclusion and poverty reduction.
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