Automated credit limit increases and consumer welfare
利用美国监管微观数据，研究银行主动提升信用卡额度是否主要针对循环债务者，并构建模型量化该行为对家庭福利的影响。
In the United States, credit card companies frequently use machine learning algorithms to proactively raise credit limits for borrowers. In contrast, an increasing number of countries have begun to prohibit credit limit increases initiated by banks rather than consumers. In this paper, we exploit detailed regulatory micro data to examine the extent to which bank-initiated credit limit increases are directed towards individuals with revolving debt. We then develop a model that captures the costs and benefits of regulating proactive credit limit increases, which we use to quantify their importance and evaluate the implications for household well-being.