A randomized encouragement design yields null average effects of a credit builder loan (CBL) on consumer credit scores. But machine learning algorithms indicate the nulls are due to stark, offsetting treatment effects depending on baseline installment credit activity. Delinquency on preexisting loan obligations drives the negative effects, suggesting that adding a CBL overextends some consumers and generates negative externalities on other lenders. More favorably for the market, CBL take-up generates positive selection on score improvements. Simple changes to CBL practice, particularly to provider screening and credit bureau reporting, could ameliorate the negative effects for consumers and the market.

中文翻译：

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信用建立还是信用崩溃？信用建设者贷款对美国消费者行为和市场效率的影响

随机鼓励设计产生信用建立者贷款 (CBL) 对消费者信用评分的无效平均影响。但机器学习算法表明，空值是由于根据基线分期付款信贷活动而产生的明显的抵消治疗效果。对预先存在的贷款义务的拖欠推动了负面影响，这表明增加 CBL 会使一些消费者过度扩张，并对其他贷方产生负外部性。对市场更有利的是，CBL 的使用会在分数提高方面产生积极的选择。对 CBL 做法的简单改变，特别是对供应商筛选和征信机构报告的改变，可以减轻对消费者和市场的负面影响。