Low take-up of interventions is a common problem faced by evaluations of development programs. A leading case is financial education programs, which are increasingly offered by governments, nonprofits, and financial institutions, but which often have very low voluntary participation rates. This poses a severe challenge for randomized experiments attempting to measure their impact. This study uses a large experiment on more than 100,000 credit card clients in Mexico. The study shows how the richness of financial data allows combining matching and difference-in-difference methods with the experiment to yield credible measures of impact, even with take-up rates below 1 percent. The findings show that a financial education workshop and personalized coaching result in a higher likelihood of paying credit cards on time, and of making more than the minimum payment, but do not reduce spending, resulting in higher profitability for the bank.

中文翻译：

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摄取量低时用大数据估算治疗效果：在金融教育中的应用

干预措施采用率低是发展计划评估面临的一个普遍问题。一个主要的例子是金融教育计划，越来越多的政府，非营利组织和金融机构提供这些计划，但其自愿参与率通常很低。对于尝试测量其影响的随机实验而言，这构成了严峻的挑战。这项研究对墨西哥超过100,000个信用卡客户进行了一项大型实验。该研究表明，财务数据的丰富性如何允许将匹配方法和差异差异方法与实验相结合，以产生可靠的影响度量，即使采用率低于1％时也是如此。调查结果表明，金融教育研讨会和个性化教练会导致按时支付信用卡的可能性更高，