Publication:
Estimating Treatment Effects with Big Data When Take-up is Low: An Application to Financial Education

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Date
2019-12-14
ISSN
0258-6770 (print)
1564-698X (online)
Published
2019-12-14
Author(s)
Lara Ibarra, Gabriel
Ruiz-Ortega, Claudia
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Abstract
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.
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Lara Ibarra, Gabriel; McKenzie, David; Ruiz-Ortega, Claudia. 2019. Estimating Treatment Effects with Big Data When Take-up is Low: An Application to Financial Education. World Bank Economic Review. © World Bank. http://hdl.handle.net/10986/40781 License: CC BY-NC-ND 3.0 IGO.
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