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Learning the impact of financial education when take-up is low (anglais)

This note shows how big data can help combine experimental with non-experimental approaches in impact evaluations when take-up is low. In this study, author have access to a large administrative data set (of 660 MB), which follows the monthly financial indicators of each client for up to 18 months prior to the intervention and 6 months after it. Moreover, from the experimental approach their also had a large pool of clients randomly assigned to the...
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