that were not able to be detected using experimental methods alone. It is worth re-iterating how little power experimental methods will have to detect treatment effects of the size we find, given the low take-up levels. For example, our power to detect the 5.9 percentage point improvement in the likelihood of paying more than the minimum that we see for the coaching treatment, given that take-up is 6.8 percent, is only 17.9 percent.<sup>14</sup> Note that our estimated treatment effects are all within the (very wide) confidence intervals seen for the LATE in Table 3.

It is important to note that these treatment effects are for the set of clients who will take up the interventions when invited. We have seen there is positive selection into participation, so that individuals who have the worst initial financial behavior in terms of late payments, not paying more than the minimum required, etc., are less likely to participate. The treatment effect may be larger for these individuals if they could be induced to participate, since they have more room for improvement, or potentially smaller if such individuals are less likely to implement the changes suggested in the workshops.

Finally, the cost per client of providing these programs was 131 MXP (7 USD) per person coached, and 86 MXP (5 USD) per person participating in the workshop. Using the impact on paying on time, this equates to a cost of 7 MXP/0.026 = 269 MXP per additional client induced by coaching to pay on time. If this were the only impact, this would appear an expensive way for banks to get clients to pay in a timelier manner. However, we see that the training and coaching get clients to pay their accounts on time and pay more of their bills, but do not get them to cut back on spending. In fact, perhaps because they are not experiencing as many payment problems, they spend more on their cards. The result is that this training does increase the likelihood these clients remain profitable for the bank.

# **5. Conclusions**

The reliable estimation of treatment effects in impact evaluations relies heavily on the implementation efforts of teams to get individuals assigned to a treatment to be effectively treated (and to the extent possible ensure those assigned to the control group do not receive treatment). In settings where individuals have little incentive to participate and those that do tend to be selfselected, the identification of the program effects through experimental methods is challenging. Unfortunately, and despite their recent popularity, financial education programs constitute a perfect example of these issues.

In this study, we take advantage of the richness of administrative data from a financial institution to implement a novel approach that overcomes the low-take-up problem in RCTs. The availability of monthly administrative data over a two-year period for a large pool of individuals assigned to the control group allows for a clearer estimation of the impacts of the workshops and coaching sessions. By selecting a group of individuals within the control group that is statistically comparable in their financial behavior (previous to the treatment) to that of the effectively treated group, our approach improves the empirical evaluation of the treatment in several ways. The approach improves upon the simple application of experimental methods. The experimental

> 14 This uses the autocorrelation of approximately 0.4 in our data, and the following command in Stata: sampsi 0.69 0.694012, n1(2504) n2(3626) sd1(0.46) pre(14) post(9) r01(0.4) r1(0.4) r0(0.4).

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