most preferred approach, is to match just on the month-by-month pre-intervention data for an outcome at a time. This last method ensures that the control individuals look as similar as possible on levels and dynamics as those receiving treatment, but does mean, in contrast to the other approaches, that different controls are used for each outcome.

Figure 3 illustrates how the five different approaches define counterfactuals for the coaching treatment and outcome of paying more than the minimum payment owed. The top left panel compares the full control group to those receiving treatment. We see that the group which received coaching starts from a much higher mean level than the control group, reflecting positive selection into training in terms of pre-existing credit behavior. The trends seem broadly similar preintervention, suggesting difference-in-differences may be able to control for this selection. This difference in baseline means becomes smaller, but is still there, when we condition on being in the common support. In contrast, all three nearest neighbor approaches look much more similar on baseline levels, and appear to match reasonably well on baseline trends. These different nearest neighbor approaches do select different individuals from the control group: only 2 clients are selected by all three methods, so we are forming multiple plausible counterfactuals.

Appendix Table 1 shows how these three nearest neighbor matches achieve samples from the control group which are much more comparable on baseline observables to those who took up treatment than is the case for the full control sample. The first column shows baseline means for those who took-up treatment. We then follow Imbens and Rubin (2015) in considering the normalized difference ��<sup>�</sup> � −�<sup>�</sup> � �� + �̅���/2 as a measure of balance, where �<sup>�</sup> � and �̅�� are �/���̅ the sample mean and variance of the variable for those receiving treatment ( _j=T_ ) and the comparison subsample from the control group ( _j=C_ ) respectively. These normalized differences provide a scale-invariant measure of the difference in locations, with differences less than 0.2 standard deviations typically considered to indicate balance. We see that normalized differences exceed this level for pre-intervention averages in our key outcomes when using the full control sample, or the sample within the common support, but are all less than this when using any of our three nearest neighbor methods. As a result, we cannot reject equality of means of our financial outcomes averaged over all pre-intervention periods when using nearest neighbor.

# **4.2 Impacts of Workshops**

Table 4 presents the difference-in-difference estimates of the impact of workshops on our five outcomes of interest (Panels A through E). Each column presents the results of one of the five approaches we use to form a control group. For each outcome and approach, we test whether the treated and control groups followed common linear and non-linear trends in the pre-intervention period. The p-values of the tests are included in the table. In Figure 3, we show a graphic representation of the different approaches. The figure shows the trajectory of the outcome of paying more than the minimum payment for the clients that participated in the workshops and the clients assigned to the different control groups.

The last column of the table presents our preferred specification, the nearest neighbor approach that matches on the monthly pre-intervention data for a specific outcome of interest. As Figure 4 14