shows, this approach allows us to generate a control group that tracks very closely the evolution of each outcome for the treated group in the pre-intervention period. After the intervention, the mean outcomes of the clients that participated in the workshops begin to separate from the mean outcomes of the control group. While the outcomes of clients in the control group deteriorate over time (i.e., lower fraction of clients paying more than the minimum required and increased likelihood of delayed payment), the outcomes of clients who took the workshops remained stable.

The results from Table 4 show that these differences are statistically significant for all outcomes, except for bank profitability. The p-values of the common trends tests suggest that we cannot reject that the outcomes of clients who took the workshop and the control group formed by our preferred approach followed common linear and non-linear trends before the interventions. The economic impact of the estimates is also robust across the different matching approaches. The results of our preferred specification suggest that participating in the workshop results in an 11 percentage point increase in the likelihood of paying more than the minimum payment, a 3.4 percentage point reduction in the likelihood of delaying payment, 63.7 percent higher monthly spending on the credit card, and a 2.7 percentage point increase in the likelihood of owning a deposit account with our partner bank.

# **4.3 Impacts of Coaching**

Table 5 provides the difference-in-difference results for our five different approaches to forming a control group, along with tests of whether the two groups follow a common linear trend, and a common non-linear trend, before the intervention. Figure 5 shows the trajectory of mean outcomes for the group that received coaching, compared to our different matched control groups.

Figure 6 shows that after the time of the intervention, the control group is becoming progressively more likely to not pay more than the minimum payment, to delay in their credit card payments, to no longer have a deposit account with the bank, to cut back on spending, and are becoming less profitable clients for the bank. The coaching treatment is halting these trends from occurring, so that those who receive coaching appear more similar to their pre-intervention levels.

Table 5 shows that these impacts are statistically significant after matching for all but having a deposit account, are reasonably robust in magnitude to different plausible ways of defining this matched control group, and that we cannot reject that the matched control groups display parallel linear or non-linear trends pre-intervention. Using our preferred specification in the last column (which matches Figure 5), we find that receiving coaching results in a 5.9 percentage point increase in the likelihood of paying more than the minimum payment, a 2.6 percentage point reduction in the likelihood of delaying payment, 51.9 percent higher monthly spending on the credit card, and a 7.8 percentage point increase in the likelihood that the client is profitable for the bank.

# **4.4 Discussion**

This combination of nearest neighbor matching, difference-in-differences, and the random assignment enables us to find a subset of clients within the full experimental control group who look similar on baseline observables to those who take up the interventions, and who also follow similar pre-intervention trends. Using this strategy, we detect treatment effects of the interventions 15