or due to portfolio risk concerns, pass use still peaks at the first quarter of the loan duration.<sup>9</sup> The proportion of extension passes increases over time as clients have less remaining time to repay the skipped balance within the original loan duration.

We report the reasons for pass use given by clients in Panel A of Appendix Table 7.<sup>10</sup> Forty-one percent report using the pass to make an investment in the business and separate qualitative data indicates that these business investments include making use of an opportunity for discounted bulk buying of inputs, financing inputs for a large customer order and covering lost revenue from temporarily closing the business for renovations. Dealing with shocks is another important reason why clients use passes --- 44% of flexible clients in the phone survey sample who used a pass did so to deal with a personal or family calamity while 19% used a pass to deal with business problems.

Appendix Table 7 Panel B reports client satisfaction using data from the lender phone survey. To keep answers comparable across treatment arms, questions about satisfaction were asked _before_ questions about pass use. While most borrowers feel confident about repaying their loan five months after disbursement (p-value of t-test of equality between flexible and standard loan borrowers is 0.51), borrowers of the flexible loan are 7 percentage points more likely to report higher quality of service from FMSD. Among the reasons given for good service, the flexibility of the product was the only one that was statistically significantly different at conventional levels (p-value 0.00).

# **Default, Business, and Stress-Related Treatment Effects**

We estimate the average treatment effect of a flexible contract relative to a standard contract, pooling across initial offers as discussed above. Since the probability of assignment to a flexible credit offer changed during the experiment (see Figure 1 and Appendix A), we adjust the standard estimation equation to avoid potential bias from correlation of client characteristics with the assignment probability. Following Gibbons, Suarez Serrato, and Urbancic, (2019), we estimate treatment effects separately for the two periods and calculate a weighted average based on the two periods’ sample frequencies. Formally, we estimate the following regression equation for client _i_ : where T _i_ is an indicator for assignment to a flexible contract and R1 is an indicator for receiving an offer in the initial period, Yi is the dependent variable. We include as additional controls the pre-intervention

> 9 We note that portfolio risk concerns raised by some credit officers in our study would be consistent with the lenders policy in Battaglia, et al. (2023), in which loan officers screened applicants for flexible loans based on their repayment histories and that of Barboni and Agarwal (2023)  in which the “repayment vacations” could not be taken in the first three months of the loan.

> 10 The rate of pass use among the sample of clients interviewed in the phone survey is only 18%. This is lower than the final rate from the administrative data since phone surveys were carried out, on average, six months into the loan. When controlling for time elapsed since loan disbursement, the reported rates of pass usage match closely with those of the administrative data.