it is crucial to understand whether these consumer protection benefits are experienced broadly or whether certain subgroups are left behind. Our study population spans a heterogeneous set of individuals by gender, education, financial literacy, household composition and structure, control over household resources, and work experience. One might predict, ex ante, that any of these dimensions might lead to heterogeneous treatment effects.<sup>45</sup> In order to test for treatment heterogeneity in a disciplined way, we implement the recently-developed methodology of Chernozhukov et al. (2018). We are especially interested to examine whether there is predictable heterogeneity in the two main findings reported above: (i) “learning by doing” and efficient use of the account (ii) expanding savings by cutting discretionary spending.

**Methodology.** To proceed, we follow the method and road map laid out by Chernozhukov et al. (2018). Namely, we consider an environment in which we want to learn about the conditional average treatment effect (CATE):

_Y_ (1) and _Y_ (0) are the potential outcomes under the treatment and _Z_ is a vector of covariates. The method laid out by Chernozhukov et al. (2018) uses machine learning (ML) proxies _S_ ( _Z_ ) for the CATE, _s_ 0( _Z_ ), and provides a method to conduct inferences on different features of the underlying heterogeneous treatment effects.<sup>46</sup> In addition to providing an estimate of the heterogeneous treatment effect, Chernozhukov et al. (2018) demonstrate how to construct two additional features of the data. First, the authors show how to estimate group average treatment effects (GATES), which gives measures of the treatment effects separately for the most and least impacted groups. Following Chernozhukov et al. (2018), we consider the highest and lowest quintile groups in our GATES analyses. Second, once the GATES have been constructed, Chernozhukov et al. (2018) show how to conduct a classification analysis (CLAN), which gives the average characteristics _Z_ of the most and least affected groups. We follow these steps to ask if there is any detectable heterogeneity in our observed treatment effects as a function of 17 baseline characteristics.<sup>47</sup>

**Results.** We consider heterogeneous treatment effects for our key outcomes. Table 7 presents a GATES analysis, comparing the most affected quintile with the least affected quintile, by baseline characteristics. Panel A includes the main savings and consumption outcomes and pools across the bank and mobile direct deposit treatments relative to the control group. Panel B includes the main “learning-by-doing” outcomes. Because these outcomes mostly involve mobile account usage

> 45These are all listed as potential sources of heterogeneity in the outline of our study in the AEA RCT registry.

> 46All of our estimates employ a Random Forest algorithm in the ML step.

> 47 We use the following set of baseline characteristics: gender, marital status, indicator for parent, education, indicator for literate in the local language, digit span test score, the fraction of income shared with others, control over food expenditures within the household, an indicator for strictly positive savings, an indicator for positive formal savings, an indicator for being able to save BDT 5,000, an indicator for sending remittances in a typical month, an indicator for having prior experience with mobile transactions, and indicator for needing to borrow to pay for basic consumption at the end of the month, number of years in current job, and expected tenure in current job.

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