# **6 Heterogeneous Treatment Effects**

It is not clear whether digital market access can substitute for in-person exposure among low-mobility women. Although these might be more market-constrained, they might also have limited capital, time, skills, or aspirations. If that’s the case, there might not be a significant share of productive but market-constrained women for the intervention to impact.

To shed light on this tension, I first present heterogeneity in treatment effects by women’s mobility levels. Second, I present consistent heterogeneous treatment effects as predicted using machine learning algorithms, following Chernozhukov et al. 2018. Finally, I present results by whether a business is home-based, which is a pre-specified measure of business owners’ mobility and market exposure.

## **6.1 Heterogeneity by Baseline Mobility**

I create an index for respondents’ physical mobility using mobility variables collected from the baseline survey. Following Kling et al. 2007, the index averages the standardized values of baseline mobility variables. These variables are summarized in table 1 and consist of: (1) Ability to take weekly trips alone for business-related reasons; (2) Willingness to operate the business outside the home if rent is covered; and (3) The number of times the respondent went out alone last month.

### **6.1.1 Differences across high vs. low-mobility women:**

In table 5, I document how high vs. low mobility women differ in demographics and business outcomes, defining high and low mobility as above vs. below median mobility index. As one might expect, high and low mobility women have significant differences in their demographics, values, digital practices, and business characteristics. These differences are not necessarily confounders for mobility, but underlying reasons for it.

For example, high mobility women are significantly less likely than low mobility women to live in rural areas, in clan communities, to be married, to have children, and to have a working husband. They are also more likely to have a high school degree and to have ever been employed, and they overall have significantly higher household incomes than low-mobility women. High mobility women also less likely to think that the children of a woman that works outside the home suffer and that society judges working mothers.

Interestingly, there are little differences in high and low mobility women’s use and perception of Facebook, but high mobility women are 9 percentage points more likely to use profile pictures that show their faces on Facebook. They are also more likely to use Facebook to market their business and to display their personal names and pictures on their online business page.

Also note that high and low mobility women differ in how they operate their businesses. High mobility women are 17 percentage points less likely to operate their business from home. They are also more likely to offer a service (e.g. a salon) as opposed to trading products. High mobility women’s businesses also have more formalized practices: they are more likely to register their business and to offer delivery services, and they are less likely to sell exclusively to friends and neighbors and to sell on store credit. Overall, high mobility women have significantly higher business profits than their counterparts. Finally, figure 3 presents two maps showing that the approximate distribution of high vs. low mobility women across the country is comparable.

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