Given concerns on multiple hypotheses testing, I follow Kling et al. 2007 and construct a business index averaging the z-scores of business survival and performance outcomes. The index exhibits an increase of 0.13 standard deviations (p-value=0.02) as a result of the intervention. As table A.5 shows, the results are qualitatively similar when using inverse hyperbolic sine transformations, but effects on weekly revenues become insignificant at conventional levels (p-value=0.16). Additionally, table A.7 reveals that most of these effects are coming from business survival rates: conditional on business survival, the treatment is associated with positive but statistically insignificant increases in business revenues and profits.

Higher business survival for the treatment group is consistent with recent evidence suggesting that female microentrepreneurs are particularly likely to face market access constraints, which are often underestimated in standard enterprise surveys (Hardy et al. 2022). Results are also promising, especially when compared to existing evidence on growth-targeting policies among microenterprises. For example, an experiment in Sri Lanka randomizing (male-owned) enterprises’ access to wage subsidies for a worker over a 6-month period found significant effects on business survival, but not on sales and profits during the subsidy period or after it (De Mel et al. 2019).

Next, I look at whether the intervention impacted client type. I start by looking at the effects at the sample level in columns (7)-(12) of table 3. Broadly, we see an increase in the likelihood of selling to strangers, the number of online clients, and the likelihood of offering delivery services. This composition effect, however, might be coming mechanically from business survival, since closed businesses have no clients at all. Similarly, we see a decrease in informal practices usually adopted with customers within business owners’ social networks, such as selling on store credit. These effects might also be mechanical, since closed businesses have no sales.

In table A.7, I reports on these outcomes among the sample of operating businesses, which reflects both the treatment effects and selection into business survival. While we expect that businesses induced by the intervention to stay open are more negatively selected, compared to those that stayed open in the control group, we still see an enhancement in business outcomes conditional on survival. While noisy, the likelihood of selling to strangers among surviving businesses is 5 percentage points higher in the treatment group (p-value=0.15). Additionally, treated business owners reach 41% more online clients (an increase of 0.71 clients coming from online platform over a month period, up from a control mean of 1.81). Also conditional on survival, treated business owners still report a lower share of store credit out of revenue and a smaller number of store credit clients, compared to their counterparts in the control group. Qualitatively, women complain about their neighbors, friends, and family members borrowing from them through store credit requests, or informal ‘buy now pay later’ requests. Such informal practices could become a form of ‘kin tax’ (Jakiela and Ozier 2016) limiting the growth of businesses (Squires 2018). Access to online markets offered by the intervention seems to have increased sales to new clients found online, shifting the composition of customers that women sell to away from social network members who might ask for store credit. Finally, among surviving businesses, we see that the treatment group is only 2 percentage points (SE=0.03) more likely to offer delivery services than the control group (42% of which offer delivery). The small difference suggests that improvements in business outcomes might be coming particularly from women who were already offering delivery services. Qualitatively, women do not report constraints on their ability to offer delivery services, even if they themselves rarely leave the home. Most women report that a male 18