average 10 years of professional experience, and more than 90 percent have already completed a business training either in 2017 or in 2018. Almost all characteristics are balanced across the three groups. There are only some minor differences for religious affiliation, age, and gender. But even these differences are relatively small in absolute terms. Their firms exist on average for eight years; more than 50 percent are in agriculture including livestock, 20 percent are in services, and about 15 percent are in manufacturing. They have on average a capital stock of US$3,500–US$6,500 and generated profits of about US$500 over the six months preceding the baseline survey, yielding an annual return to capital ratio of about 15–30 percent and a profit margin of about 30–40 percent, yet profits may include a substantial part of the owner’s labor income. They employ on average 2.5 workers. About 40–45 percent keep books. Almost all characteristics are balanced across the three groups, yet there is substantial variation for the reported capital stock, driven by some extreme values, though even these differences are statistically not significant given the high variance of the distribution. Despite the overall very good balance, we will, for robustness reasons, include in all regressions a large set of controls to redress any potential bias. It will also increase the precision with which we estimate the impacts.

Since the sample comprises already existing firms and new firms and for many outcomes it makes sense to analyze impacts separately for these two types, we also did balance tests for these two subgroups. They are shown in the baseline report (Grimm, Soubeiga, and Weber 2020). The balance tests suggest that most observable characteristics are also balanced for these subgroups and hence already existing and new businesses are quite evenly distributed across the two treatment groups and the control group. Yet, in general, it must be noted that any subgroup analysis, whether by firm status, or by gender or grant size, is subject to lower power. Hence, at this stage, we mainly focus on the sample of all firms and the sample of already existing firms and leave a further analysis of heterogeneity for later stages when more data are available.

# _4.3 Attrition_

Of the 1,200 entrepreneurs, 92.7 percent responded to the follow-up survey, implying an attrition rate of 7.3 percent. In our case, attrition has two major sources: respondents who could not be found, for instance, because they moved away or because they were temporally not available because of traveling or the like and respondents who refused to participate in the second wave of the survey. Yet, in each case, several attempts had been made to reach out to all participants interviewed at baseline and to limit attrition to its absolute minimum. With respect to the treatment arms, the attrition rate is higher in the group of matching grant beneficiaries (9.3 percent), followed by the rate in the control group (7.5 percent), and the group of cash grant beneficiaries (5.3 percent). Yet, if we run regressions of attrition on respondents’ characteristics and the treatment arms, the coefficients associated with the latter are insignificant. The observables that are correlated with attrition include the sector of activity, the number of employees, and a range of household characteristics such as marital status, family size, religious affiliation, ethnicity, and household’s assets. When estimating impacts, we will use regressions and control for these factors. According to MEBF, no one dropped out because of the inability to pay the 20 percent contribution as they were allowed to find an arrangement with the BDS provider to somewhat delay the payment of their own contribution if that was needed, but could already receive the support service in the meantime.

12