Mel et al., 2008, 2009; Bardasi et al., 2011), there is a sizeable profitability gap (as measured by profits per worker) between male and female-owned businesses (with female-owned businesses earning approximately 45% less in monthly profits per worker than their male-owned counterparts). A similar pattern emerges when productivity is measured by sales per worker; female-owned businesses earn on average 266 USD per worker per month,<sup>19</sup> 72% of the level for male-owned businesses.<sup>20</sup> In five of the countries where both the Informal Sector Enterprise Surveys and micro-enterprise surveys are available, informal businesses are much less productive than micro-enterprises. Importantly, the size of this gap differs by country: in Zimbabwe, the average sales per worker for informal businesses is just a tenth of the corresponding value for micro-enterprises, while in Somalia, it is about 60%. Figure 12 plots the kernel densities of the log of sales per worker for informal and micro-enterprises. In Zambia and Somalia, there appears to be considerable overlap in the productivity of these two types of businesses. In contrast, in Zimbabwe, the majority of micro-enterprises earn more per worker than the top decile of informal businesses. The reasons for these relative differences between countries are left for future research. Overall, these results are consistent with previous studies that documented that informal businesses are less productive than their formal counterparts (La Porta and Shleifer 2014; Ulyssea 2020; Aga et al., 2021).

Figure 13 provides a more formal test of the equality of the distributions of sales per worker of informal businesses and formal micro-enterprises.<sup>21</sup> This figure presents the (unconditional) cumulative distribution function (CDF) of sales per worker for informal businesses and formal micro-enterprises. The thick horizontal line along the horizontal axis highlights the values of sales per worker where the two distributions are statistically significantly different from each other (using the familywise error rate, FWER, at 5%). In Zimbabwe, the equality of the two distributions is rejected across almost all values of sales per worker, confirming what we alluded to based on the kernel density plot given on Figure 12. Similarly, the two distributions are significantly different across considerable ranges of values of sales per worker in India, Mozambique and

> 19 Male-female differences in sales per worker, though large, are statistically insignificant.

> 20 A large body of research seeks to explain these differences. One of the potential explanations is that businesses owned by women in developing countries are more likely to be credit-constrained (Rose, 2000; Fletcher, 2009). Though we are unable to directly addresses this hypothesis with the descriptive statistics presented here, female owned businesses are just as likely to have an existing loan for the business as male owned businesses (9.95% compared to 8.05%).

> 21 We use the _distcomp_ command in Stata which provides a test that allows to check for equality of two distributions, thus enabling to test for a restricted form of the first order stochastic dominance (see Kaplan 2019).

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