where the explained variable lnvahi is the log of the ratio of value added with the total hours worked in firm _i_ . lnKi stands for the log of physical capital. Given that in our sample some firms do not possess any capital, a dummy variable nocapitali that equals 1 when the firm has no capital is also introduced. This allows us to avoid obtaining unbiased estimates of the returns to capital when some firms have zero observations of capital (Battese 1997; Grimm, Knorringa, and Lay 2012). The vector Xi includes standard characteristics of the firm’s owner, (age squared, gender and the age of the firm). The vector Hi includes educational attainment, financial knowledge, reading knowledge and a dummy taking the value 1 if the owner is member of a business association. The vector Mi includes proxies for firm’s management skills such as lack of customers, lack of credit access, management issues, employees’ discipline problems, and problems of location and lack of equipment. The equation also includes sector dummies Si and region fixed effects Ri.

Equation (7) explains the performance drivers of the three groups of firms. Appendix A, figure A.7 shows that the higher a firm’s value added per hour worked, the better the firm performs. The results are displayed in table 11. The first column shows the results when we estimate equation (7) with only some basic characteristics of the firm’s owner, including education. The second column includes the variables estimated by vectors Xi and Hi . And the third column displays the results if one estimates the equation with all the variables.

The estimated capital elasticity is about 0.115 (column 3). This elasticity is lower than the capital elasticity (0.150) found using the 1-2-3 survey for seven Sub-Saharan African countries in the paper of Grimm, Knorringa and Lay (2012). The results show that the older the firm’s owner, the better is the firm’s, but at a narrowing rate. Human capital, including secondary educational attainment, vocational training, and reading knowledge, is significantly and positively correlated with the performance of a firm. For instance, some other post-primary educational attainment, such as vocational training, raises the performance of firms by around 41 percent (column 1).

Membership in a business association is another important factor in the success of firms. The availability of a professional network seems to be a key advantage that can help entrepreneurs gain access to resources such as information and credit.<sup>10</sup> However, there is no clear gender effect. The estimated coefficient is positive, but not significant. All the coefficients associated with the factors used as proxies for management skills have the expected signs except the variable for the lack of equipment, which may not be a great problem for those entrepreneurs providing services such as hairdressers or luggage carriers. In any case, building performance-enhancing skills is always central to enhancing productivity and reducing inequality and poverty among informal firms. Entrepreneurs in the informal sector,

> 10 The lack of credit access is not significant (table 11, column 3). Regressions include another financial variable – which is not subject to endogeneity. While we are concerned about the potential reverse causality between access to credit and performance of firms, this paper does not discuss the literature analyzing the link between financial development and economic growth and bi-directional causality.

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