With respect to other firm characteristics, the Enterprise Surveys include information on whether a firm adopted a new technology during the past three years. Female-owned firms are 5.8 percent less likely to have adopted a new technology. As mentioned above, the Enterprise Surveys do not contain any owner characteristics other than gender. The other surveys, however, provide the owner’s number of hours worked during the past week and also the owner’s year of schooling. Both are statistically significantly smaller for female owners than male owners in Mexico and Bolivia. Women work about 25 percent fewer hours than men and have 0.75 fewer years of schooling than men in Mexico and 1.8 fewer years of schooling in Bolivia. The differences in hours worked and schooling are smaller in Peru and are not statistically significant, although they are still negative. The Bolivian data also provides information on whether the firm owner has ever participated in any business training. Women are 14 percent less likely to have done so than men. Another variable collected in the Bolivian data is whether the business owner’s mother has any formal education. This variable is a proxy for family background and could shed light on whether female business owners are from more or less disadvantaged families than men. There is, however, no statistically significant difference in mother’s formal education status across female and male business owners.

Overall, the data indicate that female-owned firms are smaller than male-owned firms along all dimensions and that they are less profitable than male-owned firms. Table 3a investigates whether female firms are also less productive than male-owned firms. The table displays the regression results of an OLS regression of sales on the female owner dummy, controlling for the inputs into the production process. The regressions are based on a Cobb-Douglas production function of the form Y = (AL)<sup>1-α-β</sup> H<sup>α</sup> K<sup>β</sup> . Here, L stands for the labor input and is measured by the number of employees and hours worked by the owner. H stands for human capital and is approximated by the owner’s years of schooling. K stands for capital. The regressions control for three different types of capital separately, property, machinery, and material inputs. Note that not all firms in the Bolivian, Mexican, and Peruvian data have property, machinery, or material inputs. I address this in the regressions by replacing the log values of these variables with zero when their non-log value is zero and then adding a dummy for whether the non-log value is zero. A measure of technology, A, is only available in the Enterprise Surveys, which ask the respondents whether the firm adopted a new technology during the past three years. All regressions in Table 3a control for industry fixed effects since the production function is likely to vary across industries.

In the Mexican data, female- and male-owned firms are, on average, statistically significantly less productive than male-owned firms. The size of the difference is on the order of 9 percent. In the Bolivian data and the Enterprise Surveys, this difference is much smaller (1 to 2 percent) and it is not statistically significant. The Peruvian data displays the largest difference in productivity across female-owned and male-owned firms, 15.5 percent. Note, however, that the Peruvian data do not include information on property and machinery values. Machinery value is positively 11