Econometric analysis

We now test formally for efficiency. We estimate the following equation, derived from the theoretical model: ⁴⁵

(2.12) In (2.12) is the value added of enterprise in household . is a vector of enterprise characteristics (experience and education of the enterprise head). is a dummy variable that indicates the gender of the enterprise head. captures effects of unobservable differences across households (such as household wealth) and is an error term, assumed to be random.⁴⁶ The hypothesis is that the value added in an enterprise led by a woman does not differ from the value added of a

45 Zero and missing values for value added, labour and capital have been excluded in order to avoid a downward bias. However, running the regression with missing values as zeros did not affect the overall findings.

46 In all regressions, we drop influential outliers from our sample (and sub-samples). These outliers are identified by the DFITS-statistic. As suggested by Belsley et al. (1980), we use a cutoff-value | |

√ ⁄ with, k the degrees of freedom (plus 1) and N, the number of observations. By applying this approach to correct for influential outliers our sample is reduced by about three to five percent.

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male headed enterprise in the same household, after controlling for enterprise characteristics ( ).

The results of estimating (2.12) are reported in the first column of in Table 2-3.

For every specification we also report the mean and standard deviation of the dependent variable. The results reported in column 1 show that value added in female headed enterprises are significantly lower (by about 230 Int. USD) than value added in male headed enterprises, even when controlling for all household characteristics using a fixed effects specification. The difference is equivalent to 70 percent of mean value added.

Table 2-3: OLS fixed effects estimates, dependent variable value-added on enterprise level.

(1) (2) (3) (4)

Notes: * p<0.05, ** p<0.01, *** p<0.001. Robust standard errors corrected for clustering at the

‘segment’-level (around 10 observations) in parentheses.

Source: Authors’ computation based on 1-2-3 surveys (Phase 2, 2001/02, AFRISTAT, DIAL, INS).

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However, it could be the case that unobserved characteristics that are correlated with value added are also correlated with sector, and that these differences explain the significance of the gender dummy. If this is the case, and we assume that the households cannot change the sector of their enterprises in the short term, the allocation of resources may not be inefficient, even if the gender dummy is significant, as seen above. Udry (1996) deals with a similar concern regarding differences in productivity between crops, by restricting the sample to households where both men and women farm the same crop, on separate plots. To control for such sectoral effects we estimate a sector fixed effects model. The results are reported in column 2 and show that the gender effect prevails and is of similar magnitude to the results in column 1.⁴⁷ The findings of the first two columns are inconsistent with efficiency and the main hypothesis that arose from our empirical model ( ) is rejected.⁴⁸

In column 3 of Table 2-3 we relax the assumption that capital and labour allocations are purely driven by enterprise characteristics. This is done to test the hypothesis that lower value added in female-led enterprises can be explained by lower allocations of capital and labour.

In column 3 the gender dummy remains significant on a ten percent level but is much smaller in magnitude than in our first two specifications. This shows that some of the variation in value added is explained by lower allocations of labour and capital to female headed enterprises.⁴⁹ However, these variations in input factor allocations do not fully explain the gender bias. Average value added in female headed enterprises is still lower by 80 Int. USD, or 25 percent of mean value added, in the sub-sample used in column 3, which controls for capital and labour allocations.

There are several possible explanations for the persistence of gender bias, even after controlling for capital and labour. It is probable that women look after the children at the same time as working in the informal enterprise (Udry, 1996). The outputs in terms of this ‘household public good’ are not measured. This would mean that time spent by a female entrepreneur creates skills for the future entrepreneurs in addition to the enterprise value added. This could change our conclusion on efficiency. Child labour is reportedly used more frequently in male headed enterprises, but younger children may be looked after by women, reducing the productivity of their labour.

47 The results also hold when we limit our sample to households where both firms operate in the same sector.

The gender dummy remains significant at the ten percent level and explains more than 40 percent of mean profits.

48 Because of the cross-sectional nature of our data set we are unable to control for endogeneity of the input choices.

49 One possibility is that capital allocations are driven by the sector the enterprise operates in. Nonetheless, one can argue that choice of sector in itself reflects a gender effect, as social norms restrict men and women to working only in certain sectors. The descriptive statistics in Table 2-2 lend support to this hypothesis.

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To test the robustness of our results to our assumption that labour of the owner is indistinguishable from labour of the employees we also estimate (12) including capital and labour endowment for a sub-sample of enterprises which do not use external labour. The results are reported in column 4. The gender dummy is significant at the 1 percent level and remains very large in this robustness test. It shows that value added in female headed enterprises is about 60 Int. USD lower than for male headed enterprises. If there were differences in productivity of the labour of the owner and employees one would expect the coefficient on labour to vary between columns 3 and 4. However, the coefficient on labour in our fourth specification changes very little compared to column 3. Also, the other coefficients are relatively stable. The only exception is the coefficient on capital, which is almost zero. This might be caused by measurement error combined with the small sample size. Overall, there is no evidence that our assumption that hired and owner’s labour are indistinguishable is inappropriate.

The gender dummy is significant and negative in all our specifications.⁵⁰ This is inconsistent with efficient allocation of resources. Under efficient allocation of resources, the gender dummy should be equal to zero, as described in our theoretical model. In contrast to this prediction we find that value added in female headed enterprises, after controlling for enterprise and household characteristics including unobservables, is between 25 and 40 percent lower, depending on the specification. This effect is partly caused by the inefficient allocation of capital and labour. It is remarkable that our findings are very much in line with the finding of Udry (1996) who also finds the gender differential to be over thirty percent in a rural setting.