With respect to the structure effect, since coefficients cannot be interpreted causally, it is not possible to trace back the source of gender differences in this case. Still, the detailed decomposition gives us valuable information since it identifies the factors to which the productivity generating function is more sensitive. As explained in section 4, the structure effect reflects differences in the returns to observable and unobservable characteristics between the female and male structural form equations. Therefore, differences in changes of the productivity generating function as a result of marginal changes to the covariates will be captured here.

The main factors that explain structural differences include years of schooling, access to extension services, number of crops produced, use of non-labor inputs (mainly fertilizers), and land characteristics (mainly distance to household). The components related to female structural disadvantages include:<sup>34</sup> (i) access to extension services, (ii) manager's hours per week for agricultural activities, (iii) land size, (iv) number of fields managed by the manager, (v) land certification, (vi) fields' distance to the household, (vii) fertilizer access, and (viii) oxen availability (per hectare). Covariates related to male structural advantage are:<sup>35</sup> (i) access to extension services, (ii) land certification, (iii) fertilizer access, and (iv) oxen availability (per hectare). Also, the following variables can be related to female structural advantages or male structural disadvantages:<sup>36</sup> (i) years of schooling, (ii) number of crops produced (measure of diversification), (iii) irrigation access, (iv) organic fertilizer availability, (v) pesticide, herbicide, or fungicide use, (vi) chemical fertilizer employed (per hectare), and (vii) household child labor use. Most of these female advantages (or male disadvantages) could be explained by decreasing returns to inputs and lower quantities provided for female managers. This would be interesting to analyze in a withinhousehold Pareto-efficiency framework as in Udry (1996) and Akresh (2005), nonetheless, data limitations from the ERSS survey prevent this.

# **5.3 Female subgroups**

Traditional gender roles generally link females to "home economics" production which involves activities like horticultural production and raising poultry (World Bank and IFPRI 2010). Extension services targeted to females are even designed ad-hoc according to their common roles in production. This implies that when comparing female to male managers it is relevant to distinguish between different roles that female managers might be representing within the household. In particular, female managers embody very different levels of responsibility as spouses or household heads. Typical gender studies only compare male to female household-headed units (World Bank 2011). Since the ERSS survey specifically asks for the manager at the plot level, the data set allows analyzing productivity differences for different female subgroups.

Table 5 shows the decomposition results by distinguishing between married and not-married female managers (columns 1 and 3). Furthermore, the not-married group is subdivided in single, divorced and widowed females (columns 2 and 4). As evidenced in the results, not-married female managers are 30.2 percent less productive than male managers. Most of this difference results from the structural effect since the difference after controlling for covariates is only reduced to 23.9 percent. Contrastingly, married female managers are non-significantly less productive than male managers (3.9 percent) and after controlling for covariates, the difference favors female managers by 15.8 percent (not statistically significant also). Columns 2 and 4 show that single and widowed females

> 34 This includes the positive and significant coefficients of Table 4, panel C, column 3.

> 35 This includes the positive and significant coefficients of Table 4, panel C, column 2.

> 36 This includes the negative and significant coefficients of Table 4, panel C, columns 2 and 3.

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