||**Log**<br>|**[Self-reported**<br>|**Productivity (XO**<br>|**F/ha)]**<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||All crops|All crops|Export crops|Food crops|
|Dependency ratio|0.009|0.007|-0.010|0.034|
||(0.025)|(0.024)|(0.027)|(0.030)|
|Household Male Labor Use (Number)|0.033*|0.032|0.019|0.057***|
||(0.020)|(0.020)|(0.017)|(0.022)|
|Household Female Labor Use (Number)|-0.016|-0.005|0.003|-0.014|
||(0.018)|(0.018)|(0.019)|(0.022)|
|Household Child Labor Use (Number)|-0.048|-0.039|-0.103|-0.052|
||(0.069)|(0.069)|(0.071)|(0.078)|
|Hired labor use (Number)|0.022***|0.022***|0.011**|0.025***|
||(0.004)|(0.004)|(0.005)|(0.005)|
|Number of Fields Managed by the household|0.111***<br>(0.021)|0.104***<br>(0.021)|0.032<br>(0.023)|0.199***<br>(0.029)|
|Total number of crops produced|0.007|0.011|0.070***|-0.064**|
||(0.021)|(0.022)|(0.023)|(0.031)|
|Household used any purchased seed|-0.078|-0.058|-0.044|-0.005|
||(0.052)|(0.052)|(0.053)|(0.064)|
|Plots that Use Chemical Fertilizer (% of total)|0.255***<br>(0.081)|0.264***<br>(0.080)|0.333***<br>(0.081)|0.233**<br>(0.116)|
|Plots that Use Pesticide (% of total)|0.348***|0.357***|0.240***|0.192**|
||(0.067)|(0.066)|(0.069)|(0.086)|
|Export crops dummy (1 if Household||0.474***|||
|produced any export crop)||(0.072)|||
|<br>Constant|11.043***|<br>10.874***|11.763***|10.579***|
||(0.466)|(0.438)|(0.401)|(0.481)|
|Adjusted R-squared|<br>0.132|<br>0.148|<br>0.122|<br>0.179|
|**% gap**|**21%**|**12%**|**6%**|**19%**|
|Observations|3936|3936|2823|2749|

_Note: ***/**/* indicate statistical significance at the 1/5/10 percent level respectively._ To isolate the relative importance of the different factors in explaining the gender gap, we run an OaxacaBlinder decomposition analysis (Table 9). As already observed in Table 7, the overall unconditional gap is statistically significant, while the unconditional export crop productivity gap and food crop productivity gap taken separately are not. The explained component (endowment effect) of the gender gap is both significant for the overall gap and for food crops, working to close the gap for women.

Within the explained portion, we see that the factor that contributes the most to the endowment effect is the total land area cultivated by the household. This variable contributes negatively to the endowment effect, thus reducing the gender gap; again, female-headed households cultivate smaller plots and there is an inverse relationship between land area and productivity. Interestingly, the lower rates of secondary and tertiary education among female-headed households work to reduce the gap for them, since these lower rates are positively related to productivity.

Again, given the additive linearity property of the decomposition, it is possible to determine the contribution of each component towards the gender gap. The key factors that increase the gap in agricultural productivity between male- and female-headed households are the latter’s lower adoption of export crops (91% of overall gap), lower use of pesticide (34% of gap) and lower use of chemical fertilizer (13% of gap). A lower number of fields managed by the household also emerges as a key driver for the gap (24% of overall gap), 19