Regarding returns to endowments (the structure effect), we see that the female-headed households’ advantage related to their higher returns from education is mainly driven by higher-productivity households. Higher returns from lower land area cultivated only help close the gap for the highest- and lowestproductivity deciles. Lower returns to hired labor use and to chemical fertilizer contribute to the productivity disadvantage of female-headed households in top productivity deciles.

A key result of interest is that export crops drive more than half of the productivity gap for the highest- and lowest-productivity households.

In the Appendix, we provide Tables of the RIF results separately for export (Table A.3) and food crops (Table A.4). A notable result specific to export crop productivity is that gender gaps are most strongly driven by the middle of the distribution, with no significant gender gap in productivity at the bottom. Also, lower tertiary education increases the gender gap for the upper half of the distribution but is not significantly correlated at all with food crop productivity. While labor use almost entirely disappears as a driver of the gap (except for the lower returns to female household labor prevalent in the middle of the distribution), lower use of fertilizer and especially pesticide among female-headed households remains a key driver across the distribution.

For food crop productivity, on the other hand, household labor use has a strong and significant association: lack of male labor increases the gap in every productivity decile, while a higher number of female workers helps shrink the gap for the lower half of the productivity distribution. Moreover, the lower number of fields managed by female-headed households increases the gender gap for the lower end of the distribution. Lower use of chemical fertilizer only significantly increases the gap for mid-range productivity households.

# **3.2 Determinants of the gender gap in 2016**

In this section, we analyze results for agricultural productivity eight years later, in 2016, in order to determine how the gender gap and its drivers have evolved. The results from the simple OLS regression displayed in Table 7 below show that the gender gap is statistically significant with and without region fixed effects (columns 1 and 4). Similar to 2008, the export crop productivity gap is not significant in the fixed effect specification. The food crop productivity gap is only significant in the fixed effect specification, and drops by 10 percentage points over the period.

**Table 7. OLS regressions of agricultural productivity without control variables (2016)**

|||**Log [S**<br>|**elf-reported Pro**<br>|**ductivity (XO**<br>|**F/ha)]**<br>||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||All crops|Export crops|Food crops|All crops|Export crops|Food crops|
|Female-headed|-0.197***|-0.119|-0.129|-0.163**|-0.016|-0.155*|
|households|(0.077)|(0.091)|(0.087)|(0.077)|(0.093)|(0.080)|
|Constant|11.697***|11.772***|11.633***|11.510***|11.884***|10.879***|
||(0.025)|(0.024)|(0.033)|(0.418)|(0.398)|(0.442)|
|Region fixed effects|No|No|No|Yes|Yes|Yes|
|Adjusted R-squared|0.003|0.001|0.001|0.063|0.068|0.116|
|**%gap**|**18%**|**11%**|**12%**|**15%**|**2%**|**14%**|
|Observations|3936|2823|2749|3936|2823|2749|

_Note: ***/**/* indicate statistical significance at the 1/5/10 percent level respectively._ 17