Informel, which was collected between May and August 2016 (ENSESI 2016). Both surveys include detailed agricultural modules, with production and input data at the plot-crop level, allowing for comparability across the two years.

The third contribution of our paper is the decomposition of gender gaps along the productivity distribution, offering insights into the drivers of the gender gap for both poorer and richer farmers. We first use the traditional Oaxaca-Blinder technique employed in the gender wage gap literature, based on the gender of the household head, to estimate: (i) the proportion of the gender productivity gap that results from differential characteristics of household heads, land, and unequal access to productive inputs (endowment effect); and (ii) the proportion that results from unequal returns to the previous components (structure effect) (Oaxaca 1973; Blinder 1973). This method allows us to identify the main drivers that explain the gender differences in productivity, and changes in these drivers between 2008 and 2016. We then use Recentered Influence Function (RIF) regressions proposed by Firpo et al. (2009) to identify how the previous results change along the productivity distribution.

A large and significant unconditional gender difference in productivity of 21 percent favoring male-headed households is found in 2008. The gap increases to 31 percent once introducing region fixed effects and controlling for household and farm characteristics. It is attenuated – dropping by 4 percentage points – when we control for whether any export crops are farmed by the household. When the analysis is conducted separately for food and export crops, the productivity gap within food crops is over twice as large as that within export crops (40 vs. 17 percent) suggesting that the gender gap in agricultural productivity is closely related to occupational segregation between crop types.

Between 2008 and 2016, we note a large reduction in the gender gap between female- and male-headed households. The unconditional gender gap drops to 18 percent in 2016, while the conditional gender gap drops to 21 percent: a 32 percent reduction over 8 years. Further, when we control for whether the household farms any export crops, the gender gap becomes statistically insignificant in 2016. Thus, farming of export crops remains a very strong determinant of agricultural productivity: in 2016 the gender gap within food crops is significant at 19 percent, while that within export crops is no longer significant. These gaps must be interpreted as lower bounds of agricultural productivity gender gaps: the 2008 data provides information on the plot manager’s gender as well on the plot owner’s gender, and we are able to show that estimated gender gaps are even larger when using either of these variables – rather than household headship – to differentiate between male and female productivity.<sup>1</sup> The Oaxaca-Blinder decomposition shows that in 2008, the single largest driver of female-headed households’ lower agricultural productivity was the low number of household male laborers, closely followed by their lower adoption of export crops, and then lower use of non-labor inputs (pesticides and chemical fertilizer). The low number of household male laborers disadvantaged poorer female-headed households most, while for chemical fertilizer it is the converse. The poorest female-headed households also faced the lowest returns on factors of production. Overall, gender gaps are observed all along the productivity distribution in 2008; apart from being slightly larger in the bottom half of the productivity redistribution, they are remarkably stable. A second key finding is that while the lower use of non-labor inputs matters more for export crop productivity (chemical fertilizer does not matter at all for food crops, while pesticide use matters less), the lower use of household male labor matters more for food crops. Moreover, while higher female labor use helped to close the gap in food crops between 2008 and 2016, it did not matter for export crop productivity.

> 1 See Table A.1a - A1b for plot manager and A.2a – A.2b for plot owner in the Appendix.

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