years, as does their lower use of fertilizer in all but the very bottom decile. When examining the results separately by crop type, the effect is somewhat more consistent for export crops.

While lower rates of marriage among female-headed households do not show up as a driver in 2008’s distribution analysis, they work to increase the gender gap in the bottom and top deciles in 2016. In 2008, the lower use of male household labor widens the gap all along the distribution. Lower household male labor still widens the gap in 2016, but only towards the bottom half of the distribution – which is where the gap remains. This effect is particularly strong for food crops: while in 2016 lower levels of male labor are a driver of the gap only for the 20<sup>th</sup> and 30<sup>th</sup> deciles for export crops, they are a driver for the bottom five deciles, plus the 80<sup>th</sup> decile, for food crops.

Another interesting finding related to labor use is that while the higher use of female household labor by female-headed households works to reduce the gap in all but the top two deciles in 2008, it no longer does so in 2016, either for food or export crops. Moreover, only in 2016 do female-headed households seem to benefit from lower levels of education, particularly tertiary education (though the incidence of tertiary education is trivial in rural areas of Côte d’Ivoire, so precise estimation is difficult). This is entirely explained by export crops, where it holds for every decile, while it does not hold for any part of the distribution for food crops.

A key change over the past decade is the emergence of total number of fields managed by the household as a driver: while in 2008 there was no big difference in this variable between female-and male-headed households, in 2016 female-headed households have approximately a half-field less than their male counterparts, and this works to increase the gender gap in every decile of the productivity distribution. This comes almost entirely from food crops: while in export crops it matters only for the bottom decile, for food crops it increases the gender gap in every single decile of the distribution, and substantially so.

Another substantial change occurred for export crop adoption as a driver of the gender gap. Interestingly, whether a household cultivates export crops drove the gap at the lower end (10<sup>th</sup> and 20<sup>th</sup> deciles) and upper end (80<sup>th</sup> and 90<sup>th</sup> deciles) of the distribution in 2008, exhibiting a U-shaped relationship with the gender gap. In 2016, export crop cultivation drives the productivity gap for every decile in the distribution but matters significantly less at the top of the distribution. Top-decile female household heads in 2016 are relatively more likely to cultivate export crops, which partly drives the productivity advantage of womenheaded households at the top of the productivity distribution.

While female-headed households saw no effect, or a positive effect, from returns to land area cultivated in 2008, in 2016 they witness lower returns in every decile of the distribution, despite their smaller land size. This comes entirely from food crop cultivation. Moreover, returns to the household head’s marriage status start mattering a lot more: while in 2008 marriage status is only weakly related to the gender gap, in 2016 female-headed households see lower returns to not being single—i.e., being married, divorced, separated or widowed—in every single decile except for the very top and bottom. Lower returns to marriage are large and matter up to the 60<sup>th</sup> decile for export crops in 2016, while in food crops they matter more for the middle-top of the distribution.

Moreover, while in 2008 female-headed households saw no effect or positive returns to their education levels, in 2016 they see no effect or negative returns, particularly for primary education. This may be connected to the fact that while primary education shows up as having no impact on agricultural productivity overall, female-headed households increased their relative primary education rates over the past decade. Specifically, they moved from the likelihood of having primary education being 50 percent that of a male household head in 2008, to 75 percent in 2016. While returns to pesticide either do not matter 28