# **4.2 Reasons for the Gender Difference**

We two reasons behind the differences in propose potential gender coping strategies. the female-headed households are the femaleFirstly, relatively poorer. Although headed households are not more likely to have unfinished floors, they bear relatively higher childcare burden (Figure 2b, and Table 4, column (3)). The child-adult ratio is about 7.2 percent higher in female-headed compared to male-headed households. In male-headed ones, the average child-adult dependency ratio is 0.47, that is, there are 2.13 adults for every child in the household. However, the female-headed households have an average child-adult dependency ratio of 0.54, and there are only 1.85 adults for every child. In addition, the average livestock value in the male-headed households is about 60 percent higher than in the female-headed ones (Figure 2c, and Table 4, column (4)). Moreover, the female-headed households are less likely to engage in agricultural production or have wage employees to generate income (Table 4, column (5)-(7)). Secondly, female-headed households are more dependent on social networks prior to the COVID-19 outbreak (Figure 2d, and Table 4, column (1)). These two factors might explain why male-headed households are more likely to cope using household internal wealth (since they are relatively richer) but female-headed one rely more on social network (as they are more dependent on network even before the COVID-19 shock).

Table 4: Channels - Gender Differences in Social Network, Poverty, and Source of Income

||Network (0/1)|Dirt Floor (0/1)|Child-Adult Ratio|ln Livestock|Agriculture (0/1)|Wage Employment (0/1)|Family Business (0/1)|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Female Headed Household (0/1)|0.071<sup>_∗∗∗_</sup>|0.006|0.136<sup>_∗∗∗_</sup>|-0.600<sup>_∗∗∗_</sup>|-0.046<sup>_∗_</sup>|-0.064<sup>_∗∗∗_</sup>|0.003|
||(0.017)|(0.025)|(0.027)|(0.160)|(0.024)|(0.020)|(0.015)|
|Household Head Age|0.001|0.004<sup>_∗∗∗_</sup>|-0.004<sup>_∗∗∗_</sup>|0.001|0.005<sup>_∗∗∗_</sup>|0.001<sup>_∗_</sup>|-0.002<sup>_∗∗∗_</sup>|
||(0.001)|(0.001)|(0.001)|(0.003)|(0.001)|(0.001)|(0.000)|
|ln Household Size|-0.051<sup>_∗_</sup>|0.124<sup>_∗∗∗_</sup>|0.827<sup>_∗∗∗_</sup>|0.028|0.208<sup>_∗∗∗_</sup>|0.105<sup>_∗∗∗_</sup>|0.028|
||(0.027)|(0.033)|(0.027)|(0.241)|(0.031)|(0.033)|(0.022)|
|Child-adult Ratio|-0.000|0.040<sup>_∗_</sup>||-0.114|-0.026|-0.012|0.001|
||(0.020)|(0.022)||(0.196)|(0.022)|(0.016)|(0.022)|
|Urban (0/1)|0.002|-0.132<sup>_∗∗∗_</sup>|0.009|-0.100|-0.119<sup>_∗∗∗_</sup>|-0.031<sup>_∗_</sup>|0.006|
||(0.010)|(0.023)|(0.029)|(0.188)|(0.024)|(0.016)|(0.012)|
|County, Wave, Strata FE|✓|✓|✓|✓|✓|✓|✓|
|<br>Observations<br>|31,187|31,481|31,703|6,095|31,703|28,930|28,389|
|_R_<sup>2</sup>|0.058|0.252|0.501|0.161|0.311|0.140|0.050|

This table shows OLS regression estimates using the first five waves from the Rapid Response Phone Survey data in Kenya. All regressions include the set of controls described in Table 1. Robust standard errors clustered within counties are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

When we interact the household head gender dummy with the two hypothetical channels, and include the channel variables into the regressions, we find further 11