Table 2: Gender Differences in Household Consumption

|||E|xpenditures|on ...||# Days Hu|ngry At Night|# Days Ski|pped Meal|# Days No|Meal All Day|
|---|---|---|---|---|---|---|---|---|---|---|---|
||Food|Personal|Durables|Services|Communication|Adult|Child|Adult|Child|Adult|Child|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|Female Headed Household (0/1)|-127.920<sup>_∗∗∗_</sup>|11.330|12.742|-36.903<sup>_∗∗∗_</sup>|-32.174<sup>_∗∗∗_</sup>|0.015|0.027|0.097|0.097<sup>_∗_</sup>|-0.031|-0.022|
||(39.799)|(15.679)|(36.370)|(11.247)|(8.736)|(0.033)|(0.029)|(0.066)|(0.056)|(0.027)|(0.024)|
|Household Head Age|-3.201|-0.427|-0.263|-1.627|-0.828<sup>_∗_</sup>|-0.000|0.000|0.006<sup>_∗∗_</sup>|0.006<sup>_∗∗∗_</sup>|-0.000|-0.001<sup>_∗_</sup>|
||(2.240)|(0.499)|(1.673)|(1.160)|(0.442)|(0.001)|(0.001)|(0.003)|(0.002)|(0.001)|(0.001)|
|ln Household Size|594.504<sup>_∗∗∗_</sup>|141.558<sup>_∗∗∗_</sup>|-5.293|52.107<sup>_∗∗∗_</sup>|44.311<sup>_∗∗∗_</sup>|0.069<sup>_∗_</sup>|0.095<sup>_∗∗∗_</sup>|-0.025|0.200<sup>_∗∗_</sup>|0.033|0.049<sup>_∗∗_</sup>|
||(61.717)|(21.376)|(54.955)|(13.782)|(14.886)|(0.035)|(0.023)|(0.128)|(0.085)|(0.032)|(0.024)|
|Child-adult Ratio|-76.848|-65.192<sup>_∗∗∗_</sup>|-66.381<sup>_∗∗∗_</sup>|-49.983<sup>_∗∗_</sup>|-36.264<sup>_∗∗∗_</sup>|0.065<sup>_∗_</sup>|0.114<sup>_∗∗∗_</sup>|0.382<sup>_∗∗∗_</sup>|0.498<sup>_∗∗∗_</sup>|0.017|0.032|
||(66.662)|(18.201)|(16.521)|(21.458)|(8.186)|(0.034)|(0.031)|(0.077)|(0.076)|(0.032)|(0.027)|
|Urban (0/1)|71.933<sup>_∗_</sup>|0.025|-60.108|-20.670|33.342<sup>_∗∗∗_</sup>|-0.014|-0.014|-0.014|-0.031|-0.010|-0.012|
||(37.714)|(15.620)|(52.100)|(35.968)|(9.677)|(0.035)|(0.018)|(0.078)|(0.052)|(0.021)|(0.015)|
|Dirt Floor (0/1)|-247.111<sup>_∗∗∗_</sup>|-92.461<sup>_∗∗∗_</sup>|-75.471<sup>_∗∗∗_</sup>|-45.094<sup>_∗∗_</sup>|-71.131<sup>_∗∗∗_</sup>|0.152<sup>_∗∗∗_</sup>|0.085<sup>_∗∗∗_</sup>|0.322<sup>_∗∗∗_</sup>|0.236<sup>_∗∗∗_</sup>|0.065<sup>_∗∗_</sup>|0.045<sup>_∗_</sup>|
||(44.332)|(16.593)|(25.237)|(20.850)|(9.668)|(0.035)|(0.025)|(0.069)|(0.061)|(0.030)|(0.025)|
|Network (0/1)|7.281|35.702<sup>_∗_</sup>|-32.669|13.936|-13.611|0.010|-0.003|-0.141<sup>_∗_</sup>|-0.177<sup>_∗∗∗_</sup>|-0.028|-0.046<sup>_∗∗∗_</sup>|
||(51.534)|(18.055)|(35.492)|(10.783)|(8.153)|(0.035)|(0.029)|(0.074)|(0.054)|(0.026)|(0.012)|
|County, Wave, Strata FE|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Observations<br>|30,550|30,545|24,354|30,739|30,788|30,884|30,896|30,892|30,900|30,897|30,901|
|_R_<sup>2</sup>|0.108|0.042|0.018|0.087|0.068|0.099|0.092|0.079|0.151|0.061|0.063|

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 it comes to household actions specific to COVID-19, similar gender differences are observed. Male-headed households have a higher probability of selling assets and female-headed households are more likely to receive help from the social network (Figure A.1a-A.1b). In addition, the regression analysis in Table 3 suggests that female-headed households are less likely to sell assets and take loans, but have higher probabilities of receiving assistance from family members in light of the COVID-19 shocks.

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