households have sold any asset. However, households with a female head are more likely to receive transfers from family members (Figure 1c). Specifically, female-headed (male-headed) households have a probability of 18.6 (12.7) percent of receiving any transfer or remittance from family. Although both the male-headed and female-headed households tend to reduce meals, there is no significant difference in the probability of reducing meals between these two types of households.

The regression analysis further confirms the differences in coping strategies between female-headed and male-headed households (Table 1). Compared to male-headed households, female-headed households are three percentage points less likely to sell assets or take loans in the past 14 days (columns (2) and (4)). Moreover, they have five (or two) percentage point higher probability of receiving transfers from families (or gifts and assistance from friends), relative to male-headed households (column (6) and (7)).

Table 1: Gender Differences in General Coping Strategies

|||House|hold Internal|||External - Fa|mily/Network|
|---|---|---|---|---|---|---|---|
||Reduce Meal<br>(0/1)|Sell Asset<br>(0/1)|ln<br>Asset|Loan<br>(0/1)|ln<br>Loan|Family<br>Transfer (0/1)|Gift/Assistance<br>(0/1)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Female Headed Household (0/1)|0.021|-0.032<sup>_∗∗∗_</sup>|-0.142|-0.028<sup>_∗∗_</sup>|-0.006|0.036<sup>_∗∗_</sup>|0.009|
||(0.018)|(0.012)|(0.104)|(0.012)|(0.130)|(0.014)|(0.011)|
|Household Head Age|0.002<sup>_∗∗∗_</sup>|-0.000|0.001|-0.002<sup>_∗∗∗_</sup>|0.009|0.002<sup>_∗∗∗_</sup>|0.001<sup>_∗∗_</sup>|
||(0.001)|(0.000)|(0.004)|(0.001)|(0.007)|(0.001)|(0.001)|
|ln Household Size|0.006|0.022|0.200<sup>_∗_</sup>|-0.009|0.197|-0.092<sup>_∗∗∗_</sup>|-0.039<sup>_∗_</sup>|
||(0.028)|(0.015)|(0.116)|(0.020)|(0.178)|(0.020)|(0.023)|
|Child-adult Ratio|0.067<sup>_∗∗∗_</sup>|0.010|-0.215<sup>_∗∗∗_</sup>|0.010|-0.053|0.043<sup>_∗∗∗_</sup>|0.003|
||(0.019)|(0.015)|(0.079)|(0.014)|(0.114)|(0.012)|(0.013)|
|Urban (0/1)|-0.029<sup>_∗∗_</sup>|-0.004|-0.108|-0.013|0.132|-0.002|0.002|
||(0.014)|(0.011)|(0.105)|(0.015)|(0.133)|(0.013)|(0.010)|
|Dirt Floor (0/1)|0.087<sup>_∗∗∗_</sup>|0.008|-0.146|-0.055<sup>_∗∗∗_</sup>|-0.266|0.041<sup>_∗∗∗_</sup>|0.036<sup>_∗_</sup>|
||(0.015)|(0.014)|(0.102)|(0.018)|(0.172)|(0.015)|(0.018)|
|Network (0/1)|0.014|0.023|0.051|0.043<sup>_∗_</sup>|0.132|0.159<sup>_∗∗∗_</sup>|0.131<sup>_∗∗∗_</sup>|
||(0.022)|(0.020)|(0.160)|(0.024)|(0.162)|(0.023)|(0.020)|
|County, Wave, Strata FE|✓|✓|✓|✓|✓|✓|✓|
|Observations<br>|30,967|30,978|2,521|30,978|4,666|30,934|30,968|
|_R_<sup>2</sup>|0.073|0.027|0.273|0.042|0.179|0.078|0.083|

This table shows OLS regression estimates using the first five waves from the Rapid Response Phone Survey data in Kenya. Dependent variables are different coping strategies utilized by the household. These include reducing meals, selling the assets, taking loans, receiving transfers from family members, and getting assistance from friends. In all regressions, the household age, household size, child-adult ratio, the urban dummy, a dummy indicating whether the floor is made of dirt, and county, wave, strata fixed effects are adjusted. Robust standard errors clustered within counties are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

In addition, as shown in Table 2, female-headed households would have smaller expenditures on food consumption and services, and more children in female-headed 7