household survey data.<sup>19</sup> We expect greater access to extension advice to lead to more productive and climate-resilient farming.

Table 4 provides the descriptive statistics for the explanatory variables used in the analysis, for 2013, and then by flood affectedness category for 2015. Of key interest are the potential risk-coping strategies and the social safety nets. First, we note that _ganyu_ labor market participation and earnings increased substantially from 2013 to 2015, but that there is no significant difference by flood-affectedness in 2015. Participation in self-employment also increased significantly between years, but again we see no significant difference between flood-affectedness groups in 2015. We do see that the high-affected were relatively less likely to have a member with formal wage employment. This relationship was the same in 2013, and overall, there is no significant change between years in incidence of wage employment. There are no other significant differences between flood-affectedness groups in 2015 in terms of risk coping strategies.

With respect to the social safety nets, we note that there was an increase in children having access to school feeding over time, but a decrease in households accessing food aid or engaging in the MASAF program. The overall decrease in household access to food aid is somewhat surprising given that food aid was provided extensively in response to floods; nonetheless, households in high affected areas were significantly more likely to receive food aid than those in medium and low affected areas, and those in medium affected areas were significantly more likely to receive food aid than those in low affected areas. Also, if we combine food aid to the household and to the children via school feeding programs, we see that the incidence of receiving any food aid increased significantly from 27.5 to 39.4 percent of households: a 43 percent increase.

# **4. Empirical Strategy**

We have a balanced, two-period panel data set, and three outcomes of interest, namely the logarithmic transformations of real household annual food consumption expenditures per capita and household caloric intake per capita, and the household food consumption score. These outcomes are denoted as _Y_ for household _i_ at time _t (2013, 2015)_ in the following linear regression: ��� = ��� + ����� + ����� + ����� + �(���� ∗����) + ����� + �(���� ∗����) + ����� + ���� + ���� + ���� + �����+ �� �� [�] where **_F_** is a vector of dichotomous variables capturing households’ medium and high flood affectedness status in 2015, as defined above, with low flood affectedness excluded as the reference category; **_RF_** is the percent difference of absolute flowering season rainfall from the long-term mean; **_RC_** and **_SN_** are

> 19 In cases where we had very few FIAS households in a district due to households moving between survey rounds, households were matched to their district from the previous round. There were 25 households that moved to districts that had 5 or fewer surveyed households located in the new district. We ran the regressions dropping these households; results are nearly identical in terms of signs and significance of coefficients, and thus we include the full sample in our analysis. These results are available upon request.

14