Another potential explanation is that food aid deliveries kept the market prices of maize flour in check. The WFP Malawi country office provided us with district-level data on food aid deliveries over the period January – July 2015. The simple correlation coefficient between calories of food aid delivered and unrefined maize flower prices in 2015 is significant but fairly low, at -.22. In many districts, households fell into all three flood categories, meaning that the food aid delivery data may be too coarse to adequately capture local market price effects.<sup>14</sup> Finally, despite the increase in calories per capita, the food consumption score decreases significantly across time as seen in Figure 4. Although the reductions are significant across the board, the drop is particularly marked for the high-affected households. The shifts in FCS suggest that although diet quantity is remaining steady or increasing, the shifts in household consumption are coming at a price in terms of diet quality, and thus weakening food security.

# **_3.1.2. Weather and Climate Variables_**

In our regressions, we use the percent difference of absolute flowering season rainfall from the long-term mean, which captures the potential impact of deviations from expected rainfall in both years, 2013 and 2015. We complement this variable with the dichotomous variables for medium and high floodaffectedness, with low affectedness being the omitted category.<sup>15</sup> The regressions also control for a longterm measure of rainfall variability, the coefficient of variation of flowering season rainfall, calculated over the period of 1983 – 2015. It has long been recognized that farmers subject to riskier climates are more likely to grow lower-yielding but more stable crops, use fewer purchased inputs, and invest less in land (Hardaker et al., 2004; Hazell, 1992; Hurley, 2010 and references cited therein). Additionally, McCarthy and Kilic (2015) provide empirical evidence that this long-term measure of risk has a significant negative impact on maize yields in Malawi. Thus, we hypothesize that the coefficient of variation of growing season rainfall will have a negative impact on crop productivity, with subsequent negative impacts on household welfare outcomes of interest.

# **_3.1.3. Household Demographics and Wealth_**

Household demographic controls include the number of adult equivalents; the dependency ratio calculated as the number of household members below 15 years old and over 60 years old divided by the number of members between 15 and 60 years old; the natural logarithm of the age of the household head; and a dichotomous variable capturing whether the household head is female. We expect that larger

> 14  We believe being able to document the impact of food aid deliveries on local prices may show important indirect impacts on consumption, and hope that such data will be made available on a more disaggregated scale in the future. 15 We considered using in our regressions the flood affectedness PCA index itself, in addition to the dichotomous variables for flood affectedness categories. The index did not perform as well as the dichotomous variables, particularly when we include interaction terms with our social safety net variables. As our results highlight, the interaction terms indicate that impacts on consumption outcomes are inconsistent with a linear specification of flood intensity.

11