maize yields and value of crop production per hectare, and find that it is a strong negative predictor. Below, we will instrument for the value of crop production per capita as a regressor in our consumption equations to examine the link between production and consumption.

Using the three flood-affected categories, we can look at the impacts on food expenditures per capita, caloric intake per capita, and the Food Consumption Score (FCS). The Food Consumption Score (FCS) is a standardized composite score that brings together information on dietary diversity, food frequency, and considers relative nutritional importance of different food groups (WFP, 2008). The indicator was developed by the World Food Programme (WFP) and is calculated using a module specifically designed to capture the required information built into the FIAS and the IHPS questionnaires. The FCS has been shown to relate strongly with detailed measures of household calorie consumption in a study across Burundi, Haiti, and Sri Lanka (Weismann et al., 2009).

For food expenditures, in order to compare expenditures over time, we convert all unit values to 2015 real values, adjusting for the difference in inflation between the household’s interview month in 2013 and 2015 using publicly available NSO consumer price index data for rural households, and differentiating between food and non-food inflation. Figure 2 shows that food expenditures per capita were significantly lower in 2015 versus 2013 for both low- and medium-affected households. The drop in food expenditures for high-affected households, however, is not significant. On the other hand, when we look at the distributions of food expenditure per capita over time, applying the Kolmogorov-Smirnov equality of distributions test shows that all three flood category distributions have shifted significantly to the left from 2013 to 2015.<sup>13</sup> Turning next to calories per capita, Figure 3 illustrates that calories per capita in fact increased over time for both the medium and high affected households, but there was no significant change for low-affected households. There are many reasons why total real expenditures fell but calories increased in 2015 versus 2013. The primary reason is that the real unit values for many food items fell over the period. As noted above, the Malawi economy was suffering high inflation resulting from the currency devaluation in May 2012. As shown in Table 3, the real unit values fell for all categories shown, including for the two most important calorie sources, refined and unrefined maize flour. And, households shifted consumption of maize towards the cheaper unrefined maize flour. Thus, even though calories per capita were higher in 2015, lower real unit values and the shift to cheaper maize flour led to lower overall food expenditures per capita in 2015.

In particular, total calories from the two types of maize flour and other grains rose from just over 1,600 calories per person per day to close to 1,900, a statistically significant increase. Calories from other sources increased for certain food items and decreased for others. The increase in meat, fish & dairy is primarily driven by increases in consumption of fish, which is relatively low in cost compared to other components of the food type.

> 13 The p-values for the Kolmogorov-Smirnov tests are (.114) for the low-affected, (.003) for the medium-affected, and (.029) for the high-affected.

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