(Columns 1 and 2: water depth and the number of days water covered). The number of days evacuated from home significantly decreases school progression (Column 4).

In Columns 5 to 8, we disaggregate the household asset portfolios into four measures: the maximum education in the household (years of schooling), land size, household size and livestock (value). First, except for the number of days evacuated, the flood measures have significant and negative effects on school progression. Second, in these cases, maximum education significantly mitigates the negative impacts. In two cases, we also find significant effects of household size and livestock. Therefore, though the flood had negative impacts on schooling investments in the subsequent six years, households with more asset holdings were better able to mitigate the impacts of flood.

Figure 1 shows the flood impacts on schooling progression (based on the estimates in Columns 5 to 7). We use the sample mean of water depth, the number of days covered by water and repair cost to quantify the impacts. Case 1 shows direct effects (without asset). Though the estimate in repair cost effect is relatively small, the water-depth and day effects show nearly a reduction of 0.6-0.7 year due to the flood. In Case 2, we assume that someone in household has attained 8 years of education. Our estimates suggest that the impact is substantially reduced. Case 3 supposes household size of 10 members to assess how the effect of the number of days covered by water changes. This effect is almost equivalent to the education effect in Case 2. Case 4 shows livestock effect using the effect of repair cost. Using the mean value of livestock, we confirm that the mitigation effect is nearly the same as what was found in Cases 2 and 3. These exercises demonstrate the effectiveness of human capital accumulation: both quality and quantity, and livestock in mitigating the flood impact on child schooling.

# Table 5 and Figure 2 to be inserted

Next, we use the flood exposure measure constructed by the IFPRI team, that is categorized into not exposed, moderately, severely and very severely exposed (Del Nino et al., 2001). The results are summarized in Table 5. Column 1 includes only the flood exposure index, all of which are insignificant. Columns 2 and 3 include interactions with household assets. Consistent with the previous findings, we confirm that the total asset value, and maximum education and household size (severely exposed case) significantly mitigate the adverse impacts of the 1998 flood.

Figure 2 evaluates the impact of very-severe flood exposure on schooling progression (based on the estimates in Column 3). We use the same assumptions for Cases 2 and 3. The direct impact of very-severe exposure is a reduction of 1.5 years. The simulation shows that maximum education of 8 years and household size of 10 members substantially decrease the impact.

# **5.1.2. Ethiopia**

Table 6 summarizes estimation results on grade progression in Ethiopia. In the case of Ethiopia, we investigate the impact of the 2001 drought on child schooling, using 1997 as the initial round.<sup>1415</sup> In all specifications, age and male dummies are

> 14The 1997 round is the fourth round of the full Ethiopian Rural Household Survey Sample (three rounds were fielded between 1994 and 1995, and a fifth round was fielded in 1999). In the analysis of dynamic human capital production, we also use information on child anthropometry from the fourth round (Yamauchi, Yohannes and Quisumbing, 2008).

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