positively to the recovery process after the flood.

# **5.2.2. Ethiopia**

Tables 7 and 8 summarize estimation results for Ethiopia. In our preliminary analysis, we have found that although the 2001 drought has the most widespread exposure, the country had also experienced many other, smaller-scale, droughts. Therefore we assess the impacts of both the 2001 drought and drought frequency on child schooling. Therefore, not only the 2001 drought indicator, but also the average likelihood (frequency) of droughts in the period 1999 to 2004 are used in the analysis.

# Table 7 to be inserted

Column 1 in Table 7 shows that the height for age z score has a significant and positive effect on schooling. In Column 2, the estimation controls for household fixed effects, which confirms that the above finding remains robust even despite upward bias in the estimate. Column 3 adds the 2001 drought indicator, which shows that the drought had a negative impact on child schooling (marginally significant).

In Columns 4 and 5, we include interaction terms of drought(s) and the height z score. First, the 2001 drought indicator as well as the drought likelihood (two of three) have significant and negative effects on grades completed. Second, it seems likely that healthier children (i.e. taller) had experienced larger negative impacts from droughts than less healthy children. This result is contrary to those found in Bangladesh probably due to the difference in timeframe. This result implies that the inequality of human capital among children has decreased with the frequent exposure to droughts.

Columns 6 and 7 split the sample based on the ranges of the height z score. To maintain reasonable numbers of children in both estimations, the ranges overlap. The results show that the negative impacts of droughts are larger among children embodying less biological human capital in early childhood. This is consistent with our hypothesis (also confirmed in the Bangladesh example) that human capital accumulation prior to disasters increases resilience to the adverse effects of disasters.

# Table 8 to be inserted

Table 8 reports results on asset effects. As in Bangladesh, we used total asset value, maximum education (years of schooling) in the household, land size, household size and livestock value. Column 1 shows the total asset effect with the 2001 drought indicator. Interestingly, the negative impact of the drought is magnified by asset level. Wealthier households (as measured by the total asset value) experienced larger negative drought impacts on child schooling than poorer households. Again, the height effect remains robust with the drought indicator. Column 2 similarly shows that land size magnifies the negative impact of drought on child schooling.

Columns 3 and 4 use the empirical frequency of droughts, interacted with household assets. At the low levels of drought frequency, direct effects of the droughts are significantly negative. However, this negative effect is significantly magnified by asset level, especially land size, if droughts are very frequent in the period of 1999-2004. However, holding livestock and a larger household size seem to help mitigate the 14