# Table 4 to be inserted

Table 4 uses alternative measure of flood exposure, constructed by the IFPRI research team (Del Nino et al., 2001). The results are consistent with those in Table 2. First, in Column 1, the height for age z score has a significant and positive effect on schooling at a later stage. Having been severely exposed to flood significantly increased schooling within the same union. Second, in Column 2, healthy children (measured by the height for age z score) achieve more schooling if they had experienced severe exposures to the flood. We do not observe a significant direct effect of the height for age z score, but this was significantly influencing schooling through the flood severity. In Columns 3 to 5, we check robustness of these findings by altering the range of the height z score for estimation. To maintain a reasonable number of observations in each estimation, the ranges overlap in these exercises. It shows that for children with relatively large height for age z scores, we find positive effects of the 1998 flood and interaction terms with the height for age z score. That is, healthy children experience a faster recovery from the disaster in the subsequent 6 years.

# Tables 5 and 6 to be inserteed

In Tables 5 and 6, we investigate how pre-disaster assets affect the flood impacts on schooling, together with the early-stage human capital. We use total asset value, maximum education (years of schooling) in the household, land size, household size, and livestock value to represent asset allocation (holding) before the disaster. The maximum education measure covers children as well, so we include older siblings who may affect income smoothing at the household level. First, Column 1 in Table 5 confirms the previous result that the flood severity increases the subsequent investment in child schooling-that is, households who are most severely affected by the flood invest more in child schooling. In addition, the more assets they hold, the larger the positive impact of the flood on schooling investments. This suggests that households with more assets are better able to play ”catch up” with respect to human capital investments. In Column 2, we use more disaggregated measures of household assets. Interestingly, investments in human capital after the flood are higher for households with higher maximum education. However, with the inclusion of these asset variables, the height effects and direct effects of the flood become insignificant.

In Table 6, we use water depth, the number of days submerged, repair cost and the number of days evacuated from home to measure the 1998 flood exposure. The results are qualitatively similar. First, the height effect is robust to various measures of flood exposure in all estimations. Second, total asset value increases the catch up effect on human capital investment after the flood in the specification with the number of days covered by water Third, and more dramatically, the maximum education within the household significantly increases the positive impact of the flood in Columns 5 to 8.

These results imply substitution between the early-stage child human capital and the household asset holding in the sense that households may choose one of the two to mitigate (increase) the flood impact on schooling in a dynamic context. In the Bangladesh example, our results showed a post-disaster recovery process in the 6-year period, with asset holdings and human capital within the household contributing 13