areas. As discussed more carefully in the next section, the analysis utilizes data collected after actual natural disaster events: the 1998 flood in Bangladesh, and 2001 droughts in Ethiopia and Malawi.

As discussed in the previous section, the use of child schooling to measure disaster impacts may be potentially problematic since disasters may affect not only the marginal utility (due to income reduction) but also the opportunity cost of schooling investment (i.e., a decrease in labor-market wage). The former decreases schooling investment in order to smooth consumption over time, but the latter increases the investment since a decrease in the market wage increases the incentive to allocate more time to schooling. However, many disasters are different from economic recessions. For example, floods can destroy school facilities to disrupt normal school activities. Severe droughts - those analyzed in the Ethiopian and Malawi examples in this paper - cause substantial decreases in crop production, which threatens food security and human survival and therefore increases the real necessity for children to earn incomes for their families.

The focus of this paper is on the role of pre-school human capital in overcoming and recovering from the adverse impacts of disasters. As clarified in the model, recovery from disaster could be quicker if the child embodies large (biological) human capital prior to the disaster. Capturing this dynamic process empirically depends on the exact timing of data collection and disaster occurrence. Although the direct effect of a disaster on schooling (or child growth) is negative during and immediately after the disaster, once the recovery process begins, the experience of such a disaster may hasten the rate of investment in schooling. The empirical question is under what conditions the recovery process begins, and whether the negative impact of disaster persists over time. We use years of grades completed to our measure of child schooling, with child height in the previous period as a key explanatory variable. Controlling for child height (our indicator of investments in biological human capital in early childhood), we look at human-capital growth from pre-school to school periods.

where _hijl_ 2  _t_ 1 is grades completed up to _t_  1 for child _i_ in household _j_ and village _l_ , _D jl_ is disaster indicator (indexes) or its continuous measure such as repair cost,<sup>_h_</sup> _ij_ 1 _l_  _t_ is child height in _t_ , _a kjl_ 0 is pre-disaster asset of type _k_ , _villagel_ is the village fixed effect (this could be a wider geographic unit than village, depending on the empirical context), _agei_ denotes a set of age dummies to control for age-specific grade progression and  _ijl_  _t_ 1<sup>is the error term. In the above notations, we used time</sup> 0 and 1 to denote pre-disaster asset (before _t_ ) and post-disaster public assistance (before _t_  1) respectively.

_k_ We assume that _E_ [  _ijl_  _t_ 1 _a jl_ 0 ]  0 . Pre-disaster assets are also uncorrelated with shocks to schooling investment in _t_  1. Also assume that _E_ [  _ijl_  _t_ 1 _D jl_ ]  0 , implying that the disaster occurred before _t_  1 and actions taken in _t_  1 are conditioned on 8