round could include some of immediate adverse impacts of the flood. Overall, the Bangladesh setting provides both long-term and short-term dimensions.

The 2004 surveys conducted in the three countries have retrospective information on past disasters. This is useful for knowing the probability of disasters. The probability is defined as the empirical average of incidences in the period from the initial to the last round. Therefore, it is the probability of disasters in the future from the standpoint at the initial round. This preliminary work showed that Ethiopia and Malawi experienced several droughts between the initial and follow-up rounds. In Bangladesh, however, the 1998 flood was the single and most devastating incident for many households in our sample.<sup>11</sup> We have the following distributions for the three countries (Table 1).<sup>12</sup> Table1 to be inserted In Bangladesh, we also use the flood exposure index that measures the severity of the flood (Del Ninno, et al. 2001). In this measure, households were classified into categories depending on flood exposure: no exposure, moderately exposed, severely exposed and very-severely exposed. Given that the 1998 flood was the single and most severe disaster experienced by many households in the sample, it is appropriate to use this exposure measure, rather than the frequency. In addition, the Bangladesh data provides some details of the flood impacts such as the depth of water, the number of days covered by water, repair cost and the number of days evacuated from home. The former two measures are objective, but the latter two could be endogenous. Repair cost is an actual expenditure, so this involves household decisions and also depends on their asset holdings. The number of days evacuated is correlated with number of days submerged, but it also measures the duration of staying safely away from the disaster, so it is higher among those who had resources to keep them away from the flood (e.g., evacuating to other regions). Though these measures principally capture the disaster impacts, we may need to be careful in interpreting the results.

# **5. Empirical Results**

In this section we summarize empirical results on (i) disaster impacts on schooling progression, (ii) ex-ante actions and ex-post public responses, and (iii) pre-disaster asset allocation (ex-ante actions) and disaster risks. In the following analyses, we use the sample of children aged 6 to 12 in the initial rounds.

> 11Floods are a normal part of the agricultural cycle in Bangladesh. However, the 1998 floods were exceptional both for their severity and duration. Unlike normal floods, which cover large parts of the country for several days or weeks during July and August, the 1998 floods lastes until mid-September in many areas, covering more than two-thirds of the coutry, causing over 2 million metric tons of rice crop losses (equal to 10.45 percent of target production in 1998/99) (del Nino et al. 2001).

> 12Alternatively we can use historical meteorological data to construct some measures of too-little and too-much rain. In this way, however, we must define drought and floods with some thresholds in rainfall amount. The proposed method using actual drought (or flood) incidences between the intial and final rounds has an advantage that households were not able to know the future disaster incidences at the time of the initial round. Though historical data reduce the noise in the frequency estimates, both actual incidences and the disaster probability are in the agent’s information set. However, our estimates should have relatively large measurement errors.

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