5 Yet not only are subjectively reported shock modules often not included in standard household surveys, they also raise issues of endogeneity, especially when incorporated in consumption regressions. These can typically only be overcome through the use of panel data, which are rarely available at the national level. The location, time, and household specificity of the subjective shock data also limits generalizations across time and space. In contrast, knowledge of the historical distribution of weather patterns allows the construction of probability distributions of weather events, which is useful to explore patterns of adaptation over time. It also helps in controlling for potential endogeneity of the extreme weather event measures in the absence of panel data. Interpolation of the extreme weather event data across space further permits the construction of disaggregated natural hazard maps, a useful tool in the spatial allocation of interventions and the simulation of the welfare effects of changing climate patterns.

Several global meteorological databases are available. But with the exception of the cyclone databases (UNEP/GRID‐Europe, 2007a,b) they do not have a high resolution typically. This paper exploits access to Vietnam’s detailed historical daily weather station data from 166 stations across the country to generate geographically disaggregated rainfall maps at the 0.1‐degree resolution (corresponding nominally to 11 km at the equator).<sup>4</sup> These data form the basis to derive geo‐ referenced local measures of drought and localized floods/heavy rainfall. To also capture riverine floods, which may occur downstream following heavy rainfall in the mountains or as a result of storm surges in coastal areas, riverine flood indicators will be constructed based on the satellite based flood data from the Dartmouth Flood Observatory (DFO) (2008). The 1980‐2006 geo‐ referenced UNEP/GRID‐Europe storm track dataset is used to separately explore the effects from high winds and gusts from cyclones. As the damage from heavy rainfall associated with cyclones will already be captured by the localized and riverine flood indicators, these measures will capture the wind damage associated with cyclones.<sup>5</sup>

# _Rainfall maps_

To construct spatially disaggregated indicators of drought and excess rainfall, the weather station data must first be spatially interpolated and cut‐offs must be determined beyond which precipitation is considered too little or too much. Spatial interpolation of weather data is a discipline in itself which has developed an array of techniques that go from the most crude and obvious (such as the nearest neighbor techniques<sup>6</sup> ) to the more precise and resource intensive (such as the radial basis functions).<sup>7</sup> Nonetheless, as the complexity of the techniques increases, they also display rapidly decreasing returns in terms of precision given resources spent.<sup>8</sup> As the focus of the paper is

> 4 The frequently used CRU TS2.1 monthly rainfall database (Mitchell and Jones, 2005) only has a half degree resolution, which is around 55 kilometres at the equator.

> 5 See Thomas (2009) for a detailed description of the different data sources and a more elaborate description of the construction of the different natural hazard maps.

6 The nearest neighbour method assigns the value of the weather station nearest to the grid cell centre. 7 Radial basis functions are functions of distance used for exact interpolation of point data. They attempt to overlay a smooth surface on the point data, allowing for various degrees of bending in that surface through a smoothing parameter that is specified by the researcher. Common radial basis functions are thin‐plate spline and multiquadratic functions. See De Smith et al. (2007) for a comprehensive textbook that is reasonably accessible.

8 In a comparison of the performance of different interpolation techniques on rainfall data from May 8 1986 in Switzerland, the latter was found to be the most precise (Dubois, 2003). Yet, as highlighted by Dubois and Shilbi (2003), at 5.93 mm, the root mean square error (RMSE) for the multiquadratic function was only slightly