7 relative importance of both indices.<sup>13</sup> The regression explained 76.8 percent of the observed variation in the monthly rainfall records from these stations (R‐squared = 0.768), which provides some confidence in the predictive power of the method.<sup>14</sup> The IEW index carries substantially more weight in predicting rainfall during the wetter months (April‐November), while the IDW index is also important in predicting rainfall during the dry months (December‐March). An estimate of monthly rainfall for each grid cell was obtained by applying the estimated coefficients in Table A1 to their respective IEW and IDW indices. By way of illustration, Figure 1 presents the interpolated rainfall map for June 2006, both based on the regression prediction method and the IDW and IEW indices separately.

# _Drought maps_

While Vietnam does receive large amounts of rainfall on average, moderate and severe droughts occur across the country, albeit with diverging frequency. They are more common and more severe in the Northeastern part of the country and the Red River Delta, but have also been reported in the Mekong Delta in the South, despite widespread irrigation.<sup>15</sup> In keeping with the study’s focus on exogenous measures of a natural hazard, the focus here is on meteorological drought, defined as rainfall deficits over extended periods of time.<sup>16</sup> Unlike agricultural drought indices, meteorological ones also allow exploration of the (indirect) effects of droughts on urban households for example through water shortages and electricity outages.

A multitude of meteorological drought indices has been proposed in the literature (see Keyantash and Dracup (2002) for a review). Following Dilley et al (2005), the authors of the World Bank study “ _Natural Disaster Hotspots: A Global Risk Analysis_ ”, the cumulative precipitation anomaly index is taken. This is the deviation in precipitation from a long‐term mean or median for a specific period of time, usually expressed as the proportion of the long term mean or median. It is straightforward to calculate and does not require other meteorological input (such as temperature data) and thus also no additional spatial interpolation. It is also flexible. It does not impose a distributional structure on the rainfall data, like the widely used Standardized Precipitation Index (SPI) (McKee et al., 1993). Furthermore, unlike the rainfall decile index, which ranks the historically observed rainfall in each location and considers rainfall episodes in the lowest decile(s) as droughts, the cumulative precipitation anomaly index also allows the likelihood of a drought in a particular area to vary across space. The latter is important to explore potential adaptation to frequent drought exposure, as will be seen later on.

13 By taking log transformations, negative predictions are avoided and estimation issues related to the skewness of the rainfall data are mitigated.

14 A similar degree of precision was registered in a series of modified specifications. Using a polynomial regression of the IEW and IDW indices reduces the root mean square error by 0.01 (from 0.764 in the current specification). Similarly, application of the interpolation on the log or squared root transformed values reduces the RMSE only by 0.03. Inclusion of time varying month dummies yields virtually no improvement, while inclusion of regional dummies reduces the RMSE by 0.05.

15 The administrative regions of Vietnam are presented in Figure 0.

16 Other disciplinary conceptualizations of drought include hydrological, agricultural and socio‐economic drought, each accounting for the effect of rainfall deficits on their respective domain of interest. Hydrological droughts relate to shortages of water in river systems which can be due to meteorological droughts in subsets of the river basins but also to land use changes. Agricultural droughts consider the amount of water available in relation to crop growth. Socio‐economic droughts occur when meteorological, hydrological, or agricultural droughts cause economic distress.