particular city or village. Therefore, in this paper we only use aggregate results at the country, country‐ urban and country‐rural level.

We complement our global analysis with two country‐level case studies (Morocco and Malawi) for which small‐scale poverty maps are available (poverty data representative at the local level). In these case studies we only focus on floods. We focus on the within‐country geographical distribution of poverty. Country‐level poverty maps, which represent the percentage of the population living below the USD $1.25/day line (this is in consumption terms, using 2005 purchasing power parity (PPP) exchange rates) are developed using the methodology presented in Elbers et al. (2003), combining household surveys with census data.

# **3.3. Analyzing the relationships between poverty and floods/droughts**

To investigate the global exposure of poor people to floods and droughts, we define a ‘poverty exposure bias’ (PEB) that measures the fraction of poor people exposed, compared to the fraction of all people exposed per country. When estimating the number of people exposed (and not exposed), we multiply the exposed households with their household size and we use household weights to ensure the representativeness of our results. We compute the PEB using: Where _Ip_ is the PEB, _fp_ and _f_ are the fraction of people exposed to floods/droughts in the poorest household quintile within a country and in the entire population, respectively. If _Ip_ is lower than zero, this means that poor people are less exposed to floods/droughts than average. If _Ip_ is above zero, poor people are more exposed than average. The mean values (denoted by the over bar within Eq. 1) of all estimated values for _fp_ and _f_ , based on the geographical randomization process (see Section 3.2), are used in Eq. 1 in the case of floods. In the case of droughts, only a single value is computed because the drought indicator used has a much larger scale (0.5°) than the uncertainty in household location, and therefore this single value is used instead. Since the wealth index is comparable only within and not between countries, the PEB we calculate in this paper is an estimate of whether poor people are more or less exposed compared to the entire population _within_ a specific country. Aggregation of all wealth index data for all countries and computation of a single global PEB is not possible with the data currently available.

# **3.4. Geographical uncertainty and sample size robustness**

From the 100 results with random replacements, we assess the uncertainty of the results due to uncertainty in geographical location as described in Section 3.2. From the 100 varied results, we also establish the standard deviation of _fp_ . If, for a given country, the standard deviation of _fp_ is relatively large with respect to the mean, we treat the country’s results as uncertain.

A further uncertainty assessment is performed for both floods and droughts to test the significance of the results, which ensures that the results for countries having a very low number of exposed households can be labelled insignificant. We test how robust a positive (or negative) PEB is, given the size of exposed people by bootstrapping with 5,000 samples. Bootstrapping is a significance test to 8