or drought maps and local household surveys designed to be representative at the local scale. For example, in Mumbai, local‐scale flood data and household surveys revealed that an over‐exposure of poor people to floods can be observed (Hallegatte et al., 2016), but varies at very small scale, from one street to the next (Patankar and Patwardhan, 2014).

# **5. Limitations and recommendations for further research**

Consistent with expectations, there is high variability across countries and poor people are not over‐ exposed to natural hazards everywhere. But the analysis is limited by data availability as the DHS samples are too small to look at regions and within‐country variability. The limited number of households per country has implications for the results for droughts in particular: in many countries, there is no overlap between zones with extreme drought conditions (e.g. a minimum of 3 months drought, at 100 year return period yielded only 15 countries with significant results) and households, meaning that no estimate of the PEB for droughts could be made in these cases. A larger number of observations per country would therefore make the results of our analysis more robust. Furthermore, it would allow a more local scale analysis, as performed for Morocco and Malawi, for all countries.

A related limitation is the spatial scale of the analysis. DHS samples are rarely representative within sub‐national regions, which limits our ability to examine the poverty exposure bias within specific regions of a country. Furthermore, even the sub‐national poverty maps are at the commune or traditional authority level, while higher‐resolution data (e.g. poverty maps within a city) would be able to better capture dynamics at the local level, where lower land prices may push poorer people into more risky areas.

Furthermore, ideally, we would compare our results across countries and not just within them. However, the wealth index calculated by DHS is country‐specific, meaning that the same value for the wealth index across two different countries may imply a different level of wealth. While some authors have recently suggested the DHS wealth index may be compared across countries (Rutstein and Staveteig, 2013), country‐to‐country comparability remains difficult. This is one reason why we use relative thresholds (e.g. quintiles) rather than absolute thresholds. Another reason for which we use relative numbers is that if a standard poverty line is used, in some countries an overwhelming majority of the population would be classified as poor (e.g., 70‐80% in Malawi with a 1.25USD/day poverty line).

The flood hazard maps used in this study only show whether each grid‐cell is flooded (yes or no) for each return period. In reality, the impacts of flooding on people and their assets and productivity are dependent on several other factors. Exposure is only one component of risk: protection (e.g., with dikes or water reservoirs), vulnerability of assets and livelihoods (e.g., quality of housing, savings at financial institutions, and diversification of income), and ability to cope and recover (e.g., access to social protection and credit) are also critical to determine the impact of natural hazards on well‐being.

Households living in high‐quality elevated houses may not suffer much from a shallow flood (e.g., 10 cm depth), while informal housing may already be damaged. The inundation depth has been shown to be the main determinant of damages to assets (e.g. Penning‐Rowsell and Chatterton, 1977; Penning‐Rowsell et al., 1994; Wind et al., 1999; Merz et al., 2010; Meyer et al., 2013). In future studies the relationship between the actual depth occurring and wealth could also be examined. Numerous 20