to limited data access, outdated survey, or the size of the country unsuitable for further subnational disaggregation.

We use the national and subnational boundaries in GSAP to overlay shapefile boundaries for each region with the gridded exposure data described above. We then take the sum of the population exposed within the defined boundaries to generate an estimate of the number exposed within the administrative unit. For partially covered grid cells, the population is weighted by the fraction of the 1km grid cell covered by the region. The total number exposed is multiplied by the share of people whose per capita income or consumption falls below a given poverty line in that administrative unit to determine the number of people who are both exposed and poor at a given poverty line in that unit. This is then summed by country, region and globally to provide country, regional and global numbers on exposed and poor. Implicitly, we assume a uniform rate of poverty within each GSAP subnational administrative level. The impact of this assumption is ambiguous since poorer households may be disproportionately located in exposed locations within subnational areas in some cases, (rural drought) but not in others (urban flooding). The direction and size of this bias is something that needs to be tested in future work as discussed further in the final section of the paper.

This data is used for the following measures: • **Income** : this is calculated as the share of households that have income or consumption less than 1.5 times the poverty line of $2.15 (2017 PPP) for each administrative unit in the GSAP. The data is available for the common reference year of 2019 and is available for 168 countries. At this level of income and consumption there is very little difference between income and consumption (when assessed using countries with data on both, see Wollburg et al 2023) and for the rest of this paper we refer to this measure as income regardless of whether the data used to estimate this comes from an income or consumption aggregate.

• **Education** to switch livelihoods or to access information and resources: this is proxied by a variable reflecting whether the household has an adult that has completed primary education. This is a very low level of education that is likely mostly relevant in lower income countries. The GMD is used because this allows us to have information on education and income for the same household which allows us to know whether an individual is deprived on one or both dimensions. Unlike poverty rates which have been extrapolated or interpolated to a common reference year, the estimates for education come from the year of the survey. In order to avoid using data that is too old, when the survey is outside of the range of +/- 5 years from the reference year we would ideally not use the survey. In practice, for 2019, this means we are using the range 2015-present. When we do this, data is only available for 125 of the 168 countries in the GMD and it is not available for either India or China, two very large countries which are important for global coverage. We present results including and excluding data that falls outside of this year range. In future work we will explore mechanisms to update educational attainment using demographic trends, or other surveys that provide information on education within the country and can be used to impute educational attainment in the GMD.

• **Water** : we use whether a household has access to at least limited standard water, as in the SDG indicator. This is available for 139 countries of 168 for all years and 120 countries for the period 2015present. There are some countries that are missing data on water access but that have achieved universal access to at least a limited standard of water as per the latest data available in World Development Indicators.<sup>18</sup> For these countries we assume no household is vulnerable on the water dimension. This

> 18 Here we define universal coverage as above 98 percent of the population having access.

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