Although conceptually vulnerability is undeniably multifaceted, as work on this measure continues it will be important to assess whether the cost of each dimension (in terms of data requirements and the burden that places on coverage and updating) is worth the benefit in terms of identifying a different set of people as vulnerable than would be possible with a narrower set of measures. The limited pairwise correlation across the indicators used in this paper suggests that using several dimensions add value (compared with, e.g., using income as a unique proxy for vulnerability). However, further work will be needed to use data from multiple data sources such as by using survey to survey imputation.

Using the approach proposed and data readily available we generate an estimate for 75 countries and 77 percent of the world’s population. If high vulnerability is defined based on a single dimension, as many as 42 percent of the population are at high risk from extreme weather shocks. This is 70 percent of the population that is exposed. If high vulnerability is defined based on two dimensions or more, then 12 percent of people (20 percent of those exposed) are at high risk from extreme weather shocks.

This work extends and complements previous work. For instance, Rentschler, Salhab, and Jafino (2022) calculates the number of people exposed to floods and in poverty. This paper builds on it by looking at other extreme weather events and assessing how many people are exposed to any one of these events with a similar severity and return period as used in Rentschler, Salhab, and Jafino (2022). It also extends the work by looking not only at those that are currently poor and exposed, but also those are at high risk from the adverse impacts of those shocks. Additionally, the recent IPCC report finds that approximately 3.3 to 3.6 billion people live in contexts that are highly vulnerable to climate change (IPCC, 2022).

There are also a number of global indicators that address the dimensions of climate risk and vulnerability (e.g., ND GAIN (Notre Dame Global Adaptation Initiative), INFORM (Index for Risk Management, UNDP), <u>Global Climate Risk Index (German Watch)). The method proposed in this paper adds to this global</u> indicator work by taking a measurement approach that is much more focused on household-level vulnerability and risk. Unlike other indices which use data aggregated to the national level for each of their dimensions of climate risk and vulnerability, which makes it challenging to aggregate across dimensions, we use the household as our unit of analysis, considering exposure and vulnerability at the household level. We overlay data on exposure to extreme weather events with household data at the subnational level, thereby providing a more granular and spatial assessment of vulnerable populations globally.

Finally, there are modeling efforts that measure global exposure to multiple climate-related or natural hazards, such as UN-ISDR (2015), Pesaresi et al. (2017), Maes et al. (2022). These estimates often focus on asset losses (i.e., the repair or replacement value of damaged or lost assets). Based on estimates of asset losses from UN-ISDR (2015), Hallegatte et al. (2017) estimated the number of people falling in poverty every year due to storms, floods, droughts, and earthquakes, the socioeconomic resilience (as the ability of households to cope with and recover from disaster losses), and the risk to well-being (as the expected loss of welfare due to natural hazards). As highlighted above, this paper follows a different approach by estimating the number of people with high exposure and using proxies to measure vulnerability rather than metrics based on monetary losses. However, the key drivers of vulnerability are similar, focusing on poverty and income, financial inclusion, coverage of social protection, and ability to respond. In as much as people lack these mechanisms their ability to respond to shocks is diminished and hence are considered vulnerable.

There are important caveats to this study and estimates.

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