Networks have increasingly been used to insure against covariate shocks as mobile money and higher rates of rural to urban migration have increased the geographic reach of any network (Yang and Choi, 2007; Jack, Ray and Suri, 2013; Blumenstock, Eagle, and Fafchamps, 2016; Meghir et al., 2022; Apeti, 2023). In Kenya, mobile money has strengthened risk-sharing to the point that consumption is fully insured against shocks—because of increasing the number of transfers and the diversity of senders (Jack and Suri, 2014). Similar findings were found for floods in Mozambique (Batista and Vicente, 2023) and for violence in Kenya (Morawczynski and Pickens, 2009). In Tanzania, consumption is fully protected from small village-level rainfall shocks for those who have mobile money (Riley, 2018) and households are protected from falling into poverty and reducing investments in human capital (Abiona and Foureaux Koppensteiner, 2022). As is discussed in more detail in the next section, data on the use of mobile money is not as widespread as access to a bank account. We thus use access to a bank account to indicate whether households have access to financial services to smooth consumption in the face of a shock.

There are dimensions of vulnerability that although very important are hard to measure consistently across countries so are difficult to include in a global measure. For example, race, ethnicity and political exclusions matter, all else equal, in determining the impact of a disaster on a household and how well a household is supported and can cope with the losses. However, defining these for a global measure is challenging even though these could be included in a country level analysis.

# 4.2 DATA USED AND METHOD FOR OVERLAYING

Many of the indicators come from the same data source. We start by describing this data and then discuss the other data sources used.

## The Global Monitoring Database and the Global Subnational Atlas of Poverty

Our measure of income is based on the per capita income or consumption measure collected in national household surveys that forms the basis for poverty measurement. These survey-year income (or consumption) estimates are collated and harmonized across countries and over time to maximize comparability as part of the World Bank’s Poverty and Inequality Platform (PIP). These estimates also go into the Global Monitoring Database (GMD) which contains not only the income or consumption aggregate but other data on household characteristics such as access to improved water, access to electricity, and education of adult household members, all of which we use here.<sup>16</sup> This income or consumption data is further extrapolated or interpolated to a common reference year, in this case 2019, and the share of the population falling beneath the international poverty lines in this year—or any other line—can be determined.

To overlay with the geographic data on exposure, we use subnational data on income (or consumption) and other measures when possible. We obtain this using the latest edition (October 2023 vintage) of the World Bank’s Global Subnational Atlas of Poverty (GSAP) for the reference year 2019 for 1,733 subnational or national areas across 168 economies, accounting for 97 percent of the global population.<sup>17</sup> The subnational areas are typically administrative boundary level 1 areas, corresponding to the highest subnational unit level such as provinces or states, but they can also be a group of regions defined by the specific sampling strategy and representativeness of the household survey. There are 47 economies (24 percent of the global population) for which poverty estimates are only available at the national level due

> 16Refer to World Bank (2023b) for more information on the methodology to calculate poverty rates.

> 17 More information about GSAP is available at Nguyen et al. (2023).

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