services, access to early warning systems, sanitation, and building and infrastructure quality are also playing a key role in determining disasters’ impacts, and has been included in other estimates, but is left for future inclusion here.

# Inability to cope with losses

The first dimension of inability to cope is not having income to manage the impact of shocks. The aim of this measure is to identify individuals that have incomes that are too low to be able to meet basic needs should a shock to incomes occur. The motivation for this measure comes from work that has set vulnerability lines for identifying those at risk to falling into poverty. Many of the early estimates on measuring vulnerability to poverty utilized panel data that had repeated observations of welfare outcomes on the same individual and considered households to be vulnerable if the probability that their consumption was below a poverty line was high (Hoddinott and Quisumbing, 2010). In an extension of this work, vulnerability lines were defined—the level of consumption or income that an individual would need in order to have a low probability of falling into poverty (López-Calva and Ortiz-Juarez, 2014). LópezCalva and Ortiz-Juarez (2014) proposed using a 10% probability of becoming poor to identify those vulnerable to poverty. Using this approach they calculated that the vulnerability threshold for Chile, Mexico, and Peru was about 2.5 times the poverty line. Similar analysis in Indonesia found that the vulnerability threshold is 1.5 times the poverty line (Jellema et al., 2017; World Bank, 2019). This work is informative, but it is important to note that this approach takes into account both idiosyncratic and covariate shocks, whereas here we focus only on one type of covariate shock, extreme weather events.

This approach—of setting a vulnerability line at some multiple of the poverty line—has been used in World Bank regional reports and poverty assessments in East Asia and the Pacific (EAP), Europe and Central Asia (ECA), Latin America and the Caribbean (LAC), Uganda, Tanzania, Viet Nam, Mongolia, Cambodia, Brazil, Türkiye and others. The multiples used range from 1.25 to 2.5 times the poverty line.<sup>15</sup> In the context of the United States, several studies have considered the threshold between 1.25 and 2 times the Federal Poverty Guidelines to identify those who are poor or near poor including Montgomery et al. (1996), Heggeness and Hokayem (2013), Hair et al. (2015), Saczewska-Piotrowska (2016), Dube (2019). The appropriate multiple will depend on the context, but most studies fall within the 1.5-2 range. In this paper we use 1.5 times the poverty line. As the next section indicates for many countries this is estimated using data on consumption rather than income, as this is the aggregate available.

The second dimension of inability to cope is educational attainment. Research has consistently shown that households with higher level of education have better understanding and ability to process risk information such as weather forecasts and early warnings (Mileti and Sorensen, 1990; Hoffmann and Muttarak, 2017). More educated individuals are also likely to assess and respond to risks more effectively, thereby being better prepared to cope with natural disasters and weather shocks (Helgeson, Dietz, and Hochrainer-Stigler, 2013; Muttarak and Pothisiri, 2013; Muttarak and Lutz, 2014). Moreover, there is evidence suggesting that even the completion of primary education can enhance the ability to cope with a shock. In the Ugandan context, households with a head who has attained some primary education experience a 2.8% reduction in the negative effect of rainfall on crop income compared to those without

> 15 Recent poverty assessment reports use a range of multiples of the poverty line to define vulnerability to poverty: 1.25 for Cambodia (Karamba et al. 2022), 1.5 for Indonesia and Myanmar (Pape and Ali 2023; World Bank 2022d), 1.7 for Viet Nam (World Bank 2022b), 1.75 for EAP (Ruggeri Laderchi et al., 2017), and Turkiye (World Bank 2018, World Bank 2023),  twice for Brazil, ECA, Honduras, Peru, and Uganda (World Bank 2022a; Bussolo et al. 2018; Robayo-Abril et al. 2023; Word Bank 2023a; World Bank 2016), and almost 2.5 for LAC (World Bank 2021a).

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