of malnutrition are more prone to experience health implications due to disasters. Similarly, households with lower income or savings often lack the ability to smooth consumption shocks, rebuild their houses, keep their children in school or buy alternative sources of water (Hallegatte et al., 2020). For instance, across households affected from Hurricane Mitch in Honduras (1998), poorer households lost a greater percentage of the productive wealth (31%) compared to wealthier households (8%), and took longer to recover from this shock (Carter et al., 2007). After the Mumbai floods of 2005, poor households experienced a relative income loss that was 2.5 times larger than non-poor households (Patankar and Patwardhan, 2016), an observation similar to those made in a number of studies in Bangladesh (Brouwer et al., 2007; Akter and Mallick, 2013; Rabbani et al., 2013) and Pakistan (Kurosaki et al., 2012). Households in Indonesia that experienced an earthquake between 2002 and 2007 were almost 4% more likely to fall into poverty (Dartanto, 2022).

Hence, those already living in poverty are more likely to face disproportionate impacts of disasters, while those living close to the poverty line might be pushed temporarily or chronically into poverty (Hallegatte et al., 2020). However, while it is generally accepted that the poor and most vulnerable experience disproportionate welfare impacts, what the driving factor of this is remains inconclusive or context-specific. More specifically, disparities in impacts across socioeconomic groups could be attributed to the fact that poor households live in areas that are more hazard-prone ( _e.g._ exposure bias), poor households experience larger relative losses ( _e.g._ lower asset quality, undiversified asset base), or a lack the capacity to cope with the shock ( _e.g._ financial resources).

# **2.2 Quantifying the welfare impacts of disasters**

As discussed in the previous section, there is a plethora of _ex-post_ analysis of the impacts of natural disasters on human welfare, such as on education, income, health, and inequality, primarily based on longitudinal household surveys and econometric methods. However, quantifying the welfare impacts of natural disasters _ex-ante_ , using a risk modeling framework, has received considerably less attention. As such, most risk assessment of natural disasters or climate extremes use asset damages as their main metric of impact, obscuring how the poor might experience disproportionate impacts beyond asset damages (Markhvida et al., 2020; Verschuur et al., 2020).

A number of studies have quantified the exposure of poor households to disasters on a national or global scale (Bangalore et al., 2016; Winsemius et al., 2018). Less information is available on the vulnerability and resilience of poor households exposed to disaster. The extent of vulnerability is often related to the assets that households own, the employment they are engaged in, the type of infrastructure they depend on, and the health and socio-economic characteristics of households. A number of studies have developed regional or national-scale social vulnerability indices of hazard-prone areas using survey data (using subnational statistics), providing insights where the most social vulnerable regions are exposed to hazards (Marzi et al., 2019; Lee et al., 2021). However, these social vulnerability indices are difficult to construct on an individual household level while covering a large spatial scale. The resilience 6