of households captures their ability to smooth shocks to their income or consumption and recover quickly, and is more difficult to infer from survey data. As part of the World Bank’s _Unbreakable_ report (Hallegatte et al., 2017), Hallegatte _et al._ (2016) (H2016) developed a model that quantifies how disaster impact can result in income and consumption losses, which, consequently, lead to households experiencing welfare losses (using a utility function). This model, which was initially applied on an international scale across 117 countries, has been refined and used in additional detailed case studies, including in the Philippines, San Francisco, and Bangladesh (Walsh and Hallegatte, 2019; Markhvida et al., 2020; Verschuur et al., 2020).

The original H2016 model has several assumptions and limitations inherent to the modeling framework. First, the only disruptive channel resulting in households experiencing income losses is via asset losses, which assumes that households derive income from the assets in their vicinity. The vulnerability of a household to such asset losses is based on a proxy of household vulnerability (e.g. housing type), with households living in lower quality housing experiencing a disproportionately higher income shock given damages to assets, as it is assumed that their asset-base is more hazard-prone. While necessary to make the step from asset damages to income losses, it remains a coarse assumption ignoring which assets are affected, and how households may depend on these assets. Similarly, poor or non-poor households are determined based on their income, assuming that the only way wellbeing is affected is by experiencing a drop in household income. However, using income or consumption as a metric of poverty has its limitations (see next section), especially in low-income countries, and ignores the multidimensional aspects ( _e.g._ health, education, living conditions) that result in people living in a state of deprivation. Third, the H2016 operates using national household survey data, which is generally not available on a sufficiently granular level to provide insights into the spatial distribution of welfare losses on a subnational scale, nor the impacts to individual households. Although this has been partly resolved in the follow-up case studies (Philippines, San Francisco, Bangladesh), where regional household survey data was used and the impacts to individual households was evaluated, the survey data generally only includes a small subset of the population.

Hence the H2016 model and its extensions could be supplemented with a complementary analysis that (1) includes a larger representative sample of households on a local scale, (2) models how disasters affect specific assets, services and employment that households depend upon, and (3) quantifies the resulting impacts to the wider development potential of poor or otherwise vulnerable households. Such a model also allows evaluating, and better targeting, adaptation options. While the H2016 made a significant contribution in the sense that it allowed evaluating both hard (e.g. preventive measures) and soft (e.g. cash transfers) interventions in a single modeling framework, it did not allow the evaluation of spatial targeting of these interventions, nor of implementing interventions that make critical infrastructure more climateresilient.

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