disasters affect industrial facilities and create local pollution). The framework proposed here is for socioeconomic resilience, not for a broader concept of resilience.

Issues related to conflicts and government stability are not explicitly recognized, even though they indirectly influence our results since fragile governments usually provide little social protection and have limited ability to respond to shocks. We also do not account for the possibility that a disaster (or the response to it) magnifies pre‐existing conflicts.

Average losses for poor and non‐poor people may not capture the full impact of the disaster: in each category, losses are heterogeneous and some households may lose everything, and experience long‐term effects or fall into _poverty traps_ . During the July 2005 floods in Mumbai, India, household surveys show that the median asset and income loss per capita was approximately Rs. 9,300, while the average loss was substantially higher at around Rs. 13,700 (Patankar and Patwardhan 2016). Losses across the population follow a lognormal distribution with a long tail: median losses are moderate, but some households lost almost all their income. For the people experiencing large losses, the welfare impact of the shock is not only related to the net present value of the flow of consumption losses, but also to possible long‐term effects, such as reduction in food intake, health effects and disability, and exclusion from job markets, which can lead households to fall into poverty traps (Barnett et al., 2008; Carter et al., 2007; Kraay and McKenzie, 2014; Maccini and Yang, 2009). The risk of poverty traps is particularly acute for children, as severe health impacts or interruptions in education can have lifelong impacts on earnings. We disregard this risk as it is difficult to monetize. (Barrett and Constas, 2014) propose a definition of _development resilience_ that focuses on the capacity of people to avoid such poverty traps.

Also, the ability of individual firms to cope with the shock and continue to produce in the disaster aftermath – the _static resilience_ of (Rose, 2009) – depends on many factors that would need to be included in the analysis. Various methodologies have been proposed to assess these parameters, using input‐ output or general equilibrium models (Santos and Haimes, 2004; Rose and Wei, 2013; Hallegatte, 2014b) or explicit modeling of supply‐chains (Battiston et al., 2007; Henriet et al., 2012). But more work is needed to assess this resilience based on the data and indicators that are available in all countries.

We have disregarded the impact on natural capital, in spite of its importance for the income of poor people across the world (Angelsen et al., 2014) and the impact of natural disasters on soils (through salinization or erosion), fish stocks, or trees. Including natural capital in the assessment would meet many data related issues, on the local importance of natural capital in income and on the vulnerability of natural capital to disasters.

Further, our framework does not address the ability to “build back better” after a disaster and the possibility for reconstruction to lead to an improved situation. It also takes the current exposure and vulnerability as a given, and investigates policy options without accounting for feedback in terms of risk‐ taking decisions. Better ability to manage risks – e.g., through access to insurance and social protection – could indeed have further positive economic impacts through more risk‐taking, innovation, and specialization (Elbers et al., 2007; World Bank, 2013). It can also have negative impacts through moral hazard and excessive risk‐taking (Michel‐Kerjan, 2010). These feedbacks and relationships have to be 39