to nonpoor people. The _elasticity of the marginal utility of consumption_ is the parameter that describes how $1 in consumption loss affects differently poor and non‐poor people. Implicitly, it sets _distributional weights_ , i.e. the weight attributed to poor people vs. the rest of the population in the aggregation of costs and benefits in an economic analysis (Fleurbaey and Hammond, 2004). We use a standard value of 1.5 but, as this parameter is a normative choice, we explore in the Online Technical Note how results change for different values. Higher values give more importance to poor people, lead to higher estimates of welfare losses, and make it relatively more important to use policy instruments targeted towards poor people to reduce welfare risks at the country‐level.

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_ . In Mumbai, 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. 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.<sup>5</sup> To account for these effects, we calculate the number of people with very low post‐disaster, post‐ response consumption and assume that a fraction of them are unable to fulfill their basic needs and suffer from losses that go beyond the impact of a change in aggregate consumption. We count individuals with income levels below a threshold equal to 10% of average GDP per capita in the country. Of those with very low income levels, we exclude households who save at a financial institution, assuming that these savings allow smoothing the shock (through dissaving or borrowing to smooth the shock). Finally, we also exclude those who are assumed protected by the socioeconomic environment. We assume that in a country where healthcare is available and affordable, where children attend school at least until the end of primary school, and where unemployment is low, people are less likely to fall into poverty traps. In the absence of a full model for these effects, we assume that the fraction of people falling into poverty traps is inversely proportional to a simple indicator that averages measures of access to health, access to education, and employment (see the Online Technical Note).

People who have very low income and are not protected by their own savings or the socioeconomic environment are assumed to fall into a poverty trap (or to transmit poverty to their children), leading to an additional welfare loss. This welfare loss is estimated to be equal to the discounted value of the loss of the average individual’s income in the country, returning to normal over 40 years (one generation). This is of course a crude simplification of reality – which consists of a continuum of intertwined impacts on

> 5 (Barrett and Constas, 2014) propose a definition of _development resilience_ that focuses on the capacity of people to avoid such poverty traps.

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