then the scorecard provides the resulting increase in resilience and decrease in risk. The socioeconomic resilience can also be used to translate in welfare terms the risk reduction benefits which are typically expressed in asset losses: if a new dike can reduce expected asset losses by $1 million per year in Malawi – with its 53 percent resilience – then welfare benefits can be estimated as equivalent to $2 million of additional consumption, assuming the dike protects the “average” population and is not biased toward non‐poor people, for instance.

These estimates can serve as an input to a cost‐benefit analysis that would also need to account for the cost of these options and their benefits unrelated to resilience. For instance, developing social protection brings benefits that go beyond increased resilience and include economic benefits even in the absence of shocks: an analysis of resilience cannot alone determine the desirability of such a policy. However, the scorecards can contribute to a discussion on a broad set of options to reduce natural risks and increase resilience, and ensure all options are discussed, from preventive actions like flood zoning to ex‐post options like insurance, contingent finance and social protection. The scorecard provides an integrated framework to discuss and compare these options, and could even help break the silos in governments and local authorities, where ministries or departments in charge of social protection, building norms and urban planning may not work well together or not even consider flood risks in their decisions.

Our analysis of Malawi and Sweden – and the 88 other countries – is a first‐round estimate using globally open data. It is a starting point for policy design and should be complemented by local studies (Aerts et al., 2014; Keating et al., 2014; Michel‐Kerjan et al., 2013). At the local or national level, for instance, the flood maps from the global model can be replaced by results from local analyses at higher resolution, including flash floods and small basins. Local data on flood protection and better exposure data can often be mobilized (Aerts et al., 2014). And socioeconomic characteristics can be refined, accounting for instance for the institutional capacity to scale‐up social protection beyond what a global database can reasonably aim at providing (Pelham et al., 2011). But in spite of all these limits, our global approach may contribute to the monitoring of country and global progress in terms of resilience, and our findings already provide insights into promising policy options, such as adaptive and well‐targeted social protection, and show that “good development” increases resilience, especially if it reduces poverty and improves social safety nets.

# Discussion

Our socioeconomic resilience remains an imperfect metric, in the sense that it does not include all the dimensions discussed in the resilience field (Barrett and Constas, 2014; Engle et al., 2013; Keating et al., 2014). Our framework looks at the socioeconomic resilience, but disregards direct human and welfare effects (death, injuries, psychological impacts, etc.), cultural and heritage losses (e.g., the destruction of historical assets), social and political destabilization, and environmental degradation (for instance when 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 20