shocks: an analysis of resilience cannot alone determine the desirability of such a policy. However, the policy cards 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 115 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 risks 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.

The comparison between Sweden and Malawi illustrates that our results are country‐specific: some factors are more important in some countries that in others. For instance, a low exposure is even more important in countries with a weak social protection system. Or a good post‐disaster support delivery mechanism matters only where the government has resources to distribute after a shock. This variability supports the choice of using a model to assess resilience, instead of a weighted average of sub‐indicators, such as most other indicators for vulnerability or resilience, in which the weights are global and cannot be adjusted to local circumstances.

# 4.3. Comparison with other indicators

Several other indicators measure resilience and risk at the country and sub‐national level, work which our analysis has built on. Importantly, these indicators have different boundaries: some of them (e.g., InfoRM) include fragility and conflicts. Others – such as ND‐GAIN – try to measure the ability to adapt to climate change, and thus include long‐term trends that may not materialize through natural hazards and disasters (e.g., increasing water scarcity). As a result, these indicators cannot be directly compared among each other or with the indicator proposed in this paper. However, all these indicators tend to measure similar socioeconomic “capacities,” namely the ability to avoid, resist and absorb shocks, and are thus closely related.

In this sub‐section, we review existing indicators and compare our results on resilience and risk with three well‐known country‐level indicators, ND‐GAIN, InfoRM, and WRI, for which data are available online.

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