change are also those where data is lacking. Producing an exhaustive map of socioeconomic resilience would require data collection in these countries or developing a reduced, less data intensive, version of the model presented here (for instance based only on the parameters identified in Figures 6 to 8).

The Online Technical Note reviews other indicators that can usefully complement our approach with different methodologies or focuses (e.g., some methodologies give more weight to institutional factors; others accounts for community‐level characteristics); see also a review in (Noy and Yonson, 2016). We also provide a comparison of our measure of socioeconomic resilience and our estimates of welfare risks with two other vulnerability or resilience indicators, namely ND‐Gain and InfoRM. Our indicator adds to the literature and existing indicators because (1) it is based on a formal theoretical framework and on a formal and quantified definition of resilience (the ratio of asset and welfare losses); (2) it adds a focus on the poorest and most vulnerable by distinguishing between the characteristics of the poorest 20 percent and the rest of the population; (3) it provides an associated tool to assess the benefits from various risk management policies such as adaptive social protection or early warning systems.

# Acknowledgements

This article benefited from comments and suggestions from anonymous referees and from Paolo Bazzurro, Charlotte Benson, David Bevan, Laura Bonzanigo, Sebastian Boreux, Daniel Clarke, Carlo del Ninno, Patrice Dumas, Nate Engle, Marianne Fay, Chico Ferreira, Chris Field, Francis Ghesquiere, Ruth Hill, Maddalena Honorati, Kazuko Ishigaki, Brenden Jongman, Tamaro Kane, Kouassi Kouadio, Norman Loayza, Hugh MacLeman, Reinhard Mechler, Martin Melecky, Rick Murnane, Ulf Narloch, James Newman, Ilan Noy, James Orr, Richard Poulter, Valentin Przyluski, Rob Reid, Adam Rose, Rae Rosen, Julie Rozenberg, Alanna Simpson, Emmanuel Skoufias, Vladimir Stenek, Tse‐Ling The, Maarten van Aalst, Sebastian von Dahlen, Claudio Volonte, and Ruslan Yemtsov. This paper benefited from support from DFID and the GFDRR.

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