subsequent accumulation and recovery. We propose that additional econometric analyses can be done of the extent of resilience following a natural disaster, with the purpose of identifying heterogeneity of resilience capacity and the factors that contribute to resilience and can be financed through DRFI. Ideally, a survey is conducted immediately after a shock to measure the stock of assets, and the loss of assets using recall data for periods prior to the event. The unit of analysis can be the community/district, the firm, and/or the household. Follow-up surveys allow tracking the process of recovery. Questions can be asked about the channels through which recovery was achieved, including access to public sources of assistance and their sources of financing. Contrasts can be made according to the intensity of shock and the degree of exposure of the specific population group (e.g., a cohort of age) to which the unit of analysis belongs.

Alternatively, recent availability of global data such as the Global Climate Data (www.worldclim.org/) and the WHO’s International Emergency Disasters Database (EMDAT) allows to characterize the occurrence of natural disasters in time and space at a world scale. This information can be overlaid with socio-economic data from sources such as the DHS and Population Censuses to measure the impact of natural disasters in well-identified natural experiments. An example is the analysis of the health impact of forest fires in Indonesia by Jayachandran (2009). If expenditures in risk reduction and/or expenditures in relief, recovery, and reconstruction operations are similarly documented in time and space, their roles in enhancing resilience and mitigating impacts of natural disasters can be measured. This opens a new set of possibilities for ex-post impact analyses of SDRFI that should be explored.

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