access to services, to estimate the cost of asset reconstruction or replacement, to identify the most urgent needs, and on that basis to establish a recovery strategy.

This second approach tends to be comprehensive and has produced useful estimates of the cost of disasters, often in a matter of just weeks or months. However, one of the main challenges it faces is to convert the information on physical destruction into monetary values. A baseline reporting of the market value or investment cost of public and private assets prior to the disaster is generally unavailable and educated guesses on the spot may carry a significant margin of error. Assessing in monetary terms the change in earnings opportunities and in access to services can be even more challenging. Because of these difficulties, the precision and relevance of studies based on this second approach has been questioned (Jeggle and Boggero 2018).

An alternative to using field survey data is relying on remote-sensing technology. Proceeding this way allows for greater measurement consistency across geographic areas and provides real-time estimates. Studies based on this third approach have leveraged satellite images on built-up areas and nighttime light intensity to infer the impact of natural disasters at local levels. For example, an analysis of Chinese typhoons shows that when 50 percent of properties in an area are destroyed, local economic activity falls by 20 percent (Elliott et al. 2015).

The advantages of this third approach include the speed at which cost estimates can be generated, and their high spatial granularity. However, there are disadvantages as well. Built-up data allows assessing the extent of physical destruction, less so the market value of the assets destroyed. Similarly, nighttime light intensity provides information on long-term trends in economic activity, especially across countries and regions, but is less well-suited to assess its short-term fluctuations <mark>(Ezran et al. 2023)</mark> . And satellite images may not be sufficient to disentangle the impact of disasters across population groups.

On the other hand, the approach proposed in this paper is closely linked to the existing literature on disasters through its reliance on _microsimulations_ . These are computational exercises that use disaggregated data on assets and earnings from previous household surveys or population and housing censuses to assess how each household in the sample may be affected by specific disasters.

In this spirit, Rozenberg and Hallegate (2016) harmonized representative household surveys for 89 emerging and developing countries, and estimated that “altogether earthquakes, storm surges, tsunamis and cyclones bring between 300,000 and 2.9 million people into extreme poverty on average per year”. Similarly, Skoufias et al. (2020) developed a microsimulation model for the Philippines to estimate household vulnerability to natural disasters of different types and intensities.

An important strength of microsimulations is their ability to generate impact estimates by population groups and geographic areas. However, an analytical framework aggregating the various impacts into a welfare loss is often missing. For example, a given fraction of assets of different types and of labor earnings in different sectors may be deemed lost. But the impact on specific households is bound to vary with their endowments and occupations prior to the disaster.

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