# **1 Introduction**

Quickly assessing and estimating the damage caused after the incidence of a natural disaster is important for both central and local governments, emergency services and aid workers, so that they can respond efficiently and deploy resources where they are most needed. Recently, remote sensing technologies have been used to analyze the impact of disasters, such as hurricanes (Myint _et al._ , 2008; Klemas, 2009), floods (Haq _et al._ , 2012; Wu _et al._ , 2012, 2014; Chung _et al._ , 2015), landslides (Nichol _et al._ , 2006), earthquakes (Fu _et al._ , 2005; Yamazaki & Matsuoka, 2007), wildfires (Holden _et al._ , 2005; Roy _et al._ , 2006), volcanoes (Carn _et al._ , 2009; Ferguson _et al._ , 2010) and tsunamis (R ̈omer _et al._ , 2012). These remote sensing techniques are useful for providing quick damage estimates shortly after the disasters giving emergency services a chance to respond quickly and local governments an overview of estimated costs and necessary repairs.

In addition to their usefulness in the aftermath of a disaster, estimates of the potential damage associated with a natural disaster are also useful for policy making prior to the realization of the natural hazard event. In many cases the incidence of a natural hazard event can turn into a natural disaster simply because of inadequate preparation ex-ante. Indonesia, for example, is highly exposed to natural disasters by being situated in one of the worlds most active disaster hot spots, where several types of disasters such as earthquakes, tsunamis, volcanic eruptions, floods, landslides, droughts and forest fires frequently occur. The average annual cost of natural disasters, over the last 10 years, is estimated at 0.3 percent of Indonesian GDP, although the economic impact of such disasters is generally much higher at local or subnational levels (The Global Facility for Disaster Reduction and Recovery, 2011). The high frequency of disasters experienced has important impacts on expenditures by local governments that could be anticipated, at least in part, through upward adjustments in the annual fiscal transfers from the central government to the subnational governments.<sup>1</sup> Such ex-ante adjustments in the level of fiscal transfers would be more useful if they could be based on estimates of the potential damages associated with the incidence of a natural disaster as opposed to estimates of the intensity of the potential natural hazard that might occur. However, although in recent years there has been much progress towards the modeling of the main natural hazards, there continues to be a scarcity of estimates of the damages associated with the incidence of these disasters. The value of damage caused by a natural disaster is typically a complicated function of the size of population living in that area, the level and type of economic activity carried out, the value of the physical infrastructure in place, and the resilience of infrastructure and people’s livelihoods to the natural hazards.

This paper fills some of the gaps in the literature by using different remote sensing sources and data on the physical characteristics of the events to construct four damage indices for natural disasters in Indonesia. The indices cover floods, earthquakes, volcanic eruptions and a tsunami, and are all weighted by local economic activity in an area, and then aggregated up to a district level.<sup>2</sup> All data used in the construction of the indices are free and publicly available, making the methods used a potentially very useful alternative for both central and local governments to quickly get a rough estimate of the damages caused by a disaster (either ex-ante or ex-post).<sup>3</sup> Importantly, all of the indices constructed take into account local exposure. Given limited access to highly disaggregated local economic activity data, nightlight intensity derived from satellite imagery has proved to be a good proxy; see, for instance, Henderson _et al._ (2012), Hodler & Raschky (2014) and Michalopoulos & Papaioannou (2014). By utilizing the grid cells of approximately 1 square kilometer we can break down areas in cities and districts into where they are busiest, and thus take into account not only the local physical characteristics of a natural disaster but also the local economic activity exposed to it.

The paper is structured as follows. Section 2 of the paper discusses in more detail the incidence and types of natural disasters. Section 3 discusses the nightlights data. Sections 4-7 discuss in detail the construction of the four damage indices, while section 8 concludes.

> 1For example, Indonesia experienced 4,000 disasters between 2001 and 2007 alone, including floods (37%), droughts (24%), landslides (11%) and windstorms (9%) (The Global Facility for Disaster Reduction and Recovery, 2011).

> 2A tropical cyclone index was also constructed, but no hurricanes had strong enough winds to cause any damage on land.

> 3In a separate paper, Skoufias _et al._ (2017), we correlate the damage indices of these disasters at the district level with the ex-post allocation of district expenditures in different sectors and by economic classification.

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