# **Introduction**

Natural hazards and the damages caused by them lead to headlines in the news now more than ever. Part of the reason for the increased reporting is due to the focus on climate, but it is also caused by larger economic damages than in prior years. The latter point is due to the growing global population and the increasing economic value in settled places. With this value increase comes a need for updated local data on where people live and the economic activity there. This is particularly true in the wake of a devastating natural hazard, when it can be difficult to assess damages quickly and accurately.

A recent method to assess damages and economic activity is to use remotely sensed data. As a proxy for economic activity one has seen widespread use of nightlights (Henderson, Storeygard and Weil 2012) (Gillespie, et al. 2014) (Skoufias, Strobl and Tveit 2017) (Hodler and Raschky 2014) (Michalopoulos and Papaioannou 2014) (World Bank 2017). The modeling and measurement of natural hazards has also been extensively covered in the literature for different disaster types such as earthquakes (De Groeve, et al. 2008), typhoons (Emanuel 2011) and floods (Wu, Adler and Tian, et al. 2014) to name a few.

The aim of this paper is to provide a refined model of a previous study (Skoufias, Strobl and Tveit 2017) where nightlights were used as a proxy for economic activity and then combined with damage indices to find the economic impact. The novelty is that a new, more spatially and temporally detailed data set for nightlights, the Visible Infrared Imaging Radiometer Suite (VIIRS) product from the National Oceanic and Atmospheric Administration (NOAA), following the work of Elvidge et al. (2017), is employed. In addition, settlement layers have been used to remove nightlight cells with zero population to reduce the impact that “empty” cells could have on the analysis.

Five countries in Southeast Asia have been chosen as case studies due to their general lack of local level economic data and prevalence of different natural hazards: Indonesia, Myanmar, the Philippines, Thailand and Vietnam. These countries represent a large portion of the total GDP, population and area of the region. In addition, they experience earthquakes, typhoons and floods to a varying degree.

The VIIRS data have a number of advantages over the widely used, but now discontinued, Defense Meteorological Satellite Program (DMSP). Firstly, they have a higher resolution, 450m by 450m compared to 1km by 1km for the DMSP. Furthermore, the VIIRS data are released monthly compared to the annual DMSP data, making it possible to compare month‐ on‐month effects of natural disasters. Finally, the DMSP data are normalized and capped at 63, whereas the VIIRS data have no upper limit. However, the VIIRS data exhibit a lot more noise than what is found in the DMSP data. Part of this is due to stray light corrections that can lead to negative light values for some months. Another issue, prevalent in both data sets, occurs due to the many days with cloud cover that happens in the tropics. As a matter of fact, several months have no days with a measurement. Importantly, both the DMSP and the VIIRS data have shown strong correlation with local GDP (World Bank 2017) (Henderson, Storeygard 2