and Weil 2012) and this paper will take advantage of this to use VIIRS as a proxy for economic activity.

The settlement layers used in the analysis are from WorldPop (WorldPop 2013) (Worldpop 2016) and CIESIN (Facebook Connectivity Lab and Center for International Earth Science Information Network ‐ CIESIN ‐ Columbia University n.d.). Both sets are high resolution layers that show where people live through a combination of satellite images and census data. There are two reasons for including the settlement layers. Firstly, the data are used as a way to clean the VIIRS data by excluding cells that are unlikely to contain economic activity and population. The use of secondary data to improve recognition of human activity has previously been done for poverty (Jean, et al. 2016) and for urban markets (Baragwanath, et al. 2019). Secondly, the population data are used to distinguish between rural and urban areas.

For the disaster modeling, we model three disaster types, earthquakes, typhoons and floods, by following closely the methodology from Skoufias, Strobl and Tveit (2017) for earthquakes and floods and Strobl (2012) for typhoons. These damage indices are then combined with the other data sets and two types of analysis are performed on this combined set. The first type is one case study for each hazard type. The choice fell on typhoon Haiyan, which hit the Philippines in 2013, the 2016 Aceh Earthquake and the 2017 floods in Southern Thailand. All three disasters caused significant local damage and were for the most part spatially and temporally separate from other disasters. The case studies consist of a simple graph showing the difference in mean nightlight values for the nightlight cells that were hit by the hazard and the cells that were not hit over a period starting 12 months before the hazard and until 12 months after. The second analysis moves the focus from specific events to a national level and hazards that occurred between June 2012 and March 2018. To find any short and medium‐term impacts that each disaster type has, a fixed effect regression with 12 monthly lags is performed.

Overall, the results are not particularly significant for either analysis. Two case studies of typhoons in the Philippines exhibited the strongest results, where one found a clear drop in mean nightlight during the month that Haiyan struck and similarly a clear drop when Rammasun struck in July 2014. The 2017 floods in Thailand also exhibited a drop, but compared to noise in other months it was rather small, whereas the 2016 Aceh Earthquake exhibited no difference between cells hit by it versus those that were not affected. Part of the reason for these results could be that an influx of aid and personnel would lead to a net effect of zero, but given the noise that is exhibited overall in the VIIRS data it would be hard to draw any more confident conclusions in this regard. The issue of noise in the underlying monthly data has been well illustrated in Hu and Yao (2019), where the monthly VIIRS data showed much lower coorelation with GDP than annual VIIRS and DMSP data.<sup>1</sup> The fixed effects regressions were also inconclusive, as there was little if any significance across disaster types and months. Several methods were tried to correct for the noise, such as dropping any negative values, setting any value below a certain threshold to zero (both for

> 1 The annual VIIRS data were not used in this study, as they are currently only available for 2 years

3