this, one can use nightlights. This methodology has been used in several papers already, with significant results (Henderson, Storeygard and Weil 2012) (Gillespie, et al. 2014) (Hodler and Raschky 2014) (Michalopoulos and Papaioannou 2014). In Henderson, Storeygard and Weil (2012), Indonesia is used as an example of using nightlights to capture an economic downturn following the Asian financial crisis in the late 1990s. Their results show that swings in GDP change can generally be captured. Nevertheless, one has to account for factors such as cultural differences in light usage, latitude and gas flares. In our case this is unlikely to affect our results since nightlights are used to capture exposure within a country rather than across countries.

All the previously mentioned studies use a prior iteration of nightlight images, the DMSP satellite images. The approximate cell grid size of these images is 1 by 1 kilometer at the equator and the data provided are annual.

This paper utilizes a newer nightlight data set, the VIIRS Day/Night Band (DNB) provided by The Earth Observations Group (EOG) at NOAA/NCEI. The data are produced following the methodology of Elvidge, et al. (2017). These data are provided starting in April 2012 and go till the present, making the time series much shorter than the DMSP data which start in 1992 and go through 2013. However, the VIIRS DNB images from EOG are monthly, whereas the DMSP data are annual. Furthermore, the images have 15 arcseconds grids (approximately 450 meters at the equator) compared to the 30 arcseconds of DMSP. The VIIRS coverage spans from 75N latitude to 65S around the entire globe, meaning that all of Southeast Asia is included. The VIIRS data have also seen usage in the economics and disaster literature. Firstly, Chen and Nordhaus’s (2015) analysis finds promising results for VIIRS as an economic and population indicator, also when comparing to the DMSP product. Secondly, Zhao, et al. (2018) used the underlying NPP‐VIIRS DNB Daily Data to analyze selected natural disasters. They found that the images were useful for detecting damages and power outages, but that cloud coverage was a major limitation in the assessment.

More recent research utilizing the VIIRS nightlights also points to the limitation of monthly VIIRS in the detection of GDP and urban markets. Hu and Yao (2019) show how monthly VIIRS data have very low correlation for low income countries.<sup>4</sup> The overall correlation is significantly better for middle income countries and annual, corrected data. Even when using the annual mean of monthly data, the results are worse than for annual VIIRS and DMSP.

The data output consists of two variables: the average light radiance from DNB and the number of cloud free days. An important note regarding cloud free days is that for the tropical areas in Southeast Asia one will have months where there are no cloud free days and hence no radiance measurement. To account for months with no radiance value, this paper has interpolated between the month before and after. Furthermore, for cells with little light, one often finds negative light values due to airglow contamination (Uprety, et al. 2017). There is no established standard for how to interpret these measurements, but we did any analysis

> 4 If one includes all the low income countries, the correlation is negative (‐0.02), but it is heavily skewed by the poorest African nations. With the exclusion of the Central African Republic, the correlation improves to 0.12.

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