ter, 2012 to the present.<sup>3</sup> We use the harmonized data produced by Li et al. (2020).<sup>4</sup> We clipped the night light raster files to include only Bangladesh’s territory and downloaded the shapefile at the national level from the Humanitarian Data Exchange, which is managed by the Office for the Coordination of Humanitarian Affairs (OCHA). Next, the spatial coordinates, originally measured in degrees using the (WGS 84) coordinate system, were projected to the EPSG 9678 coordinate system, which enabled the measurement of coordinates in meters. As a result, we divided the entire territory into grids consisting of cells of one square kilometer. Then, since the night light density raster files were in geographic coordinates, the grid cells were projected back to WGS 84. Night light density at the grid cell was then computed by calculating the simple mean of the night light luminosity across all pixels within each grid cell. The final data was restricted to the district of Cox’s Bazar and consists of an annual average measure of night light density for the period spanning 1992 to 2021 at a level of one square kilometer.

2. _Deforestation._ This study uses the collection of raster images from the Hansen Global Forest Change dataset, which is available in the Google Earth Engine database (Hansen et al. 2013). This dataset leverages Landsat images to assess and characterize global forest extent and changes over time. Specifically, it focuses on identifying forest loss, defined as the transformation of forested areas into non-forested ones due to stand-replacement disturbances. For the analysis, forest loss was computed into 3Despite the availability of data, both sources of night light series are inconsistent due to differences in spatial and radiometric resolution, spectral responses, the spread function of the sensors, local overpass time at night, radiance range, and on-board calibration (Li et al. 2017; Sahoo, Gupta and Srivastav 2020). Furthermore, the DMSP-OLS sensor measures night light density annually in digital numbers (DN), while the VIIRS sensor measures monthly in radiance (Gibson et al. 2021).

> 4The authors follow four steps to inter-calibrate DMSP-OLS and VIIRS sensors: DMSP-OLS calibration, the annual composition of VIIRS, and VIIRS conversion like DMSP-OLS. In the first step, the authors calibrated the stable DMSP-OLS night lights from 1992 to 2013. This calibration process aimed to ensure consistency and accuracy in the DMSP-OLS data. In the second step, the authors addressed the noise present in the VIIRS data caused by factors such as clouds, auroras, and temporary lights like fires and boats. They applied noise-removal techniques to improve the quality of the VIIRS data. In the third step, the authors converted the higher resolution of VIIRS (15 arc-seconds) to match the resolution of DMSP-OLS (30 arcseconds) using the kernel density approach, which is similar to the method described in Li et al. (2017). This conversion ensured consistency between the two datasets. Finally, to convert the processed data into digital numbers (DN), the authors employed a sigmoid function proposed by Zhao et al. (2019). As a result, it is possible to use consistent and calibrated night light data.

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