Which night lights data should we use in economics, and where?

Abstract

Popular DMSP night lights data are flawed by blurring, top-coding, and lack of calibration. Yet newer and better VIIRS data are rarely used in economics. We compare these two data sources for predicting Indonesian GDP at the second sub-national level. DMSP data are a bad proxy for GDP outside of cities. The city lights-GDP relationship is twice as noisy using DMSP as using VIIRS.Spatial inequality is considerably understated with DMSP data. A Pareto adjustment to correct for top-coding in DMSP data has a modest effect but still understates spatial inequality and misses key features of economic activity in Jakarta.

Tracking Economic Activity in Response to the COVID-19 Crisis using Nighttime Lights – the Case of Morocco

“…These data are not subject to the top-coding problem, while the "overglow" problem, while not completed eradicated, is also much less severe (Small, 2019). Because the data undergo onboard calibration, they are also more comparable over time (Gibson et al, 2021). As such, the data appear, prima facie, better suited to detecting changes in economic activity over time than the old DMSP-OLS data.…”

“…Operational Linescan System (DMSP-OLS). 8 While in addition to global coverage, these data have the advantage of having a relatively long historical annual time series spanning the period 1992 -2013, they suffer from several drawbacks that undermine their ability to accurately proxy economic activity (Gibson et al, 2021). Most notable among these drawbacks are those of top coding, the so-called "overglow" or "blooming" phenomenon, and the absence of onboard satellite intertemporal calibration of the data.…”

Nightlight as a Proxy of Economic Indicators: Fine-Grained GDP Inference around Chinese Mainland via Attention-Augmented CNN from Daytime Satellite Imagery

“…These studies verified the rationality of considering nighttime lights (provided by the DMSP data) as a proxy of regional economic activity degrees. However, flaws in DMSP data, including pervasion blurring, no calibration, coarse spatial and spectral resolution, and inter-satellite differences [14,22], inflicted inaccuracy and even invalidity upon studies using this data source, especially for smaller units and lower density areas [23,24]. In comparison, the newgeneration VIIRS data, which became available from 2012 onward, were more pertinent to the needs of socioeconomic researchers.…”

“…In comparison, the newgeneration VIIRS data, which became available from 2012 onward, were more pertinent to the needs of socioeconomic researchers. Empirical results proved that the VIIRS data could be a promising supplementary source for socioeconomic indicator measures [25][26][27] and have better performance than the DMSP data [23,28].…”

“…It should be noticed that despite the superiority to DMSP data, estimation at small geographic units and detection of agricultural activities remained to be challenges for the utilization of VIIRS data [23,26]. The estimation of county-level GDP in China from nightlight was only affected by the limitation of data sources.…”