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

• Published in: Journal of development economics 2021-03, Vol.149, p.102602, Article 102602
• Main Authors: Gibson, John, Olivia, Susan, Boe-Gibson, Geua, Li, Chao
• Format: Article
• Language: English
• Subjects: Density; DMSP; Indonesia; Inequality; Night lights; VIIRS
• ISSN: 0304-3878; 1872-6089
• DOI: 10.1016/j.jdeveco.2020.102602

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 GDP, especially at the second subnational level, for Indonesia, China and South Africa. The DMSP data are a poor proxy for GDP outside of cities. The gap in predictive performance between DMSP data and VIIRS data is especially apparent at lower levels of the spatial hierarchy, such as for counties, and for lower density areas. The city lights-GDP relationship is twice as noisy with DMSP data than with VIIRS data. Spatial inequality is considerably understated with DMSP data, especially for the urban sector and in higher density areas. 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 big cities. •DMSP night lights data poorly predict economic activity in low density rural areas.•DMSP night lights data understate spatial inequality, especially in urban areas.•Pareto-adjustment for top-coding has only modest effect, as blurring is a bigger flaw.•VIIRS night lights data more accurate, especially for less aggregated spatial units.