 Which night lights data should we use in economics, and where?John Gibson, Susan Olivia, Geua Boe-Gibson and Chao Li Journal of Development Economics, 2021, vol. 149, issue C 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 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. Keywords: Density; DMSP; Inequality; Night lights; VIIRS; Indonesia (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:deveco:v:149:y:2021:i:c:s0304387820301772 DOI: 10.1016/j.jdeveco.2020.102602 Access Statistics for this article Journal of Development Economics is currently edited by M. R. Rosenzweig More articles in Journal of Development Economics from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.