Which Night Lights Data Should we Use in Economics, and Where?

John Gibson, Susan Olivia and Geua Boe-Gibson

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Night lights; inequality; GDP; DMSP; VIIRS; Indonesia (search for similar items in EconPapers)
JEL-codes: O15 R12 (search for similar items in EconPapers)
Date: 2019-12-14
New Economics Papers: this item is included in nep-big, nep-geo, nep-sea and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/97582/1/MPRA_paper_97582.pdf original version (application/pdf)

Related works:
Journal Article: Which night lights data should we use in economics, and where? (2021)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:97582

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().