Which night lights data should we use in economics, and where?
By John Gibson, S. Olivia, Geua Boe-Gibson, Chao Li. 2019
307 citations recorded by the source corpus.
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.
Subfields: Economics and Econometrics, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/e6dec33259feafd9be3fb6aef3720cf93f8acd48
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Citations in the corpus
- NIGHT LIGHTS IN ECONOMICS: SOURCES AND USES1 (2020, background): “Yet it is 25 Gibson et al (2019) show that in another big city (Jakarta in 2012), 82% of pixels have the top-coded DMSP value (DN=63) and 17% get DN=62.”