Noisy Night Lights Data: Effects on Research Findings for Developing Countries

Omoniyi Alimi (), Geua Boe-Gibson and John Gibson
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Omoniyi Alimi: University of Waikato, https://www.waikato.ac.nz/about/faculties-schools/management/

Working Papers in Economics from University of Waikato

Abstract: Night lights data are increasingly used by economists, especially for developing country research. Many of these countries have limited capacity to generate timely and accurate sub-national statistics on economic activity so satellite data seem attractive. Most studies have used Defense Meteorological Satellite Program (DMSP) data that are flawed by blurring, lack of calibration, and top- and bottom-coding. These noisy data are only weakly related to traditional economic activity measures for lower levels spatial units. More accurate data from VIIRS (the Visible Infrared Imaging Radiometer Suite) are available since 2012 but are rarely used by economists. This paper examines how recent published findings for developing countries based on DMSP data for very small spatial units change when the more accurate VIIRS night lights data are used. Our first example finds that economic activity is far more concentrated in low-lying, flood-prone, urban areas than is apparent with the DMSP data. Our second example shows that urbanization, as proxied by night lights, is not ceteris paribus associated with better child nutritional outcomes in Nigeria, contrary to claims in a study using DMSP data. In both examples, spatially mean-reverting errors in the DMSP data cause econometric bias that distorts policy implications.

Keywords: Anthropometrics; DMSP; flooding; night lights; satellite data; VIIRS (search for similar items in EconPapers)
JEL-codes: C80 O12 Q54 (search for similar items in EconPapers)
Pages: 41 pages
Date: 2022-09-30
New Economics Papers: this item is included in nep-big, nep-dev, nep-geo and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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