Planes, Trains, and Automobiles: Night-time Lights of the USA

Dickinson, Jeffrey (2020): Planes, Trains, and Automobiles: Night-time Lights of the USA.

Abstract

This paper seeks to advance understanding of the lights-income relationship by linking the newest generation of night-time satellite images, the VIIRS images, to nationwide, panel data on 3,101 US counties, including data on both population and income. I leverage the quality and frequency of those data sources and the VIIRS lights images to decompose the links between population growth, official GDP growth, and nighttime lights growth at the county level. I use a between-county estimator to identify the effects of time-invariant infrastructure features on night-time light. Roads, rail, ports, and airports I find to be strong contributors to increases in light. I find GDP growth is weakly linked with night-time lights though light growth is strongly linked with population growth even when controlling for substantial non-linearities which appear to be present.

• Item Type: MPRA Paper
• Language: English
• Keywords: night-time light, GDP, population, infrastructure, regional development
• Subjects: C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C82 - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data ; Data Access; O - Economic Development, Innovation, Technological Change, and Growth > O5 - Economywide Country Studies > O51 - U.S. ; Canada; R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R1 - General Regional Economics > R10 - General; R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R1 - General Regional Economics > R11 - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes; R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R1 - General Regional Economics > R12 - Size and Spatial Distributions of Regional Economic Activity

References

Aysheshim, K., Hinson, J. R., and Panek, S. D. (2020). A primer on local area gross domesticproduct methodology.Survey of Current Business, 100(3):1–13.

Bleakley, H. and Lin, J. (2012). Portage and path dependence.The quarterly journal ofeconomics, 127(2):587–644.11

Carlowicz, M. (2012). Out of the blue and into the black: New views of the earth at night.[Online; posted 5-December-2012].Chen, X. and Nordhaus, W. D. (2011). Using luminosity data as a proxy for economic statistics.Proceedings of the National Academy of Sciences, 108(21):8589–8594.

Donaldson, D. and Storeygard, A. (2016). The view from above: Applications of satellite datain economics.Journal of Economic Perspectives, 30(4):171–98.

Elvidge, C. D., Baugh, K., Zhizhin, M., Hsu, F. C., and Ghosh, T. (2017). Viirs night-timelights.International Journal of Remote Sensing, 38(21):5860–5879.

Frick, S. A., Rodr ́ıguez-Pose, A., and Wong, M. D. (2019). Toward economically dynamicspecial economic zones in emerging countries.Economic Geography, 95(1):30–64.

Henderson, J. V., Storeygard, A., and Weil, D. N. (2012). Measuring economic growth fromouter space.American economic review, 102(2):994–1028.

Hodler, R. and Raschky, P. A. (2014). Regional favoritism.The Quarterly Journal of Economics,129(2):995–1033.

Hu, Y. and Yao, J. (2019). Illuminating economic growth. Technical report, InternationalMonetary Fund.

Kocornik-Mina, A., McDermott, T. K., Michaels, G., and Rauch, F. (2020). Flooded cities.American Economic Journal: Applied Economics, 12(2):35–66.Levin, N. and Zhang, Q. (2017). A global analysis of factors controlling viirs nighttime lightlevels from densely populated areas.Remote sensing of environment, 190:366–382.

Mellander, C., Lobo, J., Stolarick, K., and Matheson, Z. (2015). Night-time light data: A goodproxy measure for economic activity?PloS one, 10(10)