Satellite Nighttime Lights as a Measurement of Economic Growth in Mexico’s Municipalities

Andrés Jerson Millán López; Daniel González Olivares

doi:10.29105/ensayos43.1-1

Autores/as

Andrés Jerson Millán López Universidad de Guadalajara. Centro Universitario de Ciencias Económico Administrativas CUCEA. https://orcid.org/0000-0003-4112-8302
Daniel González Olivares Universidad de Guadalajara. Centro Universitario de Ciencias Económico Administrativas CUCEA. https://orcid.org/0000-0002-9052-2763

DOI:

https://doi.org/10.29105/ensayos43.1-1

Palabras clave:

imágenes satelitales de luces nocturnas, dinamismo económico, municipios

Resumen

La falta de información sobre el desempeño económico y las limitaciones temporales de las cuentas nacionales y los censos económicos a nivel local en México motiva la exploración de indicadores alternativos. Con base en Henderson (2012), en este documento se analiza el dinamismo económico a través del uso de imágenes satelitales de luces nocturnas. Esta construcción complementa las omisiones de información y mide el dinamismo económico a nivel subnacional. Los resultados destacan que los cambios en la luminosidad observados desde el espacio exterior se relacionan positivamente con el dinamismo económico, por lo que la variable luminosidad puede usarse como proxy para analizar los cambios en el Producto Interno Bruto (PIB).

Descargas

Los datos de descargas todavía no están disponibles.

Biografía del autor/a

Andrés Jerson Millán López, Universidad de Guadalajara. Centro Universitario de Ciencias Económico Administrativas CUCEA.

Ph.D Universidad de Guadalajara | jerson.millan@cucea.udg.mx

Andrés Jerson Millán López (AJ, Millán) Professor at Department of Quantitative Methods of the University of Guadalajara. Doctor in Economic Studies and Head of the Data Analysis and Advisory Services Unit in the Postgraduate Coordination at the same institution. His line of research focuses on the generation of geoindicators through satellite images for the instrumentation of economic, environmental, political and social variables.

Daniel González Olivares, Universidad de Guadalajara. Centro Universitario de Ciencias Económico Administrativas CUCEA.

Ph.D. ESSEX University | daniel.gonzalez.o@cucea.udg.mx

Daniel González Olivares (DG, Olivares), of Mexican nationality, originally from Guadalajara, residing at Calle Loma Fría Sur number 7944 Loma Dorada, in Tonalá Jalisco, C.P. 45402, is an Economist by profession, has a degree in Mathematics and a PhD in Economics from the University of ESSEX in England. Since 2015 Daniel has been a research professor at the University of Guadalajara. In addition to being a member of the National System of Researchers, Daniel has publications both in international indexed revisits and in book chapters. His lines of research focus on economy politics and financial analysis, in addition, due to his studies in Big Data and Networks, he has works focused on the analysis of social networks such as Facebook and Twitter and their political impact.

Citas

Bils, M., and Klenow, P. J. (2001). Quantifying Quality Growth. The American Economic Review, 1006-30. DOI: https://doi.org/10.1257/aer.91.4.1006

Burgess, R., and Matthew Hansen, B. A. (2012). The Political Economy of Deforestation in the Tropics. Quarterly Journal of Economics, 1707–54. DOI: https://doi.org/10.1093/qje/qjs034

Carlos Petricioli, “México desde el espacio: cómo obtener, procesar y calibrar una serie de tiempo de imágenes satelitales de iluminación nocturna para estimar el crecimiento económico de Entidades Federativas” (2015).

Chen, Xi, and William D. Nordhaus. (2011). Using Luminosity Data as a Proxy for Economic Statistics. Proceedings of the National Academy of Sciences 108(21): 8589–94. DOI: https://doi.org/10.1073/pnas.1017031108

Croft, T. A. (1973). Burning Waste Gas in Oil Fields. Nature, 375–76. DOI: https://doi.org/10.1038/245375a0

Doll, C. N., Muller, J.-P., and G. Morley, J. (2006). Mapping Regional Economic Activity from Night-time Light Satellite Imagery. Ecological Economics , 75-92. DOI: https://doi.org/10.1016/j.ecolecon.2005.03.007

Elhorst, J. P. (2010). Applied spatial econometrics: raising the bar. Spatial Economic Analysis 5 (1), 9–28. DOI: https://doi.org/10.1080/17421770903541772

Elvidge, C. D., Baugh, K. E., Kihn, E. A., Kroehl, H. W., and Davis, E. R. (1997). Mapping city lights with nighttime data from the DMSP Operational Linescan System. Photogrammetric Engineering and Remote Sensing, 63(6), 727-734.

Ghosh, T., Anderson, S., Powell, R. L., Sutton, P. C., and Elvidge, C. D. (2009). Estimation of Mexico’s informal economy and remittances using nighttime imagery. Remote Sensing, 1(3), 418-444. DOI: https://doi.org/10.3390/rs1030418

Good, David F. (1994). The Economic Lag of Central and Eastern Europe: Income Estimates for the Habsburg Successor States, 1870–1910. Journal of Economic History 54(4): 869–91. DOI: https://doi.org/10.1017/S0022050700015527

Henderson, J. Vernon, Adam Storeygard, and David N. Weil. (2012). Measuring Economic Growth from Outer Space. American Economic Review, 102(2): 994–1028. DOI: https://doi.org/10.1257/aer.102.2.994

Lee, Yong Suk. (2018). International isolation and regional inequality: Evidence from sanctions on North Korea. Journal of Urban Economics 103: 34-51. DOI: https://doi.org/10.1016/j.jue.2017.11.002

Rangel-Gonzalez, E., & Llamosas-Rosas, I. (2019, November). An alternative method to measure non-registered economic activity in Mexico using satellite nightlights. In Presentation given at the 7th International Monetary Fund Statistical Forum, November (Vol. 14).

DMSP-OLS Nighttime Lights Time Series (Version 4); Image and data processing by NOAA’s National Geophysical Data Center. DMSP data collected by US Air Force Weather Agency. Available online: https://ngdc.noaa.gov/eog/index.html (accessed on 1 March 2019).

Sutton, Paul C., Christopher D. Elvidge, and Tilottama Ghosh. (2007). “Estimation of Gross Domestic Product at Sub-national Scales Using Nighttime Satellite Imagery.” International Journal of Ecological Economics and Statistics 8(S07): 5–21.

Young, A. (2012). The African growth miracle. Journal of Political Economy, 120(4), 696-739. DOI: https://doi.org/10.1086/668501

Doll, C. N. (2008). CIESIN thematic guide to night-time light remote sensing and its applications. Center for International Earth Science Information Network of Columbia University, Palisades, NY.

Ebener, S., Murray, C., Tandon, A., & Elvidge, C. C. (2005). From wealth to health: modelling the distribution of income per capita at the sub-national level using night-time light imagery. international Journal of health geographics, 4(1), 1-17 DOI: https://doi.org/10.1186/1476-072X-4-5

Bhandari, Laveesh & Roychowdhury, Koel. (2011). Night Lights and Economic Activity in India: A study using DMSP-OLS night time images. Proceedings of the Asia-Pacific Advanced Network. 32. 218. 10.7125/APAN.32.24. DOI: https://doi.org/10.7125/APAN.32.24