Nightlights as a Development Indicator: The Estimation of Gross Provincial Product (GPP) in Turkey

Basihos, Seda (2016): Nightlights as a Development Indicator: The Estimation of Gross Provincial Product (GPP) in Turkey.

Abstract

For a while in Turkey, researchers dealing with spatial economics are unable to make detailed comparative and descriptive analysis on sub-national base due to lack of data. In particular, GDP, which is a basic indicator of economic activities, has not been published in Turkey at sub-national level since 2001. In this study, we use a different data source, night-time satellite imagery, to obtain sub-national GDP and GDP per capita series for the period between 2001 and 2013 at the level of provinces which is the basic administrative division of the Country. We also re-construct the series for the period between 1992 and 2001. For the estimation of sub-national GDP, we use Neural Network Algorithm.

• Item Type: MPRA Paper
• Language: English
• Keywords: Nightlights, GDP, Gross Provincial Product, Economic Growth, Neural Network, Spatial Economics, Turkey
• Subjects: C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics; O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development; O - Economic Development, Innovation, Technological Change, and Growth > O4 - Economic Growth and Aggregate Productivity > O49 - Other; R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R1 - General Regional Economics > R11 - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

References

Bundervoet, T., Maiyo, L., & Sanghi, A. (2015). Bright Lights, Big Cities (No. 22922). The World Bank.

Chen, X., & Nordhaus, W. D. (2011). Using luminosity data as a proxy for economic statistics. Proceedings of the National Academy of Sciences, 108 (21), 8589-8594.

Doll, C. N., Muller, J. P., & Morley, J. G. (2006). Mapping regional economic activity from night-time light satellite imagery. Ecological Economics, 57(1), 75-92.

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.

Feng, L., & Zhang, J. (2014). Application of artificial neural networks in tendency forecasting of economic growth. Economic Modelling, 40, 76-80.

Fierz-Schmidhauser, R. , Zieger P. , Wehrle G. , Jefferson A. , Ogren J. A. , Baltensperger U. , and Weingartner, E. (2010). Measurement of relative humidity dependent light scattering of aerosols. Atmospheric Measurements Techniques

Forbes, D. J. (2005). Statistical Correlation between Economic Activity and.Urban Systems, 29, 179-95.

Ghosh, T., Anderson, S. J., Elvidge, C. D., & Sutton, P. C. (2013). Using nighttime satellite imagery as a proxy measure of human well-being. Sustainability, 5(12), 4988-5019.

Henderson, J. V., Storeygard, A., & Weil, D. N. (2012). Measuring economic growth from outer space. The American Economic Review, 102(2), 994-1028.

Herbrich., R.,Keilbach., M, Graepel., T., Bollmann–Sdorra, P., and Obermayer, K. (1999). Neural Networks in Economics: Background, Applications, and New Developments. Microsoft

Kuan, C. M., & White, H. (1994). Artificial neural networks: an econometric perspective∗. Econometric Reviews, 13(1), 1-91.

Maasoumi, E., Khotanzed, A., & Abaye, A. (1994). Artificial neural networks for some macroeconomic series: a first report. Econometric Reviews, 13(1), 105-122.

Mellander, C., Lobo, J., Stolarick, K., & Matheson, Z. (2015). Night-Time Light Data: A Good Proxy Measure for Economic Activity?. PloS one, 10(10), e0139779.

Narayan, P. K., & Prasad, A. (2008). Electricity consumption–real GDP causality nexus: Evidence from a bootstrapped causality test for 30 OECD countries. Energy Policy, 36(2), 910-918.

Pinkovskiy, M., & Sala-i-Martin, X. (2014). Lights, Camera,... Income!: Estimating Poverty Using National Accounts, Survey Means, and Lights (No. w19831). National Bureau of Economic Research.

Shannon, C. E., & Weaver, W. (1993). Information theory. Encyclopedia Britan.

Sokolov-Mladenović, S., Milovančević, M., Mladenović, I., & Alizamir, M. (2016). Economic growth forecasting by artificial neural network with extreme learning machine based on trade, import and export parameters. Computers in Human Behavior, 65, 43-45.

Sutton, P. C., Elvidge, C. D., & Ghosh, T. (2007). Estimation of gross domestic product at sub-national scales using nighttime satellite imagery. International Journal of Ecological Economics & Statistics, 8(S07), 5-21.

Tkacz, G., & Hu, S. (1999). Forecasting GDP growth using artificial neural networks. Bank of Canada.

White, H. (1992). Artificial neural networks: approximation and learning theory. Blackwell Publishers, Inc..

Wu, J., Wang, Z., Li, W., & Peng, J. (2013). Exploring factors affecting the relationship between light consumption and GDP based on DMSP/OLS nighttime satellite imagery. Remote Sensing of Environment, 134, 111-119.