Dickinson, Jeffrey (2020): Planes, Trains, and Automobiles: What Drives Human-Made Light?
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Abstract
This paper links the newest generation of nighttime satellite images, which offer a resolution 45 times higher than the previous generation, to nationwide administrative panel-data on population and income from the United States and Brazil for the years 2012-2019. Using this fine-grained data, I confirm that nighttime light responds strongly to changes in income even after controlling for population effects. When population is included directly in the model, light is less responsive to changes in GDP in Brazil than in the USA. In Brazil, though not in the USA, except for the highest-producing municípios, the effect of changes in population appear to track more closely with nighttime lights than changes in economic output. A between-county estimator provides identification of the effects of time-invariant characteristics and infrastructure features on night-time light. My estimates suggest that railways are associated with lower levels of nighttime light while border crossings contribute positively and significantly to nighttime light.
Item Type: | MPRA Paper |
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Original Title: | Planes, Trains, and Automobiles: What Drives Human-Made Light? |
Language: | English |
Keywords: | night-time light, GDP, population, infrastructure, regional development |
Subjects: | C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C33 - Panel Data Models ; Spatio-temporal Models E - Macroeconomics and Monetary Economics > E0 - General > E00 - General E - Macroeconomics and Monetary Economics > E0 - General > E01 - Measurement and Data on National Income and Product Accounts and Wealth ; Environmental Accounts O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development > O10 - General O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development > O11 - Macroeconomic Analyses of Economic Development O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development > O18 - Urban, Rural, Regional, and Transportation Analysis ; Housing ; Infrastructure O - Economic Development, Innovation, Technological Change, and Growth > O4 - Economic Growth and Aggregate Productivity > O40 - General 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 |
Item ID: | 112712 |
Depositing User: | Dr. Jeffrey Dickinson |
Date Deposited: | 12 Apr 2022 14:46 |
Last Modified: | 12 Apr 2022 14:46 |
References: | Abrahams, A., Oram, C., and Lozano-Gracia, N. (2018). Deblurring dmsp nighttime lights: A new method using gaussian filters and frequencies of illumination. Remote Sensing of Environment, 210:242–258. Alesina, A., Michalopoulos, S., and Papaioannou, E. (2016). Ethnic inequality. Journal of Political Economy, 124(2):428–488. Asher, S., Lunt, T., Matsuura, R., and Novosad, P. (2021). Development research at high geographic resolution: an analysis of night-lights, firms, and poverty in india using the shrug open data platform. The World Bank Economic Review, 35(4):845–871. Aysheshim, K., Hinson, J. R., and Panek, S. D. (2020). A primer on local area gross domestic product methodology. Survey of Current Business, 100(3):1–13. Banerjee, A. and Iyer, L. (2005). History, institutions, and economic performance: The legacy of colonial land tenure systems in india. American economic review, 95(4):1190–1213. Baum-Snow, N., Brandt, L., Henderson, J. V., Turner, M. A., and Zhang, Q. (2017). Roads, rail- roads, and decentralization of chinese cities. Review of Economics and Statistics, 99(3):435– 448. Berman, N., Couttenier, M., Rohner, D., and Thoenig, M. (2017). This mine is mine! how minerals fuel conflicts in africa. American Economic Review, 107(6):1564–1610. Bleakley, H. and Lin, J. (2012). Portage and path dependence. The quarterly journal of economics, 127(2):587–644. Bluhm, R. and Krause, M. (2018). Top lights-bright cities and their contribution to economic development. Bluhm, R. and McCord, G. C. (2022). What can we learn from nighttime lights for small geographies? measurement errors and heterogeneous elasticities. Remote Sensing, 14(5):1190. Bruederle, A. and Hodler, R. (2018). Nighttime lights as a proxy for human development at the local level. PloS one, 13(9):e0202231. 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. (2015). A test of the new viirs lights data set: Population and economic output in africa. Remote Sensing, 7(4):4937–4947. 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. Chen, X. and Nordhaus, W. D. (2019). Viirs nighttime lights in the estimation of cross-sectional and time-series gdp. Remote Sensing, 11(9):1057. Conley, T. G. (1999). Gmm estimation with cross sectional dependence. Journal of economet- rics, 92(1):1–45. Cook, C. J. and Shah, M. (2020). Aggregate effects from public works: Evidence from india. The Review of Economics and Statistics, pages 1–38. Dalgaard, C.-J., Kaarsen, N., Olsson, O., and Selaya, P. (2018). Roman roads to prosperity: Persistence and non-persistence of public goods provision. Donaldson, D. and Storeygard, A. (2016). The view from above: Applications of satellite data in economics. Journal of Economic Perspectives, 30(4):171–98. Egger, D., Haushofer, J., Miguel, E., Niehaus, P., and Walker, M. W. (2019). General equilib- rium effects of cash transfers: Experimental evidence from kenya. Technical report, National Bureau of Economic Research. Elgin, C., M. A. K. F. O. and Yu., S. (2021). Growth and external debt. CEPR Discussion Papers, (16497). Elvidge, C. D., Baugh, K., Zhizhin, M., Hsu, F. C., and Ghosh, T. (2017). Viirs night-time lights. International Journal of Remote Sensing, 38(21):5860–5879. Elvidge, C. D., Baugh, K. E., Zhizhin, M., and Hsu, F.-C. (2013). Why viirs data are superior to dmsp for mapping nighttime lights. Proceedings of the Asia-Pacific Advanced Network, 35(0):62. Frick, S. A., Rodr ́ıguez-Pose, A., and Wong, M. D. (2019). Toward economically dynamic special economic zones in emerging countries. Economic Geography, 95(1):30–64. Gennaioli, N., La Porta, R., Lopez-de Silanes, F., and Shleifer, A. (2013). Human capital and regional development. The Quarterly Journal of Economics, 128(1):105–164. Gibson, J. and Boe-Gibson, G. (2021). Nighttime lights and county-level economic activity in the united states: 2001 to 2019. Remote Sensing, 13(14):2741. Gibson, J., Olivia, S., Boe-Gibson, G., and Li, C. (2021). Which night lights data should we use in economics, and where? Journal of Development Economics, 149:102602. Hao, R., Yu, D., Sun, Y., Cao, Q., Liu, Y., and Liu, Y. (2015). Integrating multiple source data to enhance variation and weaken the blooming effect of dmsp-ols light. Remote Sensing, 7(2):1422–1440. Henderson, J. V., Squires, T., Storeygard, A., and Weil, D. (2018). The global distribution of economic activity: nature, history, and the role of trade. The Quarterly Journal of Economics, 133(1):357–406. Henderson, J. V., Storeygard, A., and Deichmann, U. (2017). Has climate change driven urbanization in africa? Journal of Development Economics, 124:60–82. Henderson, J. V., Storeygard, A., and Weil, D. N. (2012). Measuring economic growth from outer 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. Hsiang, S. M. (2010). Temperatures and cyclones strongly associated with economic production in the caribbean and central america. Proceedings of the National Academy of Sciences, 107(35):15367–15372. Hu, Y. and Yao, J. (2021). Illuminating economic growth. Journal of Econometrics. Huang, L. Y., Hsiang, S. M., and Gonzalez-Navarro, M. (2021). Using satellite imagery and deep learning to evaluate the impact of anti-poverty programs. Technical report, National Bureau of Economic Research. Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., and Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301):790–794. Jedwab, R., Kerby, E., and Moradi, A. (2017). History, path dependence and develop- ment: Evidence from colonial railways, settlers and cities in kenya. The Economic Journal, 127(603):1467–1494. Keola, S., Andersson, M., and Hall, O. (2015). Monitoring economic development from space: using nighttime light and land cover data to measure economic growth. World Development, 66:322–334. 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 light levels from densely populated areas. Remote sensing of environment, 190:366–382. Li, X., Xu, H., Chen, X., and Li, C. (2013). Potential of npp-viirs nighttime light imagery for modeling the regional economy of china. Remote Sensing, 5(6):3057–3081. Mellander, C., Lobo, J., Stolarick, K., and Matheson, Z. (2015). Night-time light data: A good proxy measure for economic activity? PloS one, 10(10). Michalopoulos, S. and Papaioannou, E. (2013). Pre-colonial ethnic institutions and contempo- rary african development. Econometrica, 81(1):113–152. Michalopoulos, S. and Papaioannou, E. (2014). National institutions and subnational develop- ment in africa. The Quarterly journal of economics, 129(1):151–213. Pinkovskiy, M. and Sala-i Martin, X. (2016). Lights, camera. . . income! illuminating the na- tional accounts-household surveys debate. The Quarterly Journal of Economics, 131(2):579– 631. Ranjan, P. and Talathi, K. (2021). Impact of colonial institutions on economic growth and development in india: Evidence from night lights data. Shi, K., Huang, C., Yu, B., Yin, B., Huang, Y., and Wu, J. (2014). Evaluation of npp-viirs night-time light composite data for extracting built-up urban areas. Remote Sensing Letters, 5(4):358–366. Smith, B. and Wills, S. (2018). Left in the dark? oil and rural poverty. Journal of the Association of Environmental and Resource Economists, 5(4):865–904. Tuttle, B. T., Anderson, S. J., Sutton, P. C., Elvidge, C. D., and Baugh, K. (2013). It used to be dark here. Photogrammetric Engineering & Remote Sensing, 79(3):287–297. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/112712 |
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Planes, Trains, and Automobiles: What Drives Human-Made Light? (deposited 19 Oct 2020 15:25)
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Planes, Trains, and Automobiles: What Drives Human-Made Light? (deposited 24 Mar 2021 00:29)
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Planes, Trains, and Automobiles: What Drives Human-Made Light? (deposited 01 Oct 2021 04:54)
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Planes, Trains, and Automobiles: What Drives Human-Made Light? (deposited 21 Dec 2021 14:35)
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Planes, Trains, and Automobiles: What Drives Human-Made Light? (deposited 21 Dec 2021 14:35)
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Planes, Trains, and Automobiles: What Drives Human-Made Light? (deposited 01 Oct 2021 04:54)
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Planes, Trains, and Automobiles: What Drives Human-Made Light? (deposited 24 Mar 2021 00:29)