Planes, Trains, and Automobiles: What Drives Human-Made Light?

Dickinson, Jeffrey (2020): Planes, Trains, and Automobiles: What Drives Human-Made Light?

Abstract

This paper expands on our understanding of the lights-income relationship by linking the newest generation of nighttime satellite images derived from the Visible Infrared Imaging Radiometry Suite, VIIRS, to nationwide, panel data on population and income from 2012-2018 for both Brazil and the United States including 3,095 US counties, and 5,570 municipios. I leverage the quality and frequency of those data sources and the VIIRS lights images and confirm that nighttime light responds to changes in income when controlling for population effects. I find positive effects of GDP on light in both USA and Brazil, though light is less responsive to changes in GDP in Brazil than in the USA. Consistent with the literature, I discover nonlinearities in the form of decreasing marginal effects of GDP on nighttime light. This result holds across many specifications and is robust to sub-sample analysis and placebo tests. Leveraging the large sample size, I use regressions by centile of nighttime light to present a clear picture of the effects of GDP and population on nighttime light. In many cases, results are shown for the combined USA and Brazil samples, as well as the dis-aggregated samples. Finally, I use a between-county estimator to identify the effects of time-invariant infrastructure features on night-time light. Roads, rail, ports, airports, and border crossings I find contribute positively to nighttime light.

• Item Type: MPRA Paper
• 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; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C55 - Large Data Sets: Modeling and Analysis; 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; 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; E - Macroeconomics and Monetary Economics > E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy > E23 - Production; 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 > O2 - Development Planning and Policy > O21 - Planning Models ; Planning Policy; O - Economic Development, Innovation, Technological Change, and Growth > O4 - Economic Growth and Aggregate Productivity > O40 - General; O - Economic Development, Innovation, Technological Change, and Growth > O4 - Economic Growth and Aggregate Productivity > O47 - Empirical Studies of Economic Growth ; Aggregate Productivity ; Cross-Country Output Convergence; O - Economic Development, Innovation, Technological Change, and Growth > O5 - Economywide Country Studies > O51 - U.S. ; Canada; O - Economic Development, Innovation, Technological Change, and Growth > O5 - Economywide Country Studies > O54 - Latin America ; Caribbean; O - Economic Development, Innovation, Technological Change, and Growth > O5 - Economywide Country Studies > O57 - Comparative Studies of Countries; 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

Alesina, A., Michalopoulos, S., and Papaioannou, E. (2016). Ethnic inequality. Journal of Political Economy, 124(2):428–488.

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.

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.

Bosch-Capblanch, X., Ronveaux, O., Doyle, V., Remedios, V., and Bchir, A. (2009). Accuracy and quality of immunization information systems in forty-one low income countries. Tropical Medicine & International Health, 14(1):2–10.

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.

Conley, T. G. (1999). Gmm estimation with cross sectional dependence. Journal of econometrics, 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.

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.

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., 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.

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. (2019). Illuminating economic growth. Technical report, International Monetary Fund.

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 development: 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.

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 contemporary african development. Econometrica, 81(1):113–152.

Michalopoulos, S. and Papaioannou, E. (2014). National institutions and subnational development in africa. The Quarterly journal of economics, 129(1):151–213.

Pinkovskiy, M. and Sala-i Martin, X. (2016). Lights, camera. . . income! illuminating the national 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.

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.

Van Lottum, J. and Marks, D. (2012) The determinants of internal migration in a developing country: quantitative evidence for indonesia, 1930–2000.Applied Economics, 44(34):4485–4494.