Empirical Economics

, Volume 57, Issue 3, pp 971–990 | Cite as

On measuring economic growth from outer space: a single country approach

  • Víctor M. Guerrero
  • Juan A. MendozaEmail author


This article proposes a simple statistical approach to combine nighttime light data with official national income growth figures. The suggested procedure arises from a signal-plus-noise model for official growth along with a constant elasticity relation between observed night lights and income. The methodology implemented in this paper differs from the approach based on panel data for several countries at once that uses World Bank ratings of income data quality for the countries under study to produce an estimate of true economic growth. The new approach: (a) leads to a relatively simple and robust statistical method based only on time series data pertaining to the country under study and (b) does not require the use of quality ratings of official income statistics. For illustrative purposes, some empirical applications are made for Mexico, China and Chile. The results show that during the period of study there was underestimation of economic growth for both Mexico and Chile, while official figures of China over-estimated true economic growth.


Digital number Generalized least squares Median Night lights Tchebysheff’s theorem 

JEL Classification

C2 C4 O4 


  1. Aruoba BS, Diebold FX, Nalewaik J, Shorfheide F, Song D (2013) Improving GDP measurements: a measurement-error perspective. National Bureau of Economic Research, Working paper 18954:1–34Google Scholar
  2. Bertinelli L, Strobl E (2013) Quantifying the local economic growth impact of hurricane strikes: an analysis from outer space for the Caribbean. J Appl Meteorol Climatol 52:1688–1697CrossRefGoogle Scholar
  3. Chen X, Nordhaus W (2011) Using luminosity data as a proxy for economic statistics. Proc Natl Acad Sci 108(21):8589–8594CrossRefGoogle Scholar
  4. Doll C, Muller JP, Elvidge CD (2000) Nightime imagery as a tool for global mapping of socio-economic parameters and greenhouse gas emissions. Ambio 29:157–162CrossRefGoogle Scholar
  5. Elliot RJR, Strobl E, Sun P (2015) The local impact of typhoons on economic activity in China: a view from outer space. J Urban Econ 88:50–66CrossRefGoogle Scholar
  6. Elvidge CD, Ziskin D, Baugh KE, Tuttle BT, Ghosh T, Pack DW, Erwin EH, Zhizhin M (2009) A fifteen year record of global natural gas flaring derived from satellite data. Energies 2(3):595–622CrossRefGoogle Scholar
  7. Ghosh T, Sutton P, Powell R, Anderson S, Elvidge ChD (2009) Estimation of Mexico’s informal economy and remittances using nighttime imagery. Remote Sens 1(3):418–444CrossRefGoogle Scholar
  8. Granger CWJ, Newbold P (1974) Spurious regressions in econometrics. J Econom 2:111–120CrossRefGoogle Scholar
  9. Guerrero VM (2007) Time series smoothing by penalized least squares. Stat Probab Lett 77(12):1225–1234CrossRefGoogle Scholar
  10. Harari M, La Ferrara E (2013) Conflict, climate and cells: a disaggregated analysis. Center for Economic Policy Research (CEPR) Discussion paper no. DP9277. SSRN: Accessed 21 Oct 2015
  11. Henderson JV, Storeygard A, Weil DN (2012) Measuring economic growth from outer space. Am Econ Rev 102(2):994–1028CrossRefGoogle Scholar
  12. Hodler R, Raschky PA (2014a) Economic shocks and civil conflict at the regional level. Econ Lett 124:530–533CrossRefGoogle Scholar
  13. Hodler R, Raschky PA (2014b) Regional favoritism. Q J Econ 129:995–1033CrossRefGoogle Scholar
  14. Michalopoulos S, Papaioannou E (2014) National institutions and subnational development in Africa. Q J Econ 129(1):151–213CrossRefGoogle Scholar
  15. Nordhaus W, Chen X (2014) A sharper image? Estimates of the precision of nighttime lights as a proxy for economic statistics. J Econ Geogr 15:217–246CrossRefGoogle Scholar
  16. Phillips PCB, Perron P (1988) Testing for a unit root in time series regression. Biometrika 75:335–346CrossRefGoogle Scholar
  17. Rawski TG (2001) What’s happening to China’s GDP statistics. China Econ Rev 12(4):12–14Google Scholar
  18. Ruppert D, Wand MP, Carroll RJ (2003) Semiparametric regression. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  19. Seo N (2011) The impacts of climate change on Australia and New Zealand: a gross cell product analysis by land cover. Aust J Agric Resour 55:220–238CrossRefGoogle Scholar
  20. United Nations, European Commission, International Monetary Fund, OCDE and WB (2009) System of national accounts 2008. European Communities, International Monetary Fund, Organisation for Economic Co-Operation and Development, United Nations and World Bank, New YorkGoogle Scholar
  21. Wackerly D, Mendenhall W III, Scheaffer RL (2002) Mathematical statistics with applications, 6th edn. Thomson/Brooks-Cole, GroveGoogle Scholar
  22. World Bank (2002) Building statistical capacity to monitor development progress. World Bank, WashingtonGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of StatisticsInstituto Tecnológico Autónomo de México (ITAM)Mexico CityMexico
  2. 2.Financial MathematicsUniversity of ChicagoChicagoUSA

Personalised recommendations