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Remotely too equal: Popular DMSP night‐time lights data understate spatial inequality

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  • Xiaoxuan Zhang
  • John Gibson
  • Xiangzheng Deng

Abstract

Regional science and economics studies increasingly use the Defense Meteorological Satellite Program (DMSP) night‐time lights data to measure spatial inequality. These DMSP data are a poor proxy in this context because of their spatially mean‐reverting errors, which yield significantly lower inequality estimates than what subnational GDP data show. Inequality estimates from DMSP are also lower than what newer, research‐focused and more accurate satellites show. We demonstrate this bias using county‐level data from China and the United States. The errors in the DMSP data distort estimates of both the level of and trend in spatial inequality. Los estudios económicos y de las ciencias regionales utilizan cada vez más los datos de las luces nocturnas del Programa de Satélites Meteorológicos de Defensa (DMSP, por sus siglas en inglés) para medir la desigualdad espacial. Estos datos del DMSP son un pobre indicador indirecto en este contexto debido a sus errores de reversión espacial a la media, que arrojan estimaciones de desigualdad significativamente más bajas que las que muestran los datos del PIB subnacional. Las estimaciones de desigualdad del DMSP son también inferiores a las que muestran los satélites más nuevos, orientados hacia la investigación y más precisos. Se demuestra este sesgo utilizando datos a nivel de condado de China y de Estados Unidos. Los errores en los datos del DMSP distorsionan las estimaciones tanto del nivel de desigualdad espacial como de la tendencia. 地域科学と経済学の研究では、空間的不平等を測定するために、防衛気象衛星計画 (Defense Meteorological Satellite Program:DMSP)の夜間光データを使用することが多くなっている。これらのDMSPデータは、その空間的平均回帰誤差のため、この文脈では貧弱なプロキシであり、これは地方GDPデータが示すものよりも著しく低い不平等推定値をもたらす。DMSPからの不平等推定値も、より新しい、研究に焦点を当てた、より正確な衛星が示すものよりも低い。中国と米国の郡レベルのデータを用いてこのバイアスを実証した。DMSPデータの誤差は、空間的不平等のレベルと傾向の両方の推定値を歪めるものである。

Suggested Citation

  • Xiaoxuan Zhang & John Gibson & Xiangzheng Deng, 2023. "Remotely too equal: Popular DMSP night‐time lights data understate spatial inequality," Regional Science Policy & Practice, Wiley Blackwell, vol. 15(9), pages 2106-2125, December.
  • Handle: RePEc:bla:rgscpp:v:15:y:2023:i:9:p:2106-2125
    DOI: 10.1111/rsp3.12716
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    References listed on IDEAS

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    1. Bluhm, Richard & Krause, Melanie, 2022. "Top lights: Bright cities and their contribution to economic development," Journal of Development Economics, Elsevier, vol. 157(C).
    2. Deininger, Klaus & Squire, Lyn, 1996. "A New Data Set Measuring Income Inequality," The World Bank Economic Review, World Bank, vol. 10(3), pages 565-591, September.
    3. Guohui Chen & Jie Zhang, 2023. "Regional Inequality in ASEAN Countries: Evidence from an Outer Space Perspective," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 59(3), pages 722-736, February.
    4. John Gibson, 2021. "Better Night Lights Data, For Longer," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(3), pages 770-791, June.
    5. John Gibson & Bonggeun Kim & Geua Boe-Gibson, 2022. "How effective are sanctions on North Korea? Popular DMSP night-lights data may bias evaluations due to blurring and poor low-light detection," Working Papers in Economics 22/06, University of Waikato, revised 14 Nov 2022.
    6. J. Vernon Henderson & Adam Storeygard & David N. Weil, 2012. "Measuring Economic Growth from Outer Space," American Economic Review, American Economic Association, vol. 102(2), pages 994-1028, April.
    7. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    8. John Gibson & Susan Olivia & Geua Boe‐Gibson, 2020. "Night Lights In Economics: Sources And Uses," Journal of Economic Surveys, Wiley Blackwell, vol. 34(5), pages 955-980, December.
    9. repec:lic:licosd:41920 is not listed on IDEAS
    10. Deininger, Klaus & Squire, Lyn, 1996. "A New Data Set Measuring Income Inequality," The World Bank Economic Review, World Bank, vol. 10(3), pages 565-591, September.
    11. Gibson, John & Huang, Jikun & Rozelle, Scott, 2001. "Why is income inequality so low in China compared to other countries?: The effect of household survey methods," Economics Letters, Elsevier, vol. 71(3), pages 329-333, June.
    12. Gibson, John & Olivia, Susan & Boe-Gibson, Geua & Li, Chao, 2021. "Which night lights data should we use in economics, and where?," Journal of Development Economics, Elsevier, vol. 149(C).
    13. Omoniyi Alimi & Geua Boe-Gibson & John Gibson, 2022. "Noisy Night Lights Data: Effects on Research Findings for Developing Countries," Working Papers in Economics 22/12, University of Waikato.
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