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Using night time lights to find regional inequality in India and its relationship with economic development

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  • Abhishek Singhal
  • Sohini Sahu
  • Siddhartha Chattopadhyay
  • Abhijit Mukherjee
  • Soumendra N Bhanja

Abstract

Due to unavailability of consistent income data at the sub-state or district level in developing countries, it is difficult to generate consistent and reliable economic inequality estimates at the disaggregated level. To address this issue, this paper employs the association between night time lights and economic activities for India at the sub-state or district-level, and calculates regional income inequality using Gini coefficients. Additionally, we estimate the relationship between night time lights and socio-economic development for regions in India. We employ a newly available data on regional socio-economic development (Social Progress Index), as well as an index that represents institutional quality or governance. Robust to the choice of socio-economic development indicators, our findings indicate that regional inequality measured by night time lights follow the Kuznets curve pattern. This implies that starting from low levels of socio-economic development or quality of institutions, inequality rises as regional socio-economic factors or quality of institutions improve, and with subsequent progress in socio-economic factors or quality of institutions, regional inequality declines.

Suggested Citation

  • Abhishek Singhal & Sohini Sahu & Siddhartha Chattopadhyay & Abhijit Mukherjee & Soumendra N Bhanja, 2020. "Using night time lights to find regional inequality in India and its relationship with economic development," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-18, November.
  • Handle: RePEc:plo:pone00:0241907
    DOI: 10.1371/journal.pone.0241907
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    1. Shapiro, Daniel & Oh, Chang Hoon & Zhang, Peng, 2023. "Nighttime lights data and their implications for IB research," Journal of International Management, Elsevier, vol. 29(5).
    2. Chakraborty, Tanika & Mukherjee, Anirban, 2022. "Economic geography of contagion: A study on Covid-19 outbreak in India," GLO Discussion Paper Series 1028, Global Labor Organization (GLO).
    3. 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).
    4. Pushkar Maitra & William Yu, 2021. "The Long Shadow of Infrastructure Development: Long Run Effects of Railway Construction in Colonial India," Monash Economics Working Papers 2021-01, Monash University, Department of Economics.
    5. Yi Cheng & Hui Liu & Dongmei Chen & Haimeng Liu, 2022. "Human Activity Intensity and Its Spatial-Temporal Evolution in China’s Border Areas," Land, MDPI, vol. 11(7), pages 1-19, July.
    6. Yanjun Wang & Fei Teng & Mengjie Wang & Shaochun Li & Yunhao Lin & Hengfan Cai, 2022. "Monitoring Spatiotemporal Distribution of the GDP of Major Cities in China during the COVID-19 Pandemic," IJERPH, MDPI, vol. 19(13), pages 1-29, June.
    7. Tanika Chakraborty & Anirban Mukherjee, 2023. "Economic geography of contagion: a study of COVID-19 outbreak in India," Journal of Population Economics, Springer;European Society for Population Economics, vol. 36(2), pages 779-811, April.
    8. Chindarkar, Namrata & Goyal, Nihit, 2023. "Did it increase energy consumption? A difference-in-differences evaluation of a rural electrification policy in Gujarat, India using night-time lights data," Energy Policy, Elsevier, vol. 183(C).

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