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Can higher-quality nighttime lights predict sectoral GDP across subnational regions? Urban and rural luminosity across provinces in Türkiye

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Abstract

Limited access to regional and sectoral economic data hinders effective policy design in various countries. To address this issue, this study explores the potential of higher-quality nighttime light (NTL) data to predict economic activity across various sectors within regions. We analyze the relationship between NTL intensity and sectoral GDP in 81 Turkish provinces from 2004 to 2020. Our findings reveal that urban NTL data is most strongly correlated with non-agricultural GDP, particularly in the industrial sector. This suggests that NTL data, especially its urban component, can be a valuable tool for policymakers to identify economically disadvantaged regions and sectors, monitor the impact of economic development policies at a granular level, and allocate resources efficiently. However, this study also acknowledges limitations in capturing annual GDP changes, highlighting the need to combine NTL data with other economic indicators for a comprehensive understanding.

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Notes

  1. Following the Frisch-Waugh-Lowell theorem, the two-way fixed effect regression is visualized using residualized GDP and NTL.

  2. Three observations were excluded from the regressions due to zero NTL values in the rural areas of Bartin and Rize in 2004 and 2005.

  3. The DMSP data after Pareto adjustment for top-coding by Bluhm and Krause (2022).

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Acknowledgements

Yilin Chen is a recipient of “Interdisciplinary Frontier Next-Generation Researcher Program of the Tokai Higher Education and Research System.” This work is financially supported by JST SPRING, Grant Number JPMJSP2125. Uğur Ursavaş is a recipient of the International Postdoctoral Research Fellowship Program scholarship awarded by The Scientific and Technological Research Council of Türkiye (TÜBİTAK). This work is financially supported by TÜBİTAK (Project No: 1059B192202566).

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Authors and Affiliations

  1. Graduate School of International Development, Nagoya University, Nagoya, Japan

    Yilin Chen & Carlos Mendez

  2. Geographic Data Science Lab, Department of Geography and Planning, Roxby Building, University of Liverpool, Liverpool, L69 7ZT, UK

    Uğur Ursavaş

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  1. Yilin Chen
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  2. Uğur Ursavaş
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Correspondence to Yilin Chen.

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Appendices

Robustness check using DMSP (2004–2013) and VIIRS NTL (2013–2020)

For the analysis during 2004–2013, we used the Consistent and Corrected Nighttime Lights (CCNL) dataset, a refined version of the DMSP OLS NTL. This dataset developed by Zhao et al. (2022) effectively mitigates inter-annual inconsistencies, data saturation, and blooming effects, thereby ensuring year-to-year comparability and improved data quality (Tables 3, 4, 5 and 6).Footnote 3

Table 3 Robustness check using DMSP NTL: pooled OLS and between-estimator results (2004–2013)
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Table 4 Robustness check using VIIRS NTL: pooled OLS and between-estimator results (2013–2020)
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Table 5 Robustness check using DMSP NTL: within-estimator results (2004–2013)
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Table 6 Robustness check using VIIRS NTL: within-estimator results (2013–2020)
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Robustness check using corrected DMSP data

See Tables 7 and 8.

Table 7 Robustness check using the corrected DMSP NTL: pooled OLS and between-estimator results (2004–2013)
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Table 8 Robustness check using the corrected DMSP NTL: within-estimator results (2004–2013)
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Chen, Y., Ursavaş, U. & Mendez, C. Can higher-quality nighttime lights predict sectoral GDP across subnational regions? Urban and rural luminosity across provinces in Türkiye. Lett Spat Resour Sci 17, 12 (2024). https://doi.org/10.1007/s12076-024-00375-x

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