Can night lights track the economy? For urban nighttime lights in 81 provinces of Türkiye, 2004–2020, reported between-province R² values are 0.807 for non-agriculture, 0.732 for industry, 0.809 for services and 0.511 for agriculture. Corresponding within-province models with region and year fixed effects have R² of 0.012, 0.038, 0.004 and 0.038. Connected dots compare these reported fit statistics on a zero-to-one scale. Night lights describe differences across places better than annual changes within places. These are in-sample statistics for different variation, not causal effects or holdout forecast accuracy; no R² intervals are reported.
Can night lights track the economy? For urban nighttime lights in 81 provinces of Türkiye, 2004–2020, reported between-province R² values are 0.807 for non-agriculture, 0.732 for industry, 0.809 for services and 0.511 for agriculture. Corresponding within-province models with region and year fixed effects have R² of 0.012, 0.038, 0.004 and 0.038. Connected dots compare these reported fit statistics on a zero-to-one scale. Night lights describe differences across places better than annual changes within places. These are in-sample statistics for different variation, not causal effects or holdout forecast accuracy; no R² intervals are reported.

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

Chen Yilin, Ursavas Ugur, Carlos Mendez
April 2024
Preprint Cite DOI AI Video AI Podcast AI Mindmap Published article

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.

Type
Journal article
Publication
Letters in Spatial and Resource Sciences
nighttime lights VIIRS data sectoral GDP subnational GDP Türkiye
Carlos Mendez
Carlos Mendez
Associate Professor of Development Economics

My research interests focus on the integration of development economics, spatial data science, and econometrics to better understand and inform the process of sustainable development across regions.

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