Econometric panel analysis demonstrates satellite nighttime lights track GDP fluctuations across global economies, highlighting their utility for real-time macroeconomic nowcasting.
Objective This study evaluates the viability of satellite-derived Nighttime Lights (NTL) radiance data as an objective, real-time, and unmanipulable proxy for tracking macroeconomic output and short-term GDP nowcasting. It addresses measurement lags, reporting errors, and potential political manipulations inherent in traditional national accounts—particularly within emerging and resource-dependent economies. 2. Methodology & Execution Data Integration: Programmatically constructed a global longitudinal panel spanning 1990 to 2024 using the wbstats API connector to extract real GDP per capita, Statistical Performance Indicators (SPI), and resource dependency metrics. Spatial Econometrics: Modeled top-of-atmosphere spectral radiance (NASA VIIRS / DMSP-OLS proxies) and established a spatial panel fixed-effects regression framework with Arellano robust heteroskedasticity and autocorrelation-consistent (HAC) standard errors. Stress Testing: Conducted dynamic lag evaluation ($t+1$) and isolated micro-trajectory case studies during major macroeconomic shocks (e.g., the 2008 Great Recession) to test orbital sensitivity during sudden economic contractions. 3. Key Findings & Impact Structural Elasticity: Confirmed a statistically significant positive coupling (β₁ = 0.0286, p < 0.001) between satellite nighttime luminosity and real economic output. Development Asymmetry: Exposed a structural elasticity penalty in emerging markets (β₂ = -0.0029, p < 0.001), proving that horizontal urban sprawl in developing economies expands light footprints differently than energy-efficient, capital-intensive growth in advanced states. Crisis Tracking: Demonstrated that orbital imagery registers physical industrial contractions instantaneously, serving as a reliable early-warning mechanism for sovereign risk assessment and central bank nowcasting.
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Bekdaulet Abzhamiev (2026) studied this question.
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