Can nighttime lights serve as a proxy for economic inequality at the local administrative unit scale? Evidence from Spain

Economic inequality remains a pressing issue, with localized disparities becoming increasingly visible. Monitoring these subnational dynamics is often constrained by the availability of timely, reliable data. This study evaluates the potential of nighttime light (NTL) data to proxy economic inequality at the scale of local administrative units (municipalities), using Spain as a case study. Spain is among the most unequal EU countries by the Gini index and has published municipal income distribution records since 2015. Its geographic and demographic diversity provides a robust setting for testing NTL-based measures with broader applicability. We combine two remote-sensing products (VIIRS and Harmonized NTL) with WorldPop gridded population data to compute lights-per-capita for each pixel. Within each municipality, Gini coefficients are calculated using pixels as the unit of analysis, enabling fine-grained spatial estimation of inequality. We derive Gini values for over 8,000 municipalities for 2015–2020 and compare them with official income-based Gini coefficients from Spain’s tax administration. Relationships between light- and income-derived measures are assessed using cross-sectional regressions and multilevel panel models. Results show statistically significant associations between NTL- and income-derived Gini coefficients, particularly in smaller settlements and rural areas. Panel models outperform cross-sectional models, indicating that NTL data capture temporal changes in inequality even when contemporaneous correlations are modest. Some regions exhibit elevated NTL-derived inequality despite low official estimates—often where unemployment or informal economic activity is prevalent—suggesting that light-based metrics detect disparities not fully reflected in formal income statistics. These findings demonstrate the promise of NTL-derived indicators as scalable, low-cost, and globally replicable tools for monitoring inequality in both data-rich and data-poor settings. As subnational disparities gain importance, this framework offers a practical avenue for fine-grained and inclusive socioeconomic monitoring. Future work should integrate additional spatial layers—such as urban form, infrastructure, and demographics—to refine accuracy and interpretation.