Nowcasting Italian Municipal Income with Nightlights: A Deep Learning Approach

TL;DR

This study demonstrates that deep learning models, especially a single-layer GRU, effectively predict Italian municipal income using nightlight data, outperforming traditional benchmarks and spatial econometric models.

Contribution

It introduces a deep learning approach, particularly a GRU model, to forecast municipal income from nightlights, highlighting the importance of model flexibility for capturing non-linearities.

Findings

GRU model achieves median forecast error of 1.07 million euros

Deep learning models outperform spatial econometric benchmarks

Nightlights contain predictive information for municipal income

Abstract

This paper assesses whether NASA Black Marble nightlight intensity can serve as an early indicator of annual taxable income at the Italian municipal level, where official data are released with a 12--18 month lag. Using a panel of 7{,}631 municipalities over 2012--2021, we compare four recurrent neural network architectures (LSTM, BiLSTM, GRU, Transformer) against six benchmarks: simple persistence, panel fixed effects, autoregressive distributed lag, and two spatial econometric specifications (SAR, Spatial Durbin) on a queen-contiguity matrix. Models are trained on 2012--2019 and evaluated out-of-sample on 2020--2021 with a cross-sectional Diebold--Mariano test. A single-layer GRU achieves a median forecast error of 1.07 million euros across the cross-section of municipalities -- approximately $4\%$ of the median municipal IRPEF income of 29 million euros -- statistically dominating every benchmark (DM $>4$ against persistence, $>40$ against spatial linear models, all $p<0.001$). Spatial models recover statistically significant spatial autocorrelation ($\rho \approx 0.71$) and a meaningful nightlight spillover ($\theta \approx 0.05$), but their forecasting gap with the GRU is virtually identical to that of spatially-naive linear specifications. We conclude that nightlights contain genuine predictive content for municipal income, but extracting it requires a model class flexible enough to capture cross-sectional heterogeneity and non-linearities that linear specifications, spatial or otherwise, cannot recover.