Local GDP Estimates Around the World

Esteban Rossi-Hansberg and Jialing Zhang

No 20023, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: We use high-resolution spatial data to build a novel global annual gridded GDP dataset at 1°, 0.5°, and 0.25° resolutions from 2012 onward. Our random forest model trained on local and national GDP achieves an R² above 0.92 for GDP levels and above 0.62 for annual changes in regions left out of the training sample. By incorporating diverse indicators beyond population and nighttime lights, our estimates offer more precise subnational GDP measurements for analyzing economic shocks, local policies, and regional disparities. We evaluate the precision of our estimates with a sample case of COVID-19’s impact on local GDP in China.

JEL-codes: E0 F0 R0 (search for similar items in EconPapers)
Date: 2025-03
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