Proxying economic activity with daytime satellite imagery: Filling data gaps across time and space.

Autor: Lehnert P; Department of Business Administration, University of Zurich, Plattenstrasse 14, 8032 Zurich, Switzerland., Niederberger M; Department of Business Administration, University of Zurich, Plattenstrasse 14, 8032 Zurich, Switzerland.; Department of Geography, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland., Backes-Gellner U; Department of Business Administration, University of Zurich, Plattenstrasse 14, 8032 Zurich, Switzerland., Bettinger E; Graduate School of Education, Stanford University, 520 Galvez Mall, Stanford, 94306 CA, USA.
Jazyk: angličtina
Zdroj: PNAS nexus [PNAS Nexus] 2023 Mar 29; Vol. 2 (4), pp. pgad099. Date of Electronic Publication: 2023 Mar 29 (Print Publication: 2023).
DOI: 10.1093/pnasnexus/pgad099
Abstrakt: This paper develops a novel procedure for proxying economic activity with daytime satellite imagery across time periods and spatial units, for which reliable data on economic activity are otherwise not available. In developing this unique proxy, we apply machine-learning techniques to a historical time series of daytime satellite imagery dating back to 1984. Compared to satellite data on night light intensity, another common economic proxy, our proxy more precisely predicts economic activity at smaller regional levels and over longer time horizons. We demonstrate our measure's usefulness for the example of Germany, where East German data on economic activity are unavailable for detailed regional levels and historical time series. Our procedure is generalizable to any region in the world, and it has great potential for analyzing historical economic developments, evaluating local policy reforms, and controlling for economic activity at highly disaggregated regional levels in econometric applications.
(© The Author(s) 2023. Published by Oxford University Press on behalf of National Academy of Sciences.)
Databáze: MEDLINE
Externí odkaz: