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

Patrick Lehnert; Michael Niederberger; Uschi Backes-Gellner; Eric Bettinger

doi:10.1093/pnasnexus/pgad099

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

PNAS Nexus. 2023 Mar 29;2(4):pgad099. doi: 10.1093/pnasnexus/pgad099. eCollection 2023 Apr.

Authors

Patrick Lehnert¹, Michael Niederberger^{1

2}, Uschi Backes-Gellner¹, Eric Bettinger³

Affiliations

¹ Department of Business Administration, University of Zurich, Plattenstrasse 14, 8032 Zurich, Switzerland.
² Department of Geography, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland.
³ Graduate School of Education, Stanford University, 520 Galvez Mall, Stanford, 94306 CA, USA.

Abstract

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.

Keywords: Landsat; daytime satellite imagery; economic activity; land cover; machine learning.