Proxying Economic Activity with Daytime Satellite Imagery: Filling Data Gaps across Time and Space

Patrick Lehnert, Michael Niederberger (), Uschi Backes-Gellner and Eric Bettinger
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Michael Niederberger: University of Zurich

No 15555, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: This paper develops a novel procedure for proxying economic activity with day-time 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: daytime satellite imagery; Landsat; machine learning; economic activity; land cover (search for similar items in EconPapers)
JEL-codes: E01 E23 O18 R11 R14 (search for similar items in EconPapers)
Pages: 72 pages
Date: 2022-09
New Economics Papers: this item is included in nep-big, nep-cmp, nep-geo and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published - published in: Proceedings of National Academy of Sciences (PNAS) Nexus , 2023, 2 (4), pgad099

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Working Paper: Proxying Economic Activity with Daytime Satellite Imagery: Filling Data Gaps Across Time and Space (2022)
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