Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/265776 
Year of Publication: 
2022
Series/Report no.: 
IZA Discussion Papers No. 15555
Publisher: 
Institute of Labor Economics (IZA), Bonn
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.
Subjects: 
daytime satellite imagery
Landsat
machine learning
economic activity
land cover
JEL: 
E01
E23
O18
R11
R14
Document Type: 
Working Paper

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