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

This paper develops a novel procedure for proxying economic activity across time periods and spatial units, for which other data is not available. In developing this proxy, we apply machine-learning techniques to a unique historical time series of daytime satellite imagery dating back to 1984. Compared to night lights intensity, another satellite-based proxy that economists commonly use, our proxy has the advantages of more precisely predicting economic activity over a longer time series and at smaller regional levels. We demonstrate the proxyâ€™s usefulness for the example of Germany, where data on economic activity is otherwise unavailable, in particular for the regions belonging to the former German Democratic Republic before reunification. However, our procedure is generalizable to other regions and countries alike, and thus yields great potential for analyzing historical developments, evaluating local policy reforms, and controlling for economic activity at highly disaggregated regional levels in econometric applications.