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 increasingly used 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 data on economic activity are unavailable for detailed regional levels and historical time series. This is especially true for areas of East Germany before reunification. Our procedure is generalizable to other settings, and yields great potential for analyzing historical economic developments, evaluating local policy reforms, and controlling for economic activity at highly disaggregated regional levels in econometric applications.
Contact: Michael E. Rose