Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
This study demonstrates estimations of economic activities on global, national, and subnational levels using remote sensing data, with a focus on developing economies. It extends a recent statistical framework which uses nighttime lights to estimate official income growth by accounting for agriculture and forestry which emit less or no additional observable nighttime light. The study argues that nighttime lights alone may not explain value-added by agriculture and forestry. By adding land cover data, our framework can be used to estimate economic growth in administrative areas of virtually any size.