A Geospatial Approach to Measuring Economic Activity

Anton Yang, Jianwei Ai and Costas Arkolakis

No 33619, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We introduce a new methodology to detect and measure economic activity using geospatial data and apply it to steel production, a major industrial pollution source worldwide. Combining plant output data with geospatial data, such as ambient air pollutants, nighttime lights, and temperature, we train machine learning models to predict plant locations and output. We identify about 40% (70%) of plants missing from the training sample within a 1 km (5 km) radius and achieve R² above 0.8 for output prediction at a 1 km grid and at the plant level, as well as for both regional and time series validations. Our approach can be adapted to other industries and regions, and used by policymakers and researchers to track and measure industrial activity in near real time.

JEL-codes: Q50 Q53 R12 (search for similar items in EconPapers)
Date: 2025-03
Note: EEE ITI
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