A Geospatial Approach to Measuring Economic Activity

Anton Yang, Jianwei Ai and Costas Arkolakis
Additional contact information
Anton Yang: Yale University
Jianwei Ai: Renmin University of China
Costas Arkolakis: Yale University

No 2435, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

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.

Pages: 20 pages
Date: 2025-04-03
References: Add references at CitEc
Citations:

Downloads: (external link)
https://cowles.yale.edu/sites/default/files/2025-04/d2435.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cwl:cwldpp:2435

Ordering information: This working paper can be ordered from
Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA
The price is None.

Access Statistics for this paper

More papers in Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University Yale University, Box 208281, New Haven, CT 06520-8281 USA. Contact information at EDIRC.
Bibliographic data for series maintained by Brittany Ladd ().