Using Neural Networks to Predict Microspatial Economic Growth
- (pp. 491-506)
Abstract
We apply deep learning to daytime satellite imagery to predict changes in income and population at high spatial resolution in US data. For grid cells with lateral dimensions of 1.2 km and 2.4 km (where the average US county has dimension of 51.9 km), our model predictions achieve R2 values of 0.85 to 0.91 in levels, which far exceed the accuracy of existing models, and 0.32 to 0.46 in decadal changes, which have no counterpart in the literature and are 3–4 times larger than for commonly used nighttime lights. Our network has wide application for analyzing localized shocks.Citation
Khachiyan, Arman, Anthony Thomas, Huye Zhou, Gordon Hanson, Alex Cloninger, Tajana Rosing, and Amit K. Khandelwal. 2022. "Using Neural Networks to Predict Microspatial Economic Growth." American Economic Review: Insights, 4 (4): 491-506. DOI: 10.1257/aeri.20210422Additional Materials
JEL Classification
- C45 Neural Networks and Related Topics
- R11 Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
- R23 Urban, Rural, Regional, Real Estate, and Transportation Economics: Regional Migration; Regional Labor Markets; Population; Neighborhood Characteristics