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Using Neural Networks to Predict Microspatial Economic Growth

Author

Listed:
  • Arman Khachiyan
  • Anthony Thomas
  • Huye Zhou
  • Gordon Hanson
  • Alex Cloninger
  • Tajana Rosing
  • Amit K. Khandelwal

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.

Suggested Citation

  • Arman Khachiyan & Anthony Thomas & Huye Zhou & Gordon Hanson & Alex Cloninger & Tajana Rosing & Amit K. Khandelwal, 2022. "Using Neural Networks to Predict Microspatial Economic Growth," American Economic Review: Insights, American Economic Association, vol. 4(4), pages 491-506, December.
    • Handle: RePEc:aea:aerins:v:4:y:2022:i:4:p:491-506
    DOI: 10.1257/aeri.20210422
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    More about this item

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

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