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Measuring Commuting and Economic Activity inside Cities with Cell Phone Records

Author

Listed:
  • Gabriel E. Kreindler

    (Harvard University)

  • Yuhei Miyauchi

    (Boston University)

Abstract

We show how to use commuting flows to infer the spatial distribution of income within a city. A simple workplace choice model predicts a gravity equation for commuting flows whose destination fixed effects correspond to wages. We implement this method with cell phone transaction data from Dhaka and Colombo. Model-predicted income predicts separate income data, at the workplace and residential level, and by skill group. Unlike machine learning approaches, our method does not require training data, yet it achieves comparable predictive power. We show that hartals (transportation strikes) in Dhaka reduce commuting more for high model-predicted wage and high skill commuters.

Suggested Citation

  • Gabriel E. Kreindler & Yuhei Miyauchi, 2023. "Measuring Commuting and Economic Activity inside Cities with Cell Phone Records," The Review of Economics and Statistics, MIT Press, vol. 105(4), pages 899-909, July.
    • Handle: RePEc:tpr:restat:v:105:y:2023:i:4:p:899-909
    DOI: 10.1162/rest_a_01085
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    Cited by:

    1. Stephen J. Redding & Daniel M. Sturm, 2024. "Neighborhood Effects: Evidence from Wartime Destruction in London," NBER Working Papers 32333, National Bureau of Economic Research, Inc.
    2. Chen, Liming & Hasan, Rana & Jiang, Yi & Parkhomenko, Andrii, 2024. "Faster, taller, better: Transit improvements and land use policies," Journal of Development Economics, Elsevier, vol. 171(C).

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