Measuring Commuting and Economic Activity Inside Cities with Cell Phone Records

46 Pages Posted: 1 Mar 2021 Last revised: 2 Dec 2021

See all articles by Gabriel Kreindler

Gabriel Kreindler

Massachusetts Institute of Technology (MIT), Department of Economics, Students

Yuhei Miyauchi

Boston University

Date Written: February 2021

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 achieves comparable predictive power. We show that hartals (transportation strikes) in Dhaka reduce commuting more for high model-predicted wage and high-skill commuters.

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Suggested Citation

Kreindler, Gabriel and Miyauchi, Yuhei, Measuring Commuting and Economic Activity Inside Cities with Cell Phone Records (February 2021). NBER Working Paper No. w28516, Available at SSRN: https://ssrn.com/abstract=3795036

Gabriel Kreindler (Contact Author)

Massachusetts Institute of Technology (MIT), Department of Economics, Students ( email )

Cambridge, MA
United States

Yuhei Miyauchi

Boston University ( email )

595 Commonwealth Avenue
Boston, MA 02215
United States

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