Working Paper: NBER ID: w28516
Authors: Gabriel E. Kreindler; Yuhei Miyauchi
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.
Keywords: commuting; economic activity; cell phone records; urban economics
JEL Codes: C55; E24; R14
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Higher wages (J39) | More commuters (L91) |
| Commuting flows (R41) | Spatial distribution of income (D31) |
| Transportation strikes (hartals) (L91) | Reduced commuting (R41) |
| Commuter characteristics (R41) | Impact of transportation strikes (R41) |
| Gravity model (R15) | Predict workplace income (J31) |