Measuring Commuting and Economic Activity inside Cities with Cell Phone Records

We show how commuting flows can be used to infer the spatial distribution of income within a city. We use a simple workplace choice model, which 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. Unlike machine learning approaches, our method does not require training data, yet achieves comparable predictive power. In an application, we show that hartals (transportation strikes) in Dhaka lower commuting, leading to 5-8% lower predicted income.