We show that commuting flows constructed from cell phone transaction data predict the spatial distribution of wages and income in cities. In a simple workplace choice model, commuting flows follow a gravity equation whose destination fixed effects correspond to wages. We use cell phone data from Dhaka and Colombo, covering hundreds of millions of commuter-day observations, to invert this relationship. Model-predicted income at the workplace level predicts self-reported survey workplace income, and model-predicted residential income predicts nighttime lights. In an application, we estimate that predicted commuter income is 4-5% lower on days with hartals (transportation strikes) in Dhaka.