Traditional retail involves traffic both from warehouses to stores and from consumers to stores. Ecommerce cuts intermediate traffic by delivering goods directly from the warehouses to the consumers. Although plenty of evidence has shown that vans that are servicing e-commerce are a growing contributor to traffic and congestion, consumers are also making fewer shopping trips using vehicles. This poses the question of whether e-commerce reduces traffic congestion. The paper exploits the exogenous shock of an influential online shopping retail discount event in China (similar to Cyber Monday), to investigate how the rapid growth of e-commerce affects urban traffic congestion. Portraying e-commerce as trade across cities, I specified a CES demand system with heterogeneous consumers to model consumption, vehicle demand and traffic congestion. I tracked hourly traffic congestion data in 94 Chinese cities in one week before and two weeks after the event. In the week after the event, intra-city traffic congestion dropped by 1.7% during peaks and 1% during non-peak hours. Using Baidu Index (similar to Google Trends) as a proxy for online shopping, I found online shopping increasing by about 1.6 times during the event. Based on the model, I find evidence for a 10% increase in online shopping causing a 1.4% reduction in traffic congestion, with the effect most salient from 9am to 11am and from 7pm to midnight. A welfare analysis conducted for Beijing suggests that the congestion relief effect has a monetary value of around 239 million dollars a year. The finding suggests that online shopping is more traffic-efficient than offline shopping, along with sizable knock-on welfare gains
