Abstract
We flexibly estimate demand for residential broadband accounting for congestion externalities that arise among consumers due to limited network capacity, as well as dynamics arising from nonlinear pricing. Our high frequency data permits insight into temporal patterns in usage across the day that are impacted by network congestion, and how usage responds to efforts to mitigate congestion. To estimate demand, we build a dynamic model of consumer choice and rely on variation in the timing of network upgrades and nonlinear pricing to identify the model. Using the model estimates, we calculate the welfare changes associated with different economic and technological solutions for reducing congestion, including peak-use pricing, throttling connectivity speeds, and local-cache technologies.
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