The Role of "Live" in Livestreaming Markets: Evidence Using Orthogonal Random Forest

Abstract

A common belief about the growing medium of livestreaming is that its value lies in its “live” component. We examine this belief by comparing how the price elasticity of demand for live events varies before, on the day of, and after livestream. We do this using unique and rich data from a large livestreaming platform that allows consumers to purchase the recorded version of livestream after the stream is over. A challenge in our context is that there exist high-dimensional confounders whose relationships with treatment policy (i.e., price) and outcome of interest (i.e., demand) are complex and only partially known. We address this challenge via the use of a generalized Orthogonal Random Forest framework for heterogeneous treatment effect estimation. We find significant temporal dynamics in the price elasticity of demand over the entire event life-cycle. Specifically, demand becomes less price sensitive over time to the livestreaming day, turning to inelastic on that day. Over the post-livestream period, the demand for the recorded version is still sensitive to price, but much less than in the pre-livestream period. We further show that this temporal variation in price elasticity is driven by the quality uncertainty inherent in such events and the opportunity for real-time interaction with content creators during the livestream.

Keywords: Livestreaming, Creator economy, Price elasticity, Heterogeneous treatment effects, High-dimensional data, Machine learning

JEL Classification: C10, C14, C45, C50, M30, M31