The Role of "Live" in Livestreaming Markets: Evidence Using Orthogonal Random Forest
Ziwei Cong, Jia Liu, Puneet Manchanda

TL;DR
This paper investigates how the demand for livestreamed events and their recordings varies over time, revealing that demand becomes less price sensitive during the event and remains somewhat elastic afterward, using advanced causal inference methods.
Contribution
It introduces a novel application of Orthogonal Random Forest to analyze temporal demand elasticity in livestreaming, accounting for complex confounders.
Findings
Demand becomes inelastic on the livestream day.
Post-event demand for recordings remains somewhat elastic.
Temporal demand elasticity is driven by quality uncertainty and real-time interaction.
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…
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Taxonomy
TopicsConsumer Market Behavior and Pricing · Digital Platforms and Economics · Auction Theory and Applications
