Distilling Network Effects from Steam

Abstract

This paper identifies direct network effects in the demand for video games using minimal network data, but leveraging within-consumer variation. I separately identify price elasticities on isolated individuals on the network, and on non-isolated individuals. I then use the discrepancies between estimated price elasticities to identify the direct network effect. As an empirical application, I estimate video game demands and network effects on Steam, the largest video game digital distributor in the world. I compare my method to “traditional” IV-strategies in the literature, which require detailed network data, and find similar results. A 1% increase in friends’ demands, increases demand by .3%. In counterfactuals, I find strong incentives for firms to promote the game through “influencers,” and some incentives for games to merge their gaming networks.

Keywords: Network goods, partially observed networks, direct network externalities

JEL Classification: L14, L82, C31, D62