Distilling network effects from Steam

José Tudón ()
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José Tudón: Río Hondo 1

Quantitative Marketing and Economics (QME), 2022, vol. 20, issue 3, No 3, 293-312

Abstract: Abstract This paper develops a method to estimate the demand for network goods, using minimal network data, but leveraging within-consumer variation. I estimate demand for video games as a function of individuals’ social networks, prices, and qualities, using data from Steam, the largest video game digital distributor in the world. I separately identify price elasticities on individuals with and without friends with the same game, conditional on individual fixed effects and games’ characteristics. I then use the discrepancies between estimated price elasticities to identify the impact of social networks. 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 .13%. In counterfactual simulations, I find demand increases by about 5% from a promotional giveaway to “influencers,” those users in the top 1% of popularity in the network.

Keywords: Network goods; Partially observed networks; Direct network externalities; Network effects (search for similar items in EconPapers)
JEL-codes: C10 L10 L82 L86 M3 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11129-022-09254-5

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