Peer Effects and Club Selections of a Unique Online Fishing Game

Abstract

We examine a unique large dataset in an online game setting to study how a player's spending is influenced by other club members' purchasing behavior. We develop a three-step model that accounts for self-selection, reflection and overfitting issues to properly estimate peer effects. First, we represent players' choice of clubs with a multinomial logistic model and construct correction terms based on that to capture potential self-selection bias. In the second step, these correction terms are then included in the linear-in-mean peer effects model to jointly address self-selection and reflection problems. Finally, to resolve the overfitting issue due to a large number of covariates, we implement the least absolute shrinkage and selection operator (LASSO) regression. We then run the ordinary least squares (OLS) post-LASSO using the selected variables to further improve the estimation accuracy. We find that a player's spending is positively affected by the average payment size of her/his clubmates. Importantly, we also uncover the existence of self-selection in the game. In particular, players choose clubs matched with their spending and countries. Our empirical findings shed light on the necessity of factoring social interaction into future game designs to boost revenues.

Keywords: peer effects, social interaction, self-selection, online games, decision making, data-driven analytics, digital business model

JEL Classification: M00, M1, M13