A Structural Analysis of the Role of Superstars in Crowdsourcing Contests

We investigate the long-term impact of competing against superstars in crowdsourcing contests. Using a unique 50-month longitudinal panel data set on 1677 software design crowdsourcing contests, we illustrate a learning effect where participants are able to improve their skills (learn) more when competing against a superstar than otherwise. We show that an individual’s probability of winning in subsequent contests increases significantly more after she has participated in a contest with a superstar coder than otherwise. We build a dynamic structural model with individual heterogeneity where individuals choose contests to participate in and where learning in a contest happens through an information theory-based Bayesian learning framework. We find that individuals with lower ability to learn tend to value monetary reward highly, and vice versa. The results indicate that individuals who greatly prefer monetary reward tend to win fewer contests, as they rarely achieve the high skills needed to win a contest. Counterfactual analysis suggests that instead of avoiding superstars, individuals should be encouraged to participate in contests with superstars early on, as it can significantly push them up the learning curve, leading to higher quality and a higher number of submissions per contest. Overall, our study shows that individuals who are willing to forego short-term monetary rewards by participating in contests with superstars have much to gain in the long term.