Title: Replication Data for: The dynamics underlying the rise of star performers
Subject:  Computer and Information Science
Description: In the article connected to this data set, we propose that star performers' abilities develop according to a multi-dimensional, multiplicative and dynamical process. Based on existing literature, we defined a dynamic network model, including different parameters functioning as enhancers or inhibitors of star performance. The enhancers were multiplicity of productivity, monopolistic productivity, job autonomy, and job complexity, whereas productivity ceiling was an inhibitor. These enhancers and inhibitors were expected to influence the tail-heaviness of the performance distribution. We therefore simulated several samples of performers, thereby including the assumed enhancers and inhibitors in the dynamic networks, and compared their tail-heaviness. Results showed that the dynamic network model resulted in heavier and lighter tail distri
Keywords: Talent development, Productivity, Dynamical System, Network models, Simulation, L-moments