We use the metatheoretical principle of cumulative advantage as a framework to understand the presence of heavy-tailed productivity distributions and productivity stars. We relied on 229 datasets including 633,876 productivity observations collected from approximately 625,000 individuals in occupations including research, entertainment, politics, sports, sales, and manufacturing, among others. We implemented a novel methodological approach developed in the field of physics to assess the precise shape of the productivity distribution rather than relying on a normal versus nonnormal artificial dichotomy. Results indicate that higher levels of multiplicity of productivity, monopolistic productivity, job autonomy, and job complexity (i.e., conductors of cumulative advantage) are associated with a higher probability of an underlying power law distribution, whereas lower productivity ceilings (i.e., insulator of cumulative advantage) are associated with a lower probability. In addition, higher levels of multiplicity of productivity, monopolistic productivity, and job autonomy were associated with a greater proportion of productivity stars (i.e., productivity distributions with heavier tails), whereas lower productivity ceilings were associated with a The first and second authors contributed equally to this manuscript. We thank Berrin Erdogan for sharing data described in Erdogan and Bauer (2009);