Abstract

Our focus is on persistence in research productivity over the course of an individual’s entire scientific career. We track “late-career” scientists – defined as scientists with at least 25 years of publishing experience (N=320,564) – in 16 STEMM and social science disciplines from 38 OECD countries for up to five decades. Our OECD sample includes 79.42% of all late-career scientists research-active today. We examine the details of their mobility patterns as early-career, mid-career, and late-career scientists between decile-based productivity classes (from the bottom 10% to top 10% of the productivity distribution). We turn a large-scale bibliometric dataset (Scopus raw data) into a comprehensive, longitudinal data source for research on careers in science. The global science system is highly immobile: half of global top performers continue their careers as top performers. Jumpers-Up are extremely rare in science. The chances of moving radically up (or down) in productivity classes are marginal (1% or less). Our regression analyses show that productivity classes are highly path dependent: there is a single most important predictor of being a top performer, which is being a top performer at an earlier career stage. Methodological challenges of using structured Big Data of the bibliometric type are discussed, with implications for global academic career studies.