Are Scientists Changing their Research Productivity Classes When They Move Up the Academic Ladder?

TL;DR

This longitudinal study analyzes career mobility in scientific productivity, revealing that scientists rarely change their productivity classes significantly, with early career performance strongly predicting long-term productivity across STEMM fields.

Contribution

The paper provides new evidence on the long-term stability of scientific productivity classes and the limited mobility between them during academic careers.

Findings

Scientists rarely change productivity classes significantly.

Early career productivity strongly predicts future performance.

High performers tend to remain high performers over time.

Abstract

We approach productivity in science in a longitudinal fashion: We track careers over time, up to 40 years. We first allocate scientists to decile-based publishing productivity classes, from the bottom 10% to the top 10%. Then, we seek patterns of mobility between the classes in two career stages: assistant professorship and associate professorship. Our findings confirm that radically changing publishing productivity levels (upward or downward) almost never happens. Scientists with a very weak past track record in publications emerge as having marginal chances of becoming scientists with a very strong future track record across all science, technology, engineering, mathematics, and medicine (STEMM) fields. Hence, our research shows a long-term character of careers in science, with publishing productivity during the apprenticeship period of assistant professorship heavily influencing productivity during the more independent period of associate professorship. We use individual-level microdata on academic careers (from a national registry of scientists) and individual-level metadata on publications (from the Scopus raw dataset). Polish associate professors tend to be stuck in their productivity classes for years: High performers tend to remain high performers, and low performers tend to remain low performers over their careers. Logistic regression analysis powerfully supports our two-dimensional results. We examine all internationally visible Polish associate professors in five fields of science in STEMM fields (N = 4,165 with N art = 71,841 articles).