The Great Divide in Scientific Productivity. Why the Average Scientist Does Not Exist

We use a quantile regression approach to estimate the e¤ects of age, gender, research
funding, teaching load and other observed characteristics of academic researchers on the full
distribution of research performance, both in its quantity (publications) and quality (citations)
dimension. Exploiting the panel nature of our dataset, we estimate a correlated random-effects
quantile regression model, accounting for unobserved heterogeneity of researchers. We employ
recent advances in quantile regression that allow its application to count data. Estimation of the
model for a panel of biomedical and exact scientists at the KU Leuven in the period 1992-2001
shows strong support for our quantile regression approach, revealing the di¤erential impact of
almost all regressors along the distribution. We also …nd that variables like funding, teaching
load and cohort have a di¤erent impact on research quantity than on research quality.
JEL-classi…cation: C14 ; C23 ; L31 ; O31 ; O32
Keywords: economics of science; research productivity; quantile regression; count data; random effects

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