The great divide in scientific productivity. Why the average scientist does not exist.

Abstract

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-e¤ects 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.economics of science; research productivity; quantile regression; count data; random effects;