Artificial selection versus natural selection: Which causes the Matthew effect of science funding allocation in China?

Abstract

Abstract To investigate either artificial or natural selection leads to the Matthew effect in the science funding allocation and its consequences, this study retrieves 274,732 publications by Chinese scientists from the Web of Science and examines how the disparity of science funding determines scientists’ research performance. We employ the Negative Binomial Model and other models to regress the publication’s citation times, which measures the research performance, on the number of funding grants and their amounts of currency that the publication receives, which measures the disparity of science funding. The empirical results suggest an inverted U-shaped relationship. However, the optimum number of funding grants far exceeds the actual number that most publications receive, implying that increasing the funding for academic research positively impacts scientists’ research performance. The natural disparity thus plays a major role in distributing the science funding. Additionally, China’s publication-based academic assessment system may be another main cause.