SFB649DP2017 028
Is Scientific Performance a Function of Funds?
Alona Zharova
Wolfgang K. Härdle
Stefan Lessmann
Abstract:
The management of universities requires data on teaching and research performance.
While teaching quality can be measured via student performance and teacher evaluation
programs, the connection of research outputs and their antecedents is much harder to
check, test and understand. To inform research governance and policy making at
universities, the paper clarifies the relationship between grant money and research
performance. We examine the interdependence structure between third-party expenses (TPE),
publications, citations and academic age. To describe the relationship between these
factors, we analyze individual level data from a sample of professorships from a leading
research university and a Scopus database for the period 2001 to 2015. Using estimates
from a PVARX model, impulse response functions and a forecast error variance
decomposition, we show that an analysis at the university level is inappropriate and
does not reflect the behavior of individual faculties. We explain the differences in
the relationship structure between indicators for social sciences and humanities,
life sciences and mathematical and natural sciences. For instance, for mathematics and
some fields of social sciences and humanities, the influence of TPE on the number of
publications is insignificant, whereas the influence of TPE on the number of citations
is significant and positive. Corresponding results quantify the difference between
the quality and quantity of research outputs, a better understanding of which is
important to design incentive schemes and promotion programs. The paper also proposes
a visualization of the cooperation between faculties and research interdisciplinarity via
the co-authorship structure among publications. We discuss the implications for policy
and decision making and make recommendations for the research management of universities.
Keywords:
causal inference, sample splitting, cross-fitting, sample averaging, machine learning,
simulation study
JEL Classification:
C01, C14, C31, C63