Predicting future grant amounts using topic-level features

This study addresses research funding allocation, in the form of a large-scale empirical investigation into the structural factors, at the topic-level, that predict funding levels in the form of aggregated grant amounts, using a lagged panel-data approach. The topic-centric focus sets this work apart from previous research on which features predict success for individual grant applications. Understanding the topic-level dynamics around research funding provides a crucial complement to the individual-grant perspective, with a potential for informing research strategy and scientific priorities. Employing a data-driven approach based on large-scale data across more than 1,100 topics covering over 130 million publications, the study demonstrates that in addition to topic size, signals of socio-economic impact in the form of links to patents and policy documents, as well as citation patterns and aspects of the researcher community active in the topic, are significant predictors of future funding levels. A model triangulation approach, combining conditional inference trees, linear mixed effects regression, and Random Forest, reveals a clear hierarch of predictor importance across different statistical models. Clear signals of socio-economic impact driving research funding emerge both from this hierarchy of effects, as well as an outlier analysis. Taken together, the results provide compelling evidence for the structural, topic-level traces of socio-economic impact that influence research grants.