The grant application process in scientific research is time-consuming and often diverts scientists from their core work. While securing funding is critical, the current system may not efficiently match effort with outcomes, raising questions about whether it optimally identifies the best research or unnecessarily burdens researchers. A study analyzing how scientists allocate time to grant applications—and whether the process could be improved—could unlock productivity gains across the scientific community.

Understanding the Grant Application Burden

Scientists often spend weeks or months preparing grant proposals, many of which are unsuccessful. This project suggests analyzing their time allocation decisions by first surveying researchers on how much effort they dedicate to applications and their perceptions of the process. To complement self-reported data, a subset could use time-tracking tools to minimize recall bias. A quasi-experimental approach could then compare researchers who narrowly won or lost grants to study how outcomes influence future behavior. For example, if unsuccessful applicants reduce effort in subsequent rounds, it may indicate inefficiencies in the system.

Potential Improvements and Stakeholder Impact

If data reveals excessive time costs with diminishing returns, funding agencies might streamline processes—such as shortening applications or adjusting review formats. Scientists would benefit from regained research time, while institutions and society could see faster advancements in critical fields. Early discussions with agencies like the NSF, which has experimented with simplified applications, could guide feasible reforms. However, since resistance to change is possible, piloting adjustments in smaller funding bodies before broader implementation might ease adoption.

By linking behavioral insights with process reforms, this approach could make grantmaking more efficient while maintaining—or even improving—research quality. The key would be balancing rigorous study design with practical recommendations that align incentives for scientists, institutions, and funders.