Race to the Bottom: Competition and Quality in Science

Abstract This paper investigates how competition to publish first and thereby establish priority impacts the quality of scientific research. We begin by developing a model where scientists decide whether and how long to work on a given project. When deciding how long they should let their projects mature, scientists trade off the marginal benefit of higher quality research against the marginal risk of being preempted. Projects with the highest scientific potential are the most competitive because they induce the most entry. Therefore, the model predicts these projects are also the most rushed and lowest quality. We test the predictions of this model in the field of structural biology using data from the Protein Data Bank (PDB), a repository for structures of large macromolecules. An important feature of the PDB is that it assigns objective measures of scientific quality to each structure. As suggested by the model, we find that structures with higher ex-ante potential generate more competition, are completed faster, and are lower quality. Consistent with the model, and with a causal interpretation of our empirical results, these relationships are mitigated when we focus on structures deposited by scientists who — by nature of their employment position — are less focused on publication and priority. We estimate that the costs associated with improving these low-quality structures are between 1.5 and 8.8 billion dollars since the PDB’s founding in 1971.