APPLIED MICRO: Fast or Slow: What is Revealed by Expert Decision Times?; Professor Stefano DellaVigna (University of California, Berkeley)

Abstract

Cognitive scientists and psychologists stress the informativeness of decision time, e.g., in drift-diffusion models; yet, economists have made limited use of it outside of laboratory experiments. We show that decision time provides valuable information about the preferences and decision-making of experts, focusing on the decisions of referees and journal editors to give a revise and resubmit. A simple two-period model outlines two predictions. First, decision time should be inverse U-shaped in the perceived relative value of the two options. Second, the accuracy of the decision depends predictably on the decision time as a function of two forces—selection based on signal and learning over time. We bring these predictions to the editorial decision setting, taking advantage of the fact that we observe a proxy for quality of decisions, citations accumulated years later, as well as the decision inputs and the number of days taken at each step of decision. We document that the decision time is indeed inverse U-shaped in the signals received by the editor and referees. Second, we show that selection based on signal dominates the effect of additional learning. Consequently, rejected papers on which editors and referees take longer accumulate more citations ex post, all else constant. Instead, papers that received revisions after a longer delay receive fewer citations than revise-and-resubmits decided sooner. We provide estimates of a simple structural model that can be adapted to other settings.

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