Judgment Aggregation in Creative Production: Evidence from the Movie Industry - Luo, Hong; Macher, Jeffrey; Wahlen, Michael (Management Science, 2021)
We study a novel, low-cost approach to aggregating judgment from a large number of industry experts on ideas that they encounter in their normal course of business. Our context is the movie industry, in which customer appeal is difficult to predict and investment costs are high. The Black List, an annual publication, ranks unproduced scripts based on anonymous nominations from film executives. This approach entails an inherent trade-off: Low participation costs enable high response rates, but nominations lack standard criteria, and which voters see which ideas is unobservable and influenced by various factors. Despite these challenges, we find that such aggregation is predictive: Listed scripts are substantially more likely to be released than observably similar, but unlisted, scripts, and, conditional on release and investment levels, listed scripts generate higher box-office revenues. We also find that this method mitigates entry barriers for less-experienced writers, as (i) their scripts are more likely to be listed than those by experienced writers and to rank higher if listed and (ii) within scripts by less-experienced writers, being listed is associated with a higher release rate. Yet, the gap in release probabilities relative to experienced writers remains large, even for top-ranked scripts. These results can be explained by the premise that scripts from less-experienced writers are more visible among eligible voters than scripts from experienced writers. This highlights idea visibility as an important determinant of votes and surfaces the trade-offs, as well as potential limitations, associated with such methods. This paper was accepted by Ashish Arora, entrepreneurship and innovation.