Signaling quality via demand lockout

Andreas Kraft () and Raghunath Singh Rao ()
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Andreas Kraft: University of Chicago Booth School of Business
Raghunath Singh Rao: The University of Texas at Austin

Quantitative Marketing and Economics (QME), 2025, vol. 23, issue 1, No 1, 44 pages

Abstract: Abstract Consumers face uncertainty about the quality of products and services in many consumption contexts. Firms often try to resolve quality uncertainty via price signaling, where a higher price implies higher quality. However, a host of consumption contexts increasingly involve a uniform price across differentiated offerings (e.g., streaming platforms), and hence, prices as signals become unavailable. In this paper, we propose and empirically test a novel mode of quality signal: firms’ active exclusion of a profitable segment of consumers—a phenomenon we call demand lockout. Using a theoretical model, we demonstrate that the opportunity cost of locking out a profitable segment can serve as a credible signal of quality when two conditions are met: First, the non-excluded segment is large enough, and second, a significant fraction of consumers only consume if word of mouth has reduced the quality uncertainty. The value of the lockout signal increases as advertising becomes more expensive and decreases as third-party information becomes more accurate. We provide empirical observations consistent with our model in the context of the motion picture industry, hypothesizing that studios might use R ratings to credibly signal quality by excluding a non-trivial segment from consuming its product. Our empirical analysis involves the use of a large corpus of text data from thousands of movie subtitles in conjunction with machine learning methods to control for “age-inappropriate” content of movies non-parametrically. Consistent with the proposed theory, movies are more likely to actively try to get an R rating when the value of the signal is more significant. Furthermore, box office revenue numbers are consistent with our prediction that R ratings could serve as a credible signal, and the value of this signal depends on the availability and noisiness of external information, such as film reviews.

Keywords: Signaling; Machine learning; Game theory; Information asymmetry; Movies; Reviews (search for similar items in EconPapers)
JEL-codes: D82 D83 L82 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11129-024-09288-x

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