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In this paper, I propose a framework for understanding why and to what extent people value their privacy. In particular, I distinguish between two motives for protecting privacy: the intrinsic motive, that is, a “taste” for privacy; and the instrumental motive, which reflects the expected economic loss from revealing one’s “type” specific to the transactional environment. Distinguishing between the two preference components not only improves the measurement of privacy preferences across contexts, but also plays a crucial role in developing inferences based on data voluntarily shared by consumers. Combining a two-stage experiment and a structural model, I measure the dollar value of revealed preference corresponding to each motive, and examine how these two motives codetermine the composition of consumers choosing to protect their personal data. The compositional differences between consumers who withhold and who share their data strongly influence the quality of firms’ inference on consumers and their subsequent managerial decisions. Counterfactual analysis investigates strategies firms can adopt to improve their inference: Ex ante, firms can allocate resources to collect personal data where their marginal value is the highest. Ex post, a consumer’s data-sharing decision per se contains information that reflects how consumers self-select into data sharing, and improves aggregate-level managerial decisions. Firms can leverage this information instead of imposing arbitrary assumptions on consumers not in their dataset.

Matt McGranaghan, Doctoral Student,  Cornell University

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Watching People Watch TV

Synopsis

A challenge to measuring TV viewer attention is that instant access to social media, news, and work has raised the opportunity cost of engaging with TV ads.  The result may be a significant difference between traditional engagement measures, e.g., tuning, and measures which can capture more nuanced avoidance behaviors.  This paper asks two questions relating to viewer behavior in the context of TV advertising.  First, how do traditional TV tuning metrics relate to a novel set of viewer measures that may be more aligned with broadcasters’ and advertisers’ interests?  Second,what is the relationship between these new measures and ad content?  To answer these questions,we leverage novel, in-situ, audience measurement data that use facial and body recognition technology to track tuning, presence (in room behavior), and attention for a panel of 6,291 viewers and8,465,513 ad impressions,  as well as consider four different classifications of advertising content based on human and machine-coded features.  We find meaningful differences in the absolute levels and dynamics of these behaviors, and can identify ad content for which viewers are systematically more likely to change the channel, leave the room, and stop paying attention.  Such ads reduce the pool of attention to subsequent advertisers as well as the platform itself, a negative externality.  We quantify these spillover effects for the publisher by conducting a series of counterfactual simulations, and find that requiring advertisers to improve their content can result in significant increases in the cumulative levels of viewer tuning, in-room presence, and attention.