Sophisticated Consumers with Inertia: Long-Term Implications from a Large-Scale Field Experiment

Miller, Klaus; Sahni, Navdeep S.; Strulov-Shlain, Avner

doi:10.2139/ssrn.4065098

Cited by 1 publication

(1 citation statement)

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“…As such, our focus connects two disjoint threads of literature: behavioral biases in privacy valuations, and the efficiency of data markets (Arrieta-Ibarra et al 2018, Acemoglu et al 2022, Bergemann et al 2022, Ichihashi 2021, Markovich & Yehezkel 2021. We also contribute to the empirical literature on behavioral industrial organization that takes consumers' biases and fallibility into firms' decision-making processes (Peltzman 1981, Rao & Wang 2017, Strulov-Shlain 2023, Miller et al 2022.…”

Section: Introductionmentioning

confidence: 99%

Choice Architecture, Privacy Valuations, and Selection Bias in Consumer Data

Lin

Strulov-Shlain

2023

Proceedings of the 24th ACM Conference on Economics and Computation

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We study how choice architecture that companies deploy during data collection influences consumers' privacy valuations. Further, we explore how this influence affects the quality of data collected, including both volume and representativeness. To this end, we run a large-scale choice experiment to elicit consumers' valuation for their Facebook data while randomizing two common choice frames: default and price anchor. An opt-out default decreases valuations by 14-22% compared to opt-in, while a $0-50 price anchor decreases valuations by 37-53% compared to a $50-100 anchor. Moreover, in some consumer segments, the susceptibility to frame influence negatively correlates with consumers' average valuation. We find that conventional frame optimization practices that maximize the volume of data collected can have opposite effects on its representativeness. A bias-exacerbating effect emerges when consumers' privacy valuations and frame effects are negatively correlated. On the other hand, a volume-maximizing frame may also mitigate the bias by getting a high percentage of consumers into the sample data, thereby improving its coverage. We demonstrate the magnitude of the volume-bias trade-off in our data and argue that it should be a decision-making factor in choice architecture design.

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