  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.
