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

Companies often deploy some form of "choice architecture" when collecting consumer data, designed to nudge consumers towards sharing more private information. This study examines when an emphasis on maximizing the volume of data shared when deploying choice architecture can alter the composition of the collected data, hence creating a trade-off between the quantity and representativeness of data collected. To this end, we ran a large-scale choice experiment to elicit consumers' incentive-compatible valuation for their private Facebook data while randomizing the choice frames they encountered. Within participants, we elicited WTA using a multiple-price list, followed by a free-text entry. Across participants, we randomized the choice default and the price anchor. The default varied between opt-in, opt-out, and active choice. Price anchor was the range of prices in the multiple price list, which was either $0--$50 (low) or $50--$100 (high).