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

Abstract

How does choice architecture used during data collection influence the quality of collected data in terms of volume (how many people share) and representativeness (who shares data)? To answer this question, 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 22% compared to opt-in, while a $0-50 price anchor decreases valuations by 37% compared to a $50-100 anchor. Moreover, some consumer segments are influenced by frames more while having lower average privacy valuations. As a result, conventional frame optimization practices that aim to maximize data volume can exacerbate bias and lower data quality. We demonstrate the magnitude of this volume-bias trade-off in our data and provide a framework to inform optimal choice architecture design.

Keywords: privacy, choice architecture, market for data, selection bias, experiment

JEL Classification: D12, D18, L11, L15, M20, M31, M37