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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.

Figure 1b: Illustration: How Choice Architecture Affects Sample Data Quality

In this example, the proportion of low- and high-income consumers in the full sample is roughly 2:1; consumers share data when their valuation on privacy is lower than the price (compensation) offered by the firm. In the no-frame-optimization condtion (supply curve 1), the sample data is representative. With the volume-maximizing frame (supply curve 2), the sample who share data predominantly consists of low-income consumers.
Citation

Lin, Tesary, and Avner Strulov-Shlain. “Choice Architecture, Privacy Valuations, and Selection Bias in Consumer Data.” University of Chicago, Becker Friedman Institute for Economics Working Paper 2023-58 (2023).

@article{lin2023choice,
  title={Choice Architecture, Privacy Valuations, and Selection Bias in Consumer Data},
  author={Lin, Tesary and Strulov-Shlain, Avner},
  journal={University of Chicago, Becker Friedman Institute for Economics Working Paper},
  number={2023-58},
  year={2023}
}
Awards and Recognitions
  • 2023 ACM Conference on Economics and Computation, Exemplary Track
  • 2023 Alessandro di Fiore Best Paper Award
  • 2021 Becker Friedman Institute Grant (University of Chicago)