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

64 Pages Posted: 26 Apr 2023 Last revised: 23 Aug 2023

See all articles by Tesary Lin

Tesary Lin

Boston University

Avner Strulov-Shlain

University of Chicago - Booth School of Business

Date Written: July 12, 2024

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

Suggested Citation

Lin, Tesary and Strulov-Shlain, Avner, Choice Architecture, Privacy Valuations, and Selection Bias in Consumer Data (July 12, 2024). University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2023-58, Available at SSRN: https://ssrn.com/abstract=4429431

Tesary Lin (Contact Author)

Boston University ( email )

595 Commonwealth Ave
Room 611
Boston, MA 02215
United States

Avner Strulov-Shlain

University of Chicago - Booth School of Business ( email )

5807 S Woodlawn Ave
Chicago, IL 60637
United States

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