Accepted and Published Papers
Valuing Intrinsic and Instrumental Preferences for Privacy. Accepted, Marketing Science
xxxx2019 Sheth Foundation ISMS Doctoral Dissertation Award;
xxxx2018 MSI Alden G. Clayton Doctoral Dissertation Proposal Award
I empirically separate two components in a consumer’s privacy preference. The intrinsic component is a “taste” for privacy, a utility primitive. The instrumental component comes from the consumer’s anticipated economic loss from revealing his private information to the firm, and arises endogenously from a firm’s usage of consumer data. Combining an experiment and a structural model, I measure the revealed preferences separately for each component. Intrinsic preferences have seemingly small mean values, ranging from $0.14 to $2.37 per demographic variable. Meanwhile, they are highly heterogeneous across consumers and categories of data: The valuations of consumers at the right tail often exceed the firm’s valuation of consumer data. Consumers’ self-selection into data sharing depends on the respective magnitudes and correlation between the two preference components, and often deviate from the “low types are more willing to hide” argument. Through counterfactual analysis, I show how this more nuanced selection pattern changes a firm’s inference from consumers’ privacy decisions and its data buying strategy.
Consumers interact with firms across multiple devices, browsers, and machines; these interactions are often recorded with different identifiers for the same consumer. The failure to correctly match different identities leads to a fragmented view of exposures and behaviors. This paper studies the identity fragmentation bias, referring to the estimation bias resulted from using fragmented data. Using a formal framework, we decompose the contributing factors of the estimation bias caused by data fragmentation and discuss the direction of bias. Contrary to conventional wisdom, this bias cannot be signed or bounded under standard assumptions. Instead, upward biases and sign reversals can occur even in experimental settings. We then compare several corrective measures, and discuss their respective advantages and caveats.
Work in Progress
Measuring Welfare Preferences for Privacy (with Avner Strulov-Shlain)
xxxx2021 Becker Friedman Institute Grant ($20,000)
COPPAcalypse now? (with Garrett Johnson and James Cooper)
xxxx2021 University of Pennsylvania Economics of Digital Services Grant ($10,000)
Humans in the Data Loop: Valuing Consumer Data in Digital Advertising (with Harikesh Nair, Carlos Carrion, and Xiliang Lin)
Privacy Preferences in Microloan Applications (with Ivy Dang, Mandy Hu, and Pradeep Chintagunta)