The Identity Fragmentation Bias

26 Pages Posted: 10 Jan 2020 Last revised: 2 Feb 2021

See all articles by Tesary Lin

Tesary Lin

Boston University - Department of Marketing; University of Chicago - Marketing Management

Sanjog Misra

University of Chicago - Booth School of Business

Date Written: January 29, 2021

Abstract

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.

Keywords: fragmentation, cookies, bias, inference, privacy, measurement

JEL Classification: C13, C18, C81, C55, M31

Suggested Citation

Lin, Tesary and Misra, Sanjog, The Identity Fragmentation Bias (January 29, 2021). Available at SSRN: https://ssrn.com/abstract=3507185 or http://dx.doi.org/10.2139/ssrn.3507185

Tesary Lin (Contact Author)

Boston University - Department of Marketing ( email )

United States

University of Chicago - Marketing Management ( email )

Chicago, IL 60637
United States

Sanjog Misra

University of Chicago - Booth School of Business ( email )

5807 South Woodlawn Avenue
Chicago, IL 60637
United States

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