The Identity Fragmentation Bias
26 Pages Posted: 10 Jan 2020 Last revised: 2 Feb 2021
Date Written: January 29, 2021
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: Suggested Citation