Do non-choice data reveal economic preferences? Evidence from biometric data and compensation-scheme choice

Marja-Liisa Halko, Olli Lappalainen and Lauri Sääksvuori

Journal of Economic Behavior & Organization, 2021, vol. 188, issue C, 87-104

Abstract: We investigate the feasibility of inferring economic choices from simple biometric non-choice data. We employ a machine learning approach to assess whether biometric data acquired during sleep, naturally occurring daily chores and participation in an experiment can reveal preferences for competitive and team-based compensation schemes. We find that biometric data acquired using wearable devices enable equally accurate out-of-sample prediction for compensation-scheme choice as gender and performance. Our results demonstrate the feasibility of inferring economic choices from simple biometric data without observing past decisions. However, we find that biometric data recorded in naturally occurring environments during daily chores and sleep add little value to out-of-sample predictions.

Keywords: Compensation schemes; Competition; Team; Experiment; Gender; Heart rate variability; Non-choice data (search for similar items in EconPapers)
JEL-codes: C91 D01 D03 J16 J24 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167268121001505
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jeborg:v:188:y:2021:i:c:p:87-104

DOI: 10.1016/j.jebo.2021.04.009

Access Statistics for this article

Journal of Economic Behavior & Organization is currently edited by Houser, D. and Puzzello, D.

More articles in Journal of Economic Behavior & Organization from Elsevier
Bibliographic data for series maintained by Catherine Liu ().