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

Marja Liisa Halko; Olli Lappalainen; Lauri Sääksvuori

doi:10.1016/j.jebo.2021.04.009

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

Marja Liisa Halko^*
, Olli Lappalainen
, Lauri Sääksvuori

^*Tämän työn vastaava kirjoittaja

Politiikan tutkimus

Tutkimustuotos: Artikkeli › Tieteellinen › vertaisarvioitu

1 Sitaatiot (Scopus)

27 Lataukset (Pure)

Abstrakti

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.

Alkuperäiskieli	Englanti
Sivut	87-104
Sivumäärä	18
Julkaisu	Journal of Economic Behavior and Organization
Vuosikerta	188
DOI - pysyväislinkit	https://doi.org/10.1016/j.jebo.2021.04.009
Tila	Julkaistu - elok. 2021
OKM-julkaisutyyppi	A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Rahoitus

The authors want to thank Jonas Fooken, Tuukka Holster, Peter Matthews, and Henri Nyberg for useful discussion and comments and Juho Halonen for research assistance. This research is supported by the research project aivoAALTO (Aalto University), the Yrjö Jahnsson Foundation, and the Research Foundation of Cooperative Banks, Finland. The authors want to thank Jonas Fooken, Tuukka Holster, Peter Matthews, and Henri Nyberg for useful discussion and comments and Juho Halonen for research assistance. This research is supported by the research project aivoAALTO (Aalto University), the Yrj? Jahnsson Foundation, and the Research Foundation of Cooperative Banks, Finland. The authors declare no conflict of interest.

Julkaisufoorumi-taso

Jufo-taso 2

!!ASJC Scopus subject areas

Economics and Econometrics
Organizational Behavior and Human Resource Management

Pääsy asiakirjaan

10.1016/j.jebo.2021.04.009Lisenssi: CC BY

1-s2.0-S0167268121001505-mainFinal published version, 1,06 MBLisenssi: CC BY

http://urn.fi/URN:NBN:fi:tuni-202109066953Lisenssi: CC BY

Data for: Do non-choice data reveal economic preferences? Evidence from biometric data and compensation-scheme choice
Halko, M.-L. (Contributor), Lappalainen, O. (Creator) & Sääksvuori, L. (Contributor), Mendeley Data, 4 kesäk. 2021
DOI - pysyväislinkki: 10.17632/5h9mdnjfkw.1
Tietoaineisto: Dataset

Siteeraa tätä

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title = "Do non-choice data reveal economic preferences? Evidence from biometric data and compensation-scheme choice",

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.",

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note = "Funding Information: The authors want to thank Jonas Fooken, Tuukka Holster, Peter Matthews, and Henri Nyberg for useful discussion and comments and Juho Halonen for research assistance. This research is supported by the research project aivoAALTO (Aalto University), the Yrj{\"o} Jahnsson Foundation, and the Research Foundation of Cooperative Banks, Finland. Funding Information: The authors want to thank Jonas Fooken, Tuukka Holster, Peter Matthews, and Henri Nyberg for useful discussion and comments and Juho Halonen for research assistance. This research is supported by the research project aivoAALTO (Aalto University), the Yrj? Jahnsson Foundation, and the Research Foundation of Cooperative Banks, Finland. The authors declare no conflict of interest. Publisher Copyright: {\textcopyright} 2021 Elsevier B.V.",

year = "2021",

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doi = "10.1016/j.jebo.2021.04.009",

language = "English",

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T1 - Do non-choice data reveal economic preferences? Evidence from biometric data and compensation-scheme choice

AU - Halko, Marja Liisa

AU - Lappalainen, Olli

AU - Sääksvuori, Lauri

N1 - Funding Information: The authors want to thank Jonas Fooken, Tuukka Holster, Peter Matthews, and Henri Nyberg for useful discussion and comments and Juho Halonen for research assistance. This research is supported by the research project aivoAALTO (Aalto University), the Yrjö Jahnsson Foundation, and the Research Foundation of Cooperative Banks, Finland. Funding Information: The authors want to thank Jonas Fooken, Tuukka Holster, Peter Matthews, and Henri Nyberg for useful discussion and comments and Juho Halonen for research assistance. This research is supported by the research project aivoAALTO (Aalto University), the Yrj? Jahnsson Foundation, and the Research Foundation of Cooperative Banks, Finland. The authors declare no conflict of interest. Publisher Copyright: © 2021 Elsevier B.V.

PY - 2021/8

Y1 - 2021/8

N2 - 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.

AB - 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.

KW - Compensation schemes

KW - Competition

KW - Experiment

KW - Gender

KW - Heart rate variability

KW - Non-choice data

KW - Team

U2 - 10.1016/j.jebo.2021.04.009

DO - 10.1016/j.jebo.2021.04.009

M3 - Article

AN - SCOPUS:85108117663

SN - 0167-2681

VL - 188

SP - 87

EP - 104

JO - Journal of Economic Behavior and Organization

JF - Journal of Economic Behavior and Organization

ER -

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

Abstrakti

Rahoitus

Julkaisufoorumi-taso

!!ASJC Scopus subject areas

Pääsy asiakirjaan

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Tietoaineistot

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

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