Using Genetic Information to Improve the Prediction of Individual Food Choice: A Case Study of Alcoholic Beverages

34 Pages Posted: 3 Mar 2021

See all articles by Chen Zhu

Chen Zhu

China Agricultural University - College of Economics and Management; China Center for Genoeconomic Studies (CCGS)

Tim Beatty

University of California, Davis

Qiran Zhao

China Agricultural University

Wei Si

China Agricultural University

Qihui Chen

China Agricultural University

Date Written: December 28, 2020

Abstract

Individual food choices and consumption are closely relating to one's diet, nutrition, and health. Using the case of alcoholic beverages, this study extends the random-utility framework by incorporating genetic information into consumer demand models, and demonstrates the significant impact of genetic factors on individual food choice decisions in a novel way. Integrating individual-level responses of discrete choice experiments (DCE), genotyping data, and socioeconomic/demographic characteristics of 484 participants collected from face-to-face interviews in mainland China, we employ a machine learning-based classification (MLC) approach to identify and predict individual choices. We show that genetic factors are critical to explaining variations in both general drinking behavior and choices of particular products. We systematically compared the performance of traditional discrete choice models and MLC models without and with genetic factors. The MLC predictive model with both socio-demographic and genetic features yields the highest accuracy of 74.7% and AUC-ROC of 0.85. Our findings warrant further economic studies of human behaviors with the integration of genetic data.

Keywords: Consumer preference, food choice, choice experiment, genetic factor, machine learning

JEL Classification: D12, Q10

Suggested Citation

Zhu, Chen and Beatty, Tim and Zhao, Qiran and Si, Wei and Chen, Qihui, Using Genetic Information to Improve the Prediction of Individual Food Choice: A Case Study of Alcoholic Beverages (December 28, 2020). Available at SSRN: https://ssrn.com/abstract=3757205 or http://dx.doi.org/10.2139/ssrn.3757205

Chen Zhu (Contact Author)

China Agricultural University - College of Economics and Management ( email )

Beijing
China

China Center for Genoeconomic Studies (CCGS) ( email )

Tim Beatty

University of California, Davis ( email )

One Shields Avenue
Apt 153
Davis, CA 95616
United States

Qiran Zhao

China Agricultural University ( email )

Beijing
China

Wei Si

China Agricultural University ( email )

Beijing
China

Qihui Chen

China Agricultural University ( email )

Beijing
China

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