Ownership of Cash Value Life Insurance among Rural Households: Utilization of Machine Learning Algorithms to Find Predictors

Wookjae Heo (), Eun Jin Kwak, John Grable and Hye Jun Park
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Wookjae Heo: Division of Consumer Science, Purdue University, Marriot Hall 242, 900 Mitch Daniels Blvd., West Lafayette, IN 47906, USA
Eun Jin Kwak: Austin E. Cofrin School of Business, University of Wisconsin, Green Bay, WI 54311, USA
John Grable: Department of Financial Planning, Housing, and Consumer Economics, College of Family and Consumer Science, University of Georgia, Athens, GA 30602, USA
Hye Jun Park: Division of Consumer Science, Purdue University, Marriot Hall 242, 900 Mitch Daniels Blvd., West Lafayette, IN 47906, USA

Mathematics, 2024, vol. 12, issue 16, 1-27

Abstract: This study examines the determinants of life insurance ownership with a focus on rural areas and farming households in the United States. Utilizing data from online surveys conducted in 2019 and 2021, this paper explores how psychological factors, financial knowledge, and household characteristics influence life insurance ownership. Traditional indicators like wealth, income, and age were evaluated alongside less frequently discussed variables such as farm loans and rural residency. Machine learning techniques, including neural networks, Support Vector Machine modeling, Gradient Boosting, and logistic regression, were employed to identify the most robust predictors of life insurance demand. The findings reveal that farming-associated factors, particularly holding a farm loan and living in a farming household, significantly predict life insurance ownership. The study also highlights the complexity of life insurance demand, showing that financial education and management practices are critical determinants. This research underscores the need for tailored financial risk management strategies for rural and farming households and contributes to a nuanced understanding of life insurance demand in varying contexts.

Keywords: life insurance; rural; farm loan; machine learning; farmer (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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