Understanding Farmer Financing Preferences by Segmenting the Agricultural Lending Market
Abstract
Purpose- The goal of this study is to identify the current distinct market segments within the US agricultural credit lending market, predict segment membership based on readily available characteristics, and better understand farmer financing preferences. Design/methodology/approach- A two stage clustering analysis was used to identify five distinct market segments. A multinomial logit regression was used to predict segment membership based on demographic and psychographic characteristics. Findings- The segmentation analysis produced five distinct market segments. The identified segments are service, convenience, balance, price, and performance. Practical implications- This information can aid credit lenders in segmenting the market and tailoring their sales approach to the different farmer segments. Originality/value- This paper contributes to the literature in several ways. First, previous studies of farmer selection of lending institutions rely on supply side data (Brewer et al., 2019; Dodson & Koenig, 2004; Ifft and Fiechter, 2020). While these studies are useful in knowing how farmers may be segmented according to their choice set of particular lending institutions, what we cannot examine is why the farmer is choosing that choice set. Our study incorporates psychographic and buying preferences. Prior work has highlighted the trend away from demographics and socioeconomic characteristics towards psychographic characteristics as categories for customer segmentation (Sherrick et al., 1994). Secondly, as described above, much has changed in the agricultural lending markets concerning the lending institutions available to farmers and the technology that changes how farmers and lending institutions interact. Thus, this study updates the literature as farmers preferences may have changed due to the new market structure
Degree
M.Sc.
Advisors
Brewer, Purdue University.
Subject Area
Agriculture|Agricultural economics|Economics
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