Farmers' Preferences for Crop Insurance Attributes

Abstract

Utilizing survey data from corn and soybean farmers in the Midwest, this study assesses the relative importance of different features of crop insurance products. Conjoint analysis results indicate that farmers' preferences for flexibility dominate both type of insurance and coverage level. Revenue insurance demand is greater by those who are larger, younger, and farm in more separate locations. Results are significant and consistent by size, insurance usage, leverage, and risk perception. The results permit prediction of market shares of competing insurance products within specific producer segments, and thus also provide guidance for targeting specific producer groups with new product configurations.

A choice experiment model for beef: What US consumer responses tell us about relative preferences for food safety, country-of-origin labeling and traceability

“…When convergence was achieved, the variances associated with the random parameters coefficients were not statistically significant. 7 Similar and even inferior response rates were obtained in comparable published, contingent valuation, unsolicited, bulk mailing studies where no economic compensation was used (e.g., Lusk, 2003;Roosen et al, 2003;Sherrick et al, 2003). It is possible that a monetary incentive would have increased our response rate.…”

On consumer preferences and the willingness to pay for term life insurance

“…Usage regarding insurance products has recently begun to increase. Earlier studies focused on health insurance (Kerssens and Groenewegen, 2005;Sricharoen and Buchenrieder, 2008;Van den Berg et al, 2008), livestock insurance (Buchenrieder and Fischer, 2009), crop insurance (Sherrick et al, 2003;Heenkenda, 2011), and services in the insurance industry (Von Watzdorf and Skorna, 2010).…”

“…Furthermore, there is a large strand of the development economics literature aimed at assessing the WTP for health coverage in rural areas across Africa (see, e.g., Asenso-Okyere et al, 1997;Dong et al, 2003;Ichoku et al, 2010), Asia (see, e.g., Asgary et al, 2004;Zhang et al, 2006;Chen et al, 2011), India (see, e.g., Mathiyazaghan, 1998;Dror et al, 2007), as well as Latin America (see, e.g., Vargas Bustamante et al, 2008;Eckhardt et al, 2011). Besides, preferences and WTP have been studied for crop insurance (see, e.g., Sherrick et al, 2003;Heenkenda, 2011), livestock insurance (see, e.g., Shaik et al, 2008;Buchenrieder and Fischer, 2009;Khan et al, 2013), flood insurance (see, e.g., Botzen and Van den Bergh, 2012), health insurance (see, e.g., Booske et al, 1999;Kerssens and Groenewegen, 2005;Van den Berg et al, 2008), long-term care insurance (see, e.g., Costa-Font and Font, 2009;Jacobs-Lawson et al, 2010), interest rate guarantees in unit-linked life insurance (see, e.g., Gatzert et al, 2011), weather insurance (see, e.g., Fraser, 1992;Musshoff et al, 2008), as well as auto, home, and household insurance (Hansen et al, 2013).…”

Modeling Exit and Entry of Farmers in a Crop Insurance Program

“…These studies approach insurance decisions at the aggregate level over time [see for example the work of Goodwin and Smith (1995), Goodwin (1993), Barnett, Skees, and Hourigan (1990), Hojjatti and Bockstael (1988), Gardner and Kramer (1986), and Nieuwoudt et al (1985)] or at the micro level for a single period [see Sherrick et al (2004), Sherrick (2003), Mishra and ElOsta (2002), Jose and Valluru (1997)]. From this literature has emerged a set of factors that influence decisions to purchase crop insurance such as farmer-specific characteristics (e.g., education, risk attitude, off-farm income), economic factors (e.g., crop returns, insurance premium), and farm-/region-specific characteristics (e.g., crop mix, location, soil quality).…”

“…Most studies at the county level have measured changes in these participation measures across decision units in space, although some have combined spatial and time series data. Micro-level studies have used surveys to ask whether farmers use crop insurance or not, and have used limited dependent variable regression techniques to model the dichotomous choice on participation and/or coverage level to farmer characteristics (Sherrick et al 2004, Sherrick 2003, Mishra and El-Osta 2002, Jose and Valluru 1997. These micro studies are at a single point in time, which constitutes a shortcoming in the study of dynamic crop insurance participation decisions.…”