Factors Influencing the Take-Up of Agricultural Insurance and the Entry into the Mutual Fund: A Case Study of the Czech Republic

Abstract

The objective of the study was to identify the main factors influencing farmers’ willingness to take up agricultural insurance and participate in a mutual fund for non-insurable risks in the Czech Republic. Responses from 214 representative farms were processed using descriptive statistics, paired t-tests, binary logistic regression, and contingency analysis. The regression model showed the influences of agricultural area, distrust in insurance companies, the probability of losing more than 20% of production, the price of insurance premiums, and having a developed formal strategy on the likelihood of taking up agricultural insurance. Unlike previous empirical studies, this study did not attempt to look at agricultural insurance as an isolated risk management tool but rather to show the interrelationship between farmers’ decisions to join a mutual fund and their choice of agricultural insurance. Farmers expect most agricultural production risks to become significantly more important. With the ongoing economic crisis in the EU, there is growing pressure to reduce ad hoc public spending on coverage of non-insurable risks and to seek alternative solutions. The study also shows the need for a holistic approach to the design of risk management support systems in EU countries.

Risk Aversion and Perception of Farmers about Endogenous Risks: An Empirical Study for Maize Producers in Awi Zone, Amhara Region of Ethiopia

“…Farmers with large farm holdings did not worry about the time to purchase their farm input and did not involve themselves in off-farm activities since they had a sufficient financial capacity. This finding is in line with the studies of Mesfin et al (2011), Ullah et al (2015) and Kislingerova and Spicka (2022), in which large-scale farmers have a high capacity to bear risk and less need for risk management tools. Dabkiene (2020) signified that agricultural production on small farms was not sufficient to support the families; therefore, they were involved in diversification activities.…”

“…This finding was in line with the study of Nahraeni (2012). Moreover, Kislingerova and Spicka (2022) found that large farm size provides a greater management capacity and greater economies of scale for the farmers and could lead them to apply different risk management practices.…”

“…Moreover, Sulewski and Kłoczko-Gajewska (2014) implied that farmers with large farms have low risk aversion behaviour and are not eager to use risk management strategies since they have a high economic of size. On the contrary, Vihi et al (2018), Kislingerova and Spicka (2022) showed that large-scale farmers are usually high capital base farmers and have the ability to use alternative strategies; moreover, large-scale farmers can purchase and use improved inputs and practices more easily than small-scale farmers.…”

“…Agricultural production is vulnerable to various types of risks that emanate from the natural, economic and socio-political environment (Onumah 2018). Most agricultural production risks are significantly more important for the farmers since they can cause largescale production losses (Kislingerova and Spicka 2022). Inadequate institutional support (access to credit, research and extension), inappropriate production systems and inadequate infrastructures and post-harvest management technology are the main contributors to the production risk for the farmers (Yiadom-Boakye et al 2013).…”

An Estimated Analysis of Willingness to Wait Time to Pay Rice Agricultural Insurance Premiums Using Cox’s Proportional Hazards Model

“…where L β E f ron is the maximum likelihood function of parameter β using the Efron partial likelihood method, β is the parameters of the regression model to be estimated, s j is the sum of each p predictor variable of the individual experiencing the event at time t j , D t j is the set of individuals who get the event at time t j , d j is number of cases of co-occurrence at time t j , and R t j is the set of individuals who have the risk of failing at time j. From Equation (4), the ln partial likelihood function is obtained as Equation ( 5), as follows [35,36]:…”