Participation in a mutual fund covering losses due to pest infestation: analyzing key predictors of farmers’ interest through machine learning

Abstract

In the context of intensified Halyomorpha halys infestations in Italy, this paper provides a very first investigation of key factors that drive fruit growers’ intention to participate in a mutual fund (MF) compensating production losses due to this invasive insect. Data were collected in Veneto Region in Italy, where many farmers suffered H. halys attacks, and interest in the development of innovative risk management tools is growing. The study investigates how behavioral (risk attitude, risk perception) and personality factors (self-efficacy, locus of control) explain farmers’ intention to participate in the MF, additionally controlling for a large number of primary control data (e.g. farmers’ perceptions and characteristics, farm characteristics). The study assumes approximate sparsity and applies the least absolute shrinkage and selection operator (LASSO), a machine learning technique which represents an original approach for research on risk management. Our empirical analysis reveals that farmers’ intention to participate in the MF is driven by an interplay between the perceived risk of production loss, the benefits from participation in the fund, and the farm age, rather than by socio-economic characteristics of the farm. Results provide valuable insights for policymakers and local stakeholders to implement a mutual fund close to the farmers’ needs.