Indirect estimation of farms risk aversion: mathematical programming approach

J. Zgajnar, S. Kavcic

Abstract: In the paper we present an approach how farmer’s risk aversion could be estimated indirectly. This is particularly beneficial if one analyses hypothetical farms with absence of decision makers, as for example in the case of representative farms that are usually used for systematic studying. Applied approach is based on mathematical programming methods. The main idea is to use current farm practice as a baseline and to calculate missing data with partial optimization process. Non-interactive procedure based on expected value-variance framework and quadratic programming paradigm minimising variance has been applied to locate current farms’ plans in expected value - variance space and to estimate their risk aversion. To demonstrate applicability of the approach, three representative dairy farms were analysed. Obtained results indicate high relative risk aversion in all three cases. More intensive dairy farm in flat area is less risk averse as smaller, still intensive farm with similar production conditions or farm with less intensive farming typical for hilly area. The study illustrates also discrepancy between optimal solutions considering or neglecting farmers risk aversion.

Keywords: livestock farms; mathematical programming; quadratic programming; risk aversion

Date published: 2019-10-03