Stability of risk attitude, agricultural policies and production shocks: evidence from Italy

Martina Bozzola,Robert Finger

doi:10.1093/erae/jbaa021

Abstract

AbstractThis article investigates the stability of farmers’ risk attitude over time. To this end, we estimate responses to changes in agricultural policies and production shocks. We use a unique panel data of over 36,000 Italian farms specialised in cereals, during the period 1989–2009. We find evidence of risk preference changes over time in response to changes in the European Union Common Agricultural Policy and possibly after a drought-induced production shock.

Highlights

Temporal stability of risk preferences can mean that subjects exhibit the same risk attitudes over time or that their risk attitudes are a stable function of states of nature that change over time (Andersen et al, 2008)
The results presented provide an indication of the general tendency in producers’ risk attitude changes through time, in responses to major policy changes and exogenous climate shocks, but we caution the reader to keep in mind that real world changes in inputs will be the result of all the relevant market adjustments, as well as some technological constraints, which cannot be fully accounted for in our simple empirical set-up (Moro and Sckokai, 1999)
The question whether structural parameters of human behaviour such as risk aversion and risk perceptions are stable through time become popular in macroeconomic modeling since the publication of the classic Lucas critique (Lucas, 1976)

Summary

Introduction

Temporal stability of risk preferences can mean that subjects exhibit the same risk attitudes over time or that their risk attitudes are a stable function of states of nature that change over time (Andersen et al, 2008). Finger risk perception and their interaction are important determinants of the adoption and use of specific risk management tools and practices (Pennings, Wansink and Meulenberg, 2002; Pennings and Garcia, 2004; Pennings and Wansink, 2004; Just and Just, 2016). Measuring these concepts in separation in non-experimental settings is very difficult (Iyer et al, 2020).

Objectives

Results

Conclusion