Do Risk Preferences Really Matter? The Case of Pesticide Use in Agriculture

Abstract

Even if there exists an extensive literature on the modeling of farmers' behavior under risk, actual measurements of the quantitative impact of risk aversion on input use are rare. In this article we use simulated data to quantify the impact of risk aversion on the optimal quantity of input and farmers' welfare when production risk depends on how much of the input is used. The assumptions made on the technology and form of farmers' risk preferences were chosen such that they are fairly representative of crop farming conditions in the US and Western Europe. In our benchmark scenario featuring a traditional expected utility model we find that less than 4% of the optimal pesticide expenditure is driven by risk aversion and that risk induces a decrease in welfare that varies from -1.5% to -3.0% for individuals with moderate to normal risk aversion. We find a stronger impact of risk aversion on quantities of input used when farmers' risk preferences are modeled under the cumulative prospect theory framework. When the reference point is set at the median or maximum profit, and for some levels of the parameters that describe behavior toward losses, the quantity of input used that is driven by risk preferences represents up to 19% of the pesticide expenditure. Acknowledgements: Christophe Bontemps and Céline Nauges acknowledge funding from the French National Research Agency (ANR) under the Investments for the Future (Investissements d'Avenir) program, grant ANR-17-EURE-0010.Our (benchmark) situation is fairly simple: the representative farmer, who is risk averse, uses pesticides to produce some agricultural output (crop). Output is not known with certainty due to the presence of a random and unpredictable shock. In order to make the empirical analysis easy to follow and despite contrasting empirical evidence (e.g.; Pannell 1991; Möhring et al. 2020), we consider pesticides as a risk-decreasing input. i In the traditional Expected Utility (EU) framework, the optimal quantity of a risk-decreasing input used by a risk-averse farmer is above the risk-neutral optimum, since pesticides are used by risk-averse farmers to manage production risk. We compute the share of pesticide expenditure that is used by farmers for self-insurance by comparing the optimal level of input use under a risk neutrality assumption to the optimal level of input use under the assumption that the farmer is risk averse. We also report the change in farmer's welfare induced by risk aversion with respect to the risk-neutrality case. Along the lines of Pannell ( 2006) and Chavas (2019a) welfare is measured by the certainty equivalent, which is defined as the difference between the expected profit and the risk premium. If pesticides were assumed risk increasing instead, then risk-averse farmers would use less of the input to self-insure against production risk. We do not consider this case in the empirical analysis to follow. We argue this is not a major limitation of our work since the main outcome of interest, here the change in input use and welfare ind...

Bridging behavioural factors and standard bio‐economic modelling in an agent‐based modelling framework

“…Given the high importance of risk in agricultural production, the consideration of cognitive biases with respect to decision‐making under uncertainty may add an important level of detail to the simulation of agricultural systems (Howley et al, 2017). Considering the cumulative prospect theory in simulating farmers’ decision‐making has shown that it can improve the correlation between observed and simulated land‐use data and farmer behaviour (e.g., Appel & Balmann, 2019; Bontemps et al, 2020). The advantage of FARMIND is that it allows both the expected utility theory and prospect theory decision rules to be considered in the same simulation, which provides an important entry point to diversify the representative agent model that assumes only one type of decision process (Harrison & Rutström, 2008).…”

“…Therefore, FARMIND can be used to test and develop hypotheses about farmers’ behaviour in the context of counterfactual analysis or the implementation of new agri‐environmental policy measures. Moreover, it builds a bridge to recent empirical applications of the cumulative prospect theory in agriculture (e.g., Bontemps et al, 2020; Iyer et al, 2020) and addresses the challenges in understanding decision‐making under uncertainty, which cannot be identified from empirical data (Hellerstein et al, 2013; Just & Just, 2016).…”

“…In particular, their conceptualisation in the framework of the cumulative prospect theory (Kahnemann, 2003) has proved useful when seeking to explain farmers' behaviour (e.g. Bontemps et al, 2020). Empirical evidence indicates that farmers' behaviour is influenced by their risk aversion and subjective probability weighting (e.g.…”

Risk aversion, impatience, and adoption of conservation agriculture practices among smallholders in Zambia

“…Risk preference refers to attitudes towards risk and is a key factor in determining how people make decisions [ [37] , [38] , [39] ]. Risk-averse individuals tend to be more cautious, and prioritize stability and reliability over potential, but risky gains [ 40 ]. Conversely, individuals or groups tolerant to risk might exhibit a greater propensity to adopt new ideas, technologies, or practices despite the accompanying uncertainties.…”

“…The process of embracing innovations involve a deliberate evaluation of potential risks against potential gains. Diverse risk preferences among entities or individuals often result in varying inclinations toward adopting different practices [ 23 , 32 , 40 , 50 , 51 ]. For instance, when farmers anticipate simultaneous increases in returns and risks, risk-averse households are less inclined to opt for farming strategies that escalate risk despite the higher returns.…”