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Farmers' Decision Making for Crop Insurance

2426 words (10 pages) Business Assignment

23rd Oct 2020 Business Assignment Reference this

Tags: Business AssignmentsFinance

Introduction

In the highly risky Australian agricultural economy, producers face ubiquitous and complex risks, including volatile yield, unstable market prices, possible adverse weather conditions, etc. (Aditto, Gan, & Nartea, 2012). To mitigate these risks, crop insurance is regarded as the most effective tool. Nevertheless, empirical evidence has witnessed Australian farmers’ low demand for crop insurance (Bocqu ́eho, Jacquet, & Reynaud, 2014). The unpopularity of crop insurance among producers provide a motivation to conduct research into it.There are numerous decision theories applicable to analyse producers’ choices on crop insurance, and agricultural economists have long relied on the expected utility theory (EUT) (Meyer, 2002). Nonetheless, the appropriateness of applying EUT for agricultural analysis has been questioned after years of research on its predictive effectiveness (Buschena, 2002). In more recent years, growing attention has been drawn to behavioural theories, especially the Nobel Prize-winning cumulative prospect theory (CPT) (Bocquého et al., 2014).

Research Question

This research proposes to study Australian farmers’ decisions on taking out crop insurance under the framework of EUT and CPT, since an absence of similar work has been found in the existing literature. 

Literature Review

Expected utility theory (EUT)

A simple scenario in decision making is when a decision maker maximizes expected payoffs by using the monetary values directly. In this circumstance, the decision maker is called risk-neutral and only emphasizes the expected profit regardless of the variation in potential

outcomes. In other words, he/she is indifferent between alternatives that have the same expected payoff even when one of them is riskier. Nevertheless, in reality, when most people take risk into consideration, their goals change from optimising expected profit to optimising expected utility (Pratt, 1964). In EUT, the monetary values of possible outcomes are mapped into utility values through a utility function that is defined in terms of wealth. Usually, a convex utility function represents a risk-seeking behaviour and a concave utility function suggests a risk-averse behaviour.

Cumulative Prospect Theory (CPT)

EUT provides how a rational individual would make choices, nonetheless, this model often fails to predict the actual decisions people make (Anderson, 2014). Prospect theory, on the other hand, has roots in cognitive theories of limited rationality.

Anderson (2014) summarized the four key characteristics of CPT that describes individuals’ irrational behaviour. Specifically, the first feature is the use of reference point, implying that people tend to measure an outcome around a reference point, e.g. their current wealth, classifying an outcome into a gain or a loss. The existence of a reference point will then lead to different risk attitudes on the gain domain or the loss domain, suggesting that individuals are inclined to switch between risk-seeking and risk-averse on different domains. The third feature is loss aversion, derived from the observation that individuals often try to avoid losses fundamentally. For example, the degree of someone’s vexation from losing $100 is often conceived to be higher than the degree of his/her joy from earning $100. The last attribute of PT is subjective probability, suggesting that people tend to distort objective probabilities into subjective odds, such as that a lottery buyer may often overestimate their chance of winning.

Empirical evidence

In this section, we will first review some literature that discussed farmers’ aversion to risk as described by the EUT model. We will then provide empirical evidence that on top of risk aversion, producers also exhibit the essential risk behaviours as captured by the CPT model.

Farmers risk aversion

The past decades have witnessed a large body of evidence showing that farmers are risk averse worldwide. In an early experiment, Lin, Dean, and Moore (1974) assessed farmers’ risk preferences through hypothetical lotteries from a number of Californian farms and found that utility theory of risk aversion describes farmer’s behaviour better than simple profit maximization, though neither of the optimisation methods predicted their decisions accurately. Another famous empirical study is by Binswanger (1980) who carried out a field experiment on a sample of Indian villagers and revealed that most producers show a high amount of risk aversion.

In Australia, some studies have also reported risk aversion among producers. Bardsley and Harris (1987) used a model involving the debt and asset portfolio of the farm to analyse producers’ decision making, which allows an estimation of producers’ risk attitudes with actual lotteries instead of hypothetical lotteries. Their experiment showed that Australian producers are typically risk averse. Recently, Monjardino et al. (2015) studied farmers’ choices on nitrogen application using different decision models, including profit maximization, utility maximization and multi-criteria optimization, based on four farming regions in Australia. The authors revealed that Australian producers, particularly those from dryland cropping systems, are likely to exhibit a high degree of risk aversion. It was also concluded that the multi-criteria optimization provided the best predictions on the choices that are the closest to farmers’ actual decisions. The utility maximizing method provides the second best performance, while the worst performance is given by profit maximization. The authors suggested that when we design tactics regarding improving yield output and the profit for dryland farms, the role of farmers’ risk aversion should be taken into account.

Farmers other risk attitudes

This section summarises some literature on the empirical evidence showing that farmers may follow the essential features of PT, namely the existence of a reference point, different risk behaviours on the loss/gain domains, loss aversion, and subjective probabilities, which could potentially support the appropriateness of applying CPT in agricultural economics.

First of all, EUT usually considers the overall wealth as the domain for decision making but CPT considers gains and losses. Several studies have found that farmers are likely to categorise outcomes into gains and losses instead of considering their overall wealth during decision-making, such as in their choices on profit margin hedging, agricultural pollution management, and output choices under liquidation risks (Kim, Brorsen, & Anderson, 2010).

Mahul (2000) also found that farmers usually have an S-shaped utility function around the reference point, suggesting a risk-loving behaviour on the loss domain while a risk-averse behaviour on the gain domain. In another earlier study, Collins, Musser, and Mason (1991) applied prospect theory to look at the behaviours of a population of grass seed growers in Oregon, the United States. Their results revealed that farmers who had experienced negative payoffs are inclined to switch from risk aversion to risk seeking, in the same way as described by CPT.

When it comes to the third feature, loss aversion, Liu and Huang (2013) investigated the function of loss aversion in Chinese cotton farmers’ application of pesticide based on surveys and field experiments. The authors suggested that the farmers exhibited significant loss aversion and those who are more averse to losses sprayed a lower amount of pesticide due to their aversion to health deterioration.

Lastly, there is also a growing body of study suggesting farmers’ distortion of objective probabilities. Kellogg (1983) was the earliest to use real monetary incentives to draw subjective probability distributions for farmers. Their results showed that farmers in Thailand rely on subjective probabilities when they assess potential yield outcomes. In a similar vein, Sherrick, Barry, Ellinger, and Schnitkey (2004) reported that Midwestern U.S. farmers are likely to use intuitive probabilities in their decision-making. The authors also emphasized the significance of this behaviour in developing effective and efficient insurance markets.

CPT in the agricultural context

Additionally, except the content discussed in the previous section in terms of the specific feature of CPT, there is a sparse amount of literature focus on the analysis of farmer behaviour by employing CPT directly.

Bocquého et al. (2014) explored the farmer risk preference under CPT by conducting a field study with a sample of 107 farmers’ real monetary payoffs. As a result, they summarised that the characteristics of farmers’ risk behaviour are threefold. Firstly, in terms of the value function convexity, the surveyed farmers may exhibit a concave value function for their gains while exhibiting a convex value function for their losses. Secondly, regarding the gain & loss sensitivity, the surveyed farmers are more sensitive to losses with roughly twice as their sensitivity to gains. Finally, as a result, the decision-making weighting function may exhibit an inverse S-shape as the surveyed farmers intend to overestimate the probability of unfavourable events. Those findings are consistent with the CPT’s arguments in agricultural economics which also suggest the necessity of decision-maker to consider those farmers’ behaviours into their policy-making process.

Babcock (2015) explored the farmers’ behaviours on subsidized crop insurance by a simulation study where they found that, with a rational reference point, CPT can return a favourable convergent prediction of farmer’s behaviour. Also, Du, Feng, and Hennessy (2016) emphasized the rationality of farmers’ anomalies behaviours could be CPT as well by studying U.S. farmers’ abnormal behaviour in terms of the subsidized crop insurance.

However, different findings were reported by other studies. For example, by studying the risk behaviour of Dutch hog farmers, Pennings and Smidts (2002) found that piglet buyers may have different risk behaviours on gains and losses while this is not the case for piglet breeders. Also, Galarza (2009) studies the farmers’ risk behaviour by selecting a group of cotton producers from Southern Peru. They reported that roughly 70% of the surveyed farmers exhibited CPT behaviour characteristics. However, for the rest 30% of farmers, EUT may domain their behaviours.

Conclusion

We have seen through the lens of the current literature that the recent studies on agricultural economics related to CPT or its individual features mostly reveal a positive result in favor of its performance, though with exceptions noticed. More importantly, there has not been research specifically applying CPT to study Australian farmers’ decision-making. This motivates our research question to investigate Australian farmers’ behaviour under the framework CPT, along with a comparison to EUT to garner more insights.

References:

  • Aditto, S., Gan, C., & Nartea, G. V. (2012). Sources of risk and risk management
  • strategies: The case of smallholder farmers in a developing economy. In N. Banaitiene
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  • Bocquého, G., Jacquet, F., & Reynaud, A. (2014). Expected utility or prospect theory maximisers? Assessing farmers’ risk behaviour from field-experiment data. European Review of Agricultural Economics, 41 (1), 135–172.
  • Meyer, J. (2002). Expected utility as a paradigm for decision making in agriculture. In A comprehensive assessment of the role of risk in us agriculture (pp. 3–19). Springer.
  • Buschena, D. E. (2002). Non-expected utility: What do the anomalies mean for risk in agriculture? In A comprehensive assessment of the role of risk in us agriculture (pp. 21–40). Springer.
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  • Anderson, E. J. (2014). Business risk management: models and analysis. Chichester, West Sussex, UK: Wiley.
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  • Monjardino, M., McBeath, T., Ouzman, J., Llewellyn, R., & Jones, B. (2015). Farmer risk-aversion limits closure of yield and profit gaps: A study of nitrogen management in the southern australian wheatbelt. Agricultural Systems, 137 , 108-118.
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