In this blog, Dr RM Prasad argues for integration of Behavioural Economics theories into the domain of Extension education as it will help in understanding the decision making behaviour of farmers better.
Behavioural Economics is different from classical economics. Classical economics assumes that people are perfectly rational. Rationality in economics means that the individual has perfect knowledge of all the alternatives and expected utilities, which allows choice of an option that maximises net utility. Behavioural Economics evolved with insights from the field of psychology as applied to the economic decision-making processes of individuals and institutions. The effects of psychological, emotional, cultural, and social factors on the decisions of individuals and institutions are studied by Behavioural economists, which were relatively ignored by classical economists. Extension education is an applied science which deals with the creation, transmission and application of knowledge designed to bring about planned changes in the behavioral complexes of farmers. In this context, integration of Behavioural Economics theories into the domain of Extension education will help in understanding the decision making behaviour of farmers better. A brief history of Behavioural Economics is presented in Box 1.
Box 1. Behavioural Economics – A Brief History
Historically, it seems that economics was much more connected to psychology, as evident from Adam Smith’s classic work, The Wealth of Nations published in 1776, in which he famously argued that economic behavior was motivated by self-interest. Jeremy Bentham also wrote extensively on the psychological underpinnings of utility, whose ideas influenced the development of welfarism. With the emergence of neoclassical economics that focused on supply and demand as the driving forces in production, economics became more mathematical and started forgetting that they are dealing with human beings.
WHY BEHAVIOURAL ECONOMICS?
Researchers in Behavioural Economics figured out that the biases exhibited by humans have a pattern and we could exploit those biases to influence decision making. As a response to the predictions about the future of economics, Thaler (2009) predicted that Homo economicus would evolve into Homo sapiens. In other words, Economics will become more related to human behaviour. As researchers in Social Science, we need to understand that we are dealing with Homo sapiens.
Choice architecture is a behavioral change approach employed by behavioral economists. It refers to intentionally changing a decision-making environment (such as a presentation, timing, or context) to influence a decision (Williamson 2018). Richard Thaler has proposed a specific type of choice architecture known as ‘nudging’. Nudging is the act of subtly modifying the environments within which voluntary decisions are made without actually restricting any options. He called it ‘Libertarian Paternalism’.
Dan Ariely in his book Predictably Irrational delves into the irrational nature of many decisions, and how to apply these behavioural trends in the marketing strategy. According to Ariely, ‘Most people think they have a good handle on why they make certain decisions. People often think that they’re making decisions, but in reality their decisions are very much determined by their environment.’
Extension scientists often are victims of ‘confirmation bias’. They form opinions about farmers’ problems and conveniently choose only those pieces of information that support their hypothesis. Such biases cloud their decision making, and so they won’t search for real evidence. Behavioral science insights can help extension workers to realise this fallacy. It will be all the more important to recognize that such insights will help in understanding the biases of farmers in the adoption process.
Theories of select Behavioural economists
An attempt is made to present the salient contributions of select Behavioural economists, in Table 1.
Table 1: Theories of select Behavioural economists
The concepts/theories are briefly explained below.
‘Bounded rationality’ is the idea that when individuals make decisions, their rationality is limited by the tractability of the decision problem, their cognitive limitations and the time available. In this view, decision maker’s act as satisfiers, rationality describes humans making decisions within the constraints of incomplete and imperfect information, limited time, and restricted computational ability. Rationality is thus ‘bounded’ by its limitations even though people try to decide rationally.
Bounded rationality postulates that in case of decision making in the wild (‘wild’ stands for situations where the events and outcomes are not so certain), people make use of heuristics or rule of thumb. The decisions may not be rational, but given the circumstances, it might be the right one. Behavioral sciences can map the decision making process. Once the decision tree is mapped, it can provide point of intervention to change behavior. Fast and Frugal Trees (FFTs) are simple algorithms that facilitate efficient and accurate decisions based on limited information. An illustrative case could be that of an emergency doctor facing a patient with high fever and sore throat. Here, the doctor has to take a quick decision whether to send the patient to COVID-19 care unit or to a regular hospital bed. Extension workers may also face such situations while suggesting suitable technologies to farmers affected by natural disasters.
Kreckova and Brozova (2017) had conducted a study on agricultural insurance and bounded rationality. The study confirmed the existence of bounded rationality in agricultural insurance in the Czech Republic. Bounded rationality in this study seems to originate due to insufficient information on payments in the insurance industry, excessive complexity related to the selection of the best premium, as well as from the fear of impending loss. The study concluded that it seems to be primarily more acceptable to clients to pay regularly, but smaller amounts, than to eventually pay a one-time but very high amount of money.
In 1979, Kahneman and Tversky published Prospect Theory: An Analysis of Decision under Risk that used cognitive psychology to explain various divergences of economic decision making from neo-classical theory. Prospect theory has two stages: an editing stage and an evaluation stage. In the editing stage, risky situations are simplified using various heuristics. In the evaluation phase, risky alternatives are evaluated using various psychological principles that include:
- Reference dependence: When evaluating outcomes, the decision maker considers a ‘reference level’ (for example, the previous year’s yield). Outcomes are then compared to the reference point and classified as ‘gains’ if greater than the reference point or ‘losses’ if less than the reference point;
- Loss aversion: Losses are avoided more than equivalent gains sought for.
Prospect theory explains three biases people use when making decisions. They are:
1. Certainty: occurs when people tend to overrate options that are certain, and risk averse with regard to gains, e.g., use of a drought tolerant crop variety.
2. Isolation effect: refers to people’s tendency to act on information that stands out and differs from the rest, e.g., success stories of other farmers.
3. Loss aversion: is when people prefer to avoid losses rather than acquire equivalent gains. e.g., crop insurance package.
The theory describes how people choose between different options (or prospects) and how they estimate (often in a biased or incorrect way) the perceived likelihood of each of these options happening. People make decisions using several common biases. Understanding these biases can help persuade people to take action. If we take the example of investor behaviour, when an investor is presented with an equal choice, he/she will choose the one presented with the most perceived gains. This is because according to Prospect Theory, the perceived loss would have a greater negative emotional impact.
Kahneman has described the process of ‘thinking fast and slow’, which is also known as System 1 and System 2 theory. System 1 is fast, instinctive and consumes less energy. System 2 is slower, which requires thinking and energy. While System 1 refers to automatic decision making, System 2 refers to reflective thinking.
An example of System 1 is normal driving, whereas driving in hilly terrain under snowy condition is System 2. The theory of System 1 and 2 is incredibly useful as a way to understand the complexities of human decision making.
In the field of extension education, Prospect Theory can be used to explain several biases farmers rely on when making decisions related to risk and uncertainty. These could be adoption of risk aversion practices related to climate change, insurance package for farmers, poultry disease surveillance, etc. Understanding these biases can help in persuading farmers to take action.
Richard Thaler who coined ‘Nudge’ refers to any aspect of choice architecture that alters the behaviour of people in a predictable way without prohibiting any options or significantly changing their economic incentives. Nudge discusses how public and private organizations can help people make better choices in their daily lives. It is said that Thaler has changed the Economics discipline itself in that ‘[h]e doesn’t write papers that are full of math, but writes papers that are full of common sense’.
Nudge can be considered as an approach to decision making and cognitive biases such as anchoring, framing, etc., as its tools. Nudges can integrate normative messages. For example, when communicating with people who have not adopted the desired target behavior, messages might be used to inform clientele about the number of people in a certain socio-demographic group or geographic area who have adopted the behaviour. This approach is most likely to be effective when the target audience is under-performing in relation to the reference group, but wants to be like the reference group. This is referred to as ‘framing effect’. (For further details, kindly refer AESA Blog 105 – ‘Rethinking policy from a behavioural angle: A case of Nudge’ by Muthu prasad T, Niranjan S and Aditya KS).
Ariely observes that human behaviour departs from standard economic theory in systematic and predictable ways, which he explores at length in his studies. He is more connected with neuro economics. While Behavioural economics is mostly experimental and based on field observation, in neuro economics, researchers try to observe what happens in people’s brains. Ariely observed that people often behave in ways that are completely at odds with how they would behave if they were rational utility maximizers. The most common example has to do with the effect of emotion on our behaviour. A good illustration could be of someone who goes to a restaurant saying that he is on a diet, but when the waiter comes with a new dish he changes his opinion and tastes the dish, now this is a case of irrational behaviour. There are many such irrational behaviours experienced in daily life.
Ariely believes that economics would make a lot more sense if it was based on how people actually behave, instead of how they should behave. Using this hypothesis as a guide, Ariely thinks there are unlimited social, economic and political problems that can be solved or at least addressed more effectively in a chaotic world. Some of the methods suggested are: (a) the influence of ‘free’; (b) price primacy; (c) herding; and (d) placebo effect. Getting something free makes us feel excited, and the strong emotional impact of a ‘free price’ is called the ‘zero price’ effect. After administering a placebo (resembling a real drug, but with an inactive substance like sugar), if the patient feels cured, it is not due to the properties of placebo, but belief in the placebo. This is referred to as placebo effect.
Ariely (2010) noted that the only effective way to get people to respond to suffering is through an emotional appeal, rather than through an objective reading of the great need. The upside is that when our emotions are awakened, we can be tremendously caring. By realizing that our emotions are fickle and how our compassion biases work, we can make more reasonable decisions and help people who need our care. This is particularly applicable in the case of farmers under stress during the COVID-19 pandemic.
Ariely (2016) argues that people have an innate ability to trust one another, but struggle to trust large corporations. Ariely shares tips for overcoming this barrier and creating organizations that people can trust. There are many aspects and building blocks for trust, but there are five key mechanisms suggested by Ariely that allow human beings to trust one another: long-term relationships, transparency, intentionality, revenge, and aligned incentives. Ariely’s views are very relevant in the case of establishing farmer trust in FPOs.
Incentives and behavior
Uri Gneezy’s early work on ‘when and why incentives can backfire’ has become the cornerstone in a compelling line of research that explores when traditional economic theories fail to explain real human behavior with regard to incentives. Gneezy’s research focuses on helping organizations use Behavioural economics to optimize incentive schemes.
Uri Gneezy et al. (2011) explain how extrinsic incentives may come into conflict with other motivations. If incentives are not substantial enough, this change in perception can lead to undesired effects on behaviour. Incentives might have the desired effects in the short term, but they still weaken intrinsic motivation.
The growing body of evidence suggests that the effects of incentives depend on how they are designed, the form in which they are given, how they interact with intrinsic motivation and social motivation, and what happens after they are withdrawn.
Monetary incentives have two kinds of effects: the standard direct price effect, which makes the incentivized behaviour more attractive, and an indirect psychological effect. In some cases, the psychological effect works in an opposite direction to the price effect and can crowd out the incentivized behaviour.
OTHER SALIENT WORKS IN AGRICULTURE
There are other Behavioural economists also, whose studies are relevant to the discipline of Extension education. For instance, Duflo et al. (2011) in their empirical study revealed that small, time-limited discounts in the cost of fertilizers at the time of harvest induce substantial increase in fertilizer use, compared to those induced by much larger price reductions later in the season. They concluded that a small, time bound discount programme on fertilizer could be an effective, easy to scale up policy that can encourage fertilizer use without distorting decision making and inducing excessive use of fertilizers.
Nava Ashraf et al. (2016) had applied insights from psychology and economics to understand decision making with the aim of improving the design of development programmes. Their research has focused on technology adoption and intra-household decision making, with applications in health, agriculture and microfinance.
Mani et al. (2013) hypothesize that poverty directly impedes cognitive function. The researchers examined the cognitive function of farmers across the planting cycle. It was found that the same farmer shows diminished cognitive performance before harvest, when poor, as compared with after harvest, when rich. Although farmers showed more stress before harvest, that does not account for diminished cognitive performance. Instead, it appears that poverty itself reduces cognitive capacity. These results help explain a spectrum of behaviors among the poor.
Nadia Streletskaya et al. (2020) provide a selective overview of the linkages and complementary topics in behavioral economics and agricultural adoption literatures. The authors highlight the salient differences between agricultural adoption research and Behavioural Economics research, which provides many insights to extension workers.
APPLICATION IN EXTENSION EDUCATION
- is useful not only in understanding the behaviour of individuals, but also in formulating the right policies and evaluating policy outcomes;
- Is useful in studies related to risk and uncertainty faced by farmers, supply chain management, farmer insurance, agricultural financing, input and output marketing, etc.;
- helps extension professionals modify the choice environment to support positive decision making by farmers through tools such as social proof, loss aversion, framing, decoy effect, anchoring, endowment effect, etc.;
- helps social scientists in understanding farmers’ choices – be it their decision to adopt technologies or their response to policies or choice of source of information; and
- Is useful in integrating normative messages into the decision-making environment. Inform people of the norm, that is, the decisions others make in the same situation. The use of social comparisons can help people go by what others do.
Economics is very important to the daily lives of farmers in the sense that it summarizes many aspects of farmer behaviour, such as income, cost of production, marketing services, credit and insurance, etc. The importance of understanding Behavioural Economics for extension personnel is immeasurable as it allows for a better understanding of the farmer’s mind.
Behavioural Economics, also referred to as the science of decision making, is an emerging discipline which uses experiments that observe human behaviour to uncover how we think.
It is suggested that Behavioural Economics should form part of the academic syllabus for extension students. Extension researchers could gain much from identifying and studying the biases of farmers and other stakeholders in decision making using various theories and concepts developed by Behavioural economists. Capacity development programmes may be organized for field extension personnel so as to make them aware and use these concepts in organizing extension services more effectively.
Ariely Dan. 2010. Predictably irrational: The hidden forces that shape our decisions. New York: HarperCollins.
Ariely Dan. 2016. Secrets for fostering trust in your organization. Duke Corporate Education, March 2016.
Kahneman Daniel and Tversky Amos. 1979. Prospect theory: An analysis of decision under risk. Econometrics 47(2):263-292.
Kahneman Daniel. 2011. Thinking, fast and slow. New York: Farrar, Straus and Giroux.
Duflo Esther, Kremer M and Robinson J. 2011. Nudging farmers to use fertilizers: Theory and experimental evidence from Kenya. American Economic Review 101:2350-2390.
Kreckova Jana and Brozova Helena. 2017. Agricultural insurance and bounded rationality. Agris Online Papers in Economics and Informatics 09(01):91-97.
Mani Anandi, Sendhil Mullainathan, Eldar Shafir and Jiaying Zhao. 2013. Poverty impedes cognitive function. Science 341(6149):976-980. [doi: 10.1126/science.1238041]
Muthuprasad T, Niranjan S and Aditya KS. 2019. Rethinking policy from a behavioural angle: A case of Nudge. Blog 105, Agricultural Extension in South Asia.
Streletskaya Nadia A, Samuel DB, Kecinski Mark, Li Tongzle, Banerjee Simanti, Forster Lech H Palm and Pannell David. 2020. Agricultural adoption and Behavioural Economics: Bridging the gap. Applied Economic Perspectives and Policy, Feb 2020. https://org/10.1002/aepp.13006
Ashraf Nava, Glaeser Edward L and Ponzetto Giacomo AM. 2016. I. American Economic Review 106(5):77-82.
Smith, Adam. 1776. The wealth of nations. Penguin Classics, 1982.
Thaler RH. 2000. From Homo economicus to Homo sapiens. Journal of Economic Perspectives 14(1):133-141.
Thaler RH and Sunstein CR. 2009. Nudge: Improving decisions about health, wealth, and happiness. UK: Penguin.
Gneezy Uri, Rey-Biel Pedro and Meler Stephen. 2011. When and why incentives (don’t) work to modify behaviour. Journal of Economic Perspectives 25(4):191-210.
Williamson Katie. 2018. On Nudging: Behaviour change is a journey, not just a destination. Centre for Behaviour and the Environment.
Dr R M Prasad is a retired Faculty from Kerala Agricultural University, who had served in KHDP and KMIP (EU funded projects in Kerala), NIRD, Hyderabad and Government of Meghalaya. Presently, he is the Vice President of Farm Care Foundation, Thrissur, Kerala (email: email@example.com)