Behavioral economics is perhaps the most important new paradigm in economics in the new millenium. It is based on the idea that people don’t behave rationally, like economics suggests – that they are, in Thaler’s words, Humans (homo sapiens) rather than Econs (homo economicus).

In the 1960s and 1970s, Nobel Laureate Herbert Simon and others developed behavioral economic theories assuming that people satisfy rather than maximize their profit or utility and that their rationality is bounded (i.e. confined). But the foundation for modern behavioral economics came in the 1980s, with Prospect Theory developed by Kahneman and Tversky to explain behavior under risk. While modern behavioral economics is changing the way economics and finance are practiced and which policies are introduced, agricultural economics have integrated behavioral elements into their models and practices since the 1960s. And some of these elements actually became part of mainstream economics.

Agricultural economics originated in two fields: farm economics and farm management. Farm economics was standard economics applied to production and markets of food. Farm management aimed to guide farmers how to make choices, and therefore needed to deal closer with reality, with farmers who are really Humans not Econs. Here I’d like to present some of the behavioral elements that emerged in agricultural economics.

First, decision making under risk has been a major topic of agricultural economics. One of the big challenges of agricultural economists is understanding why farmers didn’t adopt technologies that were shown to have higher yields and profits than traditional practices, especially during the Green Revolution. What agricultural economists realized is that farmers were concerned about risk, especially downside risk. Namely, they would not adopt a technology that doubles yield on average, but during droughts, that may occur 10% of the time, produces much lower yields than the traditional technology. Therefore, agricultural economists applied approaches like ‘safety first’ (where a farmer would say “I’d rather avoid a disaster than increase my average yield”) or similar methods where most of the weight in technology choice is given to avoiding significant setbacks. This extra attention to losses compared to gains is close in many ways to the notion of ‘loss aversion,’ a key element of Prospect Theory that is promoted by behavioral economists to replace the neo-classical ‘expected utility’ approach to address risky choices. Much of the insights of behavioral economics originated from hypothetical and real-life experiments and such experiments are likely to play a major role in the future. Agricultural economists were probably among the first to conduct field experiments where farmers, in India, were given money under various conditions and assessed their risk preferences, and found results that challenged existing expected utility approach.

Second, discrete technology adoption choices (i.e. yes or no)[1] haven’t been part of neo-classical economics, which emphasize continuous choices (i.e. quantity of inputs). The first analysis of adoption in economics was published in 1957 by Berkeley ARE alumni Zvi Griliches, who studied adoption of hybrid corn in Iowa. Griliches incorporated profit considerations to a sociology model of adoption that conceptualized adoption as a process of imitation. Over time, agricultural economists have developed alternative frameworks that combine behavioral assumptions, dynamic considerations, and standard economic features. One is the expanded ‘threshold model’ that has three elements: individual choice, heterogeneity and dynamic processes. Individuals are influenced by others in considering new options and then make their choices based on whether the technology fits them or improves their wellbeing. But, individuals are different and at each moment only a portion of the population will adopt any given technology. Finally, learning processes may make a technology more appropriate for more segments of the population by reducing the cost of the technology, improving its effectiveness by the user, and reducing uncertainty about its performance. Much of the analysis of adoption in agriculture was done by economic historians who, for example, suggested that adoption of new technologies may be done either by buying them or renting them.

Agricultural economists tend to emphasize heterogeneity among economic agents much more than standard economic models that frequently assume identical firms or consumers. In studying production systems, Berkeley alumni Dennis Aigner developed a statistical technique to capture how individuals differ in their capacity to utilize resources in farming and another Berkeley alumni, Yair Mundlak, developed techniques that capture differences in managerial ability. This follows the work of Nobel Laureate Theodore Schultz who coined the term ‘human capital’ which he used to explain differences in performance. Furthermore, he also recognized that unlike the theory that emphasized equilibrium relationships, modern economic systems are rarely in equilibrium. New technologies arrive, random climatic and political shocks occur, and human beings must adjust. Therefore, a key attribute of human capital is the ability to deal with disequilibria and adjust to change. That led agricultural economist Mark Nerlove to introduced the notion of adaptive expectation (more appropriate to Humans) rather than rational expectation (that fit Econs). Both Schultz and Nerlove probably believed in rational economic agents, but when they looked at the reality of agricultural life and data, they realized that some people are more rational than others (Schultz) and that it is difficult to predict the future, and therefore we need some approximation (Nerlove).

These are only a few examples that demonstrate where agricultural economics developed methodologies and theories that recognize behavior reflects cognitive limitations of humans in dealing with large amounts of information and how social norms result in outcomes that defy the neo-classical paradigm. Agricultural economists continue to identify behavioral anomalies in attitudes towards GMOs and labeling, consumption of healthier foods, and even crop insurance. Agricultural economics also emphasizes that while there are several general principles, it is important to recognize different dimensions of heterogeneity and that the world is full of diverse outcomes that reflect differences among cultures, agroecological, and socioeconomic differences. The findings and models of agricultural economics have these nuances because part of the field works very closely with practitioners; and, by definition, the field is multi-disciplinary and open to input from other disciplines.

As behavioral economics become more prominent in economics, it is important to recognize that its emergence owes to the infusion of knowledge from other disciplines, increased capacity to obtain data, but also from knowledge and methods that were developed in subfields like agricultural economics. While agricultural economists want to be like economists, it is important to understand that our differences are a major source of value and lasting contribution to economics, and society.

[1] Of course, in the case of adopting new varieties, the choices may be gradual. But there is a discrete choice whether to try the new seed or not.