Author Archives: David Pannell

339. Assuming that farmers maximise profit

Agricultural economists building models of agricultural production often use simplistic assumptions about what motivates farmers. The simplest possible assumption — that farmers maximise profits — is still quite commonly made in agricultural economic models. Can this ever be defended? 

One of the papers in the recent AEPP Special Issue of papers on adoption of agricultural innovations is on this topic. The paper is by Alfons Weersink from the University of Guelph and Murray Fulton from the University of Saskatchewan. You can hear my interview with Alfons about the paper here. My conversation with Alfons is available as an episode of the AEPP podcast series.

They point out that many motivations other than profit maximisation have been identified in research about the uptake of new practices by farmers. Other factors that influence them can include risk, leisure, family, social norms, peer pressure, environmental concerns, and altruism.

Weersink and Fulton also picked up a table from an old Pannell Discussion (PD103) in which I suggested that the factors that influence adoption of new practices are likely to vary in different stages of the adoption process. Early on in the process, when farmers are learning from others about the practice, social, cultural and personal factors are likely to play a relatively large role. Later in the process, when farmers have personal experience in using the practice on their own farm, the influence of outsiders will be reduced to some extent. Influences like social norms could still play a role, but the farmer is no longer primarily relying on outsiders for their information.

The finding that farmers are more complex than we might like to assume is reinforced by research in the booming  field of behavioural economics. This has identified a large number of biases that affect people’s decision making. Some relate to the way our brains process information, and some are more about our responses to social situations. This work tells us that farmers will often diverge from profit-maximising behaviour even if they think that’s what they are going for.

Having read the Weersink and Fulton paper a few times (in the course of editing the special issue), it got me thinking about the role of profit in adoption of innovations. Here are some further thoughts.

I think that the main thing driving long-term adoption of a practice is its relative advantage (how well it enhances the achievement of the farmer’s goals). An important element (but, as I said, not the only element) of relative advantage for most farmers is the effect of the practice on profit. There is plenty of evidence that, although various factors influence farmers’ decisions, profit remains one of the main ones. It doesn’t mean that farmers are motivated by wealth for its own sake, but they seek to make money for what it allows them to do.

At the same time, the cognitive biases identified by behavioural economics don’t seem to me to be so large that profit-motivated farmers would make decisions that are really poorly correlated with their objectives.

If I was putting a number on it, I’d say that for Australian farmers, profit explains something like 70% of the variation in adoption between different practices. We can potentially do a more sophisticated analysis to get a more accurate understanding of adoption, but even if we don’t, a 70% understanding is likely to be very useful.

Economists should think carefully about whether some other factor is likely to overwhelm considerations of profit, but in most cases profit at least gives a useful indication of the broad trend in behaviour. If a scientist, an extension agent or a policymaker wants to know whether a new practice or technology is likely to be adopted by farmers, an analysis of its effect on farm profits is bound to be a useful first step.

Why not just include those other motivations in the economic models, to pick up some of the remaining 30%? Because it is much more difficult. The big advantage of modelling profit is that it can relatively easily be quantified, whereas most of the other factors can’t.

So, in summary, my view is that basing agricultural economic models on profit maximisation is often not a crazy thing to do, because it is relatively easy and reasonably useful.

Like all of the papers in the special issue, Alfons and Murray’s paper is free to access.

Further reading

Streletskaya, N.A., S.D. Bell, M. Kecinski, T. Li, S. Banerjee, L.H. Palm-Forster, and D.J. Pannell. 2020. Agriculture Adoption and Behavioral Economics: Bridging the Gap. Applied Economic Perspectives and Policy 42(1), 54-66.

Weersink, A. and M. Fulton. 2020. Limits to Profit Maximization as a Guide to Behavior Change. Applied Economic Perspectives and Policy 42(1), 67-79.

338. Modelling COVID-19

When there is a serious epidemic or a pandemic such as COVID-19, numerous epidemiological modelling groups around the world get busy. How should these various groups be coordinated to generate the most useful information to guide how an outbreak should be managed?

I’m excited to say that I have a new paper out in Science that addresses this question. In doing this project, I got to rub shoulders (in a virtual sense) with an international team of six modellers and epidemiologists from the US, the UK and China.

I was invited to join the team by the lead author, Katriona Shea, an ecologist from Penn State University who specialises in the management of populations of plants and animals and of disease outbreaks. She spent a sabbatical with us in the Centre for Environmental Economics and Policy at UWA in 2018, learning about economics and behaviour.

Katriona found that the sorts of things we do could be useful in her world. Aspects of the design of our proposed modelling process were designed with behaviour change (by modelling teams) in mind.

Here’s an extract from the official news release from Science. The full release is here.

“A new process to harness multiple disease models for outbreak management has been developed by an international team of researchers. The team describes the process in a paper appearing May 8 in the journal Science and was awarded a Grant for Rapid Response Research (RAPID) from the National Science Foundation to immediately implement the process to help inform policy decisions for the COVID-19 outbreak.

During a disease outbreak, many research groups independently generate models, for example projecting how the disease will spread, which groups will be impacted most severely, or how implementing a particular management action might affect these dynamics. These models help inform public health policy for managing the outbreak.

“While most models have strong scientific underpinnings, they often differ greatly in their projections and policy recommendation,” said Katriona Shea, professor of biology and Alumni Professor in the Biological Sciences, Penn State. “This means that policymakers are forced to rely on consensus when it appears, or on a single trusted source of advice, without confidence that their decisions will be the best possible.”

We designed our process to achieve a number of aims.

  1. Get the modelling groups working on the issues that will be most helpful for decision making.
  2. Help decision makers tap into the expertise of the full range of modelling groups. Currently, they sometimes pick a winner and go with the predictions of a single model, ignoring the significant variation between models.
  3. Foster learning between the groups, so as to maximise the quality of predictions made. Currently, when multiple models are used, the usual approach is to just take an average of their results. Our process requires the modelling groups to discuss the reasons for their differences, and to adjust their models if appropriate once they understand those reasons.
  4. Reduce bias in the decision process. Likely biases to guard against include dominance
    effects (agreeing with field “leaders”), starting-point bias or anchoring (focusing on suggestions raised early in the process to the detriment of other ideas), and groupthink (where a psychological desire for cohesiveness causes a group of collaborators to minimize conflict and reach a consensus without sufficient critical evaluation).
  5. Don’t delay the decision-making process.
  6. Make it attractive for the modelling groups to participate in the process.

Our process works as follows.

(a). The decision-making body defines the objective (e.g., minimise caseload), and specifies the management options to be assessed and communicates these to multiple modelling teams (Aims 1 and 2).

(b) The teams model the specified management options, working independently to avoid prematurely locking in on a certain way of thinking (Aims 2 and 4).

(c) The decision-making body coordinates a process where the modelling teams discuss their results, providing feedback and ideas to each other, and learning how they might improve their models (Aim 3).

(d) The teams again work independently (Aim 4) to produce another set of model results with their improved models. The full set of results is collated and considered by decision makers, not just the average (Aim 2).

(e) Information from step (b) can be used for initial decision making, without waiting for steps (c) and (d), so no time is lost (Aim 5). If the new results from step (d) indicate that the best management response is different than initially indicated, the response can be adjusted. We’ve seen plenty of adaptations to strategies over time by governments in the current pandemic.

(f) Benefits for the modelling teams themselves (Aim 6) include that they still essentially operate independently and can publish their own work; that the final quality of their model predictions is probably better; and that they can be confident that their results will be explicitly considered by the decision makers.

In some ways, this might seem like a common-sense approach, but in practice, it is rather different from what is currently done, at least in the contexts that the team of authors is aware of.

It is particularly exciting that Katriona has managed to obtain funding to roll out this approach immediately. She is already working with a collection of modelling groups in the US. The team will share results with the U.S. Centers for Disease Control and Prevention as they are generated.

Further reading

Shea, K., Runge, M.C., Pannell, D., Probert, W., Shou-Li, L., Tildesley, M. and Ferrari, M. (2020). Harnessing the power of multiple models for outbreak management, Science 368(6491), 577-579. Journal web page

337. Developing-country farmers: how are they different?

One of the papers in the recent AEPP Special Issue of papers on adoption of agricultural innovations is about how smallholder farmers in developing countries differ from larger and wealthier farmers, and how these differences affect the farmers’ responses to new agricultural practices and technologies. 

The paper is by Rick Llewelly from CSIRO in Australia and Brendan Brown from CIMMYT in Mexico, who have worked together on this issue for a number of years. You can hear my interview with Rick about the paper here. My conversation with Rick is available as an episode of the AEPP podcast series.

Here are a few of the differences that Rick and Brendan identify and discuss in their paper. The discussion is in the context of predicting adoption of a practice that is currently relatively new to farmers.

Greater heterogeneity

“In developing country settings there is greater potential for extreme differences between farms in scale, wealth, and resources, including the influence of tenure status.” In some cases, larger farmers are more able and more motivated to adopt a beneficial technology, while smaller farmers can tend to be left behind.

Higher discount rates

There is plenty of evidence that poorer farmers in developing countries tend to have higher discount rates, meaning that they give more weight to benefits in the short term than in the long term. This could be because of the high interest rates they have to incur when they borrow money, or because their poverty means that generating income in the short term has to take priority, even if it means missing out on large benefits in the long term. This is particularly a barrier to adoption of new practices if those practices involve relatively large up-front costs or the benefits take some years to be delivered.

Inability to capture the benefits

“In some cases, farmers may expect that future benefits from investments in an innovation will mostly accrue to the land owner rather than to themselves.”

Cultural norms

Cultural norms may conflict with the use of particular practices. For example, retention in the field of crop residues following harvest can be beneficial for later crops grown in that field, but in many parts of sub-Saharan Africa, “all land tends to be considered as communal grazing land in the noncropping seasons”. So if you are a crop farmer in one of these areas, it is not socially acceptable to stop other people’s livestock from grazing on your crop residues and destroying the benefit of having retained the residues. For more on this, see my paper Pannell et al. (2014), which is summarised in PD268.

Objectives other than profit

Some farmers rely strongly on their own production of food for their own family. “The need to account for farmers’ subsistence needs can add complexity to predicting adoption of new practices, particularly where subsistence farmers are also sometimes engaged in market-directed production”.

Relative to larger, wealthier farmers in developed countries, smallholder farmers are likely to be more risk-averse, so less inclined to adopt practices that are highly beneficial on average but relatively variable from year to year.

Smallholder farmers may give less consideration to generation of public environmental benefits than at least some farmers in developed countries do, and this too would influence which practices they are willing to adopt.

Factors related to learning about a new practice

In some cases, communication between developing-country farmers in different regions or different ethnic groups is less than we typically see within a more homogeneous population of farmers in a developed country. This can slow down the spread of beneficial new practices through the farming community.

There may be a lack of education needed to understand a particular practice, or a lack of required skills and knowledge, compounded by the poor quality of advisory support in some developing countries.

Clearly, there are a number of factors that can combine to make the adoption of new farming practices in developing countries unfold in rather different ways than we typically observe in developed countries. Scientists, extension agents and agricultural policy makers need to account for these factors when making judgements about what impact a research project, an extension campaign or a policy could have.

Further reading

Llewellyn, R. and B. Brown. 2020. Predicting adoption of innovations by farmers: how is it different in smallholder agriculture? Applied Economic Perspectives and Policy 42(1), 100-112.

Pannell, D.J., Llewellyn, R.S. and Corbeels, M. (2014). The farm-level economics of conservation agriculture for resource-poor farmers, Agriculture, Ecosystems and Environment 187(1), 52-64. Journal web site (access to the paper is free) ◊ On-line video presentation ◊ IDEAS page for this paper

336. Free time at home? Do this free course!

The Australian Government is encouraging people to utilize any unexpected spare time at home by up-skilling through study. I’ve got just the thing! My course on “Agriculture, Economics and Nature” is still available for free on Coursera

The course is an introduction to the economics of agriculture, including the connections between agriculture, the environment and natural resources.

It is designed to be a six-week requiring 2-3 hours per week. But if you have more time available than that, you can do it as quickly as you like.

Here’s the blurb from the web site

Sound economic thinking is crucial for farmers because they depend on good economic decision making to survive. Governments depend on economic information to make good policy decisions on behalf of the community. This course will help you to contribute to better decision making by farmers, or by agencies servicing agriculture, and it will help you to understand why farmers respond to policies and economic opportunities in the ways they do.

You can use this course to improve your skills and knowledge and to assess whether this is a subject that you’d like to study further. The course includes high-quality video lectures, interviews with experts, demonstrations of how to build economic models in spreadsheets, practice quizzes, and a range of recommended readings and optional readings. Assessment is by quizzes and a final exam.

The key economic principles that we’ll learn about can help us understand changes that have occurred in agriculture, and support improved decision making about things like agricultural production methods, agricultural input levels, resource conservation, and the balance between agricultural production and its environmental impacts. There are literally thousands of agricultural economists around the world who work on these issues, so there is a wealth of knowledge to draw on for the course.

Here is a brief video about the course.

About 20,000 people have enrolled in the course since it was first made available. The response has been overwhelmingly positive, as you can see from the feedback reported here.

Enrol now here, and I’ll see you on the discussion forum for the course.

 

335. Behavioural economics and adoption of agricultural innovations

Behavioural economics has become a thriving field of research over the past decade or so, but research by agricultural economists on farmers’ behaviour when adopting new practices has been going on for over 60 years. What is similar and different between these two fields of research?

I was listening to a presentation by David Zilberman from the University of California, Berkeley at a conference a few years ago, and he made a comment along the lines that agricultural economists had been studying behaviour for a long time before behavioural economics became popular. He was thinking about the large body of research on farmers’ adoption on new practices and technologies.

When I was putting together a recent special issue of Applied Economic Perspectives and Policy on adoption of agricultural innovations, I remembered David’s comment and thought that a paper about this would be a good inclusion. About that time I happened to visit the University of Alberta, and a conversation about this with Maik Kecinski (now at University of Delaware) led to a team of young economists (plus a not-so-young one: me) working together. The resulting paper, “Agricultural Adoption and Behavioral Economics: Bridging the Gap” by Streletskaya et al., is now out and is open access.

To get a brief overview of the paper, you could listen to a conversation with one of the co-authors, Leah Palm-Forster, who happened to be in Perth for the AARES conference last month, which luckily took place just in time to avoid most of the travel shut downs for Covid-19. My conversation with Leah is available as an episode of the AEPP podcast series.

The paper covers a lot of ground. Here I’ll just comment on some of the similarities and differences between the two fields of research. Clearly, both fields are interested in behaviour, both recognize the influence of individual characteristics, preferences, and beliefs on decision-making, and both study economic decisions. But they tend to ask somewhat different questions. Quoting from the paper:

“Agricultural adoption investigates which factors drive or inhibit uptake within a population over time, without necessarily seeking to identify and model behavioral mechanisms that influence adoption. Behavioral economics, on the other hand, seeks to identify and explain behavior that departs from what is predicted by traditional economics through the use of new theories and models of human behavior.”

“Furthermore, agricultural adoption research traditionally has highlighted the role of extrinsic factors such as social, economic, and political context in driving adoption. Behavioral economics, on the other hand, focuses on broadly unpacking the “black box” of human decision making and explores how human cognition and the manner in which tastes and preferences are formed drive decision-making, and how factors such as biases and other social influences impact economic behavior.”

“Agricultural adoption often analyzes field data available at the plot, county, regional and national levels for different types of producers in order to obtain insights about the drivers of new technology diffusion over time and adoption patterns across different farming populations. In the behavioral economics domain, … many laboratory and field experiments rely on smaller samples and consist of cross-sectional data.”

So, there are quite a few differences. But they don’t have to be different. These differences have just evolved organically, largely reflecting the different questions that the researchers were asking.

Researchers working on agricultural adoption are increasingly looking towards behavioural economics for ideas and methods, but I reckon there is also unrealised scope for cross-fertilisation in the other direction. I suspect that many behavioural economists don’t even realise that there is this other big body of research generating knowledge that is relevant to what they do.

Further reading

Streletskaya, N.A., S.D. Bell, M. Kecinski, T. Li, S. Banerjee, L.H. Palm-Forster, and D.J. Pannell. 2020. Agriculture Adoption and Behavioral Economics: Bridging the Gap. Applied Economic Perspectives and Policy 42(1), 54-66.