Category Archives: Latest

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

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.

 

334. Making video lectures

Universities around the world have moved to online teaching in response to Covid-19. Many university teachers who have done little or no online teaching are having to upskill rapidly. Here, I share my three top tips for making effective video lectures. 

Pre-recording lectures is not the only option for online teaching, but it’s a good option in some cases. My first major experience of this was making a MOOC (a free online course on Agriculture, Economics and Nature) back in 2015. I read a lot at the time about how to make a good online course, and it paid off as we’ve had around 20,000 people enrol in the course, with overwhelmingly positive feeback.

After that, with my co-teacher Ben White, we decided to convert the lectures for our first-year environmental economics course to pre-recorded videos. We found that the great majority of students were not attending lectures anyway, just watching the videos of the slides (plus audio) that were are recorded automatically. We were sure we could produce videos that would be far better for the students than those lecture-capture videos.

It was quite a bit of work, but I feel it has been worth it. The student-survey scores for the online version of our course have consistently been high.

So what would I advise to somebody who is now getting into pre-recording their lectures?

1. Break the lecture into multiple short videos

This is so important that I was tempted to do the cliched thing and include it as my tip 1, tip 2 and tip 3. The capacity to break the lectures into multiple mini lectures that the students can watch at the speed they choose is a big advantage over a traditional stand-and-deliver lecture. It would seem quite odd to stop a 45-minute face-to-face lecture 5 or 6 times to re-boot the audience’s attention, but it is highly acceptable in a pre-recorded video format.

I include a little piece of music at the start of each lecture part, which provides an additional signal to students that they need to re-engage their attention. At the end of each part I also ask a question to get them to think about what they’ve just heard.

When I’ve asked students how they watch the videos in our unit, most of them say they watch all the videos for one lecture topic one after another, but they still are very positive about the way it is broken into mini-lectures. The fact that there is a question, then a new video starts, then there is music, helps them to keep engaged for longer.

My feeling is that this is likely to be exceptionally important in the current situation, where students are getting all their lectures by staring at a screen. If we just present them with a series of normal-length lectures, it’s soon going to be incredibly difficult for the students to stay awake.

 

2. Use an external microphone

If you use the built-in microphone in your computer to record your lecture, it will probably sound boomy, echo-y and hollow. In bad cases, it may even be difficult for the students to understand you. Using an external microphone that is located close to your mouth can make a huge difference to the listening experience of your students. It doesn’t even have to be a great quality microphone to make a difference. Even a pretty cheap headset with a microphone will probably be much better than the computer’s microphone. But there are lots of better quality USB microphones available if you are keen. Personally, I use this.

The other thing you can do to further improve sound quality is to record in a room without too many hard surfaces. Carpet, curtains, soft furniture, table cloths, wall hangings, etc. can all help.

3. Moderate your pace, explain well, give examples and tell stories

Cheating a bit here: four tips in one. A disadvantage of pre-recording lectures is that you can’t see the faces of your students, so you may fail to realise that you are moving through material too quickly, you are not explaining it well enough, or you are getting too conceptual and failing to give examples. Given that a video is less personal than a face-to-face lecture, it may pay to make it more personal by including more stories and anecdotes.

The other thing I’ve usually done to make it personal is video myself speaking to the camera (which is just my iPhone) as I record the lecture. The video of me speaking is then displayed in the bottom right corner of the slide, and occupies about one-sixth of the screen space. I’m sure this makes a difference to the student experience, but it is somewhat more time consuming to create the videos, and if you are rushing to get prepared, it might be something you choose to explore later.

In case you are interested, I use Camtasia for all my recording of slides and my video editing. It’s a fantastic program.

I upload all my all my lecture videos onto YouTube as unlisted videos (so nobody can find them if I don’t provide the link) and I organise all the mini-lectures into the right sequence using the Playlist facility in YouTube.

p.s. Here is a new free book that many people might find useful: Take Control of Working from Home Temporarily

332. Farmer behaviour and agricultural policy

An understanding of farmers’ adoption of new practices is central to the design of effective and efficient agricultural policies. Aspects of agricultural policy that can be enhanced by good information about adoption include the design of the policy, the targeting of policy effort, and the assessment of additionality. 

In PD330 I advertised a new Special Issue on adoption of agricultural innovations in the journal Applied Economic Perspectives and Policy. There is an audio interview with me about the Special Issue available here.

One of the papers, by Roger Claassen and me, focuses on the relevance to agricultural policy of understanding farmers’ decisions about taking up new practices.

One simple reason for this relevance is that much agricultural policy is concerned with getting farmers to do something they are not already doing or would not otherwise do. Examples of such policies include the following:

  • Programs of agricultural extension to encourage farmers to adopt a new technology that is believed to be more productive than the existing technology farmers are using (e.g., a higher-yielding crop variety);
  • Programs that pay farmers to adopt a practice that generates public benefits (such as protecting or planting vegetation that provides habitat for wildlife);
  • Policies that fund agricultural research, with the intent of generating information or technologies that will be beneficial for farmers or the community; and
  • Policies that use regulations to constrain the behaviour of farmers (such as regulations on clearing of native vegetation).

Since all these policies are about influencing the behaviour of farmers, of course it makes sense that their design and implementation could be enhanced if the designers had a good understanding of what influences the behaviour of farmers. Sometimes policy makers do take this seriously, but not always. I’ve been critical of Australian agri-environmental policies, for example, for often being making overly optimistic assumptions about what farmers will do if we just provide them with some information or pay them a little bit.

In practice, some practices are more attractive to farmers than others. Zero till is used by about 90% of Western Australian farmers, but a practice like variable-rate precision agriculture is used by only a minority. Any one practice is more attractive to some farmers than to others due to varying local conditions, such as rainfall or soil types. Being able to predict variations in adoption would be very helpful to policy makers for targeting their resources. Having a sense of which practices can potentially be adopted, and where, is one of the factors that ought to influence where policies like extension or incentive payments are applied.

Another policy concept that is tangled up with farmer behaviour is additionality. As we say in the paper:

A conservation action (and the resulting environmental gain) that is supported by a payment is additional if the farmer would not have taken the action if he or she had not received the payment. Environmental gains that flow from nonadditional actions cannot be attributed to the incentive program.

If the additionality of a proposed agri-environmental payment scheme is too low, it’s not worth running the scheme. Most farmers were going to adopt the practice anyway, so any incentive payment to them is just a gift, making no difference to environmental outcomes. Policies to promote some practices have high additionality (e.g. filter strips or cover crops in the U.S.) while others have much lower additionality (e.g. conservation tillage in the U.S.).

Assessing additionality is essentially about predicting behaviour. In fact, for programs that aren’t in place yet, assessment of additionality required two predictions about behaviour: what will farmers do if the program does offer them the proposed incentive scheme, and what will they do if there is no incentive payment scheme. The difference between those two predictions tells you the additional change that is attributable to the scheme.

Even assessing an incentive scheme that is already well established requires a sort of prediction. You can observe what farmers are doing with the scheme in place, but to assess additionality you still need to estimate what they would have done in the absence of the scheme.

For all these reasons, an ability to understand and predict farmers’ adoption of new practices is critically important to agricultural policy makers if they want their policies to be effective and efficient.

Further reading

Pannell, D.J. and R. Claassen. 2020. The Roles of Adoption and Behavior Change in Agricultural Policy. Applied Economic Perspectives and Policy 42(1), 31-41.

331. Conservation opportunities on uncontested lands

Not all agricultural land is productive and valuable. Looking for low-value land might be a useful strategy when seeking to increase the area devoted to conservation. In addition to being relatively cheap to purchase, it may be relatively unlikely to strike problems with social or political opposition.

I’m part of a team of researchers that is looking at this issue, led by Eve McDonald-Madden from the University of Queensland. We have a new open-access paper out in Nature Sustainability that presents a framework for thinking about whether and when restoring low-value, or “uncontested”, agricultural land for conservation purposes is likely to be a good idea.

In the paper, we talk about the different costs that are involved in acquiring and restoring a piece of land. They include the purchase price of the land, which reflects its long-term economic productivity, the transaction costs involved in acquiring the land, and the cost of restoring the land to an improved ecological condition.

We suggest that these costs are likely to be related systematically to the opportunity cost of the land for agriculture (that is, the amount of income that would have to be given up if the land was converted away from agricultural production), but that the patterns may vary.

If the reason for some land having a very low opportunity cost is that it is highly degraded and therefore unproductive, the restoration cost may be particularly high. Restoring the most degraded lands is more difficult and more expensive. In that case, it might be better to seek to acquire and restore land that is degraded, but not so extremely degraded.

If the reason for agricultural land having a low opportunity cost is low market prices for agricultural outputs, rather than land degradation, then there is no reason to expect this land to be especially expensive to restore, potentially making it an attractive target for restoration. Although, not necessarily. Whether the purchase price would be particularly low depends in part on farmers expectations about future prices, not just current prices.

In some situations, acquiring land involves particularly high transaction costs. This might be the case, for example, if there is social and political oppositon to conversion of agricultural land to conservation land. As a generalisation, we might expect that to be less of an issue if the land is degraded and unproductive for agriculture.

Another example of high transation costs could be the effects of corruption. “If corruption is socially normalized, this may lead to low levels of trust, with the result that parties incur high costs for negotiation, contracting and monitoring an agreement. If legal institutions are weak, the cost of enforcing an agreement could be very high.” In this case, even though the land might be cheap, the overall cost might mean it is better to look in a less-corrupt country for land to restore.

Recognising those complexities, we are using spatial data to try to identify cost-effective opportunities for investing in restoration of land.

Further reading

Xie, Z., Game, E.T., Hobbs, R.J., Pannell, D.J., Phinn, S.R., and McDonald-Madden, E. (2020). Conservation opportunities on uncontested lands, Nature Sustainability 3, 9–15. Journal web page (open access)