Behavioural economics offers an insight into why people make the decisions they do. In a pandemic — when even apparently small decisions may involve high stakes — the discipline can provide an insight into how such choices are made, and critically asses the emotional vs logical. In this blog, we offer an overview of some of the basic concepts of behavioural economics and how these apply to the current COVID-19 outbreak…
Many countries are moving forward and easing lockdown restrictions while there are still many unknowns about the virus and how it spreads. They are proceeding with varying degrees of caution in the face of unknowns related to both where and when the virus will flare-up as well as economic uncertainty. For example, EU countries anticipating the summer tourism season have focused on easing travel restrictions and opening up the leisure sector. But already, reports from several countries highlight the reality of recurring flare-ups of the disease.
There is widespread reluctance to re-impose lockdowns, and to incur further risks to economies as well as general morale. But in economies that are driven by consumer spending, how individuals make decisions in the face of such uncertainty will have profound impacts on both the path of economic recovery as well as the course of the pandemic. Previously mundane decisions such as whether and when to go clothes shopping, visit the mall, or to go out to a restaurant or pub, now have to take into consideration risks associated with COVID-19.
The field of behavioural economics could have much to offer as policy makers face difficult choices and trade-offs in the months ahead as the world adapts to the reality that COVID-19 is not going away soon. In the UK, the government use of behavioural insights has already been widely trailed, though not without some controversy. Behavioural economics studies the influence of psychological factors on how humans make economic decisions. It extends traditional economics to better account for real people’s beliefs and biases. Nobel prize-winner Richard Thaler summarises these extensions in terms of three 'bounds' on the behaviour economists have tended to assume:
“Bounded rationality reflects the limited cognitive abilities that constrain human problem solving. Bounded willpower captures the fact that people sometimes make choices that are not in their long-run interest. Bounded self-interest incorporates the comforting fact that humans are often willing to sacrifice their own interests to help others.”
As countries and communities move from an initial phase of tight lockdown and the associated restrictions on activity — which were necessary to “flatten the curve”, slow the spread of the virus, and avoid overloading health facilities — behavioural economics can teach us, first, about how people assess risks and, second, about how they then go on to make decisions based on the risks that they’ve assessed.
Assessing risks
Fundamentally, human beings are not always good at assessing risks. To take just three examples (though there are many more):
Probability weighting is a key element of Daniel Kahneman and Amos Tversky's Prospect Theory, introduced in one of the most cited social science papers of all time. Probability weighting tells us that we systematically overestimate small risks and systematically underestimate large ones. But we generally get certainty and impossibility right, which means there's a discontinuity or certainty effect at the extremes. As long as the risk to individuals of catching COVID-19 remains low, we might expect this effect, on its own, to lead to an abundance of caution.
We make mistakes about the independence of events. One manifestation of this is in the gambler's fallacy, through which people create imaginary dependencies between independent events. For example, the gambler’s fallacy predicts a strong intuition that a black is 'due' on the roulette wheel if we’ve just witnessed a long sequence of reds. Or that, in relation to Covid-19, we’ll feel that an individual risky behaviour becomes that bit riskier with each successive occasion that we get away with it (the total risk does increase, of course, but not the risk per occasion).
We easily confuse the probability of seeing some piece of evidence given that a hypothesis is true (e.g. the chance that I get a positive test result, given that I have COVID-19) and the probability that a hypothesis is true given that I see some piece of evidence (e.g. the chance I have COVID-19, given that I get a positive test result), when in fact these quantities are often very different. For instance, say that there's a 0.1% rate of the disease in a population, and a test for the disease gives the right answer 99% of the time. Most people who get a positive test result during a routine screening will think it is now 99% certain they have the disease. But because the rate of infection in the population is low, the true probability in this case (which statisticians can calculate with Bayes’ Rule) is actually still under 10%.
Making decisions under risk
Behavioural economics tells us a wide range of ways in which our probability judgments tend to go awry. But even given correct probabilities, the way we use those probabilities to arrive at choices often seem to lack any immediate sense.
A key influence here is framing and, in particular, whether changes are presented in terms of gains or losses. As it happens, an illustration given in a paper by Tversky & Kahneman in Science hits rather close to home. The setup is as follows:
Imagine that the U.S. is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. Assume that the exact scientific estimate of the consequences of the programs are as follows:
- If Program A is adopted, 200 people will be saved.
- If Program B is adopted, there is 1/3 probability that 600 people will be saved, and 2/3 probability that no people will be saved.
Which of the two programs would you favour?
On the average, programs A and B will save the same expected number of people (200), but A is a safe bet on saving exactly that number, while B is a gamble that may or may not save them all.
A big majority of respondents in the study (72%) favoured the safe option, program A. But then the researchers polled a second group, making a simple tweak to the language they used:
- If Program C is adopted 400 people will die.
- If Program D is adopted there is 1/3 probability that nobody will die, and 2/3 probability that 600 people will die.
These new programs do nothing but reframe the first set: C is identical to A, and D is identical to B. But, strikingly, this reframing utterly reversed the majority preference. Now, 78% of respondents preferred the risky program, D.
This is no fluke. One of us (GM) has replicated the result seven years in a row with students on a behavioural economics course. The reasons have to do with our diminishing sensitivity to both gains and losses relative to a reference point, which Prospect Theory captures with something called the Value Function (a replacement for economists’ usual workhorse, the utility function).
The upshot is that we’re generally risk-averse in relation to prospective gains but risk-seeking in attempts to avoid losses. The effect is extremely deep-seated: it has even been demonstrated, through some rather ingenious experiments, in capuchin monkeys by Chen & Santos.
Communicating risk
These sorts of effects are of clear relevance to leaders and governments responsible for formulating guidance and communicating to the wide public during times of uncertainty. The words and behaviour of a leader will strongly influence how a large majority of the population will make individual decisions that will have major economic and human impacts in consumer-driven societies.
With what we know from behavioural economics, how individuals actually decide what is gain and what is loss will influence these decisions.
For example, a political leader that states. “We’re open again for business”, and who accompanies that with a relaxation of social distancing measures may set an expectation that social activities such as congregating in bars, in parks, or beaches is again the norm. Thus, individuals could see not resuming these activities as a loss; and therefore, according to behavioural economics, engage in risk-seeing behaviour, not wearing masks and not respecting social distancing. The spike in cases we’re seeing in some US states, could potentially be due to this phenomenon.
On the other hand, a leader who leans too much towards caution — out of fear of seeing even a handful of new cases — may lead to people hesitant to venture out of their homes even for lower risk situations, such as going out to the park for a walk. There has been a lot of criticism of public health leaders: from their perspective, a high degree of caution will continue to be needed until the threshold of herd immunity is reached; which could be 1-2 years away. However, our economies will struggle to continue in suspended animation until then.
Therefore it is advisable for leaders to use wording and to communicate in ways that are balanced: it’s possible to be “open for business” while also assuring the population that “we’re going to double the number of testing centres” to retain the sense of caution.
Getting this right is crucial over the months ahead as we all manage health and economic risks.
About the authors and Outsight International
Dr George MacKerron
George is a Senior Lecturer in Economics at the University of Sussex. His research is in subjective wellbeing, behaviour and the environment. He runs Mappiness, the world's largest experience sampling study, and is a co-founder of startup Psychological Technologies. George gained his PhD at the LSE, and prior degrees from Imperial College London and the University of Cambridge.
Dr Evan Lee
Evan is a trained MD and MBA with degrees from Harvard and MIT, he has dedicated his career to improving access to health. Initially practicing medicine in community health centers; for the past 20 years, he has worked across the private sector, NGO sector, and collaborated closely with UN partners to address access issues related to medicines, diagnostics, and other health technologies.
Louis Potter
Louis is one of the Co-founders of Outsight. He has a wide range of experience covering development, health, innovation, technology and research. Having worked in the field, he is well acquainted with the practical realities of delivering impact. In recent years, he has been helping organisations to improve innovation processes and outcomes. He is an experienced facilitator and has been closely involved in efforts to improve collaborations between the nonprofit, academic and commercial sectors. He is based in Lausanne, Switzerland, and received his MSc in Global Health from the Karolinska Institute, Sweden.
We believe that understanding the motivations behind behaviour is an essential part of quality strategy planning. This can apply to governments, industry or the third sector. As Outsight, we are happy to consult Outsight International provides services to its clients in an efficient and agile way using the ‘Hollywood Model’. Outsight International builds on the range of expertise offered by a network of Associates in order to deliver quality results adapted to the specific tasks at hand. If you’d like to discuss working with the Outsight team, please get in touch or follow us on LinkedIn for regular updates.
