Although many people would say that being healthy is among their most important life goals[i], their actions often suggest otherwise. Even with the detrimental health effects of e.g. smoking, heavy alcohol consumption and insufficient physical activity being well-established, 20%, 56% and 52% of Dutch citizens smoke, consume more alcohol and exercise less than recommended respectively[ii]. Besides introducing a call for interventions that reduce or prevent the occurrence of these behaviours, this tells us that many people may know their behaviour harms their health but engage in it nonetheless.
There are many different reasons why this happens. Smoking and alcohol consumption are more prevalent in low socio-economic status rather than high socio-economic status groups[iii], suggesting that perhaps a lack of financial resources or cognitive bandwidth (i.e. due to stress) may stand in the way of living long and healthy lives. Decades of work in behavioural economics has shown that unhealthy behaviour also exists because people are not rationally weighing all costs and benefits to make the best possible decision. Instead, their decisions are affected by biases: systematic patterns in decision-making steering people to act against their wishes or best interest.
In my own work[iv], I have studied such biases extensively and generally we find that:
- people are strongly loss averse for health and money, which leads them to avoid potential losses even when considerable gains can be realized (e.g. many people don’t want to engage in gambling offering a 50% chance of gaining 100 euro and 50% of losing 50 euro).
- people assign too much weight to both good and bad outcomes that have a very small chance of occurring (e.g. opting out of treatment or vaccination because of the small chance of serious side-effects)
- people have a tendency to care more about now than the future (e.g. teenagers may find their next 20 years of living worth about the same as the 50 years that would likely follow them)
Many public policy institutions have started to realize that this type of knowledge about decision-making (about health) can be used in prevention policy, by acknowledging or taking advantage of biases in decision-making. For example, people may smoke because they value the immediate rewards of smoking (e.g. stress relief) much more than potential future loss of health[v]. A successful strategy to reduce smoking could, therefore, be to try and introduce an immediate financial reward to encourage people to stop smoking.[vi]
Offering rewards to encourage people to stop smoking acknowledges that people may assign disproportionate weight to the present. Taking it one step further, we may use knowledge about decision-making in designing these rewards such that their effectiveness is enhanced by biases in decision-making. For example, instead of offering a reward-based stop smoking program, people may be asked to put their own money on the line as a deposit to get a reward (e.g. [vii]). In these deposit-based incentive programs people are asked to put their own money at stake, which will be only returned to them if they stick to a predetermined and agreed-on goal. Deposit-based incentive programs take advantage of loss aversion and may increase motivation to quit smoking as people don’t want to lose their deposits.
Earlier work has shown deposit-based incentives can be more effective than simple rewards (e.g. [viii]), and they have also found to be effective even when no additional reward is offered[ix]. In other words, if you can convince people to put their own money on the line, they may be very motivated to change their behaviour even if there is no other reward involved. Two key concerns, however, are raised when considering the use of deposit-based incentives. First, if voluntary, few people sign up for a program in which they are at risk of losing their own money[x]. Second, when we offer deposit-based incentives to take advantage of loss aversion, we apply a one-size-fits-all approach to prevention. We motivate the use of deposit-based incentives assuming people are loss averse, but many studies including my own have shown that loss aversion is not a law of nature. On average, people are loss averse, but behind the average large variation is hidden[xi]. Some people would do almost anything to avoid a loss, others care very little about losses.
Once we realize that considerable heterogeneity exists in the biases that motivate interventions, such as deposit-based incentives, at least two questions are raised. First, would interventions motivated by a particular bias work differently for those that suffer from the bias and those that don’t? Currently, such supposed mechanisms of the intervention are rarely studied. Yet, it is likely that we would find out that different people need different interventions. This introduces the second question: how can we make sure that our interventions reflect such heterogeneity in decision-making?
Together with Nienke Boderie, Jasper Been and Hans van Kippersluis, as part of the Smarter Choices for Better Health program, I have started my journey of answering these questions. In our study, which was recently published as a working paper here, we had to start small. Ideally, we would have recruited people for a study on behavioural change, e.g. smoking cessation, but running a study of that size, scope and cost was not yet feasible. Instead, we designed an online study that shares similarities with behavioural change: participants in our study were rewarded to put effort into a boring task now for a financial reward in the future. The experiment had two sessions. After signing up, students’ (n=228, recruited online) personal characteristics, including loss aversion were measured. By completing this session, they were told they had earned 4 euro. Next, they tried the boring task - moving sliders from one place to another - and we explained the way in which they would be rewarded for completing 400 sliders.
There were two different types of reward structures in this study; participants would either earn a small amount of money for each slider completed or we would take away the 4 euro they had earned already and add that to the per-slider reward. As such, we took their 4 euro as a deposit they could only earn back by completing all sliders. This reward structure tries to invoke a feeling of loss, as not completing all sliders would mean part of the 4 euros is lost. A key component of the study was how we assigned the reward structures to respondents. We compared two modes: random assignment and ‘nudged’ assignment. Random assignment meant that we would offer deposit-based rewards to a randomly selected part of our participants. ‘Nudged’ assignment worked as follows: we described both reward structures to participants and simply asked them what they wanted themselves. Supposedly they know what works for them, right? However, we made a recommendation, by pre-selecting one of the structures that we thought would work for them (i.e. a nudge). In most cases, this was the deposit-based structure, as loss aversion was quite prevalent in our sample.
Our results show that respondents receiving a nudged assignment spent more time working on the boring task than respondents randomly assigned to the incentives. Interestingly, we find no differences in effectiveness between the reward structures, and the difference was also unaffected by loss aversion. So why offer deposit-based incentives at all? Our result suggest that deposit-based incentives increased effort for participants that chose them themselves.
What do our results mean for prevention policy? We find that giving people a choice between different programs could be beneficial, since those offered a choice performed better than the randomized group. If this finding can be generalised in other contexts this could mean that policymakers aiming to personalise prevention may consider offering different options to individuals and let them decide themselves. It may be necessary to provide an informed advice on what works for individuals, however. Yet, our study does little to explain why choice is helpful, or why almost 40% of our sample followed our advice to take deposits. Many reasons may exist, but a particularly worrisome explanation would be that only a very select group of people volunteers to deposit their own money – people that know they may need help and can afford to lose some money. Earlier work on deposit-based incentives also suggested that those with higher income were more likely to accept paying a deposit[xii]. This leads to a situation where those with lower income, who may be in worse health to begin with, are less likely to enrol into a potentially beneficial intervention. Hence, it is of paramount importance to determine who benefits from being able to autonomously choose among interventions, before arguing for widespread adoption of choice-based prevention.
My hope is that in the next few years, in my role as postdoctoral researcher in the prevention action line of Smarter Choices for Better Health, I will be able to answer some of the many questions raised by our first steps into this important research area. Please get in touch if you want to know more or want to be involved (email@example.com).
[i] Bowling, A. (1995). What things are important in people's lives? A survey of the public's judgements to inform scales of health related quality of life. Social science & medicine, 41(10), 1447-1462.
[iv] Lipman, S. (2020). Decisions about Health: Behavioral Experiments in Health with Applications to Understand and Improve Health State Valuation.
[v] Khwaja, A., Silverman, D., & Sloan, F. (2007). Time preference, time discounting, and smoking decisions. Journal of health economics, 26(5), 927-949.
[vi] Notley, C., Gentry, S., Livingstone‐Banks, J., Bauld, L., Perera, R., & Hartmann‐Boyce, J. (2019). Incentives for smoking cessation. Cochrane Database of Systematic Reviews, (7).
[vii] Halpern, S. D., French, B., Small, D. S., Saulsgiver, K., Harhay, M. O., Audrain-McGovern, J., ... & Volpp, K. G. (2015). Randomized trial of four financial-incentive programs for smoking cessation. N Engl J Med, 372, 2108-2117.
[viii] Patel, M. S., Asch, D. A., Rosin, R., Small, D. S., Bellamy, S. L., Heuer, J., ... & Volpp, K. G. (2016). Framing financial incentives to increase physical activity among overweight and obese adults: a randomized, controlled trial. Annals of internal medicine, 164(6), 385-394.
[ix] Royer, H., Stehr, M., & Sydnor, J. (2015). Incentives, commitments, and habit formation in exercise: evidence from a field experiment with workers at a fortune-500 company. American Economic Journal: Applied Economics, 7(3), 51-84.
[x] Carrera, M., Royer, H., Stehr, M., Sydnor, J., & Taubinsky, D. (2022). Who chooses commitment? Evidence and welfare implications. The Review of Economic Studies, 89(3), 1205-1244.
[xi] Lipman, S. A., Brouwer, W. B., & Attema, A. E. (2019). A QALY loss is a QALY loss is a QALY loss: a note on independence of loss aversion from health states. The European Journal of Health Economics, 20(3), 419-426.
[xii] Halpern, S. D., French, B., Small, D. S., Saulsgiver, K., Harhay, M. O., Audrain-McGovern, J., ... & Volpp, K. G. (2016). Heterogeneity in the effects of reward-and deposit-based financial incentives on smoking cessation. American journal of respiratory and critical care medicine, 194(8), 981-988.
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