Psychology and Behavioral Economics

Applications for Public Policy

Kai Ruggeri, Kai Ruggeri

• Publisher: Routledge
• Year: 2021
• ISBN:
  9781000449976
• Edition: 2
• Topic: Commerce
• Subtopic: Comportement du consommateur

Psychology and Behavioral Economics offers an expert introduction to how psychology can be applied to a range of public policy areas. It examines the impact of psychological research for public policymaking in economic, financial, and consumer sectors; in education, healthcare, and the workplace; for energy and the environment; and in communications.

Your energy bills show you how much you use compared to the average household in your area. Your doctor sends you a text message reminder when your appointment is coming up. Your bank gives you three choices for how much to pay off on your credit card each month. Wherever you look, there has been a rapid increase in the importance we place on understanding real human behaviors in everyday decisions, and these behavioral insights are now regularly used to influence everything from how companies recruit employees through to large-scale public policy and government regulation. But what is the actual evidence behind these tactics, and how did psychology become such a major player in economics? Answering these questions and more, this team of authors, working across both academia and government, present this fully revised and updated reworking of Behavioral Insights for Public Policy.

This update covers everything from how policy was historically developed, to major research in human behavior and social psychology, to key moments that brought behavioral sciences to the forefront of public policy. Featuring over 100 empirical examples of how behavioral insights are being used to address some of the most critical challenges faced globally, the book covers key topics such as evidence-based policy, a brief history of behavioral and decision sciences, behavioral economics, and policy evaluation, all illustrated throughout with lively case studies.

Including end-of-chapter questions, a glossary, and key concept boxes to aid retention, as well as a new chapter revealing the work of the Canadian government's behavioral insights unit, this is the perfect textbook for students of psychology, economics, public health, education, and organizational sciences, as well as public policy professionals looking for fresh insight into the underlying theory and practical applications in a range of public policy areas.

1
Psychology and policy

Kai Ruggeri, Kamilla Knutsen Steinnes, Maja Friedemann, and Fadi Makki

DOI: 10.4324/9781003181873-1

Chapter summary

To reach desirable outcomes within a population, we regularly rely on policies: population-based interventions aiming to optimize outcomes through guiding behaviors and decisions. Policies are developed for use by anyone from individuals and families to large groups and major organizations. While often considered a nebulous system of complex processes used by governments, policies are often very simple guidelines and can relate to very basic, everyday activities. This chapter introduces core concepts of policy, how it is distinct from – yet overlaps with – laws and regulations, the historical development of policy, and the making of policies as an area of scientific interest. This chapter sets the tone for the book, framing key terms and topics critical to maximizing the value of every section through the contributions of psychological and behavioral sciences to policy.

Learning objectives

• Gain an understanding of what policy is and what it is not
• Be able to describe what policies do
• Get a firm grasp on why policies matter
• Appreciate the history behind how policies came to be a major area of scientific research
• Explore the link between policies and psychological sciences

Introduction

Every waking moment of every day, people are faced with decisions. Once we make those choices, we then live them out through our behaviors. Many of us will have a set time we decide to wake up each morning, something that we base on our personal experience of getting ready for school or for work as well as how much sleep we need. That decision will influence almost everything throughout the day, as well as when to set the time for waking up the following day once we have made the decision to go to sleep that evening.

Decisions we have to make vary between the most mundane choices about whether to wash our hands after we use the restroom and the most complex, multilayered processes involved with piloting a commercial airliner. With every decision we face, we confront contingencies related to its success, accuracy, efficiency, and influence on other people or aspects of our lives (Beach & Mitchell, 1978; Simon, 1957b).

Confident in our decisions?

But can we, as individuals, groups, or even entire populations, be confident making those decisions day in and day out? The average person may consume three full meals in a day, but is every meal equally planned and sufficiently healthy? On what basis do we choose the food we eat, when we eat, or how much we eat? When we take a moment to consider how directly our food consumption impacts almost everything about our health, it is exasperating to realize how little time we actually put into making such choices. The rational response to this is that it would be entirely unrealistic to put substantial amounts of time into making those decisions given we do it three times in a day, 21 times in a week, and possibly 11,000 times in a decade. Evolution has shown that humans are typically very good at making such choices as a species long term (Kenrick et al., 2009), as well as at adapting when supplies are short or commodities change (Payne, Bettman, & Johnson, 1988). However, there is reason to explore just how efficient and reliable we are in the ways we approach and decide on just about every behavior we make (Tversky & Kahneman, 1985).

When we are confronted with a choice, we are essentially tasked with comparing outcomes on the basis of the information we have, such as the probability of the best chance to “win” or the risk of “losing” (Kahneman & Tversky, 1984). Unfortunately, as will be discussed in Chapter 4, psychologists and statisticians have found that our understanding of probabilities and risks are limited in any context, even when simple numbers are provided to reduce complexity (Payzan-LeNestour & Bossaerts, 2011).

In 2008, 25 percent of American and 28 percent of German participants in a comparative national survey gave an incorrect answer when responding to the question, Which of the following numbers represents the biggest risk of getting a disease? 1 in 100, 1 in 1,000, or 1 in 10? (Galesic & Garcia-Retamero, 2010). How can such a large portion of the population get such a simple question – with such clear importance – wrong? If we are able to determine the difference between 10, 100, and 1,000, why should we be less successful in distinguishing 1/10, 1/100, and 1/1,000? If one-quarter of the population cannot discern something as basic as 0.1 (1/10 – the correct answer) being a much greater risk than 0.01 (1/100) or 0.001 (1/1,000), what threat does that pose to us as individuals and communities where we are faced with far more complex choices on a regular basis?

Much of this has been understood within bounded rationality, a concept developed by the economist Herbert Simon in his 1957 book Models of Man. Bounded rationality suggests that, while we may desire to give ourselves the best chance at a successful outcome, in the face of uncertainty and risk, we tend to make choices on the basis of our gut feeling or intuition (Gigerenzer & Selten, 2002). In the earlier example, it is presumed that many of those who chose incorrectly simply saw the larger numbers of 1,000 or 100 and quickly assessed them as being greater than 10. Rather than recognizing that they were denominators, participants’ immediate response was that they presented larger values.

Beyond reliance on confidence or our gut feeling – the initial belief, reaction, or preference when confronted with information where an opinion, choice, or disposition is required, which may result in implicit bias (Jolls & Sunstein, 2006) – decision-making can be heavily biased by a number of other factors that may appear irrational (Spiegelhalter, Pearson, & Short, 2011). When we decide to buy a cup of coffee on our way to work every weekday, we are making a decision based on our previous experience of both having coffee (positive) and not having coffee (disastrous – for some), even if it causes us to be late for work or we realize the total cost in a year for buying coffee every day. When we skip our usual cigarette break at work because a new colleague mentioned how unhealthy smoking is, our choice is being influenced by the opinion of others. In the long run, we might decide to quit smoking altogether due to social norms and cultural views instead of the negative impact of smoking on physical health.

Our everyday decisions are also affected by our current emotional state. We might opt out of buying a new computer because a pushy salesperson evokes a negative emotional response such as anger or frustration. We might, however, purchase an identical product at another store after a more positive interaction with a different salesperson. Another factor that can influence decisions is level of perceived control. Students who perceive they have little control over the outcome (exam grade) of their decisions (study vs. watch TV) are less likely to try (choose studying over watching TV) than students who believe they have more control (Ajzen, 2002a). Thus, decision-making is susceptible to various extraneous influences that may seem illogical given they do not change the options being chosen from.

Intuitive choices, such as opting to eat breakfast before leaving the house, work well for us most of the time (Kenrick et al., 2009). However, they are not always optimal when we need to make more deliberate, calculated decisions (Kahneman, 2003b), such as when deciding between different medical treatments or making a choice among alternative financial loans. During these times, we are more likely to make use of risk estimates in the form of probabilities to aid our decision-making (Payzan-LeNestour & Bossaerts, 2011). For example, a cancer patient choosing between different treatment options might ask their doctor for the probability of each treatment’s recovery prognosis and make their final decision on the basis of a deliberate evaluation of which treatment has the best chance of helping them recover.

Unfortunately, as mentioned earlier, probabilities pose a notorious challenge in terms of successfully conveying them to the general population and even to experts (Spiegel-halter et al., 2011). For example, in one classic experiment, medical doctors were asked to interpret the outcomes of mammography tests carried out to check for breast cancer among patients (Eddy, 1982). A large majority of the participating doctors confused the test’s sensitivity (the proportion of positive test results among women with breast cancer) with the test’s positive predictive value (the proportion of women with the breast cancer among those who received a positive test result). Thus, even people having rigorous training and expertise in a particular domain are subject to such decision-making fallacies. This difficulty with understanding probabilities – which is evident in experts and laypeople alike – affects our ability to make rational decisions (Hoffrage, Lindsey, Hertwig, & Gigerenzer, 2000).

So how confident can – and should – we really be in our ability to make good, rational choices? Going back quite a few years, multiple studies suggest that the confidence people have in their decisions exceeds their ability in terms of knowledge and judgment (e.g. Gigerenzer, Hoffrage, & Kleinbolting, 1991; Fast, Sivanathan, Mayer, & Galinsky, 2012; Alpert & Raiffa, 1982; Soll & Klayman, 2004; Pallier et al., 2002). This overconfidence tendency applies to both experts and laypeople (Einhorn & Hogarth, 1978). Students, for example, tend to overestimate their own performance on academic exams (Clayson, 2005), and most car drivers believe they are above average compared to other drivers (Svenson, 1981). People, in general, overestimate their time needed to complete tasks (Buehler, Griffin, & Ross, 1994), and the more difficult the task, the more likely people are to be overconfident (Lichtenstein, Fischhoff, & Phillips, 1982). More specifically, difficult tasks lead people to overestimate their actual performance on the task, while also incorrectly thinking they performed worse on it compared to others. Easy tasks, on the other hand, result in people underestimating their actual performance on the task, while incorrectly thinking they performed better on it compared to others (Moore & Healy, 2008). In sum, overconfidence appears to be a very common tendency in populations. Thus, one potential option to improve outcomes for populations is via guiding decisions and behaviors – whether tedious and seemingly inconsequential or complex and impactful – through policy.

What is policy?

So how can we confront our tendency to be misguided in our decisions? We often rely on a set of guidelines when faced with a decision (Jamieson & Giraldez, 2017). This is particularly true if that choice is significant or something we repeat regularly, such as deciding on a time to set our alarm clock every morning. These guidelines may not force one particular choice over another, but they provide a process to ensure the optimum choice is made, typically by including mechanisms that highlight better choices and safeguard against mistakes or higher risk options. When these are used across a group to the extent that everyone in that particular situation is influenced in some way, even if they do not abide, we consider these policies: population-based interventions that aim to guide behavior to the optimum outcome in the most efficient way over the life span of the choice (Ruggeri, 2017a).

Through guiding decisions, policies seek to either optimize (gaining the most through using up the least of finite resources, such as going to the market and buying the most food for the least money) or maximize (achieving the greatest result based on available resources, typically irrespective of expense, such as a traditional chess match, where the ultimate indicator is taking the opponent’s king not how many pieces you have remaining when it happens) outcomes.¹ That is, policies aim to achieve the best practices or consistently repeated behavior, as often as possible. Hence, policies are sets of behaviors, decisions, and standards used by a group when dealing with significant or common challenges. Consistent approaches are therefore necessary to ensure the most ideal outcome, whether quantifiable, ideological, or simply for reliability.

At their most tangible level, policies are structured attempts to approach critical choices and practices that will have significant implications across a population (Schneider & Ingram, 1990). In these scenarios, they may be informed by a variety of sources while being carried out by a plurality of stakeholders, all by design. At their most basic, policies may be unwritten codes of practice that result in consistent actions or series of steps when a group or individual faces a common choice or obstacle (Schneider & Ingram, 1990). This may be as fundamental as deciding to wake up one hour before work every morning to get ready for the day ahead. We might think of a policy as a lever (see Box 1.1) that can be pulled to alter individual-, group-, or population-level behavior (Schneider & Ingram, 1990). Different policies are targeted toward different populations of interest, which may be individuals, groups, organizations, or the general public (Ruggeri, 2017a). Populations can, however, be established by design or by default (Howlett, Mukherjee, & Woo, 2015), as there are an infinite number of such potential tactics held by a complex overlap of individuals and groups (Ruggeri, 2017a).

BOX 1.1 POLICY LEVERS

Levers can be seen as the actions (interventions, policies) that identify specific areas for improvement, accounting for significant risks and contextual factors, leading to a population-level change if implemented as a (1) policy. The figure below represents the levers as (4) behavioral interventions that have (3) considered environments and exposures following the (2) identifi cation of strengths and weaknesses in an educational intervention between age groups.

Policies: when push came to nudge

One common challenge in discussing policies is that there is a general understanding of what they seem to be, but no formal consensus on the specifics of what a policy is or is not (Oliver, Lorenc, & Innvær, 2014b). If we consider a policy as described earlier – a population-level intervention that focuses on guiding behaviors and choices – then we can see the overlap with laws and regulations, which are terms often used interchangeably with policy. However, while policies often heavily overlap with regulations and laws, they are not the same (Ruggeri, 2017a). For example, while an effort to reduce smoking without banning it is a policy, government legislation establishing bans and punishments for violating them are unmistakably laws. However, these are interrelated because a policy can lead to regulati...