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By understanding how the financial decisions of an individual are
filtered by their automatic thinking, the social context, their mental
models and their psychological predisposition towards money,
professionals can significantly enhance that individual’s ability to
convert values into financially viable outcomes.
Perhaps the most powerful aspect of the financial well-being model is that it helps us cope with some of the most problematic contradictions in the literature around financial decision-making. On the one hand, the research cautions us that unless we can make a distinct link to what matters to individuals (their values), we are unlikely to successfully translate financial knowledge into an effective engagement with the financial decision. On the other hand, there are any number of studies that highlight just how bad we human beings are at making financial decisions.
The financial well-being model recognises the validity of both those points and provides us with a means for navigating our way through the potential minefield. At the heart of the process, we must recognise that there are distinct filters by which an individual processes financial decisions: automatic thinking, social context, mental models and mindsets about money are cases in point. The job of the financial consultant, financial coach or financial therapist is to understand how these filters may be creating a blockage to an individual’s ability to secure what matters to them.This next section provides an in-depth description of some of these blockages. An important insight is that concepts such as automatic thinking, social context, mental models and mindsets about money are not simply blockages. They also hold the key to how to positively link decisions to values.
How could we go about improving the financial decision-making and financial capability of an individual? There are any number of how-to books that profess various formulas for success. But as David Brooks, the New York Times’s pre-eminent pundit on social and political issues, explains in his book The Social Animal, these formulas are typically derived from “the surface level of life”: what knowledge needs to be acquired and what physical steps need to be taken. Getting to the heart of financial decision-making demands we go one step deeper. As Brooks points out:
“We are living in the middle of a revolution in consciousness. Over the past few years, geneticists, neuroscientists, psychologists, sociologists, economists, anthropologists, and others have made great strides in understanding the building blocks of human flourishing.1”
The critical insight: our conscious, deliberative thinking capacity plays a relatively minor role in the outcome.
Earlier this year, the World Bank published their annual World Development Report with a review of why development projects tend to fail in emerging economies. We saw the paper as providing a superb template for getting to the heart of any change management issues. The study argues along the same lines as David Brooks: to understand what informs financial decisionmaking, we need an approach that goes significantly beyond financial or economic analysis to meaningfully integrate elements of behavioural finance, anthropology, development economics, psychology and even historical context. Specifically we need to understand:
While the intent of their study is to use these insights to better inform how policymakers, development specialists and service providers could create more effective outcomes for communities and societies, we will take each in turn to illustrate how these insights are equally valuable in the world of financial advice and financial empowerment. In addition, we will look at how people’s mindsets about money affect their interaction with it. This examines how our psychological disposition towards money influences decision-making.
Daniel Kahneman’s seminal piece Thinking Fast, Thinking Slow, provides the foundation for much of our current understanding of behavioural finance. It introduces the idea that the human brain processes information in two ways: automatically (thinking fast) and deliberatively (thinking slow)1.
These automatic thinking processes can be hugely valuable. After adequate training they can lead us to quick and skilled responses or intuitions. When combined with associative memory, we can rapidly produce a coherent pattern of activated ideas that can allow us to significantly shorten problem-solving tasks.
The problem with automatic thinking, however, is that it can just as rapidly infer and invent causes and intentions. Rapid processing can cause us to focus on existing evidence, while ignoring absent evidence. It occasionally reads more into information it is presented with than is warranted. It overweights low probabilities. It is more sensitive to changes than to states. It frames decision problems narrowly, when a broader picture would lead to a different conclusion2.
To some extent this is a function of the way we are hardwired: we simply couldn’t apply deliberative thinking to the tsunami of information and decisions that we are flooded with each day. But we should not underestimate its importance. The impressions and feelings generated by our automatic systems inform the explicit beliefs and reflective choices of the deliberative system – not the other way around. Still, we simply cannot ignore the potential for our automatic thinking system to produce sub-optimal financial decisions.
dominates the marketing machinery of our financial services industry. It’s our ultimate automatic thinking quick-sort tool. No matter how many times we are reminded (and remind others in turn) about how hugely destructive our adherence to it can be, our industry continually reverts to it when trying to ‘sell a point’.
So, what is this Law of Small Numbers and why is it so problematic? Kahneman uses an example from statisticians Howard Weiner and Harris Zweiling of a Gates Foundation project to illustrate the problem.
The foundation was interested in funding schools that they believed had the highest potential for success. Researchers applying for funding produced data suggesting that successful schools needed to be small. In their study, of the 1 662 schools in Pennsylvania that they analysed, they classified 6 of the top 50 schools as small. On the surface that seemed to be fairly compelling evidence. It meant that small schools out-represented large schools in the success pool by a factor of 4. As Kahneman noted: “It was easy to construct a causal story that explained how small schools are able to provide superior education and thus produce high-achieving scholars by giving them more personal attention and encouragement than they could get with larger schools3.”
Unfortunately, in this case, the facts were wrong – although that fact wasn’t fully appreciated until after the Gates Foundation made a considerable investment in the project. Had the researchers asked the question as to the characteristics of schools with the worst success rates, the answer would have been that small schools also had the worst success rates. How could that be?
The issue is not about the size of the school at all, but what happens when we work with data that represents small sample sizes. Small sample size data is prone to producing outcomes that are significantly more variable, and potentially more extreme. The net effect is that outcomes in a larger sample size would probably be randomly distributed, which can easily become inadvertently skewed with the smaller data sample. Simply put, we are far more likely to see examples of extraordinary and extreme outcomes when we have a small data set than we are when there is a large one.
In financial services, the application of The Law of Small Numbers is most prevalent when we provide performance numbers to investors. How often do we walk past airport billboards that shout the praises of a top performing fund or investment manager? And yet a ten-year performance record of excellence is statistically meaningless as an indicator of skill in the broader scheme of things. We are using very limited and very noisy data sets to make sweeping claims about what is happening and why those outcomes have been achieved. Still, research from BNP Paribas on fund flows in the South African unit trust industry provides evidence that money flows towards strong historical performance records4.
Even when we believe that our sample set is easily large enough to justify our conclusions, our misunderstanding about probability distributions can lead us to jump to conclusions that defy substantiation. As Nassim Taleb famously pointed out in relation to Warren Buffett’s extraordinary 30-year performance history, if one considers that in this case the sample size represents the several hundred million other investors in the world (and that’s probably a low estimate), the odds are extremely high that someone could have achieved Warren Buffett’s performance results by sheer chance – and not by skill at all5. In other words, a random distribution of the performances from several hundred million investors would have yielded at least one data point, if not dozens of data points with outcomes of that order. So…is Buffett skilful? Here is where the statistics defeat us again. We simply do not have a large enough data sample representing Buffett’s range of investment decisions to say with statistical certainty that Buffett is skilful.
When it comes to our financial decision-making, it’s the reflexive, fast-thinking part of our processing, the automatic mode, that plays the greatest role in our everyday processing. Cynically put, marketing departments count on the fact that consumers aren’t thinking very deliberatively when they do deliberate.
Consider these examples:
When cash-strapped consumers consider such ‘grudge’ purchases as short-term or medical coverage, cost will often be a deciding factor. Marketing to these customers will often be deliberately framed to highlight this point. In reality, a tantalisingly low premium may mask the fact that there are high embedded excesses or copayments involved. As such, the promised protections afforded by the policy may well turn out to be significantly more expensive than initially understood. As such, a sub-optimal fast-thinking financial decision will invariably nudge out a better ‘value-for-money’ decision if the consumer is provided with no basis on which to deliberate.
What about this perennial problem? Payday comes and it’s clear an employee is not going to be able to make that required down-payment for the living-room set he has promised his family. But how convenient is the offering of a quick R300 payday loan to address this ‘consumption emergency’? For only R15 per R100 for every two weeks that the loan is outstanding, the individual’s problem is solved. What’s missing, of course, is that critical deliberation insight that helps consumers appreciate that after a mere two months, the charges on that R300 loan are now R180.
Much of financial education and advice presumes that financial decision-making falls into the deliberative thinking camp for individuals. Consumers may have been properly coached in the powers of compounding, or the wisdom of long-term investing and diversification, for example, but even this knowledge foundation can be undermined when framing or anchoring end up short-circuiting a considered outcome.
While the academic world now appears to fully grasp that ‘economic man’ (a rational decision-maker) is probably a fiction, our policies and financial advice frameworks simply don’t go far enough in terms of acknowledging this reality. Financial education starts first with trying to teach individuals basic principles. Implicitly this means that we believe we can get results by simply appealing to the deliberative side of human decisionmaking. To date, the outcome of such thinking suggests this is not a useful assumption. As we pointed out in Benefits Barometer 2014, a 2014 study on Financial Literacy, Financial Education, and Downstream Financial Behaviours6 concluded interventions to improve financial literacy have a mere 0.1% effect on financial behaviour.
Counterintuitive as this may sound, an individual’s goalsetting and prioritisation is far more likely to reside in the automatic than the deliberative thinking space. Just think how many bad choices we human beings make around the deployment of our financial resources. For that reason we need a major rethink of how we could use our insights about automatic thinking to create outcomes that speak directly to greater financial capability.
We know, for example, that automatic thinking is shaped by the accessibility of different features of the situation. Seemingly unimportant features in decision-making, such as how many choices a person has to make, can completely unhinge effective decision-making. When people think automatically, the way choices are presented and the context under which decisions are made is critical.
This means that if we are going to push back the tsunami of influence from automatic thinking that can produce a horrific undertow of sub-optimal decision-making, we need to pay more attention to:
This is our starting point. We can also provide little educational ’nudges’ to get people to think through problems more deliberatively. Or, we may need to completely rethink how we provide advice.
In the World Bank’s 2015 World Development Report entitled Mind, Society, and Behavior, they introduced the following example to show exactly how to quietly introduce fi nancial education at a critical point in an individual’s fi nancial decision-making. In this specifi c fi eld experiment, individuals received payslip envelopes that provided illustrations on the back of how the cost of borrowing from a payday lender (short-term loan or loan shark) compares to borrowing the same amount from a credit card. Providing a constant reinforced message with every pay cheque created an important level of anchoring around concepts of ‘good’ debt and ‘bad’ debt.
Now let’s tackle the bigger mind-shift. How could we completely rethink the way we provide fi nancial advice to individuals?
Consider this challenge: consumers are fl ooded with information about what the fi nancial services industry believes they need. Financial products are often sold on the basis of why they would be critical to the individual. Given that we are trying to stimulate fi nancial empowerment and fi nancial well-being, would the framing not be far more effective if we told individuals what they don’t need? We take exactly this approach in Part 2: Chapter 3 when we discuss decision-making around short-term insurance and medical aid.
In a fi eld experiment, randomly chosen borrowers received envelopes that showed the dollar fees they would accumulate when a payday loan is outstanding for three months, compared to the fees to borrow the same amount with a credit card.
Borrowers who received the envelope with the costs of the loans expressed in dollar amounts were 11 percent less likely to borrow in the next four months compared to the group that received the standard envelope. Payday borrowing decreased when consumers could think more broadly about the true costs of the loan.
What’s required in all these examples is a sharper focus on decision-making by both individuals and their advisers, such that choice architecture dramatically simplifies the trade-offs being made with each decision and helps individuals quantify the outcomes of these decisions.
In Benefits Barometer 2014 we provided one example of such a tool when we described a payroll application that could help members derive a better understanding of how their employment contracts and benefit structures provide such present, future and future perfect solutions. We also showed how better choice architecture in an investment framework could improve outcomes for umbrella fund members.
In Benefits Barometer 2015 we provide a framework for decision-making around medical and short-term insurance coverage decisions that starts with the question: what if I did nothing?
When it comes to our financial decision-making, it’s the reflexive fast-thinking part of our processing, the automatic mode, that plays the greatest role in our everyday processing.
We would get better results if we focused on how to protect individuals from the reality of their automatic thought processes. Alternatively we could consider ways to use the natural process of automatic thinking to get individuals to make more of the ‘right’ decisions to address their wellbeing objectives.
Here is the conflict, we cannot increase deliberative thinking by increasing the information we give to individuals. The more information we flood individuals with, the less likely we are to see the right decision – much less any decision. We need to change that dynamic.
Our starting point is to determine whether the financial decision in question is more likely to be addressed by the individual with an automatic or a deliberative process. Only when we know the answer to that question will our interventions be effective. This is what should dictate how we formulate our advice process and our collateral marketing documents. (The forests of our planet will thank us for taking that little extra step.)
The financial well-being challenge is in finding the most effective way to walk that thin line between presuming or dictating what would be in the best interests of an individual’s well-being, and recognising that most individuals simply do not have the wherewithal to know that answer – at least not if it involves projecting out hugely complex and variable decision trade-offs into the future.
Human sociality is like a river running through society; it is a current that is constantly, if often imperceptibly, shaping individuals, just as flowing water shapes individual stones in a riverbed. Policymakers can either work with these social currents when designing interventions or ignore them and find themselves swimming upstream.
The problem with economic theory is that it often paints individuals as autonomous decision-makers. In some schools of thought our financial actions are seen as purely self-serving, with wealth maximisation being the overriding driver. It’s assumed that ‘economic man’ does what he does for the money. A focus on material incentives, material rewards and material outcomes is thought of as inevitable.
As the World Bank study highlights, there are ‘other-regarding’ preferences2 at work in all societies. These are concepts such as the innate human desire for social status; our need to identify with one group while potentially rejecting another group; our willingness to cooperate with others who are seen as cooperating; our tendencies in some instances to behave altruistically; and our willingness to engage either in instrumental reciprocity, responding to kindness with kindness for some long-term gain or intrinsic reciprocity, where an individual will be prepared to either reward or punish the behaviour of others even if it is at a cost to oneself or the community.
Understanding where these counter-intuitive drivers of decision-making come from demands that we understand how our broader group behaves and cooperates, and identifies which decisions are in the best interest of the group. Financial decision-making by an individual is more heavily influenced by the requirements of the broader group than it is by self-interest.
On the one hand, this influence from the group helps explain phenomena such as the ‘third force’ of human drive, alluded to by Daniel Pink in his book Drive. Pink describes how human beings can also be driven, to greater or lesser degrees, by the intrinsic rewards that come from believing you are enhancing the world around you. This contrasts dramatically with the kind of explicit rewards provided by simply meeting basic needs or achieving certain performance goals.
Conversely, understanding the role of social influence by way of social context and social history also helps explain how entire societies can get stuck in dysfunctional behaviours such as corruption, over-indebtedness and xenophobia.
Through social context we derive social norms, those powerful sets of shared beliefs that dictate how community members should behave to maintain group dynamics, or how and with whom individuals should interact in their social networks. The net outcome has the potential to be profoundly destructive to a population.
Failure to understand the importance of social influences on financial decisionmaking has been the undoing of many financial wellness and financial literacy programmes. As the World Bank study cautions us, policymakers often underestimate how critical this social component is in influencing changes in our financial capability.
But if we can get down to the heart of those social norms, come to grips with the context in which they were formed, and identify how a group’s social network influences both the formation of those norms and individual decision-making, we can begin to identify what types of interactions have the greatest potential for creating the kind of new behaviours required to better serve the long term interests of the group.
Understanding who (what type of person) carries influence and why, means we can determine what kind of group role model could change the ‘mental models’ the group employs while making specific decisions. We can target specific individuals to lead and amplify change.
Let’s try to explore this dynamic more fully.
The tendency of the financial services industry is to ‘tell’ people what would be the preferred behaviours they should follow if they want to improve their financial decisionmaking. Our advisory process is often one where our clients share with us their financial concerns and we, in turn, tell them what they must do.
Consider why this might be problematic. If this advisory framework exists outside of the social context, once the individual returns to that context, it’s just a matter of time until the pre-existing social norms prevail. ‘Tell’ an individual that if they want to sort out their retirement shortfall they shouldn’t buy that new car on credit and they will likely revert back to their original plan or, more drastically, change financial advisers. The issue isn’t lack of fortitude or financial savvy. The issue is that there are greater influences at play than you, the adviser.
People will do the right thing to enhance their financial well-being, but only if they perceive other people as doing the right thing.
As an industry, we owe it to our clients to better understand the social context that they operate within – irrespective of what socio-economic grouping they may fall into. The research is alerting us to the fact that unless we develop ways to help clients grapple with potentially dysfunctional pockets of influence, financial advice will fail to make any impact on an individual’s financial capability.
There is a good news/bad news story here. We are beginning to understand better that people will do the right thing to enhance their financial well-being, but only if they perceive other people as doing the right thing. That means that people often behave as ‘conditional cooperators’. It also means that under the right conditions, social pressure can provide a powerful impetus for change3. The implication is that we should be spending considerably more time as an industry and as policymakers in determining how we can leverage the power of group coercion and group support to change dysfunctional financial behaviours. Our current model depends almost entirely on convincing the individual directly. But encouraging the right financial behaviour has been shown to be far more effective if it is reinforced by social norms that correlate with the behaviours being promoted.
“Save more”, “Save for retirement”, “Make a budget”, “Protect your families and loved ones” – this is the general litany of the advice world. Why does this fall on deaf ears?
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