If you've ever taken a long drive on an expressway, you know exactly what the last mile problem is. On a drive back home from your summer vacation, you might have zipped along the expressway only to be stuck after exiting it. There are things that happen on city streets that you don't fully anticipate. Perhaps there is somebody making a left turn and blocking up the single lane road. There might be local construction. There are one way traffic flows and potholes.

The last mile or so of a journey home is perhaps the most inefficient as compared to the highway experience. Unlike the expressway, it’s also the part where the behavior of individual agents – drivers, construction workers and pedestrians – plays a large role in your progress.

I believe that the process of creating policy and welfare programs is captured by the expressway [the first miles] and the side-street [the last mile] analogy. Much of the first mile work includes strategic discussions, idea generation, program design and development. In contrast, the last mile is where the rubber meets the road and the policy or program goes “to market”. Citizens come to websites, into service centers, call in to phone centers, fill up forms and conduct transactions. The success of these last mile interactions typically translates into the success of policies and programs. After all, policy is made with the intention of helping people make the right choices. The best policy and programs should be ones that are designed for people!

It is also my belief that organizations spend disproportionately too much time on the first mile and too little on the last. Why are the take-up rates of welfare programs often so low? Why do citizens who are eligible for benefits not claim them? Why do carefully scripted financial disclosures, or financial literacy programs, not always result in better outcomes? And in the world of business, why does brand love not translate into booming sales?

I propose that policymakers and designers of programs must start at the last mile and work their way up backwards. As all my friends from the world of transportation tell me “there’s no point in building speedier highways if your city streets are not up to speed; you’ll simply send more people to be stuck there!”

One of the big reasons for low levels of attention to the last mile is that the last mile is not a problem for technology, strategy, finance or expertise in a given substantive domain. It is a problem of psychology. And as we know, individual behavior is inherently much more unpredictable and context dependent than the behavior of organizations. Indeed, much has been written about the seeming “irrationality” of human behavior and the underlying biases we display in our decisions. I personally do not like the label of “irrationality” – just because humans do not follow the laws of economics does not make them irrational; they are merely being human. Instead, to expect them to follow the laws of economics is a more dangerous form of irrationality (Irrationality 1.0) that can create all sort of last mile issues.

A classic case of Irrationality 1.0 manifests itself in policies relating to the disclosure of information and risks. In the traditional economic model of man, the more the information a decision-maker had access to, the better off they are. But research shows that the greater the amount of information that is presented, the less likely it is to be comprehended or even read.

A second example of Irrationality 1.0 is the belief that human behavior is driven primarily by monetary considerations. In Canada, the government introduced a new welfare initiative called the Canada Learning Bond. The CLB that empowers eligible low-income families with $500 that they can use to educate their children. Since this is essentially $500 of “free” money, one would expect that the take‑up rate of this CLB would be close to 100 percent. Yet, it turned out that in the first year when the bond was introduced, the take‑up rates were as low as 16 percent. A lack of awareness or information did not explain this. However, in order to benefit from the program, an eligible family needed a bank account. Yet the target market for the CLB was juggling multiple jobs and caring for children. They did not have time to open an account at a bank, creating a last mile problem.

A good theory of the last mile is therefore incomplete without a good theory of decision-making and of behavior change. However, a walk through any textbook on psychology or consumer behavior suggests that decision-making is complex and potentially affected by a myriad of factors.

The three pillars of human decision making

Despite the seeming complexity, the good news is that there are three simple principles that explain much of decision-making. The first principle if that context is everything. If you change the context, people's preferences change. The second principle has to do with procrastination. All of us want to be good human beings — we want to lose weight; we want to exercise; we want to save more for our future; we want to spend more time with our children - but not today. We tend to procrastinate all of the good things that we want to do because life gets in the way. The third principle that explains human behavior is the principle of inertia: the idea that human beings are cognitively lazy and that unless they are pushed to make active choices, they will actually not do so.

For instance, it probably comes as no surprise to most people that the most popular size of coffee sold in coffee shops is the medium size of coffee. It turns out about 74 percent of all coffee sales are in the medium cup. What's interesting is that most people don't know how much coffee is in the medium cup of coffee. The size, the number of ounces of coffee in medium cup of coffee is different across coffee shops.

Several years back, I studied a coffee shop which offered there sizes; Small (8 oz.), Medium (10 oz.) and Large (12 oz.). The medium coffee was the most popular, and when we stopped people asked them, people who had purchased a medium size provided a response that was something along the lines of a Goldilocks story; the small coffee has too little, the large size has too much, and the middle one was just right. After a period of three or four weeks we increased the size of each cup by two ounces. Lo and behold, the new medium size became the most popular size. When we again stopped people and asked them about their choice, and we heard the same kind of Goldilocks response; that the small size had too little and the large one had too much coffee! People who were previously claiming that 12 ounces of coffee was too much were now consuming it!

Sketch2

This illustrates two important ideas. First, context truly matters – people rely on the crutches of context to construct their preferences and seemingly trivial elements (forms, brochures, envelopes, layouts of waiting areas) might have large effects. Yet, we pay little attention to the details of the context. Second, people have poor insight into their own decisions and preferences. Surveying people in a different context will likely not provide any useful insights!

Amar Cheema and I were looking to help unbanked rural workers in India to save a portion of their cash earnings towards their children’s education. Rather than simply suggesting a target savings rate, we put the equivalent cash in a while envelope and marked it “savings.” Savings rates went up. We next put photographs of their children on the envelope. Savings further went up because the goal was now psychologically salient. A simple psychological intervention did much more than a financial literacy intervention or perhaps even a banking intervention could have done to try and get people to save more money. There is perhaps an e-banking version of these interventions waiting to be done. Suppose your online bank allowed you to create categories under your savings account that correspond to a savings goal. Say you were saving for your kids and for a new home. Perhaps you could create folders with pictures of your kids and your dream home in it, and set savings targets on a monthly basis, and perhaps if you fail to meet those targets the pictures on the folders might start deteriorating and nudge you to contribute. If implemented, I suspect that a last-mile intervention like this might have a significant role to play in helping people save; as significant as appeals to save, literacy programs or increased interest rates.

Towards a Behavior Change Framework

Imagine that a lawyer, an economist, an advertiser and a behavioral scientist walk into the bar. The bartender poses a question – in a choice between A and B (let’s say two beers), how do I get people who are currently choosing A to switch to B?

The lawyer suggests a restriction approach. “You could simply ban A,” the lawyer suggests. You could withdraw product A from the shelf.  The economist has a different idea; “you could tax A or give a discount on B”. The advertiser recommends information and displays that provide details on the superiority of B over A. The behavioral scientist simply looks at the context and says “Serve everyone B, but say you could give them A if they really want it.”

I talk about these four approaches towards behavior change not so much because I believe any one of them is superior to the other, but because it is an important checklist to help us get organized about developing a theory of behavior change. The best approaches are the ones that might use a hybrid of these four different times.

Think about the recovery and rehabilitation process for someone who has gone through surgery for a broken ankle. Rehabilitation requires two distinct sets of activities; the first of which involves strengthening the wounded area through physiotherapy and diet to “equip” healing. The second set of activities involves “padding” the surroundings to make it easier for the affected person to move around and to ensure that should there be a slip-up, the consequences are not severe. If human behavior deviates from what we want it to be, we could both equip people to make better choices while simultaneously padding the environment to prevent bad outcomes.

At the last mile, everything matters! Most humans do not respond well to a lot of choices and a lot of information; take the easiest way out of decision problems; are often not influenced by economic considerations unless it fits their mental models; are significantly affected by context and by others; and are often their own worst enemies in that they end up not doing things that they wanted to do. Rather than throwing up their hands and exclaiming that this is an overly complicated problem to master, policymakers and program managers will benefit if they 1) started at the last mile in program design, 2) pick small battles and address them sequentially and 3) embrace the evidence based approach to policymaking and program design. Given all the complexity in human behavior, it is unlikely that we will have a grand unified theory of decision-making. A culture of testing is the best solution to this theoretical lacuna!

Illustrations by Yue Zhuo.

Dilip Soman is a professor at the Rotman School of Management at the University of Toronto and the author of The Last Mile: Creating Social and Economic Value from Behavioural Insights  (University of Toronto Press).