Deloitte Review 18 Behavioral economics and management
Human biases extend beyond our personal lives to impact economic, regulatory, and management decisions more than we may realize. Deloitte’s Jon Warshawsky spoke with Tanya Ott about how behavioral economics can offer tools to better shape programs, policies, and products in a human-centric spirit, as presented in the upcoming issue of Deloitte Review.
TANYA OTT: This is the Press Room, Deloitte University Press’s podcast on the issues and ideas that matter to your business today. I’m Tanya Ott, and we’re going to start with a little history. For many years, economics concerned itself with curves—moving curves around on charts. It assumed that people would buy a certain product when it reached a certain price point. It was a numbers-driven, quantitative science.
But here’s the thing: People don’t always behave as perfectly rational economic beings. We don’t make makes decisions based on logical information.
Sometimes we fall into behavioral traps—based on prejudices and biases and preconceived notions. Maybe you buy a house that you can’t quite afford, but you figure your career’s going well, and you’ll probably make more money this year. Or maybe you know you should put aside more for retirement, but you just haven’t gotten around to talking to your HR department about upping your contribution to your 401k. Either way, you’re letting emotion or inertia get in the way of what you know, rationally, you should do.
So, you’ve got dispassionate numbers and charts and graphs on one side—and emotions and aspirations and the heart on another. And in the last decade or so, the two have collided into a field of study called “behavioral economics.” It’s the focus of the latest Deloitte Review. Jon Warshawsky is the editor of the publication. I asked him to give us a preview.
JON WARSHAWSKY: You can argue about it, but I think the watershed moment, a lot of us think, is when Richard Thaler and Cass Sunstein published their book Nudge in 2008. They talked about something called “choice architecture”—how do you construct an environment that helps people achieve the desired results through something called “nudging.” This wasn’t trickery or deceit, but it’s understanding how people behave and how you can create an environment that helps people behave in a way that benefits them or creates a desired outcome.
TANYA OTT: It seems like really good timing with the beginning of a new year, because folks are really looking at their motivations for doing things and how they might change habits that they have. Was that timing significant to you in choosing this as the theme?
JON WARSHAWSKY: I think that certainly factors into it, but we’ve been running a lot of coverage of technology issues. We’ve talked about the Internet of Things at length, and [that] technology has kind of been in the spotlight for the last 10 years is probably an understatement, right? There is an article that was written by a colleague of mine, Jim Guszcza, called “The last mile.” He literally calls it the “last-mile problem,” which is, using predictive models and analytics you can certainly scope out what a problem is, you can identify what people should do, but then how do you actually get people to do it? And so we think of the last-mile problem as well. And it’s mostly behavioral: How do you get people to vote for who you want them to vote for? How do you get them to plan their retirement finances intelligently? How do you get them to make better hiring decisions? And that’s all behavioral. That’s all last mile.
TANYA OTT: Of course, that’s very timely. We’ve got a presidential election going on right now, and there were some behavioral analytics tools used back in the 2012 election as well.
JON WARSHAWSKY: Yeah, the 2012 election between Romney and Obama was interesting because, particularly thinking about what the Obama team did, you can use predictive analytics to tell you who’s likely to vote for your candidate, whether it’s Romney or Obama. So you know there are certain segments of the electorate that would be unlikely to ever vote for Romney or ever vote for Obama. But even if you know who’s likely to vote for your candidate, there’s a chance that people will say they will vote for someone, and then stay at home and not actually vote. So how do you get people to actually vote? What the Obama campaign did was use something they call “commitment cards” and “voting plans,” so that if they thought someone was going to potentially be a vote for Obama, they would give these people commitment cards and ask them to say, well, when do you plan to go to the polls, and where do you plan to do that? So some specifics. When people did that, they were much more likely to go out and actually vote because they felt like they actually made a commitment.
TANYA OTT: So did they actually go back and track who did commitment cards and who actually voted? They cross-referenced that?
JON WARSHAWSKY: They did, and the results for that were very good. They saw a significant impact. I know one thing we’re all hearing about is polling for different candidates in the 2016 election cycle, and the frustrating thing about polls in terms of analytics is they may signal an attitude, but they don’t really signal what’s going to happen.
TANYA OTT: And we’re not only talking about this in politics. Of course, it’s got applications across a broad range of industry and a broad range of policy. For instance, the government of New Mexico has an interesting case study. Tell us a little bit about what was happening in New Mexico.
JON WARSHAWSKY: I know editors aren’t supposed to say this, but one of my favorite articles in this issue is the one called “Nudging New Mexico,” and it is one of my favorites because it talked about what the state did—and it was actually coauthored by Joy Forehand, who’s the deputy cabinet secretary of the New Mexico Department of Workforce Solutions, coauthored with Mike Greene from Deloitte. The state of New Mexico, like all states in the United States, makes unemployment payments, right? So there are unemployment benefits. And the problem for all states is, if you make improper payments, it’s very hard to recover those, and it’s very expensive. And when you think of improper payments, it could be a result of dishonesty or fraud. But it could also just be a result of confusion. And in this issue of Deloitte Review, we have a feature that talks about how New Mexico applied behavioral insights to reduce improper payments.
TANYA OTT: So what did they do?
JON WARSHAWSKY: Specifically, the team working with the state of New Mexico identified key moments where people fill out forms or apply for unemployment benefits. They looked at interactions that, due to the way they’re designed, could encourage dishonesty, or they could encourage inaccuracy. And they used those key moments to revise the system in simple but, I think, really elegant ways, to lessen those temptations.
TANYA OTT: Can you point to an example of how they did that? Was it rewording a piece of paperwork, or what was it?
JON WARSHAWSKY: Yeah. Going back to when we talked about politics and commitment cards, what they did in New Mexico was a variation on the commitment idea. So when the New Mexico Department of Workforce Solutions, which is the group that administers unemployment benefits for the state of New Mexico, asks people to fill out and talk about their job-seeking activities, they ask them to be very specific, and the claimants are shown what they previously planned to do and how they actually stacked up. So it’s not just filling out a form in a generic way but saying, here’s my action plan for the week, and encouraging them to look for jobs more effectively. If someone says they’re going to do something, there’s a sense of commitment internal to that person that’s not enforced by the state, and that generates more honesty, more accuracy, and more diligence in looking for jobs.
TANYA OTT: And what were the results they found after instituting and using behavioral insights?
JON WARSHAWSKY: A significant improvement. They’ve made fewer improper payments as a result of that. And I think what’s especially interesting is, if you think about from a perspective of investment in technology and systems, I think it’s very common to invest a considerable amount of money in revising systems or adopting new technologies. What we’re talking about here are fairly low-cost interventions to implement.
TANYA OTT: I’m talking with Jon Warshawsky, the editor of the latest Deloitte Review, an issue themed around behavioral economics: the idea that sometimes simple nudges can get workers and managers—basically humans—to do what’s in their own best interest.
Think about the last time you hired someone. What was your process? Was it as effective as it could have been? What metrics did you rely on? Or was it a “gut” call? That’s the focus of the cover story for the issue, an article titled “HR for humans.” And Jon says it’s especially interesting.
JON WARSHAWSKY: I think it’s interesting because Moneyball, which is the Michael Lewis book—you probably remember there was a pretty successful movie of the same name that starred Brad Pitt—got people thinking that we need to use analytics to solve these problems or to understand what problem points are. But ironically, talent, human resources, and hiring people and compensating people and motivating people is an area where this has been least applied. So even though Moneyball inspired a movement, I think the exciting part is we have a lot of opportunities in HR and talent. And “HR for humans,” which is our cover story, gets into that.
TANYA OTT: What was it about “HR for humans” that resonated with you?
JON WARSHAWSKY: It covers the need to use analytics in HR settings, which there’s some resistance to do sometimes. I think there’s a lot of sensitivity to using analytics when it comes to talent because we all think that intuition is the right way to hire people, identify who to hire, and how to motivate people. We know now that having fourth and fifth and sixth and eighth interviews statistically is not very helpful in terms of identifying who’s going to be a better fit for a particular job. So there are ways to structure the recruitment process, the interview process, that don’t rely on six or seven interviews. There are ways to do it better. We know that from the behavioral discipline.
There was a case study that we covered several issues back at Deloitte Review about a company that, instead of using the usual interview approach, created a weekend event, kind of a weekend team problem-solving event. So that was effectively your interview, is [what got] you in there. You worked with people who are already working at this company to get involved in certain projects, and for two days you would be working actually with people at the company. And at the end of that, the company felt very comfortable identifying which people would fit in very well, who were “keepers,” and which people probably wouldn’t, because you could observe the teams working together.
TANYA OTT: You mentioned some of your favorite articles. What was the most surprising thing you read in the issue this time?
JON WARSHAWSKY: Colleague Jim Guszcza interviewed Richard Thaler this issue, and Thaler is a huge name in this field. And I thought it was funny when he was talking about, you know, the US is in the process of normalizing relations with Cuba, right? And Thaler talks about a closed-end mutual fund that happens to have the ticker symbol CUBA. Now, that actually has nothing to do with the country of Cuba, because up till now, and probably still, it’s illegal to invest in Cuba. So obviously a mutual fund or a closed-end fund that has the ticker symbol CUBA wouldn’t really be trading in Cuba-related equities because you can’t. It’s illegal. Any investor would know that, right? But what Thaler points out is that after President Obama announced his intentions to relax relations with Cuba, all of the sudden there was a 70 percent premium on this fund, which had been selling at a 10–15 percent discount, and I think now, or at the time we went to press, it was at a 40 percent premium. And there’s no rational reason, so I think it’s funny.
TANYA OTT: There’s no rational reason because, as you explained, any experienced investor would know that they’re not investing in Cuba.
JON WARSHAWSKY: But it’s some mystique around having the ticker symbol C-U-B-A that people think, well, even if it’s not dealing in Cuban securities—and we know that rationally—there’s still this impact on the value, and I thought that was amusing.
TANYA OTT: Are there other examples of things that seem completely irrational that you’re also seeing in these articles?
JON WARSHAWSKY: I also like the “mental Rubicon” article that we have in this issue. I think it’s the most personal one we have. And the mental Rubicon is about decision making, [about how] we get to a point of no return when we make a decision. You might think about a big decision that you’ve made, like buying a house or buying a car or getting married to a certain person. Big decisions. At some point, once you’ve made a decision, you get into this mode that’s described as confirmation bias. In business, that can be bad because if you’re a leader, and you say, okay, this is the decision, then you may be less open to disconfirming information after that happens.
TANYA OTT: Essentially what you’re talking about is, someone has invested a lot of mental energy and resources, be they time or money, into a decision, and so to be able to back out of that is really challenging.
JON WARSHAWSKY: It is, and I think what’s kind of challenging is we put ourselves in this sort of mental jail, if you want to call it that. So it may not be the external force, because if you’re the leader of an enterprise, you may be able to reverse a decision whenever you think you need to reverse a decision, but mentally we don’t like to do that. So even if there are no external constraints on reversing a decision, we don’t like to do that. As we get toward a decision, we start gathering information that confirms that. You know, if you want to go back to politics, you see quite a bit of that where people get more polarized. You see debates on issues that are contentious, and what you see at the end of those discussions is that the people who had this viewpoint on the issue now feel that viewpoint even more strongly than they did before the conversation even started.
TANYA OTT: And certainly in the political realm, that has a fair amount to do with living in echo chambers and only recognizing the messages that reinforce the narratives we already have in our heads.
JON WARSHAWSKY: We do. There is the concept of echo chambers, so there’s the right-wing media and the left-wing media. But I think what’s interesting is that even in the business world, we tend to create our own echo chambers in a sense. It’s not necessarily that we don’t have access to other information, but we do choose which information to access.
TANYA OTT: “The mental Rubicon,” “HR for humans,” “Nudging New Mexico”—you can access a lot of information in the latest Deloitte Review, edited by Jon Warshawsky, whom I’ve been talking to. It’s available now at dupress.com. There are other articles in this issue that step outside the theme of behavioral economics. One is on using the Internet of Things to increase efficiency and environmental outcomes in farming. That’s something we’ve talked about before . . .
WILL SARNI (author of “From dirt to data: The second green revolution and the Internet of Things”): The move right now is towards, where appropriate, drip irrigation, delivering water when the crops needs it and where they need it, and underpinning all of this really is data acquisition and analytics and the ability to really understand what is required with respect to water needs in the field and delivering that through technology.
TANYA OTT: You can find that, and more, at dupress.com. I’m Tanya Ott. Thanks so much for listening to our podcast. Be sure to subscribe so you don’t miss anything—and, hey, give us a rating and leave a comment. It helps us show up better when someone searches the podcast store. Follow us on Twitter @du_press, and you can always email us at email@example.com.
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