Home > Podcasts > BETA Podcast: Interview with Professor Dilip Soman

BETA Podcast: Interview with Professor Dilip Soman

6 December 2018

Can simply separating and labelling someone’s income, or including some extra information in a quarterly report help them to save for retirement?

Listen to Professor Dilip Soman’s journey from the shop floor of an engineering firm, to helping people in Mexico and rural India save more for retirement in BETA’s latest podcast

Professor Soman is from the Centre of Behavioural Economics in Action at Rotman, or ‘BEAR’ at the University of Toronto. He also talks about how machine learning and artificial intelligence will play a huge role in the future of behavioural economics.

This is the latest in BETA’s BX2018 podcast series, where we delved into the work and journeys of speakers at the 2018 Behavioural Exchange Conference.

Disclaimer: BETA's podcasts discuss behavioural economics and insights with a range of academics, experts and practitioners. The views expressed are those of the individuals interviewed and do not necessarily reflect those of the Australian Government.


Elaine Ung: Hello, and welcome to another episode in BETA's podcast series. BETA is the Behavioural Economics Team of the Australian Government, and the team sits in the Federal Department of the Prime Minister and Cabinet. BETA's mission is to advance the wellbeing of Australians by applying behavioural insights to public policy and administration. Hi, my name's Elaine, and I'm part of the team. In this podcast series, we interview a whole range of behavioural economics and behavioural insights academics, practitioners, and policy makers. This episode is actually part of our mini BX series, where we interviewed some of the speakers at the 2018 Behavioural Exchange conference, or BX 2018. There, speakers travelled from all around the world to share the work they've been doing in the behavioural economics and insights field.

In this episode, I interview Professor Dilip Soman, who is the Corus Chair in Communications Strategy and Professor of Marketing at the Rotman School, University of Toronto. Professor Soman is also director at the Centre of Behavioural Economics in Action at Rotman, or ‘BEAR’. The Centre looks at social and economic problems from a behavioural science lens, and designs solutions using scientifically tested tools to facilitate behavioural change. Hope you enjoy!

I have here with me Professor Dilip Soman from the University of Toronto. Welcome to Sydney.

Dilip Soman: Thank you.

Elaine Ung: To start with, could you give us a bit of background, so who you are and where you've come from?

Dilip Soman: Sure. So like you said, I teach at the University of Toronto. I head a department called BEAR, Behavioural Economics in Action at the Rotman School, and I'm a behavioural scientist by training. So my degrees are in engineering, business, and then behavioural science. I've spent most of my academic career working on problems of behaviour change in the areas of financial inclusion, health, and several other domains, applying behavioural economics to help people make better choices.

Elaine Ung: So, from an engineering background how did you become interested in behavioural economics?

Dilip Soman: Oh, that's a great question. So I started off working for an engineering company. My first job was on the shop floor. And at some stage I got the opportunity to try out the sales department and I found that fascinating because as engineers we spend a whole lot of time trying to improve the torque in our engines by one percent, or by getting the efficiency up by another quarter-percent. Our customers didn't care and they didn't know. And so I found it interesting that a lot of the things that we as engineers think were important, were not really so. And that got me thinking about sort of marketing and sales and communication as a career. And so, long story short, I ended up in advertising and then in business school, and eventually in the field of behavioural economics.

Elaine Ung: Very interesting. So I understand you don't just work in academia but you do a lot of applied work. So could you share with us what you're working on right now? Maybe a bit of an intersection between academia and applied work?

Dilip Soman: Absolutely. So just to preface the response to that question, the centre that I run, BEAR, is tasked with taking the ideas from academia and helping either consumer groups or governments or not-for-profits help make better decisions. And our work spans kind of the entire spectrum of the behavioural science field, so we obviously do choice architecture, we work in the areas of design, we work in the areas of developing better decision tools.

So for example, one of the projects we've just finished work on is in Mexico and it has to do with pension savings. The problem that the Mexican Government was facing was one of low savings rates in the pension. Mexico has a fairly interesting pension system by which there's a 6.5 percent mandatory contribution, but the government projection says that people need to set aside 11 or 12 percent in order to live a happy life post-retirement. And so the question is, how do we get people to contribute the extra above and beyond the mandatory?

And so we worked through with the pension authority, it's called Consar, to try and redesign their quarterly statements to do a few things. One is to get people to realise that they needed to save more. Second, to give them some guidance on how much extra they needed to save, and then finally helping them actually do it, implement on those plans. And so we worked through—designed a randomised controlled trial, came up with multiple versions, the usual sort of extent dimensions and so on and so forth. And the long and short is we were able to increase contribution rates significantly.

So that's an example of the kind of work we do at BEAR.

Elaine Ung: Great, and that's actually also a really good example of the work that demonstrates the potential impact of behavioural economics.

Do you actually have a favourite behavioural economics concept or a favourite bias?

Dilip Soman: I do. I'm not sure I have a favourite bias. And the fact is that I don't like the term "bias" at all. Just because human beings behave differently from the laws of economics I think doesn’t make them biased. I think the laws that describe human behaviour are biased. And so I think people are just people. But do I have a favourite phenomenon, I do. And I've spent a lot of my time in my academic career working on two areas: mental accounting, which is how people treat money; and the psychology of time. And so these are the two big things that I'm interested in.

So one quick example has to do with the notion of earmarking, which is the fact that when you take cash or you take your bank account and you artificially segregate it into different compartments and give each compartment a name, then you are more likely to spend the money on the thing that you named that particular account for. So for example, a lot of people will kind of have a separate account for spending money and education allowance and it turns out that money is sticky as a function of which account it belongs to.

So one study that I did in rural India several years ago was to try and get farmers and construction workers, people who live in a cash economy, to try and save for retirement. The government had a fairly traditional financial literacy initiative. They would go to villages and talk to the villagers and explain to them why they needed to save and help them kind of figure out how much to save. And that worked in terms of comprehension, people understood, but not behaviour.

And so we had a very simple intervention where we took the 50 rupees, 100 rupees or whatever it was that the financial adviser thought they could save, put it in a separate envelope, labelled it "savings" and essentially gave it back to the person and said, "Try and save this" right? And just the act of physically partitioning cash increased the likelihood with which they saved the amount. And so to me that was a classic example of mental accounting, which we've studied so much of in the academic literature, being put to good use in helping people save.

Elaine Ung: That's really fascinating. Do you apply any of these to your own life?

Dilip Soman: Do I apply behavioural science to my own life? I guess I do. I mean one of the biggest challenges that all of us face is the gap between intention and action. So for example I've been, like a good Canadian, I've been meaning to be fluent in my second national language, French, for the past several years, and I never got around to doing it. I still plan to be fluent, but life gets in the way. But putting self-control devices onto yourself is a fabulous thing to do. So making a public pre-commitment or getting into some sort of a contract so that you can accomplish what you want to do.

It's all about giving yourself the right reinforcement, and so I find that the whole research in the area of self-control and what you could do to impose self-control on yourself is particularly handy to not just me but all human beings.

Elaine Ung: Absolutely, I think it's definitely—there are some concepts that we can all learn from and that we can all apply, to as you say, minimise that action-intention gap.

And could you share with us your reflection on the evolution of behavioural economics and behavioural insights in public policy and tell us maybe what you think is the next big thing for behavioural economics?

Dilip Soman: Sure, and it's an interesting time to be asking that question. It's ten years since Cass Sunstein and Richard Thaler wrote "Nudge". We've made amazing progress since then. So if you look at the number of government units and other not-for-profits that are using behavioural science, it's at least a hundred and counting, thousands of trials all over the world. So we've done a fair bit, but I think it's now time to take stock and sort of think through what we've actually learned. And then to your point, what is the next big thing for the field. So reflections on what we've learned. I think we've learned that the last mile, which is where a government program or an agency interacts with a citizen, is what you'd call low-hanging fruit for behavioural science. Can we improve touchpoints? Can we simplify information? Can we remind people, and so on and so forth, and I think we've done really well at that. We do need to think more about a meta-analysis of what we've found. So can we look back at all of these thousands of trials that we've done over the past few years to identify when things work and when they don't? Can we think through other particular features, the context that tend to drive success or not? And I think we need to do a fair bit of that.

But looking forward, I think we need to think about going beyond nudging. So for example, you think about the entire value chain which starts off with policy creation, and goes all the way to the last mile, there's many other steps in which behavioural science can contribute to. So let's think about design thinking, for example. Or can we think about machine learning and artificial intelligence and bring that in. And I think there's something magical waiting to happen at the intersection of behavioural science, design, and machine learning that I think will be the future of our field.

Elaine Ung: I think that's absolutely right. There's so much excitement about where this is heading towards. And one last question, what's next for you? What's next on your agenda?

Dilip Soman: A couple of things. I think one of the things I'm interested in is really thinking through developing a roadmap for organisations that are interested in implementing behavioural science. And so, I get lots of emails and calls every day from companies, from governments who say, "Well this is neat stuff, what do I do?" And I don't think we have a good roadmap for that. So I think thinking through what the canvas of behavioural science looks like, figuring out what resources need to be put into place I think is one big area that I'm interested in.

I'm also interested in sort of machine learning and human learning. And so one of the courses I'm developing at the university is called human and machine intelligence. There's a lot of parallels and I think if you look at the popular press we hear people asking questions such as "Will machines take over our jobs?" And I think those are the wrong questions. I think the interesting question is thinking through how human intelligence can collaborate with machine intelligence to make a better tomorrow. And that's another area that I'm really interested in.

Elaine Ung: Great, I can't wait to see what your future research holds! Thank you so much for your time today and I hope you enjoy the rest of the BX 2018.

Dilip Soman: Thank you, it's been my pleasure.

Elaine Ung: Thank you.

Hi, it's Elaine again. If you want to learn more about Professor Soman's work, you can read about the projects and research he's done on the University of Toronto's website, Rotman School.

Thanks for tuning in. If you haven't heard our previous episodes, listen to them at www.pmc.gov.au/beta. Until next time!