BETA Podcast: Interview with Professor John A. List

Elaine Ung: Hello and welcome to another episode in BETA's podcast series. My name's Elaine, and I'm part of BETA, or the Behavioural Economics Team of the Australian Government. We're part of the Federal Department of the Prime Minister and Cabinet. Our mission is to advance the well-being of Australians by applying behavioural insights to public policy and administration.

You may recall, BETA hosted the 2018 Behavioural Exchange Conference, BX2018, in June. I found it very inspiring and motivating to see behavioural insights academics, practitioners and policy makers come together to share journeys, insights, work, and research they're doing in the field. And I'm so excited to bring you interviews we did at BX2018 with some of our speakers. They're the crosscutting leaders in behavioural economics. I'm flagging now, there's a bit of background noise that will come through, and that's just because we recorded where we could fit into the speakers’ schedule.

In this podcast episode, I interview Professor John A. List, who is a Professor in Economics at the University of Chicago. He's also Chairman of the Department, and co-author of the book ‘The Why Axis’ with Professor Uri Gneezy, who we previously featured in a BETA podcast. In addition to his research, Professor List has worked in the White House, advising on how to use behavioural economics to address policy problems, and he also does private consulting to companies like the rideshare company Lyft. Hope you enjoy!

Hello, I'm at the International Convention Centre at Sydney today. I have here with me Professor John A. List. He has come over from the University of Chicago to talk at the Behavioural Exchange Conference. Welcome, John.

John A List: Thanks. It's great to be here in Sydney.

Elaine Ung: Fantastic. Could you please give us a bit of background, so your academic background, a bit of who you are and where you've come from?

John A List: Sure. Sure. I think my background is decidedly bucolic. My father was a truck driver, my mom was a secretary, and when I was in high school my dream was to become a professional golfer. I went to college at the University of Wisconsin at Stevens Point on a partial golf scholarship. I learned at Stevens Point two very important lessons. One, was I wasn't good enough to play golf professionally and make money, and two, that I really loved economics.

From there, I went to the University of Wyoming and received a PhD in 1996. In the early 90s and for my dissertation, I did work using field experiments. This was somewhat of a new concept 25 years ago in that I was basically using the world as my lab and trying to use random variation to learn about the economics of the world.

I went on the academic job market in 1996 and I applied to 150 schools. I got one interview, and that one interview thankfully worked out at the University of Central Florida. I became an Assistant Professor in the fall of 1996 at the University of Central Florida. After four years there I moved to the University of Arizona to join Vernon Smith's group. Vernon Smith is one of the fathers of experimental economics in the lab. He won the Nobel Prize with Daniel Kahneman in 2002 for his work using lab experiments. But I was only there for a year because the whole group was moving because Vernon was have problems with the administration, so I only stayed at Arizona for a year.

The whole group moved to D.C. and I went to the University of Maryland. I was there for four years. Right around 2002, 2003, I took a year off to work in the White House. I worked running field experiments in the area of environmental and resource economics, and using behavioural economics in the White House in 2002, 2003 to think about questions like, what are the best ways to combat climate change? That's when Homeland Security was starting in the U.S. because I started about six months after 9/11, so thinking about the behavioural economics of border control, of Homeland security. I worked on issues like softwood lumber trading between the U.S. and Canada.

Then around 2004, I think people started to recognise the importance of field experiments. Then a bunch of schools called me and said, “We'd like you to join us.” I interviewed at that point a lot of different schools. It ended up that I moved to the University of Chicago in 2005, and I've been a professor in the Economics Department at the University of Chicago since 2005. The last six years I've been Chairman of the Department of Economics at the University of Chicago, so that entails basically corralling all the troops and all the problems, and taking care of administrative details.

But along this entire path, I think I've stayed true to my belief that we should go out and run field experiments. We should use behavioural economics to inform the experiments that we run in behavioural economic theory to inform how we should interpret the results from experiments. My experiments have essentially been around the questions like, why do people give to charitable causes? Why do women earn less money than men in labour markets? Why do inner city schools continue to fail? These broad based social questions have been the ones that I've tended to go after using field experiments.

Elaine Ung: Great. That's quite a journey to where you are now. But where along the way did you become interested in behavioural economics?

John A List: I think in the very beginning. I think that as an undergrad in the late 80s, you had these ideas floating around about loss aversion, and hyperbolic discounting, and social preferences. These sorts of things always enamoured me because they were a representation of the reality that I knew, and they weren't always included in standard neoclassical models, so I think these things always intrigued me. But what I was interested in doing is not necessarily writing more theory, I was really interested in testing that theory in the field. When the theory was right, you could say the theory is right. When theory needed adjustments, I would think about what are the proper adjustments that we should make to the behavioural theory, and then how do we go back and test it again? That was always my goal of how we should be using theory and experiment together in the field.

Elaine Ung: And I think that’s really important. I think we're starting to move towards that a bit more, but it sounds like you've been doing it for a long time and have been interested in it.

John A List: Yeah, that's right. I think more and more people are doing it. Back then, I was on an island, but at least I was there early on to see the trials and travails. A lot of people were telling me that I was dumb, that I should be doing lab experiments or I should be using naturally occurring data, but it always made sense to me to combine randomisation with realism. Which is what field experiments do. It just felt right, that this is an important avenue to take and I think it will be an important avenue for decades to come.

Elaine Ung: Absolutely. Could you share with us, please, what you're working on right now?

John A List: Absolutely. Absolutely. A good question is what aren't I working on? I've been working with firms a lot lately to leverage what for-profits and non-profits have to give us. What do I mean by that? I'm the Chief Economist at Lyft, which is a rideshare company in San Francisco. We're doing a lot of experiments around incentivising drivers and incentivising consumers.

I've always worked, for the last 20 years, on charitable giving, and thinking about what are the economics behind why people give, what keeps people committed to a cause. I've also began this very interesting line of research on scaling. The background is, I ran an early childhood intervention with Steve Levitt and Roland Fryer in Chicago Heights where we find very promising results in a city called Chicago Heights. I became interested in when we want to scale that up, we found great results. If you want to scale it up to the city-wide, or state-wide, or country-wide, what are some features that we should worry about? Or what are the threats to scalability? That's become an important part of my research agenda.

Elaine Ung: Yeah, fantastic. Could you please give us an example of some of your work that you've seen demonstrates the potential for behavioural economics?

John A List: Oh, absolutely. One good example is teacher performance pay. In the U.S., there is a continuing debate about whether teachers should be paid based on their students' performance. Most critics say, we might want to think about teacher performance pay but the evidence all suggests that it just doesn't work. And by and large they're right. When you think about teacher performance pay in the traditional sense, which I mean by saying you teach all year and then at the end of the school year you are paid your bonus. That's the way we think about a traditional bonus scheme; is you work for a while, and at the end of the work you get your bonus check. They're sort of right, that there is scant evidence on whether that can work.

What I wanted to think about is, can we leverage behavioural economics to make a performance pay scheme work? I think the poster child for behavioural economics would be loss aversion, or what some people might call the endowment effect. A simple explanation is that people are averse to losses, and if they lose, say, a dollar, that hurts a lot more than a comparable dollar gain helps. People really don't like losses.

Okay, so you can think, well how does he leverage that concept for a public policy? Here's exactly what we did. We went to teachers in September, which is the start of the school year in America. We told them that, "Here is $4,000. We're looking for you to value add for your students this year. We are going to test them again in June. If you value add at a very high level, you can keep your $4,000 and we might give you more." But we told them, "If your students do not achieve on that test in June, you might have to give money back." It's exactly like a typical bonus frame, except we move the payment to the start of the program rather than the end of the program.

So then it's sort of a baseline. We have another group of teachers that have the traditional scheme, which we tell the rules are exactly the same. We go to them in September and we say, "You are part of an experiment and you can make money. We're gonna look at your students' standardised test scores in next June, and then we're gonna pay you cash." We basically compare students in the clawback or the loss averse treatment, along with the traditional bonus scheme treatment. What we find is that the traditional bonus scheme treatment really doesn't work that well. It works about like a control group: what I mean by that is teachers who have no incentive pay.

So all the critics are pretty much right. If you do a bonus scheme in the traditional way, it really doesn't work. But if you use the clawback, or leverage loss aversion, it works splendidly. What we find is that teachers who are in the clawback, they have students that go up by 0.2 to 0.3 standard deviations, compared to the controlled group when we use the loss averse incentive. There, it's a very nice example of how we can leverage a behavioural insight to make the world a better place.

Elaine Ung: Wow, that's so interesting. I think that changing that frame can actually be applied in many other situations as well.

John A List: Absolutely. Absolutely.

Elaine Ung: Do you have a favourite behavioural economics or behavioural insight concept?

John A List: Ah boy. That's like asking, do I have a favourite child, since I have five kids. All of them are my favourite. But I would probably say it would be either the endowment effect or loss aversion, or social preferences. What I mean by social preferences would be things like gift exchange. Do people who have reciprocal preferences, do those preferences affect market outcomes? Because a lot of us engage in positive or negative reciprocity: if somebody does something nice for us, we tend to do something nice in return. It's called positive reciprocity. I'm really fascinated by how people use strategic positive reciprocity to not only engage in a ‘you scratch my back, I'll scratch yours,’ but also to get people to scratch their backs more. That's how it's strategic in a sense. I think those two concepts of loss aversion and social preferences would probably be my two favourites right now.

Elaine Ung: Right. From all the research that you've done, do you apply any of the behavioural economics to your own life? So, in other words, how do you ‘B.E.’ yourself?

John A List: Exactly. That's great question. I think I would primarily ‘B.E.’ myself with my kids. I set up incentive schemes, whether they're pecuniary or non-pecuniary incentive schemes all the time with my kids. It started when my oldest daughter, who is now 19 and she's freshman at Wellesley College, when she refused to get potty trained. I set up some behavioural incentives around getting her to go potty in the right place, and within a week she was done. I think in general trying them on my kids. I do a lot around my work with Lyft, and in my various pieces of research with charities. We do a lot of behavioural applications and insights with those companies as well. But I think my favourites are probably with my kids.

Elaine Ung: Lots of opportunities to apply B.E. there.

John A List: Exactly, with five kids, you have a lot of opportunities.

Elaine Ung: Great. Could you please share your reflections on the evolution of behavioural economics? Also how it's evolved in public policy, and where you see the field heading?

John A List: Absolutely. Absolutely. I think when you look at what was going on in the 70s and 80s, you had a world whereby you had people writing down behavioural economic theories and primarily testing them in the lab. In the 90s that slowly began to expand to not only testing the behavioural theories in the lab, but also in the field. You continue to see that expansion in the late 90s. Since 2000 until today, we have more and more explorations of where and when do these behavioural insights have deep impact. Because in many cases there are boundary conditions to some of these insights where they work in the lab, but they might not work so much in the field because the field has developed checking devices or institutions that cause them not to operate the way they should.

I think the transition from the lab to the field has been a very important one, scientifically. But I also think for policy purposes, policy makers tend to believe the insights from the field a little bit more than they believed just prima facie from the lab. I got this slapped in my face when I worked in the White House. I was arguing that when we revise the benefit cost guidelines, we should take account of these lab results on loss aversion. One of the high ranking officials said to me, "Well John, these results seem like scientific numerology because they're achieved with students who are not real people." I think the policy makers' first instinct is, well, this might not be a representative situation or population, and we want to see it in the field before we invest a lot of resources in it. I think that movement has really helped policy makers too.

Now where do I see this literature going? I think we've done a really good job in developing theory and interventions that work at a small scale. I think scientists, economics, psychology, sociology, et cetera, deserve a lot of credit for showing the beauty behind how behavioural economics can be applied in the real world. But I think where we have been not as good is thinking about what now is the science behind taking that large intervention in a city and saying, how does that scale up? And when are the cases when we think it should scale one to one, and when are the cases when we think, we found a really large treatment effect the small scale, but when we scale it, it might be one tenth of the size. Or the benefit cost ratio might be one tenth of what it was in the small scale, when we do it in the larger scale.

I think we've short shrifted that area. I think it's ripe for economic theory. It's ripe for behavioural economic theory. I see that is the next frontier because that is the path that will help to not only provide more confidence in policy makers' eyes in what we're doing as scientists, but also provide more science around how do we use the science of behavioural economics. We just don't have that right now, and I think in the coming years there will be an explosion of trying to make the world a better place through the science of using science.

Elaine Ung: Great. I think also from a policy perspective, it's really important to have a lot of those questions addressed as well so that there is that bridge between the academic research, but also how it can be applied more broadly.

John A List: Exactly. Exactly.

Elaine Ung: One final questions, and you may have already touched on this, but what's the next big thing for you?

John A List:  Yeah. I think the next big thing from a methodological sense is the scaling work. I think the next big thing in a more intervention research sense will be my work in early childhood. One way to think about that work is to say, as you know, I've had five kids so I've gone through this exercise a few times. Here's the exercise of what happens when you have a baby. Baby comes out to the world and the baby gets a bunch of shots—immunisations—you bring the baby back at three months and they get more shots. You bring the baby back at six months and they get more shots, nine months, twelve months, et cetera. We even have immunisation booklets that show us the numbers and types of shots that our children receive at each of these sign posts. I think that's wonderful.

We have used science, the medical science, and experimentation to figure out what are the correct time points to give these shots, and we know exactly the side effects. We know exactly why we're doing those exercises of giving the booster shots, et cetera, et cetera. Now, you should ask yourself, what do we do for the parent when it comes to human capital? How do we teach new parents how to interact with their child? So during this very important time of brain development and brain growth, do we give the parents the tools like we do on the medical side? The answer is we don't. Even though negative nine months to three or five years old is the most important time. Neuroscientists will tell us this, this is a most important time in the development of a human's brain.

And parents have no idea what they should be doing to make sure that they build the child's human capital. That's a change I want to work on, in what I call the community-wide intervention, which I'm doing together with my partner Dana Suskind at the University of Chicago. We've started a centre called the TMW Center. One of the main initiatives is to work with communities to do what I would call a community-wide health-based approach to early childhood interventions.

I want hospitals to give appropriate information to parents. That information should be scientifically-based, which we've done a lot of work now around early childhood, so we know a lot about what we should be telling parents. And I want to interject early, very early in the lives of these kids and do it in a scientific way like we do shots and medical interruptions. We need to do the same thing with human capital interruptions. To me, that would be the next big area in my research agenda and we're currently in the point of vetting communities in the United States, and we'll be choosing a community soon to be our full partner. It would be a community-wide intervention to try to change the way we think about development and parent-child interaction.

Elaine Ung: Wow. That sounds like a huge, huge project. But also very interesting and super exciting. Thank you so much for sharing.

John A List: No, thanks for having me. I really appreciate it.

Elaine Ung: Thanks for tuning in. If you'd like to know more about Professor List and his work or research, you can find more information on his website at the University of Chicago.

Once again, I'm Elaine, and you've been listening to a BETA podcast. You can hear our previous episodes at www.pmc.gov.au/beta. Until next time.