The Economist Who (Gasp!) Asks People What They Think

It’s not very often that a young economist comes along and introduces a whole new approach for doing economic research. My guest today, Harvard economist Stephanie Sancheva, has managed to do exactly that.

STANTCHEVA: I was so convinced that this would be useful, and that I would learn quite a lot from it, that it just seemed like, I have to do this.

Welcome to People I (Mostly) Admire, with Steve Levitt.

*      *      *

How influential has Stefanie’s research been? This spring, she was awarded the John Bates Clark Medal, given annually by the American Economic Association to the most notable economist under the age of 40. I’m almost 20-years older than Stefanie, but our academic paths have followed a remarkably similar trajectory. In addition to both winning the Clark Medal, we got our Ph.D.s under the exact same advisor at M.I.T., Jim Poterba. And directly after grad school, we were both in the Harvard Society of Fellows. It’s a fellowship that brings together promising young researchers from a wide array of disciplines, and it’s an extremely unusual path for an economist to take. Only about 20 economists have done it in my lifetime. When Stefanie started at the Society of Fellows, she was working in one of the most difficult and revered areas of economics, it’s what we call the optimal tax literature. And she was very successful. But then she took a radical turn. And it’s here that I started our conversation — with a question that I’ve always wanted to ask her.

LEVITT: You were an absolute superstar graduate student, and the path you were on was essentially the dream of every economics grad student. You’re on this anointed path to virtually guaranteed success in the profession. And then you decide that instead you’re going to focus your research around conducting surveys. And there are few things economists hate more than survey research. It is obviously career suicide to do it. What in the world were you thinking when you started doing survey research?

STANTCHEVA: So this is something that I’m thinking about a lot — how it happened — and it’s something that I was just really, really interested in. So I’m coming from this, as you said, optimal taxation field, and it’s research that I love and I still work on. But there were these nagging questions. We’re looking at all these, you know, sometimes complicated policy recommendations. We’re doing all this work to model people’s behaviors, but we have so little insight into how people actually understand these issues into what they actually think about these policies. And that seems so critical to a public economist, which is what I am. And you are completely right that surveys are something that economists really dislike. For those who are non-economist, essentially we, economists, do not believe what people say. We only believe what they do or what they show you through their behavior. But this has its limitations. There’s so many things we cannot infer from just looking at your behavior. We can’t always tell this is what you think, this is what you believe from just observing how you behave. And surveys are a way to get at these things much more directly. And of course, they have to be really well designed, really well done. And my challenge for all these years has been to get to that point — to try to have really high quality, in-depth surveys that actually teach us something about how people reason.

LEVITT: Now the people around you must have told you that you were crazy, right?

STANTCHEVA: It happened a lot. I had great mentors and many people were quite worried. I’m not going to lie, it was really an uphill battle, especially the first few years. But I think as people started seeing the value of this. Many more of them came on board and I saw more and more people starting to do this type of research. I think now if you look at the number of papers that use survey methods either as their main methodology or to compliment the other methodologies, it’s really grown so much. It was very risky, but it got so much easier and much more fun as people started seeing the value of it.

LEVITT: Yeah, I mean, I suggested that your pivot into surveys was career suicide, but obviously it’s turned out to be anything but that. It’s turned out to be a new methodology in economics that I think delivers stunning insights. And honestly, when I read your papers, it feels like something that economists should have been doing forever but they weren’t. I had to see it fully executed to finally go, Oh yeah, now I get it. So for sure as you got started, if you had asked me, I would’ve been at the front of the line telling you, “Don’t do it! It won’t be convincing. Economists will hate it.” But you had this vision, this intuition somehow that this would be a powerful methodology, something others couldn’t see. Do you remember when the kernel of the idea first came to you? Do you remember the actual moment that you set out on this path?

STANTCHEVA: It wasn’t one exact moment, but for a long time I’ve been very interested in how people think. I’ve lived in different countries. I studied in different places and I’ve always been intrigued by how people understand society, the economy. So that interest was always, always there. And it’s something that, as I mentioned, we don’t really look at in economics.

LEVITT: Obviously we have a blind spot as economists, and somehow you managed to get around that blind spot to understand that actually understanding what people think matters.

STANTCHEVA: It matters in many ways too. When we think about the effects policies we’ll have, what people understand is going to be critical. We see this in many examples, you know, take-up of welfare programs, expectations related to inflation. What people understand is going to matter for how policies act and what happens. And then there’s so many things which we can’t see in other data that will help us design better policies. Understanding people’s constraints, understanding people’s limitations, people’s preferences. These things are inputs into our models and there is no great other data from which we can magically get them.

LEVITT: Okay. I just want to pause one more moment to try to paint the picture for people of how absurd this research sounded when you started doing it to economists, because obviously economists use survey data all the time because the government surveys that tell us about inflation and unemployment are all the inputs to what we do. But I think that’s exactly what the problem was. You’re brainwashed as a young economist to think that running surveys to collect data, that’s something faceless bureaucrats do. It’s not a worthy use of the time of a star academic. It’s almost like there’s a status thing that happens in economics where doing really hard theory puts you at the top of the pecking order and then maybe doing complicated econometrics puts you kind of in the middle. But the idea that it’s just too easy. I know it’s not true, but I think there’s this view among economists that it’s just too easy to design a survey and go ask people what they think. So there shouldn’t be any credit for that in economics. Do you agree that’s really been a prevailing view and I think a hurtful view to us moving forward?

STANTCHEVA: I think that view has been very dominant. And, it goes hand in hand with this other view that we spoke about, which is that people won’t tell you what they really think or we don’t believe what people will say. We only believe what people will do. When some people reached out to me to run their own survey, it has almost always started with, “I didn’t realize how hard this is!” That has happened a lot. And I’m very happy when I can share some tips and advice that prevents people from having to reinvent the wheel and saves them some of the agony that I had to go through. But yes, I think that’s a reaction that I’ve seen a lot.

LEVITT: Okay, so let’s talk about how to do these studies and what we learned from them, and I wonder if the best way to do that isn’t to talk very specifically and in some detail about one particular application of your methods. Okay. So these are big studies. The one you did on immigration, I think you surveyed 20 or 25,000 people across five or six countries for that paper, right?

STANTCHEVA: That’s right. 

LEVITT: How long does it take you to do a paper like this and how much does it cost?

STANTCHEVA: It took a long time. It takes just a really long time and a lot of effort to design a survey properly. So in this case, we’re interested in what people know about immigrants in their country, how educated immigrants are, how many there are, where they come from. We’re also interested in their policy views in order to relate their views on policy to their views on immigration. And these are complicated questions. It requires asking people about specific numbers, about economic terms. So it takes a lot of work to be able to write questions that are simple, understandable, intuitive; that are not going to make people say a given thing that is actually wrong, just because the question was poorly phrased. You know, it took more than a year to get that survey in a state where we thought this is going to be understandable, be clear, be simple, and then it takes several months to run it in the field, to get that large sample size.

LEVITT: So let’s get really deep into the nitty gritty. If I’m a participant in your immigration study, what sorts of questions will I be asked? ‘Cause these are very different surveys than what, say, Gallup would conduct to measure approval ratings of the president.

STANTCHEVA: Exactly. This is very different from, say, opinion surveys, which could be very short, very immediate. You would enter into a survey that’s 20 to 25 minutes long. We always start by asking respondents their age, gender, income, where they live, and then some variables that might be relevant to the specific question. For instance, in the study on immigration, we would ask them whether one of their parents is an immigrant; whether they are an immigrant. We would adapt these questions to the topic before we then dive into the questions that are actually about their views and perceptions. So for instance, you would be asked, in your country, in the United States, what share of people do you think are immigrants? We would define what we mean by immigrants, and then we would have you select the number using a nice interactive chart.

LEVITT: Do people have accurate perceptions? About the basic facts surrounding immigration.

STANTCHEVA: The answer is a pretty resounding no. There are many areas where people are quite correct, but immigration is an example of an area where we have really wide misperceptions. And this is true in all the countries.

LEVITT: Yeah, so the countries were the U.S., the U.K., Sweden, Germany, France?

STANTCHEVA: Germany, Italy, and France.

LEVITT: So six countries. So what are the true numbers? What are the actual share of immigrants in these countries?

STANTCHEVA: So the shares differ, obviously, across countries but if we take the U.S. as an example, at the time of the study, the actual share of documented immigrants, so that’s what we asked people about, was around 10 percent. And the average perception of people was 36 percent.

LEVITT: To me that is shocking. I don’t even understand how people can think that because — don’t they look around? Obviously, most people don’t live in neighborhoods where there’s 35-percent immigrants. It completely catches the economist in me off guard.

STANTCHEVA: Yes. And this was a very widespread result across different countries. So the U.S. was the country with the largest misperception, but we saw this in other countries too. For instance, in Italy the share of documented immigrants is also around 10 percent. and the average perception was around 26 percent. So a smaller gap than in the U.S. but still there. We tried to prod a bit further. For instance, maybe people also included second generation immigrants when they answered this, even though we specifically asked them about the share of foreign-born. But that does not close the gap. It reduces it, but doesn’t close it at all. So I think there is this very wide perception that the U.S. is a country of immigrants, so people tend to really inflate it. And in the other countries too, immigration is such a salient topic; it is in the media all the time. And so that leads people to really overestimate the number of immigrants they perceive. You mentioned people don’t live in places where there’s that many immigrants. That is true, but we see that it is correlated. So if you live in areas with more immigrants, even very locally, say, at the zip-code level in the U.S., you will also think there are more immigrants in the country as a whole. So it is correlated with what you’re seeing.

LEVITT: So a key issue in Europe has been about Muslim integration. How are people’s perceptions of that? Accurate or off?

STANTCHEVA: People tended to overestimate by quite a lot the share of immigrants who were Muslim. And on the flip side, they were underestimating what is usually the dominant religion in these countries, which is Christian. So they tended to underestimate the share of Christian immigrants coming in.

LEVITT: And I think you also asked people’s perceptions about unemployment among the immigrants, did they get that right?

STANTCHEVA: So that was a great question because it gives a good example of something you have to be very careful about in survey design. If we just ask people about what share of immigrants is unemployed, we would get very high answers. people said very large numbers, but it is actually because people tend to not quite understand unemployment rates the same way as we economists define them.

LEVITT: Because what we mean by unemployment rate is someone who’s actively trying to find a job. And they are not successful in doing that, which is of course, a small subset lots of people aren’t trying to find a job, so they’re not unemployed according to economists.

STANTCHEVA: Exactly, and this is why we also ask what share of non-immigrants are unemployed in your view? And that allows you to benchmark it. This allows you to say, Okay, there is this general misperception about what unemployment is. But let’s look at the gap between what people say for non-immigrants versus immigrants. And that gap is really overestimated. So people tend to think immigrants are sort of disproportionately more unemployed.

LEVITT: When in fact, is it true that immigrants have roughly the same unemployment rate as native borns? Is that true?

STANTCHEVA: It depends on the country. In the U.S. it’s absolutely true. It’s not true in all the European countries, but the gaps people imagined, in terms of unemployment, were really, really large. And similarly when we ask people about the reliance of immigrants on the welfare state, which is another key point that is in the policy debate. We see that people really overestimate how much immigrants rely on the welfare state. So people have this view that immigrants tend to free ride on government services, much more so than non-immigrants. 

We’ll be right back with more of my conversation with economist Stefanie Statcheva after this short break.

*      *      *

LEVITT: Virtually every economist thinks that immigration’s good. It’s helpful for countries when you get immigrants, especially immigrants who have high levels of skill. And yet when you look at the totality of your results across six countries, what you see is that regular people think there are an immense number of immigrants. These immigrants are different from them. They’re poor and uneducated and unemployed. It’s not hard to understand why people don’t like immigration when you see their perception of the facts about immigration and you then go from factual questions to trying to understand how people feel about immigrants and what drives those feelings, their perceptions. So what sorts of questions do you ask to try to sort that out?

STANTCHEVA: So after we ask about these facts, we turn to policy views. And we ask people obviously about immigration policy, but also about what we call redistribution policy, which is their views on progressive taxation, redistribution from richer people to poorer people, the social safety net welfare programs. So all these programs that are supposed to help lower-income people in the country. And our intuition was that generosity doesn’t travel as well across ethnic and national lines as it does within. So that people might be more averse to redistributing to another group, let’s say immigrants, as opposed to their own group. And indeed we saw that this was true. If you just asked people the questions about immigrants, all these questions we spoke about, How many immigrants are there; How unemployed are they, etc. If you just ask them those questions before asking them questions on policy, you saw actually a really strong effect. The people who are asked those immigrant questions before suddenly started saying that they want less redistribution. They wanted less progressive taxes. They wanted less transfers to lower income families. And this is just from having seen those questions before. So having been prompted to think about immigration, it sort of mimics what might happen if you open the newspaper and read a story about immigration. If in that moment, I ask you, are you supportive of the welfare state? Would you like more distribution? You’re likely going to say, “No,” or support it less than someone who didn’t just see that story. So this is our first finding that there is this link. It is true that when you think about who you’re giving to, who you’re redistributing to, this generosity is not the same when you’re thinking about immigrants versus not.

LEVITT: What’s really key here is that you always build randomizations — randomized experiments right into your study, and you find clever ways then to get at causality. So in the immigration paper, one of the key randomizations that you did was for some people, you asked them about their policy preferences towards redistribution before you talk to them about immigration and for others who did it in the reverse order. And that randomization allows you to actually get at really deep issues about causality that you couldn’t otherwise.

STANTCHEVA: Yes. So one example is the order of the questions that they see, as you said, some people will randomly be put in a group that sees questions about immigration before they see questions on policy, and the other group will have the opposite experience. This means that some of the people were just asked a whole range of questions about immigration. By the time they reached the policy questions, they’ve just been thinking about immigrants and immigration. So they come charged with all this “priming,” as we call it. And what we see is that the people who saw the questions about immigration first are going to be systematically less supportive of redistribution. So even though we didn’t provide them any additional information, we just made them think about immigrants, they’re going to say they want less progressive tax systems, less social safety net programs, less welfare programs. So they’re going to have this backlash against redistribution.

LEVITT: Redistribution generally, not specifically giving money to immigrants. It’s just as long as they think about immigrants and we’ve already heard they don’t like immigrants, they’re too many, they’re poor, they’re lazy, then that really poisons them towards helping anyone in need.

STANTCHEVA: And it is triggered by this observation that there is somewhat a backlash against redistribution in many societies. And people have conjectured that it might be due to this social fragmentation that’s happening, this ethnic fragmentation, and this idea that generosity just doesn’t travel as well in these fragmented societies as it does in very homogeneous, uniform societies. So at least this first finding very much speaks in favor of that and redistribution being potentially a casualty of these views peoples have about immigrants. And there’s another type of experiment you can do, you can give them different information. And so different groups in our survey will see different information about immigrants. So one group will see information about how many immigrants there actually are in their country. Another will see information about where they come from. And the third will see not information, but rather a story, an anecdote, about a very hardworking immigrant sort of a day in the life of a really hardworking immigrant. And what we see is that just the factual information does really nothing. People believe the information, but it does really nothing to their views about redistribution or even immigration policy. What does work is this anecdote or narrative about a hardworking immigrant, that actually has really positive effects in terms of making people, support more redistribution and support more immigration. And the way we interpret this is that the key concern is this idea that immigrants free ride on the welfare system. And so that is the one that you have to address. And a narrative is something that works much better here because immigration is something that is very prone to misperceptions and to narratives already. And so this might be the better approach if your goal was to change views on redistribution.

LEVITT: I don’t doubt for a second that everything you just said is true, but don’t you find it disturbing? Because, obviously, the flip side is that people concoct stories about the immigrant who is murdering people. And it’s sad that these stories are so powerful because in the end it just shows how science and facts and reasonable thinking can be overwhelmed by ideology.

STANTCHEVA: I will make you feel better by saying that this is not true for all topics. Immigration is incredibly prone to narratives. So this is a topic that is quite emotional, but it is not true for all policy issues.

LEVITT: Let’s take a different one. It’s a little one, but one of my favorite things that you’ve worked on is the estate tax. Could you just first explain what the estate tax is?

STANTCHEVA: Yes, and I think you picked a great example because that is an area where facts work like magic. So the estate tax is actually a relatively poorly known tax. It is the tax that is paid when someone dies on their whole estate, on their total wealth. So part of that wealth is taken away, and goes, to the government as tax revenue before it is distributed to the heirs and to the people who will inherit it.

LEVITT: And it’s different from other taxes, ’cause I think it’s the only tax on wealth that we have, right? We have income taxes and we have sales taxes, but I can’t think of another example where we actually tax people based on the wealth that they’ve accumulated.

STANTCHEVA: The only other example is the property tax, but it is on a subset of our wealth, it is basically on the value of our home. But you’re absolutely right, the estate tax is basically the wealth tax we have in the U.S. It is not paid every year. It is paid during a specific event, which is death, and that’s why it’s also known as “the death tax.” And it really affects a very small, small share of households in the U.S. because you need to have a pretty sizable wealth. When we did the study, it was quote-unquote, only 11 million, but that was still a very large wealth. So if it’s a household with two people, it’s twice that. So you’re allowed to pass all that tax-free to your children.

LEVITT: So you went out and you surveyed thousands of Americans. And in the factual part of your survey, you ask them to tell you what share of people when they die will pay the estate tax. And do you remember roughly what that number was? What people thought the share that would pay would be?

STANTCHEVA: Yes, people thought it was going to be around a third. And it was very consistent with when you ask them about the exemption threshold, people thought it was much, much lower. In people’s minds, they or their friend had potentially the chance to reach that threshold in their lifetime so that they would potentially be affected by it. There was this salient view that you might fall victim to the estate tax as well, when in fact, less than one in a thousand would be subject to that.

LEVITT: One in a thousand! That was surprising even to me how low it is. But, again, it just points out the value of what you’re doing and how bizarre it is that economists have not seen it to be very important to understand people’s perceptions. But think about it, there’s this tax that is paid by one person in a thousand, and Americans think that one in three people are going to pay it. It’s pretty clear that people’s view of this tax is incredibly distorted when the perceptions are so far off.

STANTCHEVA: Exactly. And this is a case where just providing that information, just saying, Actually, this is how the estate tax works and this is the share of people who will end up paying it, changes people’s views on it drastically.

LEVITT: And you do that as part of randomized experiments. Some of the people, after they tell you their opinions, you just give them the real facts. And then you ask them their policy views about redistribution and about the estate tax and everything else. And then others answer the questions first and never get to hear about the facts.

STANTCHEVA: Exactly. And the people who hear that fact suddenly become much more supportive about the estate tax than the group that didn’t see the information. They think it’s a good tax to have; that it should potentially be increased; that the threshold should potentially be lowered; that there should be more done to increase opportunities for poor kids in the U.S. As we spoke about immigration, that’s an area that’s very prone to narratives, but tax policy is an example where facts can actually be very powerful.

LEVITT: Did you do a narrative version for the estate tax? Did you tell a story about a really nice, rich person or a really awful rich person?

STANTCHEVA: We did. It is a little funny actually. We thought about just providing the same information but putting a photo of a really big mansion. And, it is a really nice mansion. That has a bigger effect than just the fact, but only a little. So the fact is just so powerful.

LEVITT: Yeah. And so by putting the big mansion, you’re highlighting the fact that not only is it one in a thousand, but it’s the kind of person who would live in this big mansion that you’re never going to live in that reinforces the idea to the person that this is not a tax that they’ll pay. Now, just to be clear, it’s not necessarily a good thing for public policy that people’s view is, if I don’t have to pay the tax, that’s a good tax. ‘Cause I was implying that I thought that immigration was good and that things we could do to make people more sympathetic to immigrants would be good. But this is a case where I think it’s actually a terrible precedent for people to say, “If I know I’m not going to pay the tax, then that’s a good tax. Let’s make it really high.” That’s not the way we think economics should work.

STANTCHEVA: Actually people don’t think that way either. For many policies, self-interest plays a role, like, If I’m affected, how is it going to hurt me? How is it going to hurt my family or benefit my family? But what does play a sometimes even bigger role is broader concerns people have about the impacts on society, about the fairness they perceive of this policy, how it’s going to impact others. So this is where it becomes so interesting to study this because it is not all driven by very narrow-minded self interest at all.

LEVITT: I want to step back from the individual studies to reflect on what I love so much about them compared to a typical economic study. In general, the best way to get an academic study published is to tackle a really tiny question and to answer it absolutely definitively. And the reason is that the incentives of the referees, the people who evaluate research submissions, are really skewed. When I’m a referee on a paper, I feel that my job is to poke holes in the author’s argument. And it’s usually really easy to cast doubt on ambitious papers with big ideas because they aren’t airtight. And so there’s a real tendency for economists to write, what I would call, small papers that aren’t really very important but they game the system that exists in economics. But you do the exact opposite in your papers. You take on huge topical economic problems, more or less in their entirety. Your methodology isn’t airtight in the way economists usually think about it. You get some facts and then you collect some background information, you do some correlations, run a few randomizations. And I wonder, have you thought about — there’s something in your methodology that is giving you permission to break the standard rules in economics? Do you get what I’m talking about? Have you thought about this at all?

STANTCHEVA: Definitely. It’s not airtight, you’re completely right. We’re talking about large-scale samples with lots of different people. You design the questions the best way you can with a lot of back and forth, but nothing is going to be perfect. There is just no other way to learn about these things. We’re doing the best we can and obviously we keep improving and other people keep improving this methodology and refining it. And now with A.I. there’s actually new, exciting opportunities to do things, but there is no other way to get at what people really think, how people reason. We learn things even though it’s not airtight and there is value in learning it, and that’s why I think this is useful.

LEVITT: Over and over in your studies, what comes out is that economics rarely’s the most important thing that’s driving people’s policy preferences. It’s considerations of fairness, whether they have trust in government or confusion — all of these other things matter much more. And so it’s naive at best and idiotic at worst to think that economic arguments are going to win the day when it comes to policy outcomes. And your papers really encouraged me to shed my economic blinders and think much more holistically. I think that’s a really important contribution.

STANTCHEVA: I’m really glad to hear that. And when we’re discussing these example papers, we picked examples where there’s very strong misperceptions and so it might sound like we’re there to see where people are wrong, etc. But a lot of this work is to really understand what we, economists, might be missing; what we need to add to our models; what we need to learn. So we learn a lot from people in these surveys. And one example in recent work we did on climate change policy, which was done in 20 countries. We tried to understand what drives people’s views on climate, why some people support more climate action, others not. And, you know, we found some very standard expected things like, your self-interest matters; if you think, like, climate change policies are going to be costly to your family, to your budget, you’re less willing to support them, etc. But some really unexpected thing came out. For instance, people cared deeply about how progressive climate change policies are; how equitable they are; that they’re not going to hurt lower income people disproportionately. So beyond their own self-interest, regardless of their income, they care about the equity of these policies. And currently they perceive many policies to be quite regressive; the burden is falling too much on lower income families. So that tells us something really important that we might not have known, which is, it’s not about trying to convince them climate change is a terrible disaster. Most people are already very convinced of that. It’s now about discussing concrete policies and explaining which ones are progressive, which ones are less progressive, what can be done to compensate people who might lose from them, etc.

LEVITT: So people who listen to this podcast regularly know that I often talk about a carbon tax and how perplexed I am that almost every economist thinks it’s a good idea. But non-economists, even staunch environmentalists, are almost always against it. Did your survey shed light on what it is that bothers people so much about carbon taxes?

STANTCHEVA: Absolutely. And it is exactly this equity concern. So we had a whole range of questions on the carbon tax, and what we see in all these 20 countries, a carbon tax, where the revenues are to just be plugged back into the government budget, has really low support. It critically matters what you do with the revenues. So we give people different options. What if we used the revenues and gave it back in a progressive way? So not just equal lump sum transfers, but rather give more to lower-income households or vulnerable households more exposed to the climate change policies? That has much bigger support. What if we use the revenues and earmark them for environmental causes so it’s guaranteed to be plugged back into, say, more green infrastructures or clean energy programs? Again, much higher support. So people do not like the idea that this carbon tax is just going to be there, potentially falling disproportionately on lower income people. And then the revenues might just disappear into a government budget that they don’t trust, that they don’t control, and they’re much more supportive once it’s either directed towards climate change or given back in a much more progressive way.

LEVITT: And that really hints at something that has emerged from your studies. And I want to move away from any particular public policy and talk even bigger picture, which is that you find across your different surveys that there are a handful of factors that exert an outsized impact on the views people hold across the gamut of economic policies. And these are factors that economists don’t talk about or even think about. One example is the degree of trust or distrust that people have of government. But I think an even more interesting one that you’ve been working on is what you call the zero-sum mentality. Could you talk about what you’re finding there?

STANTCHEVA: Yes. And you’re right that a way to think about all this is that there’s things we might expect to affect our policy views, like our narrow self-interest. What is this policy going to cost me? What are the benefits of it for me? Those are pretty easy to study, but it turns out we have much broader concerns. We have concerns about who else might lose and win, our distributional concerns. We might have different views on how fair this is.. We might have views about the government and its trustworthiness. And then finally we have what we call mindsets. So these are kind of lenses through which we’re going to see the world and which are really going to color all of this. And one important mindset is zero-sum thinking, something we studied in really recent work. The zero-sum mindset is this idea that if you or a group wins, it means that someone else must lose. So the world is a fixed pie, there’s a limited set of resources. So if someone gets a larger slice of the pie, someone else must be getting less.

LEVITT: This is like the epitome of Trump’s view on every issue I can think of — immigration, tariffs, everything. Trump just very much puts forth this view that there’s a bargain, and when you bargain over stuff, somebody wins and somebody loses and he wants to be the winner.

STANTCHEVA: So there’s a lot of zero-sum thinking today in the U.S. We see this when we look across generations. A very striking fact is that the younger generation today in the U.S. holds much more zero-sum views than older generations. This might appear quite puzzling, but it is a very strong pattern and you see it in other rich countries too. And what we trace it back to is actually the economic environment. So both at the macro level, we see that people who grew up in generations that had higher growth, higher mobility, are less zero-sum thinking today. But it’s also true at the individual level. We have questions asking people about their whole ancestry, their whole family history, and if you have done better than your parents, or if your family has experienced upward mobility, has done better than the previous generation, you are less likely to think in zero-sum terms. So it is very much correlated with your economic experiences and these positive experiences make you less likely to be zero-sum. We actually see the opposite pattern in poorer countries or in developing countries where things used to be lower growth, lower mobility. Today there’s higher growth and higher mobility, and there it’s the flip image of the U.S. Younger generations are less zero-sum than older ones.

LEVITT: What you’re saying is that people tend to be either more zero-sum thinkers or less zero-sum thinkers. And then the real power is you take that basic fact about a person and then you can predict a seemingly disparate set of policy views. So let me just try and to give an example, a zero-sum mindset would make me want to tax the rich because the rich have too big a share of the pie of the economy and the size of the economy’s fixed. And so I want to take some away from the rich and give it to the poor. But that same person who has a zero-sum mentality would wanna keep out immigrants because immigrants tend to also take a part of a fixed pie. But then you have this setting where wanting to tax the rich and being against immigrants, like it crosses traditional partisan lines. And so it gives you this new predictive tool for understanding what people believe and how they’ll act.

STANTCHEVA: That’s exactly right. This mindset is really important for policies and politics because unlike many other beliefs today, zero-sum thinking is not a clearly partisan issue. It’s not a left-wing or right-wing mindset. It doesn’t fall neatly along party lines. It leads people to support very different, very specific policies. It involves this thinking that one group is being taken advantage of, and a government needs to step in to help that group. So for instance, if you think rich people gain their wealth at the expense of poor people, and you’re against this idea of trickle down that prosperity will lift all boats, then you’re much more likely to support higher taxes on the rich and more redistribution to help the poor. If you think immigrants are benefiting at the expense of non-immigrants, you’re much more likely to favor stricter immigration rules. And so this tendency to think in zero-sum terms will lead you to policy views that are not clearly left wing, not clearly right wing.

LEVITT: In the background as you talk about this and write about this, and as I think about my own intuition, I think it’s fair to say that at some level it’s bad to live in a world that people have a knee-jerk reaction of thinking in zero-sum. Because if you have a zero-sum mentality, all you need to know is that someone else is benefiting for you to conclude that you’re being hurt. And that’s a really terrible premise to base public policy on because what you really want to strive for are these win-win situations where by opening up trade, everyone can be better off. And if people unthinkingly revert to some idea that what’s good for you is bad for me — it happens in my own family. So my children are absolutely zero-sum mentality. If they see anything good happen to a brother or sister, they immediately assume it’s got to be bad for them. And it makes life really hard, and I can see it very distinctly as that unfolds. I’m curious what you think about that. Do you think we should be trying to educate people to the value of having a mindset in which it’s possible that everyone can benefit, that there are win-win situations?

STANTCHEVA: So what we see in this research is that it is really a result of people’s reality. It is not a bias or misperception. So people have lived through things that have been zero-sum and so they have adopted that mentality. We show in the paper it’s related to your family’s immigrant history, whether your ancestors were subject to oppression and enslavement. And some situations truly are zero-sum, for instance. When jobs are really scarce, resources are limited; there’s a lot of competition. If you get a promotion at work, I might not get it. So to me the way to think about this is not, What can we do to change people’s mindsets?, but rather, What can we do for policy? It is policy’s role to try to create a better environment here. And it’s true as well that because you’ve lived through these zero-sum situations, you might apply this lens above and beyond those specific situations, you might start to apply to many more things than is justified. And that is definitely very unfortunate. But I still think the first lever here is policy. 

You’re listening to People I (Mostly) Admire, I’m Steve Levitt. And after this short break, I’ll be back with economist Stefanie Stancheva to talk about how her childhood in Bulgaria and East Germany prepared her to study inflation.

*      *      *

We haven’t talked much about Stefanie’s personal story which starts behind the iron curtain in communist Bulgaria, of all places, although her family moved to East Germany when she was young.

STANTCHEVA: So my family left because my parents actually wanted to do a Ph.D., in Dresden, which had a really good technical university.

LEVITT: What did they do Ph.D.s in?

STANTCHEVA: In electrical engineering. And we then moved to France when I was six years old. So I grew up mainly in France. And that really actually kindled my interest in economics before I knew what economics is. I knew things like people are watching the news every evening to check what the currency exchange rates are because it was a very important time for that. Or people are monitoring inflation because there was hyperinflation in Bulgaria. So all these concepts which I didn’t know were economics were so omnipresent and so important in people’s lives that I was always very interested in them.

LEVITT: You brought up inflation and I have to say in my life as an economist, I’ve never really fully understood why both economists and regular people seem so worried and upset about inflation. You actually dove into that in surveys to try to make sense of why regular people are so upset and worried about inflation. What did you find?

STANTCHEVA: So we did a study about inflation that was actually titled “Why Do We Dislike Inflation?” To really try to understand what’s driving this major hate for inflation that we have. And what we see is that people very much feel like their wages just don’t keep up with prices. Their wages are growing slowly, it will take a long time to adjust. So their standard of living is falling, and it will take them a long time to recoup it, if at all. And it’s compounded by two additional things. One is that people attribute a lot of discretion to their employer. So they will say that if their employer is not increasing their wage, it’s because they don’t want to. They don’t think that firms are subject to the market forces that will make them do something, but rather they have a lot of discretion and they just choose not to. And the other thing that’s compounding it is a strong sense of inequity, namely that the wages of higher paid people are much better able to keep pace with prices as opposed to those of lower-income people. So that generates this feeling of inequity as well. So taken together, this means, people really dislike inflation quite a lot.

LEVITT: That actually makes sense because I’m going to sound like a horrible person when I say this, but when I think about, as an economist, what’s good about inflation, it’s exactly what these people understand, which is, it is really difficult for employers to reduce the nominal wages, the actual amount that they’re paying to their workers. But sometimes you might need adjustments. Sometimes it might be that people’s productivity or a certain set of jobs aren’t as valuable as they used to be. And so inflation ends up — you can leave the actual dollars paid to someone the same, but it’s worth less in terms of purchasing power. So it actually is a relatively easy way for companies to get wages set up. And so it’s funny you say that people really perceive this as being such a negative side of inflation ’cause it’s the one side of inflation that I’ve always thought, as an economist, actually is good because it gets rid of a constraint that firms face in that people go crazy when you lower their nominal wages. How do you react to what I just said?

STANTCHEVA: I actually used this study as a great example of cases where we economists learn from people. When this study came out, I had a lot of journalists reach out to me to say, “Oh, look, people are really overestimating inflation your study shows,” because that was one of the other findings is that people think that inflation is higher than what our headline number says, “so people are clearly wrong.” And I kept pushing back against this saying, “No, I think we’re actually really learning that the perception of inflation is telling us something that our headline numbers are not measuring.” So when we economists measure inflation, we use typically the consumer price index, the C.P.I. Broadly it’s trying to measure what a typical household will spend on a basket of goods and services. And so major things would be food and beverages, housing, clothing, drugs, transportation. Our numbers exclude many things that people very much consider to be part of their cost of living. For instance, people consider their financing costs to matter. If they’re thinking of purchasing a house, what will be the mortgage rates they pay? What will be the auto loan financing; their credit card loan? All these, which are not counted in the core baseline inflation measure, matter to people. There’s also inflation inequality. People with different incomes will consume different things. We don’t have those fine-grained measures in our core C.P.I. index, for instance. So to me, this was actually learning, people have perceived this cost of living to be increasing much more for valid reasons than what our headline numbers say. And they also have reasons for disliking it that we might not take into account, but that we really should. This was a case for me where it’s very clear that we have to learn and we have to somehow adjust and incorporate this better.

LEVITT: I have this theory, probably a really bad theory, but my theory is that typically the only people who end up becoming academics are upper middle class people who always had more money than they needed because the kind of people who can be tenured at Harvard in economics can make 10 times as much money doing something different. I don’t think you grew up extremely wealthy. 

STANTCHEVA: No.

LEVITT: Do you not care about money? why do you think it is that you’re drawn to academics instead of going out and making a lot of money?

STANTCHEVA: I’m just really curious about these important questions it’s been a very longstanding quest to try to inform better policies, to try to learn as much as we can about this and I actually feel like I’m running after time. Time is never sufficient to do all these studies, to get at all these answers. So it is something that’s truly, a passion right now and that I love doing. I’m very much driven by this and I hope I can continue doing it because there’s still so, so much to be answered. It’s scary how much more there is to be done.

If you’d like to learn more about Stefanie Stantcheva’s research, check out the website socialeconomicslab.org. That’s socialeconomicslab.org. And if you’re even more ambitious, you could try reading some of her academic papers. She’s one of the few economists who writes papers that are intelligible to a noneconomist.

LEVITT: So this is a point in the show where I welcome my producer Morgan on to tackle a listener question.

LEVEY: Hey, Steve. So we got an email from a listener named Leah. You have talked in the past, Steve, about how you think that individual consumers should be able to get paid by big tech companies like Google for their data. It would incentivize consumers to share more information. So Leah is writing because she saw an interview with the founder of this tech startup called Verb.AI, and it sounds like Verb.AI is doing this. People can sign up and get paid $50 per month to allow the company to access and monitor some things they do on their phones. So she’s curious what you think of this company, Verb.AI, and if you think it’ll get traction, and is this a solution to the problem that you see?

LEVITT: First let me compliment Leah for remembering that I said that people should own their own data. I don’t know so much about Verb.AI, but what I do know about it suggests to me that it is definitely a step in the direction that I had imagined regarding getting paid for one’s data, but really only a baby step. If I understand what Verb.AI wants to do with your data when they have it, is I think they’re trying to help firms do market research. So a fast food chain is trying to figure out how people decide where they’re going to go eat. And in the past they might hold focus groups or do surveys to try to sort that problem out, but here you can see the incredible advantage that they would have of having detailed data from the phones of real people in real time to try to sort out how people are making those decisions. So that’s a really good use of data, but it doesn’t strike me as being the primary reason why your own data is really valuable. So I think that’s only a baby step because I think there’s much more to be monetized than just the market research angle on it.

LEVEY: What do you see as the other ways that our data could be monetized?

LEVITT: What I see is a totally different relationship that a consumer has to a search engine or A.I., in which right now I ask Google or ChatGPT to help me do research about which car to buy. And so the current Google model is that auto manufacturers pay to be at the front of the line so that Google shows me their information first. And that’s not a bad model. It serves everyone. But what’s missing is the actual teamwork between Google and me. Because we’re really on the same side Google’s helping me find the best car. And Google has relationships with all these car companies already. So if I’m willing after I buy the car to take a picture of the VIN number of my car and to show me driving it around and that I actually purchased it, then it’s possible for Google to get away from simply ad placement to much better deals. You could imagine Google saying, Hey, if you’re looking for a new car, as long as you’re willing to work with me and to show me at the end that you bought a new car, I’ve got this deal from Ford that says Ford will give you $400 in cash if you end up buying a Ford car in the next three months and VW — 550. Subaru will give you 600. Subaru and VW and Ford don’t just care about getting their ads in front of you. They care about actually closing the deal. So my hunch is that if they could really find a way to confirm that it’s actually leading to a sale, then they’ll be willing to pay some money. Maybe Google’s getting $800 from Ford and only giving 400 to you. But now you and Google are essentially teammates who are trying to play off the auto manufacturers in a way that maximizes the joint surplus to you and Google, which will only happen in a world in which you’re splitting the profits. In a world in which Google keeps all the profits, you’ll never want to cooperate. I’m not sure about the specifics of any particular implementation, but it’s just a thought that when your data are available to firms then there’s ways to split profits.

LEVEY: Leah, thank you so much for bringing Verb.AI to our attention. If you have a question for us or a problem that could use an economic solution, send us an email. Our email is pima@freakonomics.com. That’s P-I-M-A@Freakonomics.com. We read every email that’s sent and we look forward to reading yours.

In two weeks, we’re back with a brand new episode featuring Seth Berkley. He ran an organization called Gavi. You probably never heard of it, but through the distribution of vaccines, it can credibly have been said to have saved 18.8 million human lives. As always, thanks for listening and we’ll see you back soon.

*      *      *

People I (Mostly) Admire is part of the Freakonomics Radio Network, which also includes Freakonomics Radio and The Economics of Everyday Things. All our shows are produced by Stitcher and Renbud Radio. This episode was produced by Morgan Levey, and mixed by Jasmin Klinger. We had research assistance from Daniel Moritz-Rabson. Our theme music was composed by Luis Guerra. We can be reached at pima@freakonomics.com, that’s P-I-M-A@freakonomics.com. Thanks for listening.

LEVITT: How many languages do you speak?

STANTCHEVA: I speak five. I speak French, German, Bulgarian, English, and Spanish.

LEVITT: You have to work hard to find a country where you don’t speak the language.

STANTCHEVA: I was lucky to have Italian co-authors.