The future of inequality

22 Aug 2025 · 35 min · 15 chapters

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In short

The episode argues that inequality is not all the same. It distinguishes “good inequality” (pay reflecting value creation) from “bad inequality” (excess compensation enabled by power, discrimination, or unequal starting conditions). It claims bad inequality reduces GNP and violates equal-opportunity norms, and proposes “pre-distribution” reforms plus better causal evaluation.

Guest

Dave Grusky, Stanford professor of sociology and economics; an expert on inequality and faculty director of Stanford’s Center on Poverty and Equality.

Key claims

Bad inequality is compensation above the value of output, often via leverage (e.g., cronies on boards, monopsony employers, restricted professional accreditation). Birth-lottery advantages create “illicit advantage” and “talent left on the table” (e.g., children in the top 1% are 77x more likely than bottom 20% to attend Ivy League Plus; equal SAT doesn’t mean equal admission). Progressive taxation is “blunt” because it doesn’t separate good from bad inequality.

Notable examples

CEO packing boards; company town single labor buyer; occupational associations controlling accreditation; neighborhood and school inequality via exclusionary zoning and unequal school spending; discrimination “taste” overpaying preferred groups; quasi-experimental “doppelganger” methods (Raj Chetty) and “silicon sampling” using LLM clones to test interventions.

Written by AI. May contain mistakes. Listen to the episode to check what was said.

Chapters

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Understanding Inequality

0:45 to 1:47

Discussion on the commitment to equal opportunity and the implications of inequality.

“We have a profound and deep commitment in this country to equal opportunity.”

Introduction of Guest

1:47 to 2:18

Introduction of Dave Grusky and his focus on inequality research.

“Some inequality is justifiable, whereas other inequality is quite bad.”

Defining Good and Bad Inequality

2:18 to 3:40

Dave Grusky explains the difference between good and bad inequality.

“economically, it's not always like that.”

Examples of Good Inequality

3:40 to 4:30

Examples of acceptable inequalities based on productivity and value creation.

“extreme stark inequality as that critical source.”

Exploring Bad Inequality

4:30 to 6:10

Discussion on how bad inequality arises from misuse of power and privilege.

“And most people think it's fine if they're compensated in accord with that amazing work they're doing.”

The Birth Lottery and Its Implications

6:10 to 8:06

How the birth lottery contributes to unequal opportunities in society.

“So in that case, part of the inequality is good because it reflects the extraordinary product of the CEO.”

Addressing Inequality in Society

8:06 to 10:36

Debate on the importance of addressing inequality and proposals for change.

“They have extraordinary leverage by virtue of that, right?”

Financial Opportunity and Social Class

10:36 to 13:14

Discussion on the distinction between financial inequality and social status.

“in CS than the rich kid who did and made even more money.”

Good Jobs vs. Bad Jobs

13:14 to 14:00

Exploring the complexities of job satisfaction and mobility in relation to inequality.

“So that's why we don't get worried about this.”

Understanding Good vs. Bad Inequality

14:00 to 20:18

Explore the differences between economic inequality and various amenities, and their implications.

“Let's say their parents can afford to send them off to Juilliard and get trained up.”
Show all 15 chapters

Addressing Bad Inequality Through Policy

20:18 to 27:10

Discuss strategies for targeting bad inequality and pre-distributional reforms in economic systems.

“Welcome back to the Future of Everything.”

Innovative Approaches to Causal Inference

27:10 to 28:00

Learn about the cutting-edge methods in social sciences for evaluating policy effectiveness.

“Of kind of cutting edge quasi-experimental methods.”

Exploring the Doppelganger Approach in Research

28:00 to 29:19

Learn how researchers use doppelganger methodology to study social interventions.

“But if you've got a big data set, you can do that.”

Silicon Sampling: Creating Synthetic Doppelgangers

29:20 to 31:04

Discover how synthetic doppelgangers can be created using AI for research.

“find those matched except for one thing or two things that you then can study.”

The Potential of LLMs in Mimicking Human Choices

31:05 to 33:31

Understand how large language models can simulate human decision-making.

“So they're reading this extraordinary, long conversation that conveys what this person is all about.”
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Transcript

Automatic transcript. May contain errors.

0:00David Grusky:This is Stanford Engineering's The Future of Everything, and I'm your host, Russ Altman. I thought it would be good to revisit the original intent of this show. In 2017, when we started, we wanted to create a forum to dive into and discuss the motivations and the research that my colleagues do across the campus in science, technology, engineering, medicine, and other topics. Stanford University and all universities, for the most part, have a long history of doing important work that impacts the world. And it's a joy to share with you how this work is motivated by humans who are working hard to create a better future for everybody.

0:37David Grusky:In that spirit, I hope you will walk away from every episode with a deeper understanding of the work that's in progress here, and that you'll share it with your friends, family, neighbors, co-workers as well. We have a profound and deep commitment in this country to equal opportunity. It's a normative commitment. We think that's how the world should be. It shouldn't be the case that the birth lottery determines, or not determines, but affects your fate. And also relieving talent on the table and our GNP is being driven down because it matters so much. So there's lots of reasons why we shouldn't say, oh, that's just the way it is.

1:10We should double down, do our work, build a social infrastructure that's what we deserve.

1:21David Grusky:This is Stanford Engineering's The Future of Everything podcast, and I'm your host, Russ Altman. If you're enjoying the show or if it's helped you in any way, please consider rating and reviewing it on the platform that you're listening to right now. We like to get a 5.0 if we deserve it. Your input is extremely valuable and will help others discover the show and learn about the future of everything. Today, Dave Grusky will tell us that all inequality is not equal. Wrap your head around that. Some inequality is justifiable, whereas other inequality is quite bad. He'll tell us about it. It's the future of inequality.

1:57David Grusky:Before we get started, another reminder to rate and review the show so that others can discover it and enjoy it.

2:09David Grusky:So inequality is bad, right? All people are created equal. We should all be the same. Well, in the eyes of the law, that's reasonable. But economically, it's not always like that. If you're more productive at work, you show up, you work hard, you have a colleague who doesn't show up, doesn't work hard, your paycheck might be bigger. That might be an inequality of paycheck that's perfectly reasonable. On the other hand, bad inequality where you're taking advantage of your power to take money that you really haven't earned in any way. We've all seen situations like that. That might be truly bad and insidious for society.

2:46David Grusky:Well, Dave Grusky is a professor of sociology and economics at Stanford University and an expert on inequality. He studies it, he thinks about it, and he wants to reduce it. Dave, I'd like to start out by just asking you, how did you decide to focus on inequality in your research? Well, I have a lot of stories about that, and it's hard to know which ones really are on the mark. But let me go with what I think might be true. I mean, that's a tough question, actually, right? I think a lot of the problems that we're facing as a country and as the world at large are due to the vastly unequal distribution resources.

3:23Some people are getting shafted and that's going to have some downside consequences that are pretty bad. I think we're seeing that in a very stark way right now. And I think if we want to address problems at their source, we would think long and hard about vast, extreme stark inequality as that critical source.

3:44David Grusky:Great. Okay. So I know that one of the things that you're able to do is define inequality for us. And in particular, I know that you've written about two types of inequality and that it's very fundamentally important that people understand these. So I'd like to take some time to understand what they are and how we should think about them. Yeah. You know, if you speak very colloquially and, you know, we can get deeper about this because there are normative judgments behind this colloquial distinction that I'm about to make. But if you speak colloquially, I think it's important just to think about good inequality and bad inequality.

4:21So what do I mean by good inequality? That's just the compensation, the extra compensation that goes to workers who are really productive. So, you know, if you have a doctor who's great at saving lives or a contractor who owns lots of great houses, inventors who are responsible for amazing inventions, those are creating product that people want and are willing to shell out money for. And most people think it's fine if they're compensated in accord with that amazing work they're doing.

4:48David Grusky:And just to make sure I understand that, does that extend all the way to the CEO inventor who has created a whole new company that has billions and billions of dollars of capitalization? We count that as a good inequality because they had the idea, they executed, and now there's an inequality, a big inequality in their net worth versus other people's. But are we calling that pretty good? It depends. So that gets you right to the question of the distinction of what good and bad. It may be good. It may not be all good. It may be partly good. So let's think about that. So what's bad inequality? That's when you're getting compensation that goes to workers or firms or inventors that's in excess of the value of their product.

5:38And usually that means they're using power. to leverage that excessive compensation. So let's go back to that CEO. Let's say the CEO did an amazing thing, but now they've packed the board with their cronies, the board of directors, and their cronies, they're getting a lot of benefits from being on the board. And in return, they're going to exceed to a very excessive compensation package in excess of the value of the product that that CEO is actually delivering to the firm. So in that case, part of the inequality is good because it reflects the extraordinary product of the CEO. But part of it is a reflection of the CEO's power that they're using to extract resources in excess of the value of their product.

6:26David Grusky:OK, so we have if I can summarize very briefly, you just said it very clearly. The good, acceptable inequalities are because of value creation differences across different people and different organizations. The bad is taking advantage of it sounds like it's taking advantage of power mostly to to extract money that maybe is not value. And I know you've you've talked about this in the context of the GNP, the gross national product. Good inequality contributes to the GNP. Absolutely. Absolutely. We all benefit because we have a more powerful, stronger economy that has all sorts of downstream benefits.

7:09Good inequality is all about building a GNP that's large and that we collectively benefit from. Bad inequality is just a transfer of money from the weak to the powerful. No benefit to anyone but the powerful.

7:22David Grusky:Right. Now, another thing that you've written about that's very intriguing is that you said essentially nobody should be in favor of bad inequality except perhaps those few who are benefiting. And I guess my question is, is it really a few or do we see this so widespread across all spheres of life that it actually adds up to a ton of people who are benefiting from bad inequality? It's more the latter. There's a lot of people who are benefiting from bad inequality. We gave this one contrived example of the CEO, but there are many examples. There could be, for example, a company town in which there's just one buyer of labor.

8:06They have extraordinary leverage by virtue of that, right? You have to sell your labor to that company or no one. So they exploit that leverage and get and get excessive returns. They can drive those wages down to the rock bottom, right? Or it could be occupational associations that are handing out accreditation. And only people who have that accreditation can actually practice that occupation. They can restrict the number of accreditations that are handed out, make that body of occupational workers scarce, and drive up wages. So there's lots and lots of this type of bad inequality out there. There's another type, if I could just go a little bit further.

8:45David Grusky:Absolutely, absolutely. There's another type that's a bit more subtle, but I think it's super important. And that's a type of bad inequality that's based on the birth lottery. the winners of the birth lottery are getting unfairly compensated in some cases so what do i mean by the winners of the birth lottery these are people you know we know all the how it works you know you're born the stork takes you to your new house right and the stork's flying you know and it could it could drop you into into a family that's super rich and into a neighborhood that's super uh uh full of amenities of various sorts or or into a family that's that's low income and to a neighborhood that's not as attractive on these amenities.

9:30So that's the winners of that birth lottery, the ones who get into a rich family and a great neighborhood, right? What does that do? Well, that means that you're going to have probably great training, you know, good primary school, good secondary school. You have social networks that are wonderful. If your parents are rich, let's say they went to Stanford and they did really well. Now you're going to have a legacy advantage that might help you get into Stanford yourself. Let's say you do, you know, you go to these great schools, you do wonderfully, you have wonderful parents who are well off and can make huge investments in you.

10:03You get into Stanford, you major in CS, and you do really well. You're great. You make a lot of money. You say, well, they're making what they deserve. Well, here's the rub. They've had a lot of privilege all along the way. And so folks who are born into low-income families in less advantageous neighborhoods didn't really have the same advantages and didn't have the opportunity to get into, say, Stanford. They may be great if they had gotten into Stanford. That's the counterfactual. If they had gotten into Stanford, they would have been even better in CS than the rich kid who did and made even more money.

10:41But they've been locked out of the competition. And that's kind of talent left on the table. And our GNP is driven down because of that. Right. Right. So that's bad inequality, too. It looks good because they've got great credentials. It looks good, but it's not good.

10:57David Grusky:It's not all good. And that is a really tough one. I mean, I just if we can pause for a moment on that one, because the folks who were lucky, I imagine, and in fact, you and I might be staring at two such people right now on the video screen. Those people might think, but yes, maybe I was lucky, but I also had to work very hard. There's many ways I could have failed, and I didn't. And so what are you going to do? So the question is, do we feel so confident in that next step that we're actually going to try or are there proposals to actually try to even that field and say, we need to help the folks who were born in the less privileged situations.

11:36David Grusky:We need to give them an equal chance versus, you know, the world has luck and not luck. And we're just going to let that dice roll and we're going to deal with it. And how do you as a sociologist and economist think about that's a very difficult conversation. And I think we've all seen two people have that conversation. And it's an extremely tough one. Yeah, yeah. I think, you know, we have a profound and deep commitment in this country to equal opportunity. It's a normative commitment. We think that's how the world should be. It shouldn't be the case that the birth lottery determines, or not determines, but affects your fate.

12:11And also relieving talent on the table and our GNP is being driven down because it matters so much. So there's lots of reasons why we shouldn't say, oh, that's just the way it is. We should double down, do our work, build a social infrastructure that's what we deserve. So, yeah, I don't think we should let it go by. And I should say it's partly because it's really big, this kind of illicit advantage. So just to give you one statistic.

12:32David Grusky:You mean the magnitude of the advantage. Oh, good. Tell me about that. Yes. Yeah. So if you looked at the probability of attending an Ivy League college, kind of a broad definition of Ivy League that includes, say, Stanford, Ivy League Plus, you're 77 times more likely as a child in the 1 % than a child in the bottom 20 % to get into an Ivy League college. So it's a huge advantage. And moreover, it gets worse. The low-income child who has exactly the same SAT score as a high-income child, they're also much less likely to get into a place like Stanford. So there's huge advantage in play here. And so we're leaving a lot of talent on the table and the cost is profound.

13:14So that's why we don't get worried about this.

13:16David Grusky:Great, great. So we've been talking mostly about financial opportunity, but there's also status, social class. Tell me how those factor into inequality. Do we mostly have to worry about money and economics, or is status and social class in some ways different or require perhaps different levers to be adjusted? Yeah, that is a great question. And actually, there's a lot of research going on at the Center on Poverty and Equality, which, for which I'm the faculty director, that takes on exactly this question. I think it's a bit of an open question. I worry a lot, and we're examining whether or not this worry is on the mark, I worry a lot that people make trade-offs.

13:54So for example, the kid who's born in a very rich environment might want to be a professional dancer. Let's say their parents can afford to send them off to Juilliard and get trained up. Now, let's say they succeed. They're not going to get paid much, right? And it will look like, oh my gosh, it's the American dream. They're born into rich circumstances, they just weren't any good and they didn't get much money. But that would be a misunderstanding, right? They're just trading off the amenity of being a professional dancer, which they value greatly for having a lot of money. And so we need to take both types of mobility into account.

14:31Sorry, both types of amenities have to be taken into account, both the amenity of having money and the amenity of having a job that means a lot to you. And we have to look at both of those together in order to understand how much illicit advantage this is in play in this world. So we're taking that on now at the Center on Poverty and Equality, I think the answer is not yet clear on that very important question.

14:52David Grusky:You talk sometimes about good jobs and bad jobs. And I think, and I wonder if you can kind of describe in a context of this kind of conversation, what would be a good job? And, you know, people are thinking about this because now there's this other thing, which is AI. And so a good job and a bad job has changed because now people are looking at the degree to which they're protected from an AI revolution that might take their job. But I think even before that, you were thinking about the different effects that jobs can have on economic and social opportunity. Yeah, so I was mainly focusing up to now, when I was talking about inequality, I was focusing on economic inequality.

15:33And that's what most people instinctively think is the most fundamental type of inequality is the type of inequality that's been taking off in the U.S. and many countries throughout the world. It's the type of inequality that has massive implications for one's life chances. And so it's a really profound and important form of inequality. And that's why I've been focusing on that and talking about good and bad inequality in economic terms. But that's not to discount these other sorts of amenities about which people care, but is perhaps somewhat less fundamental. Often status and prestige flows out of having money.

16:06There are other ways to get it, no doubt about it. We're involved in a profession that maybe has status and prestigious in excess of the compensation we get. But nonetheless, I would say that the core, the fundamental inequality is economic.

16:20David Grusky:I want to start the conversation about what we do about all this, because I know that in addition to studying what is, I think that you've put a lot of thought into what could we do to try to narrow some of these gaps. And so let's start that conversation. How do you approach it as an academic? It's such a multifaceted problem. It requires you to get many people on board, governments, non-governmental organizations, certainly industry. How do you think about getting people moving in a direction that you think might be profitable? And I use that word with some irony for the country. Yeah, yeah, yeah.

16:57Well, let's think a bit about what we do now because of all those problems. that you've mentioned, we do remediation in a very blunt way. Remediation doesn't distinguish between good and bad inequality. What do we do? We mainly do progressive taxation, right? So what we're trying to say is, well, the market will do what it does. We'll get paychecks of vastly different sizes. Some of it's because of bad inequality. Some of it's because of good inequality. And now we'll just look at the sizes of those paychecks and we're going to do some progressive taxation. Those who make a lot, those who earn a lot will be taxed at a bit higher rate than those who earn less.

17:40A very blunt instrument, right? Super blunt. Why is it problematically blunt? Because it doesn't distinguish between good and bad inequality. The person who has that high earnings might be garnering high earnings simply because they're making a lot of great product that people want. Or they could be that CEO who's packed the board with cronies and is getting excessively compensated. Or the member of an occupation that's held the man down really low, held the number of people who get the accreditation down to a really low number, created artificial scarcity, and they're getting paid more than they deserve.

18:18So there's lots of people who are getting taxed at that high bracket who are productive and some who aren't, it's a blunt instrument. And then people say, well, you know, I don't know, but I want to do this because you're, you're, it's kind of like a bad cancer treatment, a cancer treatment that, that kills the good cells as well as the bad cells, all the, all the, the work in cancer treatment about targeting the cells that are cancerous, right? Yes.

18:41David Grusky:I'm also, I'm also guessing that you can have people who mix those two kinds of inequality to their own benefit. So imagine if you are actually doing some things that are good inequality, You're making your increase. You have increased productivity. You're doing a bunch of things that are worthy of, you know, of merit. And then at the same time, you're also doing some of the bad inequality stuff that makes the dissection of what your tax rate in this example that you've just that makes that dissection incredibly difficult, doesn't it? Yeah. So you don't want to make it kind of an individual problem.

19:15You want to fix our institution so they're generating less bad inequality. Right. Right. So that's why taxation. Well, we don't have taxation that's very progressive at the end of the day because we can't agree on it because it's very blunt instrument. It's doing some good work. It's doing some bad work. It's all mixed up in a horrible mess. And so we end up with taxation is not very progressive, you know, like the bottom 50 percent of the income distribution pays 25 percent of their income in taxes. When the dust settles, you know, all different types of taxes, middle class, the upper middle class pays a shade more between 25 and 33 percent.

19:48And then the top 400 families, they paid 23%, right? So it's pretty flat. We're not really progressively taxing. We're not really getting much work done. I think in part, there's a lot of reasons why, but in part because we don't think it's a good instrument. It's blunt. So we got to figure out how to do it right and well and really targeting, like a good cancer treatment, target the bad inequality. That is our job as social scientists, I would say.

20:16David Grusky:This is the Future of Everything with Russ Altman. More with Dave Grusky, next.

20:35David Grusky:Welcome back to the Future of Everything. I'm Russ Altman, and I'm talking with Dave Grusky from Stanford University. In the last segment, Dave told us, somewhat surprisingly, that there's good inequality and bad inequality. The bad inequality, however, is really bad and forms divisions in our society and gets people frustrated and treated very unfairly. How can we address the bad inequality? That's going to be the topic of the next segment. So Dave, I guess the obvious question is, how do we go about targeting bad inequality so that we can basically lift all boats, increase the GNP, but also have people feel like the world is more fair in both compensation and in general life?

Read the full transcript

21:18Yeah, yeah, that is the million dollar question. So, you know, just slapping taxes on those who make a lot doesn't get that targeted work done. It's really hard to get the targeted work done because we have to go deep into the institutions that generate our paychecks and figure out what it is in those institutions that's generating these vastly unequal paychecks and in particular, vastly unequal paychecks that are due to the illicit use of power and leverage, right? That's tough institutional work, but it's, you know, it's the bread and butter of social sciences, which would be able to get it done.

21:53Now, how do we do it? The main kind of distinction that's made is that we're going to do pre-distributional work rather than redistributional work. By pre-distributional work, it means that we're looking at the institutions that generate pay, right? And it's really great if you can get paid more equal? Because people think that's what they earn. When the long arm of the estate reaches and then it takes your money, that's illicit. People do not like redistribution.

22:19David Grusky:It rankles. So we need to get those paychecks more equal. So let me give you an example of what would be a good predistributional reform. Very simple. And there's many others. But let's just say that there's an employer who's discriminating against a given group. They don't like this group. It's not that the group is less productive. They just don't like them. They may think they're less productive, but they're not. And they don't like them. They're prejudiced. Now, this so-called taste for discrimination against a particular group or set of groups means you're going to overpay for the labor of the preferred group.

22:54Everyone wants that preferred group. They all have the same discriminatory prejudice against another group. And so they're going to all gang up and bid up the prices of the preferred group. And that's deeply inefficient. We're overpaying for labor. We, you know, you'd be better off going for, for, for anyone who can get the job done, not just your preferred group, right? So that's a type of, of illicit advantage that, that is going to harm the total GNP, right? So what do you do? Well, you could, you, in this case, it's kind of simple. It's a contrived example. You could install or enforce discrimination and, you know, discrimination law, right uh you that's just and so that's pretty straightforward um but there's lots of other types of of pre-distribution reforms that you might want to think about for example there's a lot of work now on neighborhood level reforms as we talked about this more subtle type of a bad inequality that arises because people historic drops them into a really high amenity neighborhood so we can do a lot of work to try to equalize neighborhood conditions.

23:59Eliminating exclusionary zoning that leads to having really rich houses all in one neighborhood and really small and low-cost houses in another neighborhood. If we get rid of exclusionary zoning, we can integrate neighborhoods and make it possible for them to be more mixing. Or you could do school-level reforms. You could equalize school spending across primary and secondary schools. There's lots of things you to do. But it's all about going deep into the institution to generate this illicit inequality and rooting out the problem.

24:30David Grusky:Now, some of those things have been tried. And for example, we do have anti-discrimination rules. We have some zoning rules. I'm gathering you would argue that they haven't worked or they haven't been strong enough yet. And one of the reasons, I think it's obvious, as you pointed out in the first segment, that the people who benefit from bad inequality often have power. And so that's when you start to do these things. That's when they get the most aggressive at defending their turf. Have you thought about how to get them to back down a little bit? Are they going to back down in the face of reason?

25:06David Grusky:Or is that not likely? You're a sociologist. You know how people react. Yeah. So I think there's two things that need to get done. First off, we have a boatload of programs that are supposed to take on this problem. but we haven't yet done the evaluation research that shows which of these work and which of them don't and it's hard to combat the the folks who are pressing their interests if you do not have rock solid science-based evaluations that can say this is what we need to do it's been shown to have massive payoff and it's just a matter of of overcoming entrenched interests if you don't have that science at your back, it's going to be harder to take it on.

25:54David Grusky:Great. So tell me, and I know this is the cause for hope for you. So tell me about why you're hopeful. What has happened and what is happening that makes you think that we will be able to gather this data? I love what the phrase you used, rock solid data, that even the most skeptical, entrenched interest would say, yeah, I'm losing money because of this policy I have. Yeah. So the key thing is we need to evaluate what works and what doesn't work. That means we need to do causal inference. And often the social sciences has been seen as handicapped because the gold standard for causal inference is a randomized controlled trial, right?

26:32You allocate people to the treatment group randomly in the control group, and then you see how they fare. And the difference tells you whether or not the treatment is working. In the social sciences, often you can't do that because it's very expensive or it's unethical or all sorts of other reasons. And so we've been stymied. But there's now the rise of quasi-experimental methods that take into account natural randomization and allow us to carry out a natural experiment. They've been immensely powerful. We're getting better and better at the job of exploiting these quasi-experiments and then being able to sort out what works and what doesn't work.

27:10I can give you a few examples.

27:12David Grusky:I would love to hear some examples. Okay. Of kind of cutting edge quasi-experimental methods. They may or may not bear out, but it'll just give you a sense of the excitement in the field. And I'm calling these the doppelganger version of quasi-experiments. And so the idea here is that you want to find for every person, their doppelganger. And there are basically two ways you can find a doppelganger. If you have a huge data set, you're likely to find someone that's very similar to me somewhere in that data set. You need a lot of data. I'm a rare and special person, but there's going to be someone that's basically my doppelganger.

27:51You need a big data set to do it, and you just find that person. So if I'm in the treatment group and it's not randomly assigned, that's the problem, right? You need to find someone that's like me anyway, so we mimic random assignment. But if you've got a big data set, you can do that. And work by Raj Chetty and others have shown that actually when you have a super big administrative data set, you can find that doppelganger. And they've actually taken known RCTs where they did randomly allocate people to the treatment and then to a control group. They know what happened, but then instead they got a pseudo control group where they just looked for someone who's similar to the person who was in the treatment group.

28:29They just have a huge data set. They find somebody just like that person and they have a pseudo control group. And you know what? It nailed the RCT result. It just nailed it.

28:39David Grusky:So that's what I said. Let me make sure I understand though. It has to be my twin, except for some important intervention that by chance I got and that they didn't. So that we're twins except for the thing that you're studying. So it might be, did he get a chance to go to a fancy college. In all other ways, we looked just the same, but then there was something that was important that differed between us. And then that allows you to study the impact of that thing where normally you would, and this is what you were saying would be unethical. You say, okay, you're going to go to this fancy college and you're not.

29:13David Grusky:That's not going to fly. That's unethical. But that might have happened by chance in society. And if you have a big enough database, you can find those matched except for one thing or two things that you then can study. I just want to make sure I get that right. Exactly. Now, there are other types of quasi experiments that can be deployed here. For example, you could say that the last person who didn't get admitted to that school, they just were just one cut below admission. Well, they're not going to be any different. That's a trivial difference, right? So we'll take a look at those people. So there's lots of tricks like this, but the doppelganger trick is one that's been explored and should be very promising.

29:49David Grusky:What else? You said you had a couple of examples. I want to hear more. These are good. Yeah, yeah. So there's a second type of doppelganger trick. Now, this is getting even more speculative, but I want to share with you my enthusiasm and excitement for what's happening in the social science right now and how we're at the brink of major headway. So another way to go with respect to doppelgangers is if you don't have access to a big data set and you can't find that identical person saved for the treatment, right? If you can't find them, you make them. So this is called silicon sampling, and this is how it works.

30:28You can administer a long-form, qualitative interview with someone. In fact, the Center on Poverty and Equality is working on the American Voices Project. It's one type of these long-form, extended conversations. You ask people, tell me the story of your life. You talk about their family, their religion, their politics, everything is a really intimate, cathartic experience.

30:50David Grusky:Yes. Take these. Now, we haven't done it with the American Voices Project because we don't have a consent agreement for that. But you could take that kind of conversation and then you feed it to a large language model and you say, be this person. So they're reading this extraordinary, long conversation that conveys what this person is all about. And then you tell the, be this person. So now you could say now there are two of these people. One will be assigned to the treatment. Let's say you have a little nudge treatment. Like you want to make people less discriminatory. And you think if you talk to them about how this group that they're prejudiced against actually is very productive.

31:31Now, are you going to do this?

31:32David Grusky:Is the intervention going to happen on the AI, LLM, large language model based model or on the real person? Or it doesn't matter? Or you do both? well the simplest cheapest way is your treatment group you you you you convey the treatment to the to to one of the clones right large language models you know trying to be this person you convey the treatment but you can have another clone that didn't get the treatment identical person right so in perfect matching right they didn't get the you don't read the treatment to them uh where you say hey you know this group you're discriminating against is super productive they don't get the treatment and then you see to what extent then the behavior do they for example if you give them a resume from you know from from two yeah so this is fascinating from what you've seen and we're running out of time but i just want to get this final point because it's so fascinating from what you've seen can an llm can a can a chatbot when it has seen a long bit of text about my life, can it do a pretty good job at then making choices and decisions that are similar to the choices and decisions that I might make in my life?

32:42Yeah.

32:43David Grusky:So I should say, you know, there are people at, you know, our colleagues at Stanford, like Michael Bernstein, D.E. Yang, and Park, and Willer, and lots of folks here who are taking on that question, they're making extraordinary headway on it. But there's early evidence suggesting that if you have a long form qualitative transcript, not just survey data, but if you give the LLM really, really high quality, deep information about a person, and you ask them then questions after they ingest that transcript, their answers will be very similar to the answers of the actual person who generated that transcript.

33:20So this is the result coming out of Bernstein's lab, but there are other folks involved in this too. And it suggests there may be a very, very low cost way of getting high quality evidence.

33:31David Grusky:And then, of course, just to complete the circle, with good data from these kinds of experiments, we might generate the rock solid evidence that gets institutions and people to move to create a more equal society. I couldn't have said it better myself. That's absolutely right. It's so important. It makes me so excited because we have an opportunity now, the likes of which we've never before seen. Thanks to Dave Grusky. That was The Future of Inequality. Thank you for listening. Don't forget, we have lots of back episodes in our catalog, and you can spend hours listening to the future of everything.

34:04David Grusky:Also, please remember to tell friends, family, and colleagues about the show, and remember to follow it on whatever app you're listening to. You can connect with me on many social media, at rbaultman or at russbaultman at threads, mastodon, bluesky. You can also follow Stanford Engineering at Stanford School of Engineering or at Stanford ENG.

34:36David Grusky:If you'd like to ask a question about this episode or a previous episode, please email us a written question or a voice memo question. We might feature it in a future episode. You can send it to thefutureofeverything at stanford.edu. All one word, thefutureofeverything. No spaces, no underscores, no dashes. Thefutureofeverything at stanford.edu. Thanks again for tuning in. We hope you're enjoying the podcast.

From the publisher

Sociologist David Grusky argues that all the usual debilitating debates about inequality can be sidestepped if we focus on the worst forms – those rooted in cronyism, racism, and nepotism – that everyone can agree are nothing more than a pernicious transfer of income or wealth from the powerless to the powerful. To fight this “worst form” of inequality, Grusky shows how powerful interventions can be identified with new quasi-experimental methods, including those that use naturally occurring or AI-generated doppelgangers instead of very expensive randomized controlled trials. “We’re leaving a lot of talent on the table. And the cost is profound,” Grusky tells host Russ Altman about the price of inequality on this episode of Stanford Engineering’s The Future of Everything podcast.

Have a question for Russ? Send it our way in writing or via voice memo, and it might be featured on an upcoming episode. Please introduce yourself, let us know where you're listening from, and share your question. You can send questions to thefutureofeverything@stanford.edu.

Episode Reference Links:

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Chapters:

(00:00:00) Introduction

Russ Altman introduces guest David Grusky, a professor of sociology at Stanford University.

(00:02:57) Studying Inequality

David explains his motivation for studying inequality.

(00:03:44) What Is Good Inequality?

How productive contributions create justifiable inequalities in income.

(00:04:48) Example of Bad Inequality

When legitimate productivity becomes mixed with exploitation

(00:07:22) Widespread Nature of Bad Inequality

Different groups within society who benefit from bad inequality.

(00:10:58) The Birth Lottery Problem

How the circumstances of birth create hidden advantages.

(00:13:15) Status & Social Class Inequality

Whether prestige and non-financial rewards intersect with inequality.

(00:14:52) Good Jobs vs. Bad Jobs

What constitutes a good job in an era of rapid technological change.

(00:16:20) The Limits of Progressive Taxation

Why progressive taxation fails to distinguish between inequalities.

(00:21:01) Predistribution Solutions

Preventing bad inequality before it occurs with institutional reform.

(00:24:31) Reform Challenges

How entrenched interests and weak evaluation block reform progress.

(00:25:54) Inequality Research Tools

Quasi-experimental methods that evaluate inequality interventions.

(00:28:39) AI Clones for Policy Testing

Using large language models to simulate individuals and test policy ideas.

(00:33:55) Conclusion

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Episode Transcripts >>> The Future of Everything Website

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