The issue of income inequality is one Americans continually wrestle with the COVID 19 pandemic bringing to light how housing, health, and general wellbeing are impacted by the unequal distribution of wealth. Income inequality in the United States is the focus of this episode of Stats and Stories with guest Joseph Gastwirth.
Episode Description
Dr. Gastwirth is a Professor of Statistics and Economics at George Washington University. Over the course of his career he has written over 300 peer-reviewed articles, which have appeared in the Annals of Statistics, Journal of the American Statistical Association, Journal of the Royal Statistical Society, Econometrica, Review of Economics and Statistics, Statistical Science, Annals of Human Genetics, Human Heredity, Jurimetrics and Statistics and Public Policy. His research has covered a variety of topics in statistical methodology and applications. Of special note are: his early work on order and non-parametric statistics, his research on estimating measures of economic inequality, fairness and discrimination and on the role of statistical evidence in jury discriminations, equal employment and other types of legal cases. The American Statistical Association awarded him Noether Award for his contributions to nonparametric statistics in 2012 and the Karl E. Peace Award for outstanding statistical contributions for the betterment of society in 2019.
+Full Transcript
Pennington
The issue of income inequality is one Americans continually wrestle with, with the covid 19 pandemic foregrounding. How housing, health and general well being are impacted by the unequal distribution of wealth. Income inequality in the US is the focus of this episode of Stats and Stories, where we explore the statistics behind the stories and the stories behind the statistics. I'm Rosemary Pennington. Stats and Stories is a production of Miami University's Department of Statistics in media journalism and film, as well as the American Statistical Association. Joining me as always is regular panelist John Bailer, Chair of Miami statistics department. Our guest today is Joseph Gastwirth, professor of economics and statistics at George Washington University. Over the course of his career, he's written more than 300 peer reviewed articles, which have appeared in such journals as the Journal of the American Statistical Association, Journal of the Royal Statistical Society, and the review of economics and statistics. His research has covered a variety of topics and statistical methodology and applications, including economic inequality, the role of statistical evidence and jury discriminations, and Equal Employment cases. The American Statistical Association awarded him the Karl E. Peace Award for Outstanding statistical contributions for the betterment of society in 2019. And Gastwirth recently co-authored an article for Significance magazine about the changing nature of wealth inequality in the US. Joseph, thank you so much for joining us today. My pleasure. I wonder just to get the conversation going, if you could talk about you know why you felt compelled to write this particular article now?
Gastwirth
That's a good question. And I did some work on income inequality. And I was going to try some, develop some new measures. And I was going to try that out on wealth inequality. And I went to the major data sources, the Federal Reserve, or has a survey every three years. And when I was looking at the Federal Reserve Board data, I noticed something that was a little bit surprising that they exclude the Forbes 400 richest households in America. So think for a moment, that can't be right, because those people are the very upper upper end and inequality is how much things are spread around relative to the typical. So that got me thinking to look at, how shall we say, to the very, the super rich homos are doing versus the rest of us? Because it was just very surprising to me that you would study both inequality by excluding the very, very richest people in America.
Bailer
Just as a quick follow up to that, could you make a distinction or help us clarify the difference between wealth inequality and income inequality?
Gastwirth
Income is usually measured in what you earn, either through your work, or through interest and dividend in one year. So that's kind of a one year shot, how much you've you've heard, and brought in through other sources. Wealth is something that one accumulates over time, for example, many of us contribute, a certain percentage of our salary often is matched to some extent by your employer, and you build up a pension plan of some sort. So that obviously over time that that accumulates, interest, you get an interest in interest or the interest. So the thing is, if you show everything you had at one moment, this is what it would be worth. Now, some other technical issues, which I don't want to get into, but that's the general idea is wealth is accumulated over your lifetime. So obviously, age is going to be a factor, which unfortunately, the dad isn't too good on, but somebody who was older, will have more of a pension and pay money in their pension plan than someone who's younger.
Pennington
I wonder, just to go back to the beginning of our conversation. Joe, you mentioned the fact that these families, the Forbes 400 are cut out of that gauge, is there any explanation for why they aren't included in that sort of picture of inequality,
Gastwirth
All I read is that they spoke to the Treasury Department, and they decided that they shouldn't do that. Now, it may also be that they don't expect these people to respond to the survey. These surveys are voluntary. So I'm not saying it doesn't mean that they won't. You're saying they might not have expected it. Or they might, you know, the sample size might not be big enough that they would have a high probability of getting one of them anyway. But there is a way which I hope to include a paper buddy with the same co author, I believe, approximately adjusting for that. But just an odd thing that they My mind was, it's a bit odd that they exclude them. But you could always make some kind of adjustment.
Bailer
So that kind of begs the sort of the natural follow up question, which is, is how is inequality measured? You know, what are some of the ways that that's framed? Or that you think about it? And then how would you use it in terms of the comparison? I would, you know, so I was accused of asking multiple questions at one time. But, you know, the one thing that I liked about the Significance paper is that it met this sort of metric that you defined as the NFN metric. So if you could give just a couple of remarks about the general measurement of inequality, and then transition into what the NFN metric means, that'd be great.
Gastwirth
Well, the standard measure of inequality that you will find for most governments is the Gini coefficient or Gini index. A simple intuitive approach to that is think of two people who might ask, what is their income inequality? Well, suppose you take one person making 30,001 50,000, you might say the income inequality is 20,000. If you run into a problem, when you think about it, since I'm in Washington, we have these very high priced lawyer lobbyists or former congressman lobbyists, and you might have two of them and what makes 510,001 makes 490,000. So that difference is 20,000. Now, in the first case, that difference between somebody making 50 and somebody making 30,000 is quite substantial. That same difference for people making a bit of 500,000 is pretty minor. So you might say, one way of standardizing this is to say, how much is the difference over that total? How much is the difference over the total. And so in the first case, it's about a quarter. And the second case is less than 1%. So now, if you think about, oh, you can do this average over every family in the country, just take all the pairs and then average them up? That is approximately equal to the Gini index. It's not exactly. But that's the easiest way to get it.
Bailer
That's great. Thank you.
Gastwirth
Now how I got to the Nfn, was when we had the southern measure, which I developed, and I wanted to explore by asking a question, how many I looked at income inequality. Take the top few percent, how many people accumulated from either the bottom or the middle? Do I need to have the same amount of income? So that's what started me off looking at the wealth. But when I realized that the very, very top I didn't have, it didn't make sense to try to use that. So then I thought, well, let's look at how many people we need and how many families we need to have the same amount of wealth as 400. And there, we decided that we should look at people at different points in the income curve, the median, which is the middle point, 50%. That's 50% more different percentiles, your 95th percentile, and the average of the top 10%. And so that's how we decided that. And so we lifted that up from 89 to two or one, six and two papers. Now, I've updated that in the future. But we looked at this data. And for example, in 1989, you needed about 5.7 billion households, at the median income to have the same amount of money, and things like that. Now, one way you can think about this, there is in fact, the number of families. When you get to 2019. We had you at a meeting and you needed 24 million, million families. Let's go back to the 1989 figure of 5.7 million. You can think of football stadiums, like the Rose Bowl, those stadiums, approximate we have roughly 100,000 people attending, so you can think of each family sending one person to the Superbowl to see and in that case, back in 89 you would need 57 stadiums the same amount of money for reading people Now you will need today to address 43 such stadiums that tells you how great the quality, that ratio is almost five.
Pennington
You're listening to Stats and Stories. And today we're talking to George Washington University's Joseph Gastwirth about wealth inequality. That number to me that you were talking about is so astounding that that changed from 1989, where you needed 5.7 million households to equal the income of the upper upper little level. And now it's 24.3, I think million households, I guess I wonder what thoughts you might have about how journalists might thoughtfully cover this issue. There's lots of reporting about income inequality and wealth inequality. But it often feels like the same stories are told over and over again, in a way that doesn't really I think, it doesn't always make the numbers meaningful. And I wonder if if given the work that you've been doing over the course of your career, and now this new sort of measurement that you're you're writing about, if you have thoughts about how journalists can talk about this, and write about this in a way that helps people understand the the change that we have lived through, and in really helped them make sense of this for their audiences?
Gastwirth
Well, one way you can look at it might be that it is changing over time. What is interesting, if you look at the numbers, is that you have a very big change after 2007. In 2007, we needed 12.6. In the median household, we'll also look at the very, very successful people to 12, you need a 12.7. In 2010, you needed 17. And in 2013, you at 24.5. That's it. So there's a big doubling, right between 2007 and 2013. You ask yourself, what happened? What happened was we had a big financial collapse in 2000. And we had government programs. So what did the government programs do? They had a small program to help people with mortgages. That was not particularly effective. But they bailed out. The banks, the major insurance companies like AIG at 100 cents to the dollar. So they obviously did very well. People who own a lot of stock, and those companies did very well. I'll give you another example. The Economist tells you there's no such thing as a free lunch. But there was almost a free lunch when they bailed out Fannie Mae and Freddie Mac, although they had federal Natal, they did not have a US Treasury guarantee. And that's why their interest rates were at about 1% above US Treasuries and about half a percent against the Ginnie the government national mortgage rates because those were guaranteed, but it's a kind of a lesser guarantee in the Treasury. Well, when they bailed out Fannie, or the two mortgage giants, Fannie Mae and Freddie Mac, they again did it 100 cents on the dollar. So that they essentially gave those people US Treasuries. So pretty. That's really a gift. It was really, not only is it fair to the taxpayers who are hustling faster, the people who are more prudent. And what the Treasury's retirement plans are, so it's very funny. So that was something in SharePoint about that. If there was a simple thing they could have done, they could have said, Oh, if we're going to do this, we have to lower rate of interest you get to Treasury and then say, pass it on, pass that interest rate cut off to the people who are paying your mortgage that really helped a lot of citizens.
Bailer
You know, when I was reading about this NFN, one of the things that I thought about was like in clinical studies, they'll talk about number needed to treat nnt I think they're sort of To me, it was like, Okay, this is kind of a it's it's a it was a natural unit to think about for the comparisons because when you start getting into these massively large numbers of you know, and billions and trillions in terms of these, this total wealth number, having to find this this metric that makes sense whether it was number of families of medium of the median wealth family, or the you know, the number of football stadiums of medium families that would Do it I thought that was that was really a lovely way of framing this, the challenge that I would would think would be just even getting to the point of, of defining what is what is wealth or measuring wealth? Or, you know, how do you know these are people? Or you know, these are you said this is a data source for the Federal Reserve for some of this. So are these, you know, surveys of people responding to kind of what they're identifying assets and income as part of this wealth calculation?
Gastwirth
Yes, yes, I think they probably ask people because I don't know exactly what they do there. But I would imagine, they probably asked more detailed questions, which possibly half the people are asked to get their information out before the survey. Or if you would get your bank statement, if you have stocks, mutual funds, get those statements, if you talk about some things that are easier, you have a car, somebody is driving a brand new car, someone's driving an older car. And those prices are pretty well estimable, you know, through the sort of blue book and things. So there's, there are some, there are a lot of issues. Now, one thing that the conservative economists have criticized the Fed for is that they don't include your Social Security, your expected Social Security payment, which say you could convert that pension plan. On the other hand, on the other side, you can say that, when I take cash for how much you have in your pension plan, for most people, you have a deferred pension plan. Sure, John has that university, because most of us do. That means that when you take the money out, when you're retired, you pay tax on it. So it would be very complicated to make those adjustments because people are in so many different tax brackets. And it's very hard, of course, to predict the tax bracket you're going to be in when you're retiring, especially for younger people, you know, how do you know it's gonna happen, but, but a squat, if you think of it as a train, it doesn't make that much difference, I mean, that those differences, I'm not going to explain that tremendous job inequality that occurred after the financial collapse.
Pennington
I wonder, given the work that you've done, Joe, and sort of the research you've been doing to create this new measurement, sort of, if you see if we don't take some steps to address this, we don't take a measure is this, this gap just going to get worse?
Gastwirth
Unfortunately, Rosemary, it probably will, because unless you give some grace, have some incentive to progress, we're saving like matching contributions. I mean, the trend now is to see if there's another cause to which you can't control. That is often as we've had more women work, which I think is a good thing was that people get married. And often you have two income earners. And quite often the two income earners that they both met, they met in college or graduate school. And they tend to have higher average salaries. And that also makes household income in income, wealth inequality is greater. Now, of course, you really can't tell people you shouldn't marry so and so marry this person instead. So some of this, you just have to accept as part of the social glue. But I think the big thing is the education because once you get these people for families that are in the, say, the lowest quartile trained, and they now begin to get even reasonable job jobs, pay the middle level, a set of a lower score, they will begin to save and well, so it's really a long term project. Especially our representatives, though, Congress and the White House, don't think further ahead than two to four years. And this is 25 or 30 years. So you and your business now look at your quarterly earnings. And that's the big deal. That you can also do your previous say, options have to be exercised 10 years from now, so that people will say that let's look at the business. How are we going to be growing and doing 10 years from now? We just have to make that incentive.
Pennington
Well, that's all the time. We have for this episode of Stats and Stories, Joe, thank you so much for joining us this morning.
Gastwirth
Okay, well I hope I didn't offend you with what I said.
Pennington
No, no. Stats and Stories is a partnership between Miami University’s Departments of Statistics, and Media, Journalism and Film, and the American Statistical Association. You can follow us on Twitter, Apple podcasts, or other places you can find podcasts. If you’d like to share your thoughts on the program send your email to statsandstories@miamioh.edu or check us out at statsandstories.net, and be sure to listen for future editions of Stats and Stories, where we discuss the statistics behind the stories and the stories behind the statistics.