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Speaker 1: Every three years, the Federal Reserve puts out this extremely

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Speaker 1: rich data set called the Survey of Consumer and Finances,

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Speaker 1: where they touch on everything from how much debt people have,

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Speaker 1: how much stocks they own, how they feel about being

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Speaker 1: approved for loans. It's like Christmas every time it's release.

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Speaker 1: This episode is brought to you by nat X, the

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Speaker 1: Binary Options Exchange. Binary Options let you limit your risk

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Speaker 1: exchange with transparency, free market data, and fairness guaranteed. The

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Speaker 1: e X, dot com futures options and spots. Trading involves

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Speaker 1: risk and may not be appropriate for all investors. Hi,

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Speaker 1: and welcome back to Bloomberg Benchmarket podcast about the global economy.

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Speaker 1: It is Thursday, January one. I'm Tori stillwell and economics

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Speaker 1: reporter with Bloomberg News in d C. And I'm joined

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Speaker 1: by my co host Akiedo in Tokyo. Hello, Hey, Sorry,

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Speaker 1: how's it going? Pretty good? What's new? Um? You know,

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Speaker 1: not too much on my end. We are supposed to

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Speaker 1: get like double digit inches of snow. This weekend, so

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Speaker 1: that should be very exciting. Yeah, got a little bit

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Speaker 1: of um snow over the past two days in Tokyo too.

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Speaker 1: Everything shuts down. Yeah, it's kind of what happens in

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Speaker 1: d C. There's like maybe a centimeter now and like

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Speaker 1: all the flights got canceled, all the trains are delayed.

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Speaker 1: It was pretty miserable. You know, we have a very

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Speaker 1: special episode today, don't we, Tori. We do. UM. We

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Speaker 1: have been working on a pretty big story for a

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Speaker 1: while now, and it will be available to our readers

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Speaker 1: on Bloomberg dot Com in the terminal by the time

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Speaker 1: this podcast reaches our listeners. I'm so excited for this

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Speaker 1: to finally be out, Hocky, I know, I know, it's

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Speaker 1: been a long labor of love. Do you want to

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Speaker 1: tell everyone what it's about? Absolutely? Um. A few months back,

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Speaker 1: after reading some research from the Federal Reserve Bank of St. Louis,

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Speaker 1: ACKI and I wondered whether it would be possible to

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Speaker 1: tell everyone in America their odds of being a millionaire,

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Speaker 1: not only at their current age, but also in future

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Speaker 1: stages of life. So we asked the St. Louis FED

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Speaker 1: and they very kindly agreed to help US out every

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Speaker 1: three years, the Federal Reserve puts out this extremely rich

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Speaker 1: data set called the Survey of Consumer and Finances, where

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Speaker 1: they touch on everything from how much debt people have,

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Speaker 1: how much stocks they own, how they feel about being

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Speaker 1: approved for loans. It's like Christmas every time it's a release. Um,

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Speaker 1: it is like nerd Christmas, right, Docky, I would say

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Speaker 1: that's appropriate characterization. Absolutely, And the fend makes all of

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Speaker 1: this data available to the public and some of it

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Speaker 1: is pretty easy to find it. The data that we

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Speaker 1: had the St. Louis said calculate for us is pretty complicated.

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Speaker 1: You need a very advanced level knowledge of statistics and

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Speaker 1: also the cystical software to be able to actually manipulate this.

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Speaker 1: So it was really great that we had some extra

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Speaker 1: help on this. Absolutely. So the last Survey of Consumer

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Speaker 1: Finances came out in and it covered the three years

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Speaker 1: prior and it has to tie us over until sev

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Speaker 1: But there's plenty of data to be mined, just like

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Speaker 1: Ay said, and that's what the St. Louis Fed economists

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Speaker 1: used to create this wealth analysis for us, so they

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Speaker 1: were able to calculate the probability of someone having a

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Speaker 1: net worth of a million dollars or more. Looking at

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Speaker 1: the variables of race, age, and education, it is really amazing. Yeah,

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Speaker 1: your odds of being a millionaire are really different depending

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Speaker 1: on how old you are, or you know, what your

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Speaker 1: race is, or how much schooling you got. And by

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Speaker 1: combining all those different factors, we have this incredibly rich

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Speaker 1: and rare data set available to us kind of give

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Speaker 1: us a really nice snapshot of where we are in

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Speaker 1: American society right now. I guess, Tori, it's really hard

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Speaker 1: to do this story justice without the visuals of the

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Speaker 1: charts created by our colleague Chloe Whittaker. So if any

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Speaker 1: of our listeners right now haven't been our very beautiful

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Speaker 1: story on Bloomberg dot Com or the Terminal yet, it

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Speaker 1: really might be worth pausing the show right now and

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Speaker 1: pulling up the graphics as we walk through some of

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Speaker 1: the main points, or you know, at least uh, take

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Speaker 1: a look at the story after the show. Definitely. Okay,

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Speaker 1: So let's go over the main findings of the story

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Speaker 1: and the analysis. But first let's welcome the economists who

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Speaker 1: calculated this data for us, Bill Anons and Brian Knoweth,

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Speaker 1: who are both economists at the St. Louis fed Center

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Speaker 1: for Household Financial Stability. The two of them, along with

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Speaker 1: their colleague Lowell Ricketts, did all the heavy lifting for

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Speaker 1: us with this data. So thank you all so much

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Speaker 1: and thanks for joining us. Very welcome. Let's look at

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Speaker 1: the first the first big point here, which is that

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Speaker 1: there are huge racial differences across the board. So if

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Speaker 1: you're looking at the story in print form on the web,

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Speaker 1: this is our first chart. The big takeaway here if

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Speaker 1: we're looking at statistics, you guys found that about or

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Speaker 1: so of Asians and Whites who are middle aged and

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Speaker 1: college educated are millionaires. And that compares with about seven

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Speaker 1: percent of Hispanics and about six percent of Blacks with

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Speaker 1: those same characteristics, So middle aged, college educated. What is

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Speaker 1: what is really the big takeaway here with regards to

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Speaker 1: race and wealth. I think the most surprising thing is

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Speaker 1: that education doesn't appear to level the playing field. We

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Speaker 1: and a lot of other people probably thought that people

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Speaker 1: of any race, uh and maybe the same age if

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Speaker 1: your care fully thinking about this, that the same level

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Speaker 1: of education should probably translate into roughly the same wealth,

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Speaker 1: and we found that wasn't true. Yeah, and I think

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Speaker 1: the statistic story that you just cited, these are people

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Speaker 1: who are of the same age and have the same

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Speaker 1: level of schooling, and the only thing different is really race.

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Speaker 1: So you know, a lot of times when we talk

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Speaker 1: about these racial differences, the data is just so broad

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Speaker 1: that we're like, well, you know, maybe it's just that

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Speaker 1: these people in minority racial groups are just younger, or

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Speaker 1: maybe it's that they haven't gotten enough schooling or something

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Speaker 1: like that. But there are really no excuses you can

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Speaker 1: make when you look at this specific data. That's right.

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Speaker 1: That brings us nicely to our second point, which is

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Speaker 1: the role education plays in wealth. And I think you

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Speaker 1: can broadly make the statement that education helps with your

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Speaker 1: odds of accumulating wealth and being a millionaire. But the

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Speaker 1: degree to which those ads are improved a is drastically

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Speaker 1: different for Whites and Asians compared to blacks and Hispanics,

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Speaker 1: you know, stripping out age. Just looking at the link

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Speaker 1: um the link with the race and education, a white

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Speaker 1: person with less than a high school degree has just

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Speaker 1: under a two percent chance of being a millionaire, and

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Speaker 1: that climbs to thirty seven percent if they get a

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Speaker 1: graduate degree, whereas for a Hispanic those very same odds

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Speaker 1: go from a less than one percent chance to a

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Speaker 1: ten percent chance um. And and it's pretty similar story

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Speaker 1: with for blacks as well. It's just such a big

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Speaker 1: difference there in what education can do. Yeah, a couple

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Speaker 1: of points. Uh, Certainly, higher levels of education are no

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Speaker 1: guarantee even though people with a lot of education, many

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Speaker 1: people do have a lot of wealth, but also a

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Speaker 1: lot of people with a lot of education don't. We're

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Speaker 1: just talking about the probabilities here. Another point that I

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Speaker 1: think is interesting kind of surprised us when we got

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Speaker 1: into these numbers was how um wealth increases at an

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Speaker 1: increasing pace, if you will, or what economists would call

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Speaker 1: non linear, so that these step up going from less

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Speaker 1: than a high school degree to a high school diploma

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Speaker 1: is noticeable, but not very large, and then stepping up

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Speaker 1: to college two or four year college degree, you notice

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Speaker 1: it's significantly bigger jumps, and then going up to post

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Speaker 1: graduate it's much much bigger. So in other words, it's

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Speaker 1: kind of like it shapes out a curve that that

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Speaker 1: is shooting up as you get to higher levels of education.

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Speaker 1: So there's almost extra payoffs, it seems, with those extra

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Speaker 1: levels of education. And then, as you were saying, even

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Speaker 1: though those basic points are true across all racial and

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Speaker 1: ethnic groups. It does look like these these curves are different,

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Speaker 1: you know, they're sort of at different levels. So even

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Speaker 1: though the basic dynamics are there, we still see these

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Speaker 1: racial and ethnic differences, right right, And I guess our

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Speaker 1: third point, the third point that we make in our

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Speaker 1: stories that um, along with education, everyone's odds generally get

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Speaker 1: better at the age. So the older you are typically

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Speaker 1: the greater your chances of having become a millionaire. But

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Speaker 1: this age effect is really different depending on your race too.

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Speaker 1: So what people and Asian people they tap at it

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Speaker 1: about chance. Um, if you are stripping out the effect

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Speaker 1: of education, but Blacks and Hispanics that's closer to two percent.

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Speaker 1: So gosh, that's a gigantic difference there too. Right again,

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Speaker 1: several things going on, probably you know, one is simply

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Speaker 1: having had more time to save means that older people

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Speaker 1: will generally have accumulated more wealth. Going along with that

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Speaker 1: is the miracle of compound interest, So the returns on

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Speaker 1: having accumulated wealth of really really add up over time.

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Speaker 1: Another factor is that it's really sounds counter intuitive, but

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Speaker 1: it's people in their fifties, sixties, seventies who are more

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Speaker 1: likely to be receiving inheritances, and so families or groups

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Speaker 1: of families that tend to have wealth passed down through

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Speaker 1: the generations. That's really going to show up in what

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Speaker 1: we're capturing in the in the you know, sixty sixty

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Speaker 1: two and up category. And one thing I want to

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Speaker 1: add is that UM, along this age dimension, these are

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Speaker 1: actually different generations. So going forward, UM, this pattern might

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Speaker 1: not hold. Perhaps UM certain groups will actually see their

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Speaker 1: probability increase over time, and perhaps it's a generational effect

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Speaker 1: that's that's causing UM what I would call a hump

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Speaker 1: in the fraction of millionaires along the age distribution. Right.

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Speaker 1: One more thing throw in is that there's probably some

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Speaker 1: survivorship bias, and we can't really quantify that very well.

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Speaker 1: You would need a sophisticated model to try to estimate this.

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Speaker 1: But the basic point is that people who are UM

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Speaker 1: able to take or you know, maybe be able to

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Speaker 1: they've succeeded in the job market, and they We know

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Speaker 1: that there are correlations between wealth and health, for example,

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Speaker 1: so people who are healthier are typically wealthier, and people

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Speaker 1: who are healthier tend to live longer, and so part

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Speaker 1: of what we're seeing in the old groups are people

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Speaker 1: who were generally healthy throughout their lifespans, tended to have

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Speaker 1: lower medical costs, tended to live longer, which gave them

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Speaker 1: more chance to accumulate wealth and allow that compounding to occur.

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Speaker 1: And that's one of the reasons that we um we

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Speaker 1: like to break off that older category at somewhere in

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Speaker 1: the sixties because especially if you start looking at groups,

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Speaker 1: you know, in their eighties, people in their nineties or

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Speaker 1: even older, that's kind of an unusual group because we

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Speaker 1: know that their systematically, as they say, they were, tended

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Speaker 1: to be healthier, they tended to have more education, they

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Speaker 1: tended to be different than than other groups. And we're

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Speaker 1: looking at net worth here, right, so when we're thinking

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Speaker 1: of millionaires, it's not just people who have a million

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Speaker 1: dollars in their bank account, but also you know, hundreds

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Speaker 1: of thousands of dollars, and that this is the net worth. Great,

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Speaker 1: let's actually take a quick break. We'll take a quick

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Speaker 1: break to hear a word from our sponsor, and then

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Speaker 1: we'll come back to analyze some of these big overarching

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Speaker 1: trends and themes that this analysis have shown, which are

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Speaker 1: which are really interesting. Stay with us after the break.

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Speaker 1: is a CSTC regulated exchange with transparency, free market data

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Speaker 1: and fairness guaranteed innovations of financial industry needs and nat

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Speaker 1: X already has. That's why we think binary options are

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Speaker 1: the future of trading, and it's year now at n

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Speaker 1: A d e X, dot com, futures options and swaps.

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Speaker 1: Trading involves risk and may not be appropriate for all investors.

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Speaker 1: It seems like we are being seen here and the

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Speaker 1: odds for white people and Asians are almost universally better

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Speaker 1: than for Hispanic people and black people, no matter what

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Speaker 1: sort of combination of age and education you look at.

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Speaker 1: Why Why is that? That's one of the things we're

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Speaker 1: struggling with to understand. I think some of the ideas

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Speaker 1: that people have suggested include the impacts of very early

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Speaker 1: experiences Black and Hispanic children more often than white and

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Speaker 1: Asian children in the United States grow up in disadvantaged backgrounds.

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Speaker 1: So there is a lot of evidence that black and

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Speaker 1: Latino children enter school with with some deficits in terms

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Speaker 1: of readiness for school, and those deficits persistent under you know,

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Speaker 1: some measures. It looks like they even increase throughout schooling,

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Speaker 1: and so that could could point to maybe differences in

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Speaker 1: school quality. It appears that there are a lot of

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Speaker 1: you know, fairly significant, long lasting factors that maybe compound

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Speaker 1: over time. And so, you know, we also know their

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Speaker 1: differences in the in the college experience across race and ethnicity,

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Speaker 1: and it's unfortunate. But there is evidence that there continues

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Speaker 1: to be some discrimination in the job market, and the

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Speaker 1: famous examples of sending out otherwise identical resumes under different

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Speaker 1: names exactly. We mentioned this in our story. Yes, so

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Speaker 1: there is some evidence that that continues to exist. I

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Speaker 1: think a good exercise might be for us to discuss

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Speaker 1: our own probabilities to just put a little bit more

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Speaker 1: of a human face on all these numbers that we

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Speaker 1: read out. So personally, I'm bi racial, I'm half white,

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Speaker 1: half black. But given that our race options are white, Black, Hispanic,

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Speaker 1: and Asian, I'll just select black. For our exercise, I

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Speaker 1: am younger than forty and I have a bachelor's degree.

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Speaker 1: So Bill and Brian your analysis and the sample that

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Speaker 1: you analyze, there were sixty nine people who met this description,

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Speaker 1: sixty nine heads of household households. Not a single one

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Speaker 1: of them millionaires. Uh So, I mean we could say

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Speaker 1: that that's a zero percent chance, but we have to

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Speaker 1: think that this is you know, keep in mind that

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Speaker 1: this is a small sample, um, and you know, there

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Speaker 1: maybe someone like that in the US. But that's a

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Speaker 1: pretty accurate reading. Zero percent chances, probably accurate for me

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Speaker 1: because I'm nowhere close to having that much wealth. And

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Speaker 1: it makes sense because I'm young and I haven't had

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Speaker 1: time to accumulate that. But it also sounds a little depressing.

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Speaker 1: Um Aki, what's yours? Our only difference should be race, right,

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Speaker 1: That's right. So I am also younger than forty, and

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Speaker 1: I also have a bachelard's degree. Um, However, I am Asian,

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Speaker 1: so my chances are about five percent, you know, so

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Speaker 1: it's not like astronomically higher, but it's certainly better than

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Speaker 1: zero percent. And we thought what was interesting was then

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Speaker 1: to allow you to look forward and say, if you

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Speaker 1: don't change your educational status. What would you expect when

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Speaker 1: you're middle aged and older? And I thought it was

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Speaker 1: really interesting that there was one group that the best

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Speaker 1: probability for being a millionaire UM, which was also the

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Speaker 1: only group that had a probability of more than fifty

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Speaker 1: UM where there were enough families in the sample that

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Speaker 1: actually produce a reliable estimate. And and these were white

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Speaker 1: people who are sixty two years old or older who

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Speaker 1: had a graduate degree, and their probability was fifty two percent,

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Speaker 1: which I guess makes a lot of sense since you know,

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Speaker 1: these people have all three things going for them, age,

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Speaker 1: as you, Asian, and grace. So the way to maybe

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Speaker 1: think about it is the next time you meet an

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Speaker 1: old white person with an advanced degree, you'll know that

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Speaker 1: it's lightly and more likely than not that they're a millionaire. Right.

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Speaker 1: I think also the Asian older Asian Americans with postgraduate

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Speaker 1: degrees also in that ballpark. You know, another way to

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Speaker 1: think about that is that say, it's odds for a

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Speaker 1: white family in that category in a sense, that's nothing

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Speaker 1: special to have achieved that because most of your or

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Speaker 1: at least half of your peer group has done that,

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Speaker 1: and you know I think we often, uh, certainly people

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Speaker 1: who achieved some success would like, you know, it's just

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Speaker 1: a psychological naturally thing to do, is you think, well,

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Speaker 1: I'm responsible for that. But one of the things that

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Speaker 1: this whole research project is getting at as well, there's

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Speaker 1: a lot of, if you will, head winds and tail

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Speaker 1: winds associated with you know, who your parents were, as

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Speaker 1: Brian mentioned, when you were born, makes makes a difference

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Speaker 1: all the sorts of things that go into making you

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Speaker 1: able to achieve a certain level of education. And certainly

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Speaker 1: we're not saying that there's no role for individual effort

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Speaker 1: or that people do of course control much about their

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Speaker 1: their economic and financial lives. But these these numbers, you know,

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Speaker 1: as you're pointing out, are just make it, make it

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Speaker 1: I think more clear that as you said, you some

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Speaker 1: people hit the trifecta, you know, they get everything going

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Speaker 1: for them, and conversely, there are families or individuals who,

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Speaker 1: based on these demographic and socio economic factors, have nothing

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Speaker 1: going for them other than their individual effort. And I

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Speaker 1: want to go back to tracking our our percentages in

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Speaker 1: the future. So looking forward, I'm under forty right now.

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Speaker 1: If we move to the middle aged category, and I

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Speaker 1: keep my bachelor's degree. Um, I have a six point

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Speaker 1: four percent chance as a Black American of being a millionaire.

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Speaker 1: And then as I move into the old category sixty

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Speaker 1: two and older, still with a bachelor's degree, something interesting happens. There.

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Speaker 1: Doesn't look like there was enough data to calculate a

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Speaker 1: reliable estimate for that. What goes on there? What? What is?

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Speaker 1: Why is that happening? Because this happens in some other

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Speaker 1: data sets too, with some other permutations of the data,

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Speaker 1: there just aren't enough estimates to produce a reliable probability

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Speaker 1: for Yeah. So, um, I think you had alluded to before.

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Speaker 1: But this is actually surveyed out of this comes from

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Speaker 1: the survey sumer Finances. It's a triannual survey that asks

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Speaker 1: about six thousand families. I will say the unit of

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Speaker 1: analysis here is is the family. So we're asking, um,

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Speaker 1: kind of the the pool that the family has. UM.

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Speaker 1: But say, for example, you know, in prior generations, African

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Speaker 1: Americans did not you know, we're primarily excluded from a

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Speaker 1: lot of higher education. So it makes sense that we'd

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Speaker 1: see that this older category would you know, have very

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Speaker 1: few observations for that that group, that older group with

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Speaker 1: bachelors of graduate degrees. So it's it's tough to draw

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Speaker 1: inference on on these probabilities using that limited data. Yeah right,

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Speaker 1: and Tori, how do my chances improve? Okay, so for

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Speaker 1: an Asian with a bachelor's degree in middle age you

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Speaker 1: are at roughly and yes, very big jump and bachelor's

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Speaker 1: degree Asian old groups sixty two and older, we again

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Speaker 1: have we can have the threshold problem, um, not enough

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Speaker 1: households in the sample to produce a reliable estimate, but

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Speaker 1: it is still what we do see is pretty high.

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Speaker 1: We would need more data to be more conclusive, but

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Speaker 1: already that that big jump from young to middle ages

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Speaker 1: pretty remarkable. Wow. Well I look forward to aging, I

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Speaker 1: guess well. Bill Brian, thank you so much for joining us,

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Speaker 1: and first and form Ust for doing all this data

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Speaker 1: crunching for us. It's just a really interesting data set

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Speaker 1: and I hope our readers and listeners enjoy it as

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Speaker 1: much as we do. Thanks very much, Tory and Acky,

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Speaker 1: and thanks to you all for listening to Bloomberg Benchmac.

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Speaker 1: We will be back next week and until then you

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Speaker 1: can find us on the Bloomberg terminal on Bloomberg dot

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Speaker 1: and while you're there, please take a minute to rate

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Speaker 1: and review the show so more listeners can find us.

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Speaker 1: Feel free to tweet at Tory and Me and um

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Speaker 1: We'll do our best to actually send you your specific

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Speaker 1: probability for being a millionaire if you include your demographic

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Speaker 1: traits of your age and your race and your maximum

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Speaker 1: level of going. You can talk to us and follow

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