[MUSIC]
I want to turn to using the census in the American Community Survey to show
you some household data.
And this data, Pete's great about getting all these obscure data sets that you
have to jump through.
Ops, I'm not like that at all, I get data that's relatively easy to get,
and so the general table is very easy to get,
it turns out all this individual data in the census is amazingly easy to get.
And a lot of these microdata sets that are used, especially for people,
are pretty easy to get.
So I'm going to talk about two of our papers, the allocation of talent, and then
a new paper that we're working on race and economic well-being in the United States.
So this allocation of talent papers I mentioned with Chang,
and Erik Hurst, and Pete.
And it's motivated by another key fact, again, it's,
you kind of know it's true and yet it's good to be reminded that it's true.
So, look at the United States in 1960.
In 1960 and I gotta around the numbers,
95% of doctors and lawyers were white men, 95%.
86% of managers were white men, in 1960.
Whereas by 2008, it was more like 60%.
So, my favorite example of this, but there many, is Sandra Day O'Connor.
So, Sandra Day O'Connor is the first woman Supreme Court Justice in
the United States.
She graduates from Stanford Law School in 1953, number 3 in her class.
So, clearly a brilliant, brilliant, talented person.
First woman Supreme Court Justice, number three in her class at Stanford,
she can't get a job in the private sector except as a legal secretary, right?
Ruth Bader Ginsburg, same kind of story, right?
And closer to home, David Blackwell,
so David Blackwell's contraction mapping theorem.
I won't tell the story, I'll let you google it afterwards.
David Blackwell, eventually, became a professor at Berkeley.
But because of discrimination, it was a long time coming before he became
a professor at Berkeley, relative to what should have happened.
So, times of discrimination and barriers, and you just see it in the numbers,
it jumps out at you when you look at the numbers like this.
So here's just another version that it applies across high-skilled occupations,
more generally.
And, yeah, let me skipped that graph now.
So, the question we ask in this paper is the following,
suppose you think the distribution of talent in these
high-skilled occupations is identical for whites and blacks, men and women, right?
Well, what that means is in 1960, there were all these crummy marginal white men,
white men at the margin, who shouldn't have been doctors and lawyers.
And instead, Sandra Day O'Connor should have been taking their jobs, right?
That's what should have happened, and
yet, there were barriers that prevented that from happening.
And yet, over the past 50 or 60 years, evidently, those barriers have
changed somewhat because the allocation of talent has improved.
That's what the red bars are here, and that's the 60% number here.
And so, obviously, this is a growth paper.
So, what we ask is, how much of the growth between 1960 and
2010 was due to this improved allocation of talent, okay?
Due to these declining barriers?
And here's an example of how we sort of uncover the barriers.
So, what we've got on the horizontal axis is the propensity of white men
versus white women to be in an occupation.
So you can see, and this is in 1980.
In 1980, women are 64 times more likely to be secretaries than white men.
But if we look over here at lawyers, that's at about one-quarter,
young women were four times less likely to be a lawyer than white men were.
And so, they're 256 times more likely to be a secretary than a lawyer, right?
So, enormous differences in quantities, 256 times more likely.
And yet, look at the wage gaps.
The wage gaps are both 30% for those two occupations.
More, generally,
the wage gap is uncorrelated with this relative propensity.
And what this tells you, and
the lawyer, secretary example is really good at illustrating this.
The wage gaps for lawyers and secretaries is the same.
Women get paid 30% less than men by simple measures, there's not controlling for
a lot of things.
But the propensities are 256 times more likely.
So, if you want to see this discrimination across occupations,
you'll want to look at quantities, and not prices, okay?
And so, the way we're going to back out the barriers is we're going to say, look,
in a world with no misallocation,
you would expect 50% of lawyers to be men and 50% to be women.
The population shares, 50% of secretaries to be men, 50% to be women.
To the extent that the data don't look like that,
we're going to back out the barriers, these towels that Pete highlighted before,
that must be active in order to prevent women from being lawyers and
forcing them to be secretaries, in some sense.
Okay, so we write down a model, we do all the calculations, what's the bottom line?
The bottom line is the growth differences are really big.
So, actual growth in GDP per person between 1960 and 2010 was 1.7%.
This is a census number, so it's why it's not the 2% number, okay?
Had the barriers been unchanged, if we kept the barriers at the 1960 level,
growth would have been only 1%.
So the difference is seven-tenths of a percentage point, or 40% of growth.
So 40% of growth in GDP per person, according to our calculation,
is explained by this improved allocation of talent.
24% of GDP per worker, and 90% of the rise in labor force participation,
we just obviously do predominantly to women, and here earnings numbers, as well.
Let me skip over that now.
It looks like it's mostly human capital frictions instead of labor market
frictions.
It might be like 60, 40 in our benchmark calculation, I think.
Okay, then finally, the last project I want to talk about is this new project in
progress, I think we just posted a working paper and
started presenting it over the summer at the NBR.
It's a paper with Pete and with a graduate student at Stanford,
Jean-Felix Brouillette.
It's on race and economic well-being in the United States.
And, again, using census data and
other sort of available micro datasets that are really easy to get.
The idea here is to apply this beyond GDP paper that I just showed you.
Instead of applying it across countries,
let's apply it to different groups within the United States.
So, let's ask,
what's the consumption equivalent welfare differences between blacks and whites?
Or between white Americans over time, black Americans over time,
across groups and over time?
We're going to include consumption, life expectancy, leisure, and inequality.
Again, and in our robustness checks, I won't show you them today, or
our extensions, I should say.
We include treating unemployment differently,
looking at incarceration rates, those don't make huge differences,
the health quality differences, morbidity, differences that make a pretty big
difference, those show up as being important.
So here's consumption per person imputed from census data.
So, we use the CEX, I'll show you some CEX numbers in a second.
Consumer expenditure survey to figure out if we see your income,
how much consumption are you doing, and then we apply that to the census data.
So, we can go all the way back to 1940.
And you can see, the gaps are closing, somewhat, between blacks and
whites in consumption, but it doesn't jump out at you that it's closing a lot, right?
There's been lots of growth in both, but
the gap's not closing maybe as much as you might have thought, right?
I don't know, I'll show you exactly the gaps.
Here's the gap in life expectancy, there you see much more of a closing of the gap.
So, life expectancy for
black Americans in 1940 was just 54 years versus 66 years in the US.
So, something like an 11 or 12 years gap, and
by the end, it's sort of 5 or 6 years gap, I think.
So, life expectancy's close to that gap a lot.
So here's our consumption equivalent welfare number with the census in 1940,
black Americans had a consumption-equivalent
welfare about 28% of what white's had.
And that by the end, this is more like two-thirds, right?
So the two-thirds number, I think you can see it in two different ways, and
it's okay to have both of these things in mind, that's what I think you want to see.
The gaps are still large, two-thirds of white Americans today in 2019, right?
So there is a big gap between blacks and
white, I'll show you the decomposition in a second where it comes from.
Two-thirds versus one to the 33% gaps today,
but the gaps were much, much larger historically.
So there's been enormous progress, but there's still a lot more progress to
be had, and I think that's the picture you ought to take away from this.
Okay, here's the decomposition, you can see consumption's closing the gap a little
bit here, this doesn't really tell you, I forget the exact number,
it does close the gap, somewhat.
But a big piece of the closing gap is the life expectancy piece.
We don't have the inequality numbers here, we do it when we do the CEX numbers, but
the leisure and inequality numbers are pretty small.
They're not a big piece of the story.
It's really life expectancy, first, and then consumption second.
Another important point,
which is just that, so,
what am I doing here?
Look consumption per person in the United States in 1940, call that 1,
by how much did consumption per person in the United States rise?
So, today, it's a factor of 5.
We're 5 times richer, on average, measured in consumption per person units,
than we were in 1940.
That's economic growth, that's wonderful.
It turns out when you add in the gains from life expectancy, you get much,
much bigger numbers.
So for white Americans rather than this five-fold increase for all Americans in
consumption, welfare for white Americans increased by a factor of 13.
So 13 instead of a factor of 5 and for black American welfare, consumption,
political welfare, increase by a factor of 29.
So I think the factor of 29 works out to be a growth rate of about four and
a half percent per year.
The factor of 13 works out to be like 3.3% per year, and
5 times is something like a 2% per year growth rate.
And so, you can see the US economy has grown much
faster when you add in these gains to life expectancy.
Because, again,
each year of life expectancy is worth about 5% of consumption.
And we've added a lot of years of life expectancy, and so
those are incredibly valuable.
That's kind of one of the things I would say that shows up across a range of papers
that I've worked on, related to this topic.
Okay, if two minutes, let me talk for these two minutes about the data and
where you can get it from.
Again, the ChadMatlab.zip file has a bunch of the functions that you can call.
So, if you go to this webpage, I won't click through and show it to you.
It's remarkably easy to use, it's the IPUMS database, and they have
all the census data, annual censuses back to, I guess, a long way back.
We only use them back to 1940.
You can get a login very easily, you can download the data.
There's the American Community Survey, which after, maybe 2003 or 2004,
we started doing annual surveys that were smaller than the census, but
they're stratified random samples, and so
you can get annual data rather than just decadal data.
You can get all these variables, so, household income or family income.
You can get people's states and their zip codes, you can get how many
people live in their household, do they own their house or do they rent?
Just enormous, rich data in the census that you can download and
manipulate there if you want.
Hours worked, obviously, we use that to get our leisure numbers.
You then view you carts, select the data extract.
One thing I'll say, and this is not always, so,
the census datasets are really, really huge.
They're big, even if you're playing with, I think the 1% or
5% sample, you could get in the hundreds of megabytes and
obviously, if you want more, you can get even bigger.
And so, they allow you to customize your sample size when you're downloading
the data, and I really encourage you to do this.
If you select a 0.01% sample, that's only going to be 3 megabytes, and
then you can make sure the download worked, that it gave
you the variables you want, that your program runs on the small sample.
And then you can just go back and grab the 1% sample or the 5% sample or
whatever you want and get lots of data, do it for multiple years.
So, again, a program that I have included in the,
I guess it's on the Dropbox link, is this readcensus.m.
And this tells you in Matlab, again, which is what I tend to use,
easy code using, I think, it uses the table command.
Matlab, it's gotten more and more user-friendly because of
competition from Python, and Stata, and things like that.
And so the code for reading in large datasets and
treating them like tables or belonging in white,
then you can do in Stata, I don't use Stata, it's really nice there.
So, what would I say is the bottomline?
I would say, some takeaways are times of great publicly available data for
countries and for individuals.
This kind of census data, or this CEX kind of data or labor force survey data
you can get for lots of different countries very, very easily.
And then, I think the other thing I will encourage you is, Stata, for
example, is horrible about plotting graphs, it doesn't label axes well.
Really pay attention to your graph.
Something I always tell people is, I think, legends are evil.
So, when you have five lines in your graph, label each line, individually.
Don't have three, five different colors, and the reader has to look them at
the bottom and figure out what line is what, that's terrible.
I really care about making things easy for
readers to incorporate.
[MUSIC]