[MUSIC PLAYING]
JACQUELINE: Well, today
I'm delighted to introduce
one of Google.org's
newest partners,
and one of the world's
leading experts
on opportunity and mobility.
And also a personal
hero of mine.
My team knows I talk
about him all the time
as someone who I think is doing
some of the most fantastic work
globally around inclusion,
future of work inequity.
So we are thrilled
to actually say, just
today, that Google.org
is going to be providing
a $250,000 grant to help
support some of the work
that he is doing around
Opportunity Insights.
[APPLAUSE]
We don't do this with all of
our speakers, just so you know.
But a little background
on Dr. Chetty,
for those who don't know.
So he's a Professor of
Economics at Harvard University,
and he was awarded a
MacArthur Genius Fellowship,
and is the winner of the
John Bates Clark medal,
given to the best American
economist under the age of 40.
Raj's work asks, how
can we give children
from disadvantaged
backgrounds the best
chance of economic prosperity?
And one thing I find very
powerful about his work
is that he and his team
have also figured out
how to inject the data--
and more importantly,
the meaning--
into the central
narrative of this country.
And so his work has been
popularized, and been written
about, and is part of
conversations that probably
all of you are having,
without realizing perhaps
sometimes his work
is undergirding it.
So, for example, he has found
that the generation born
in the 80s is the
first in modern history
to have less than even odds
of outearning their parents.
I won't give away all
of your good stuff.
But a personal favorite of mine,
as the wife of a public school
teacher, that high-quality
elementary teachers
generate long term gains
for their students,
including higher college
attendance and larger earnings.
And that in 99% of neighborhoods
in the United States,
black boys earn
less in adulthood
than white boys who
grew up in families
with comparable income--
99%.
So this kind of work
is shaping not only
our work in policy in
academia, but the conversations
around America, and
why it's so important.
So we're really pleased that
Google's own chief economist,
Dr. Hal Varian, is going to
join us and do a fireside chat.
And so we're going to start with
Dr. Chetty coming up and giving
us some context.
Then we'll open
up to have a chat,
and there'll be lots of times
for questions from all of you.
So thank you.
[APPLAUSE]
RAJ CHETTY: Thanks
so much, Jacqueline,
for the warm introduction.
And thank you all
for being here.
I really appreciate the
generous support of Google.org.
Looking forward to
partnering with you.
So I'm going to tell you a
little bit today about what
our research group is focused
on-- broadly on restoring
the American dream.
And I'm going to talk about how,
in our own local communities
and our local
institutions, we can
make a big difference on
issues like inequality
and economic opportunity.
But I want to start at a
much bigger picture level,
by talking about
the American dream.
Which, of course, is a
multifaceted, complicated
concept that means different
things to different people.
But I want to distill
it to a statistic
that we can measure
systematically
in the data, which is a way
that the American dream has been
traditionally conceptualized.
The idea that through hard
work, any child in America
should have the
chance of going on
to have a higher living
standard than their parents,
or going on to earn
more than their parents.
And so what we're starting
out with in this first chart
is just an assessment
of whether that's
true in the United States today,
and how the American dream has
fared over time.
So what we're doing here is
calculating, very simply,
the fraction of kids who go on
to earn more than their parents
did, based on the year
in which they're born.
So as you can see here, for kids
born in the 1940s and 1950s,
it was a virtual
guarantee that you
were going to achieve the
American dream of moving up,
relative to your parents.
92% of kids born in
1940 went on to earn
more than their parents did.
If you look at what
has happened over time,
you see a very stark fading
of the American dream.
Such that, as
Jacqueline mentioned,
for kids born in the 1980s,
who are turning 30 today,
when we're measuring
their earnings,
It's essentially a coin
flip-- a 50-50 shot--
as to whether you're going to
do better than your parents.
Now this broad
trend, I think, is
not just of economic interest.
I think it's a fundamental
social and political interest
in the United States,
because I think
it underlies a lot
of the frustration
that people around the
US are expressing--
that the US is no longer a land
of opportunity, as it once was.
So motivated by that trend,
in our research group,
Opportunity Insights,
based at Harvard,
we're essentially trying
to tackle the big picture
question of, how can we
restore the American dream?
What is driving that trend?
And our approach has, really,
three central elements.
The first, which will be very
familiar to all of you here,
is that we try to harness
large data sets, typically
structured data coming from
government data sources--
census data, tax data,
things like that--
in order to study how to
increase upward mobility.
And I think this is
really a special moment
in social science where, much
as you all are using data
in your work to improve the
products that people have
access to, I think our
knowledge of social science
is going to be revolutionized
by modern big data in the coming
decades.
Second, we focus greatly,
as Jacqueline mentioned,
on not just doing this research,
but trying to translate
that research to policy change.
By trying to inform
public conversations,
or as I'll give you a couple
of examples of, increasingly
partnering with local
government agencies, colleges,
other institutions, to really
create change on the ground.
The third point,
kind of a departure
for what I'm going
to show you, is
that rather than
approaching the data
at an aggregate
or national level,
we really try to hone in on
specific geographic areas
or specific subgroups,
to try to understand
what the drivers of inequality
and opportunity are.
And so the starting
point for what
I'm going to talk about
today is that there
are very sharp local differences
in rates of upward mobility
that I think should shape how we
think about the broader problem
of the fading American dream.
And so I'm going to
start by showing you
that data with this map here.
It's just to give you
a bit of background
before I describe what it shows.
We construct this map using
data following 20 million kids
from birth to adulthood.
That's using anonymized
information from tax returns
connected to census records,
where we basically tracked
these 20 million kids--
essentially all kids born
in America in the early 1980s--
follow them to adulthood,
and look at how they ended up
doing in terms of their earnings
conditional on growing up
in a low income family.
And so what we're doing in
this map is taking that data
and assigning kids to locations
based on where they grew up.
We divide the US into
740 different metro
and rural areas.
And in each of those
areas we take the set
of kids who grew up there in
low income families-- that
is families of the 25th
percentile of the income
distribution, earning
about $25,000 a year.
We follow those kids
into adulthood and ask,
how much do the kids who grew
up in each of these places
earn, on average, in adulthood?
So think of this as
a way of measuring
how upward mobility varies
across areas in the United
States.
And so the map is colored
such that blue areas,
like in the center
of the country,
represent areas with higher
levels of upward mobility,
and red colors represent
areas with lower levels
of upward mobility.
So start by just looking at
the scale in the lower right.
You can see that there's a
dramatic range in kids' chances
of moving up across
the United States,
depending upon
where they grow up.
In the darkest red
colors, you see
that kids who grow up in
families earning about $25,000
a year, their own incomes,
on average, are about $27,000
a year in adulthood-- like
in a place like Charlotte,
North Carolina, for example.
In contrast, if you
look at the center
of the country, or a place like
Salt Lake City, for instance,
or some areas on the coasts,
you see numbers like 45,000.
Kids starting out in families
earning $25,000 a year
are, on average, ending up
earning about $45,000 a year.
So there's a very
broad spectrum in terms
of rates of upward mobility
within the United States.
That aggregate national picture
that I started out with really
is driven by a great
heterogeneity across areas.
Now naturally, the question of
interest to us as academics,
and to policymakers, is
to ask what is driving
this variation across areas.
And what, ultimately,
might we be
able to do to increase
upward mobility in places,
or for subgroups,
where we currently
have less economic opportunity.
One thing I'd like
to point out on that
is that one of the first
explanations that might come
to your mind is
maybe differences
in upward mobility
across places are
driven by differences
in the types of jobs
that people have access to,
or things like developments
in technology, and so forth.
As you can see from this map,
though, that doesn't entirely
appear to be the case.
Right?
So if you take an example
like Charlotte, some of you
might know that
Charlotte, North Carolina
is one of the most rapidly
growing cities in the United
States.
It's had one of the
highest rates of job growth
over the past 20 or 25 years.
Yet, as you can
see here, Charlotte
is one of the places in America
with the very lowest levels
of upward mobility, among
big cities, for the kids
who grow up there.
So how does that add up?
How can you both
have very high job
and wage growth, and have
very low upward mobility?
So what's happening in
places like Charlotte,
or Atlanta, or a number of
other high-growth cities
is that they basically
import talent.
Lots of people move to
Charlotte, move to Atlanta
to get those high paying jobs,
but they don't necessarily
grow up there.
And it's not always the case
that high job growth benefits
the local residents--
something we
see in many different contexts.
This is one example of that.
Now, in this big map,
your eye naturally
gravitates to the broad
regional variation.
Places like the Midwest look
better for upward mobility
than the southeast, or places
in the industrial Midwest--
like Cleveland, and so forth.
But what is striking,
and what we've
been focusing on
more recently in work
that we released a
couple of months ago,
is that there's substantial
variation-- not just regionally
across the country,
but even within cities
at very small geographies.
So to illustrate that, I'm
going to turn and flip over
to this tool that
we've developed, called
the Opportunity Atlas,
which you can access and--
sorry, if we can just--
there we go.
So you can access this you
know freely by just going
to opprotunityatlas.org,
and type in any address--
very much like a Google map--
and zoom in to any area.
So I'm starting out with the
map that I just showed you,
at a national level.
And now I'm going to zoom
in-- given local interest
here-- to the Bay
Area, and show you
the same data now, at a census
tract level in the Bay Area.
So census tracts are--
there's 70,000 census
tracts in the United States,
and each of them have
about 4,000 people.
So to give you a sense
of scale, a place
like Palo Alto is
itself divided into 17
different neighborhoods, and
what we're showing you here.
So this is a very
fine-grained information.
Same statistic that
I started out with,
take low income kids
growing up in each place,
and essentially calculate their
average incomes in adulthood.
There's some statistical
details in the background,
but that that's basically
what we're doing here.
And so the first thing I'd
like you to see on this map
is, if you look at
the Bay Area, you
see the same spectrum of
colors that I showed you
at the national level.
Right?
So you can go from
a place that looks
like Alabama or
Charlotte, in terms
of rates of upward
mobility, to a place that
looks like Iowa, within a few
miles within the Bay Area.
Now some of the
broad patterns here
will strike you as intuitive.
So you see, for instance,
that the peninsula, much
of San Francisco, has higher
rates of upward mobility
than parts of the East Bay--
in particular, Oakland.
Right?
But let me now zoom in further
to discuss this variation
in a little bit more detail.
In particular, to the areas
where we have large low income
populations-- in Oakland
and surrounding areas--
to show you how you can pick
out more granular variation
that I think illustrates some
of what might be going on here.
So if we look here
in Oakland, you
see that there's a very
sharp, almost a dividing line
between Oakland and San Leandro,
where the colors almost trip
discretely from red to blue.
So part of that is driven
by something Jacqueline
alluded to, which is
racial differences
in economic opportunity.
So what you can do
with these data is,
not just look at the
data in aggregate,
but look at specific subgroups.
So what I'm going to do
here is click on black,
to subset the data
to black Americans.
And you can see that some of
that divide is driven by race.
But even conditional
on race, there
are sharp differences
in Oakland,
relative to San Leandro.
So one thing I should
note, just as a side note,
as you pointed out, even
within a given area,
there are really
sharp differences
in rates of upward mobility
between blacks and whites--
and in particular, between
black men and white men.
If you look at black
women versus white women,
you see very similar outcomes.
But in particular, for men, race
plays an extremely important
role.
And so it's important
to see that even when
you focus on the
black population,
there's still this substantial
variation across very
nearby neighborhoods.
Now if I look
specifically at black men,
where you see some of the
most striking patterns,
as I was just saying.
And I'm going to look at black
men growing up in the very
lowest income families.
You see that there are certain
places here, for instance,
let me click this tract,
which is a neighborhood called
Fruitvale, in Oakland.
And you see some really
striking, I think,
disturbing statistics
about the United States.
So here you see that for black
men growing up in low income
families in Fruitvale, their
average income in adulthood
is $5,600.
Right?
And importantly here,
one thing I should note
is we're tracking these
men, no matter where
they live in adulthood.
So these are people who
grew up in Fruitvale.
They might not be
living there now.
Might be somewhere
completely different.
But among the set of
people who grew up there,
they have an average
income of just $5,600.
So how is that possible?
The way you get such
a low average income
is by having lots of people
who are not working at all.
And in particular, what you
can do with these types of data
is look, not just
add income, but look
at a variety of outcomes.
So if I click over here now to
look at incarceration rates,
you see a really
striking statistic,
which is that 43% of the
black men who grew up
in this particular
neighborhood are
incarcerated on a single day--
on the date of the 2010 census.
So if you think
about lifetime odds
of being incarcerated, growing
up in that neighborhood,
it's going to look like 60%,
70%, something like that.
Which is how you end up with
an average earnings of $5,600.
Right?
So the point here is that
there's this incredibly
granular variation.
At some level you might
react to that by saying,
I'm shocked by the
magnitudes, that we
see such adverse
outcomes, but maybe I
had some sense that there
were parts of Oakland that
are really tough places--
high poverty, high crime--
so qualitatively maybe I'm not
so surprised by that picture.
But what I think is perhaps
much more surprising is,
you can go to places that are
just a few miles south, like
down here in San Leandro, where
you have rates of incarceration
that are dramatically lower--
5% or 1%-- compared to the 40%
that we were seeing in Oakland.
Right?
And so the point I'm
trying to make here
is that when we think
about economic opportunity
in America, really,
we should be thinking
at a much more local scale.
It's not even about the Bay
Area versus other cities.
It's actually
within the Bay Area,
and within a few
neighborhoods that
are within a couple of
miles of each other,
that you see extremely
different outcomes for kids.
And so understanding what's
driving that variation,
in our view, can
potentially provide a path
to figuring out how you increase
opportunity more broadly.
So let me now,
with that example,
come back here and walk you
through some further analysis
we've done to try to
understand what's driving
that very local variation.
And ultimately, what we might
be able to do from a policy
perspective to change
outcomes in the red colored
parts of the map.
So the variation that I
was showing you in Oakland
occurs not just there,
but in many other cities.
And I'm going to focus
on a different example,
here in Seattle, where
we're doing a bunch of work.
Where this is showing
you the same kind of map
of upward mobility
in Seattle, you
see a similar pattern of a
very broad spectrum of colors,
going from red and the center of
the city to much greener colors
in places like Normandy
Park, south of the city.
All right?
So what is driving this
variation across places?
Why does upward mobility
differ so much across areas?
So I'm going to show you a
couple of results on that.
The first point
I want to make is
that most of the variation
in upward mobility
that we're seeing
across these areas
is caused by differences
in childhood environment.
So there are really
two key words there.
The first is the word caused.
As many of you might
know, in social science,
traditionally it's
very difficult
to distinguish correlation
from causation.
But my view is, with
the availability
of these large data
sets, as I'll show you,
we can make great strides
in distinguishing causation
from correlation.
And I'll illustrate
that in a second.
We think we're
actually able to show
that this is about the
causal effect of where
you're growing up.
Second, it's about
childhood environment,
and not about labor markets
or conditions in adulthood.
So the way we establish
those findings
is by studying seven
million families that
move across areas, and these
data that we're working with.
Rather than getting into
the statistical details
of that study, I'm going
to summarize what we find
with a very simple example.
Let's imagine a set of families
that move from the Central
District, which, as
we saw in the map
that I just showed you of
Seattle, kids who grew up there
from birth, they
have average earnings
and adulthood of $26,000 a year.
And take a set of families that
move from the Central District
to Normandy Park, which was in
a much bluer color on the map--
higher rates of upward
mobility for kids
who grew up there from birth.
So imagine now, set
of families that move
between these two places,
with kids of different ages,
starting with families who move
when their child is exactly
two years old.
So what we do is take those kids
who move when they're exactly
two, track them forward 30
years using the tax data,
look at how much they're
earning in adulthood.
And we see that, on
average, these kids who
move at exactly age two are
earning $39,000 when they're
adults, in their mid 30s.
All right?
So that's for the kids who
move when they're exactly two.
Now let's repeat that analysis
for kids who move when they're
three, four, five, and so on.
They make the exact same move.
What you can see is a very
clear declining pattern.
The later you make that move
from the Central District
to Normandy Park, the
less of a gain you get.
And if you move after
you're in your early 20s,
the relationship
is completely flat,
and there's no impact at all.
So what do you see
from this chart?
I think there are really
three key takeaways.
The first is that, apparently,
where you grow up really
matters.
Places have a causal effect
on kids' long term outcomes.
It's not just that
different people
live in different places.
Apparently, if you
move a given child
to a place that's
a few miles away,
you can see very different
life outcomes for that child.
That, I think, is a
very encouraging result
for trying to tackle
problems like poverty
and inequality,
which we've all been
concerned about for decades.
Because it shows that the
answer doesn't necessarily
lie in switching to a completely
different economic structure--
like Scandinavian countries,
or something like that.
Often the answer
can be five miles
down the street, where you see
completely different prospects.
Second, you see that what
really seems to matter here
is childhood environment, rather
than conditions in adulthood.
So things like jobs and
labor markets, you know,
obviously they matter.
But where you're growing
up, childhood conditions
seemed really central.
And third, you see
that every extra year
of exposure to a better
childhood environment
improves kids' outcomes.
It's not just the
very earliest years
that matter, which is often
emphasized, especially
recently, in policy circles.
But also, if you move to a
better place when you're 10,
instead of you're 20, there's
still a substantial gainful
to be had.
So environment matters
throughout childhood, not just
in the very earliest years.
So how can you use
these types of data
to actually create policy change
to increase upward mobility?
So given what I've just
been showing you here,
one thought that might
come to your mind
is, what if we help low
income families move
to better neighborhoods
that we see
will potentially generate
better outcomes for their kids?
Now that's not going to
be a scalable solution.
You can't move everyone.
But it is actually something
practical to think about
on a small scale.
So just to illustrate
how we're translating
some of this research to
policy change, this map--
we're now showing the
same map, but we're
overlaying where families
that receive housing vouchers
in Seattle most commonly live.
So we spend about
$45 billion a year
in the US on various
affordable housing programs.
And what we see
here is that most
of those vouchers that people
get from the government
are used to rent housing in
pretty low opportunity areas.
They're clustered in the red
colored parts of the map.
So in light of this evidence,
we've been working with
the Seattle Housing Authority,
and the Housing and Urban
Development Agency in the US,
to develop a pilot program which
we're testing using a RCT-- a
randomized controlled trial--
where we're helping families
with housing vouchers
move to high opportunity
areas by providing them
information, by
recruiting landlords
to make them more
willing to rent
their apartments
to these tenants,
and providing
housing broker search
services to help you find units
in these higher opportunity
areas.
And while I don't have
results to show yet,
preliminary signs are this
is working incredibly well.
At essentially no incremental
cost to taxpayers,
you can help families access
much higher opportunity
areas where their kids
will do much better.
So this is a
concrete illustration
of how you can take this
type of research and data
and translate it to
change on the ground.
Now I want to take a
couple more minutes
to discuss a
different direction.
So this is, I think,
a solution that's
relevant for some families.
But as I was saying
earlier, it's not scalable.
You can do this, maybe, for
a couple of million families,
but you can't move everyone.
So the deeper question is, how
can you improve upward mobility
in the places that
currently have
low levels of upward mobility?
And so that's what
our research agenda
is centrally focused on now,
and in the coming years.
As a first step, one useful
way to think about it
is, what are the
characteristics of areas where
we see high levels of mobility?
Are there certain common
features of those places?
And the answer is yes.
They tend to have four
systematic characteristics.
They tend to have lower poverty
rates, more stable family
structures-- so a larger
share of two parent families.
They tend to have higher
levels of social capital--
so think of the old adage
that it takes a village
to raise a child.
Places with a lot
of social capital,
for instance, Salt Lake
City with the Mormon church,
those types of places tend to
have high levels of mobility.
And then finally, as you
might expect intuitively,
places with better schools--
both at the K through 12 level,
and access to high quality
higher education-- tend
to have higher levels
of upward mobility.
So this gives you some sense
of the types of areas I
think we should be focusing on.
It doesn't necessarily
tell you, though,
what exact interventions you
should implement in order
to increase economic
opportunity.
And so the direction that we're
headed in is partnering with
many cities across
America-- in this case,
I'm giving you an
example from Charlotte--
where we're using this
Opportunity Atlas data
to ask what types
of programs, what's
going on in neighborhoods
in your city
where we're seeing
much better outcomes?
Can we then learn something
about interventions
that might work, by
testing new things,
or looking at historical
data about interventions
that have been attempted and
looking at the outcomes that
have emerged?
OK, so let me wrap
up, since I want
to make sure we have time
to get to your questions,
by just making one
final point here.
Which is, usually we
think about issues
of equality of opportunity from
the perspective of principles
of justice.
Everyone should have a
shot at the American dream,
no matter what
their background is.
But improving opportunities
for upward mobility,
I think it's important
to keep in mind,
can also be important simply
for increasing economic growth.
And so to illustrate
that, I want
to show you one
final piece of data
which focuses on a specific
pathway to upward mobility
that's very relevant here
in Silicon Valley, which
is innovation.
And so to do that,
we're going to study
the lives of 750,000
patent holders in the US
by linking the universe of
patent records to the tax data
that we used in the previous
studies that I described.
So I'll start out with this
chart here, which shows you,
in some sense, your probability
of becoming an inventor,
using patents as a proxy.
I recognize that has
some limitations.
But plotting your probability
of having a patent in your mid
30s-- by the time
you're 35, or so--
versus your parents' income.
And you can see
that if you happen
to be born to parents in the top
1% of the income distribution,
you're about 10 times as likely
to go on to become an inventor
as if you happened to be born
to parents below the median
of the income distribution.
Now, one potential explanation
for why you see this big gap
is that this is about
differences in environment,
and resources, and schools--
the types of things
I've been talking about here.
A different explanation
is that this is
about differences in ability.
Perhaps the kids born to parents
at the top 1% of the income
distribution-- you
know, your parents
had to be pretty talented
in order to get there.
There's genetic transmission
of ability across generations,
maybe that's why you're more
likely to become an inventor.
One way to distinguish
between those two explanations
is to use data on test scores
early in childhood as a way
to get a rough sense of whether
aptitude seems to matter here.
And so this chart--
same y-axis, we're
plotting pattern rates--
but now versus your third
grade and math test scores,
as opposed to your
parents' income.
And so you can see
here that if you
happen to be at the top of
your third grade math class
you're much more likely to go on
to have a patent than if you're
below something like
the 90th percentile.
Now, more interesting for
what I'm talking about here
is, if you then split
that out, looking
at kids from high income
families in the green--
families in the top 20%--
versus low and middle
income families in the gray,
you see a striking pattern.
Which is that high scoring
children are much more
likely to become
inventors if they're
from high income families.
If you're from a
low income family
and you're at the top of
your third grade math class,
it doesn't do a
whole lot in terms
of your odds of
becoming an inventor.
So to put it differently,
apparently, in America,
in order to become an inventor
you seem to need two things.
You need have high
quantitative aptitude,
as measured by your
third grade math test,
and you need to be from
a high income family.
And so you can see why,
from that perspective,
increasing equality
of opportunity
could potentially be of
benefit to all of us.
Bringing these talented
kids through the pipeline
wouldn't just benefit
them but would potentially
benefit all of us by increasing
the amount of innovation.
And so let me end
by just quantifying
the magnitude of that.
If women, minorities, and
children from low income kids
were to invent as the same
rate as high income white men,
the data that I've
been showing you
imply that the patent rate, or
the innovation rate, in America
would quadruple.
So there's a tremendous amount
of talent, in some sense,
being left on the table.
And I think figuring
out a way to harness
that talent would be of
great benefit to those kids,
and more broadly help us
restore the American dream.
So let me stop there.
Thanks very much.
[APPLAUSE]
HAL VARIAN: We'll have
a few questions from me.
And then I'll turn to Dory,
and turn to the audience
here in the room.
So we've got a lot of
engineers here at Google.
And everybody wants to
know what technology did
you use to build the Atlas--
briefly.
And what was the most
difficult part of it?
What would you like
to see improved?
RAJ CHETTY: So we
were most focused
on the statistical analysis.
Right?
So we're doing a bunch
of work internally
at government agencies,
Census Bureau, tax data,
just constructing these big
tables that underlie what
was visualized in those maps.
We then worked with a firm
called Darkhorse Analytics that
does data visualization, that
created the actual interface
building off of a mapbox tool.
And I think the limit
of how much I'll
be able to talk about how that
data visualization happened
is very low, compared to what
all will know about this.
But what would we
like to see improved?
I mean, I think we've been
encouraged by the fact
that half a million people
have visited that site,
and various policymakers
are using it, and so forth.
My sense is that
data could be used
in lots of other applications.
So having it pulled
into, you know,
situations where
people are searching
for housing or other
types of things,
kind of day to day practice,
would be really useful.
The other thing I think that
we've struggled with a bit
is, there's a tremendous
amount of information there.
Lots of different variables,
lots of different ways
to cut the data, and so forth.
So how do you balance presenting
that information in a way that
is comprehensive, yet
is not overwhelming
for the typical user?
And, you know, this
is where, of course,
Google excels, in clear
presentation, things like that.
I think thinking
about ways to do
that would be really valuable.
HAL VARIAN: One thing we have
announced, a few months ago,
is this Google Public
Patent Database,
which actually joins
together all the patent
offices in the world.
So you can look at the US,
China, Japan, and Europe
in a unified way, and do all
sorts of nice cross country
comparisons on the innovation.
RAJ CHETTY: Yup.
HAL VARIAN: But as
I looked at this,
as a data person myself, how do
you get kids' third grade math
scores from 40 years ago?
RAJ CHETTY: Yeah.
[LAUGHTER]
So I didn't go into
the details there.
HAL VARIAN: And what
was mine, by the way?
[LAUGHTER]
RAJ CHETTY: So
that specific data
was for all kids who went to
New York City public schools.
So for a separate
study-- which is actually
what Jacqueline was
mentioning, you know,
showing the long-term
impacts of teachers-- the way
we did that study was
by linking data for two
and a half million kids
who went to New York City
public schools between 1989
and 2009 to the tax data.
New York City happened to
maintain on tapes, et cetera,
information on kids test
scores going way back.
We got that digitized.
It was a whole process to link
everything, as you can imagine.
But, you know, that shows
you the potential of what
you can do, I think
especially when
you combine these
different data sets.
HAL VARIAN: I just read a paper
a few days ago by Stephen Rose,
who's at the Urban Institute.
And it's a meta study of
looking at different measures
of inequality.
So, for example,
Piketty and Saez--
who I know you know well--
found that between '79 and
2002 medium income growth
declined by 8%.
But then they came back
and visited it this year--
Piketty, Saez, and
Zucman showed a gain
in real median
income of 33% when
you account for taxes,
transfers, price index
choices, and household size.
And this is similar to many
other findings, of course,
the Congressional Budget
Office and other groups
have studied this, and so
much depends on what exactly
goes into this estimate
of household income.
So I think that figures
you were showing us
were all numbers without
including transfers, right?
RAJ CHETTY: Yes,
straight earnings.
HAL VARIAN: What's
your view on this?
How should you account for the
existing transfer programs?
How should you account for
changes in real income?
Household composition
is another biggie.
I'd be very interested hear
what you had to say on that.
RAJ CHETTY: So my general
view on questions like that,
Hal-- you know, issues
of measurement--
is it really depends
upon what question
you're seeking to answer.
Right?
So there's no globally
correct measure.
It depends upon what question
you're seeking to answer.
So in this context, the
fading American dream,
we chose to measure earnings
in our baseline analysis
at a pre-tax earnings
level, rather than including
taxes and transfers.
The reason we did
that is, we felt
the notion of the American
dream, at some level,
was the idea of being able to
do better than your parents,
in terms of your own
earnings, perhaps,
rather than getting transfers
from the government.
Of course, you could define
it in different ways,
and then the appropriate
statistic would differ.
I think one thing
that's clear, regardless
of the 8% decline
or 33% increase,
is that there's been
much, much larger gains
at the top of the distribution
than in the middle
of the distribution.
Right?
No doubt, even 33%
pales in comparison,
relative to the gains
that people in the top 1%
or the top 5% of the
distribution have experienced.
And that, I think,
is very important,
and connects to what I was
showing you at the beginning,
on the fading American dream.
The fundamental
driver of why you only
have 50% of kids doing better
than their parents today,
as opposed to 90% for kids
born in the '40s and '50s,
is that the way in which
economic growth is now
distributed is extremely
different from the way
economic growth was
distributed in the past.
So growth rates, as you know,
are a little bit lower today
than they were in
the 1950s and 1960s.
But we show in that paper that
if you had the same growth
rates that you had in the past--
high growth rates like we had
in the '50s and '60s--
but divided that growth in the
way that it's shared today,
across the income distribution,
you would stem only one third
of the decline in the
fraction of kids doing better
than their parents.
Most of the decline
is due to the fact
that median wages,
however you measure it,
have largely stagnated
relative to growth at the top.
HAL VARIAN: So there's
this other side--
your colleague Chad Jones has
written a very interesting
paper about why we saw such
increases in productivity
during the '60s and '70s.
And this was-- potentially,
he doesn't establish this,
but by simulation-- it's
potentially the fact
that lots of barriers
broke down for women
and for African-Americans,
and they
were able to enter the labor
force, dramatically increasing
productivity.
But at the same time, they
were pulled away from the jobs
as teachers, and librarians, and
other low income service jobs.
So what was an opportunity
for one group of people,
turned out to be a
problem in terms out
as the community they served.
So you have to balance these two
interests to really understand
what kinds of behaviors
should be improved.
RAJ CHETTY: And I think
that's very interesting, Hal.
I mean, there might
even be dynamic effects.
Right?
So if you imagine--
people have argued that
in the past the quality
of the teaching
workforce was greater,
because you had many
high-skilled women who would
choose to go into teaching,
whereas now they don't.
And that, as you noted, could
have potentially contributed
to greater productivity
in the '50s and '60s.
But when you now think about
the current generation,
and you think about the
long-term impacts of teachers,
it may actually have adverse
effects on productivity now.
HAL VARIAN: Yes.
RAJ CHETTY: Yeah.
HAL VARIAN: One of the
interesting findings
that you said, when high income
people move into an area, that
displaces, or
potentially displaces,
people who are already there.
On the other hand, there been
other studies from, Moretti
and so on, that show that
when you see high tech,
high paying firms
move into an area,
five new jobs are created
over the course of a decade.
So there's a lot of--
I suppose I shouldn't call
it trickle down-- but trickle
across where you bring
economic resources in an area,
it tends to impact most
of the participants.
RAJ CHETTY: So I think
that's absolutely right.
This is something we're studying
in more detail at the moment.
Basically, what are the effects
of a new company coming in--
Amazon, of course,
the new headquarters,
things like that--
what impact would that
have on the people
who were already there?
So the fundamental
advance we're able to make
with the types of data
I've been showing you today
is that we're able to look
at things longitudinally.
You're able to track a
given person over time.
Why is that important?
You might see that
when a company moves
in, lots of new jobs
are created, et cetera,
the place starts to look
much richer, in terms
of the average incomes
of the people who
are there, and so forth.
A lot of that could be
driven by migration, right?
Lots of new people
come in, lower income
people get displaced, the
place looks much better.
But have you actually improved
the outcomes of the people who
were actually there?
We actually, I think, do not
really know the answer to that.
What I've been showing
you here is that,
at least for the kids
growing up in those places,
it's not a one to
one thing, that they
capture those benefits.
And that's because there's
so much mobility in the US.
You know, of course
lots of people
are going to move
to Silicon Valley
to get the great
jobs that are here.
It doesn't necessarily mean that
the kids growing up in San Jose
are going to directly benefit.
And I think we have to
think hard about how
you make that connection.
I think companies like
Google can potentially
play a role in that, thinking
beyond attracting the best
talent.
How do you cultivate
the talent in the areas
where the company is?
HAL VARIAN: Yeah, and I
think this mobility, which
you referred to,
geographic mobility
has really slowed
down significantly
in the last few years.
It's much harder, much
more expensive, much rarer
to see lower income people
moving into high income areas,
because, of course,
they can't afford them.
RAJ CHETTY: Too expensive.
HAL VARIAN: Yeah.
On this social responsibility,
when Google was, of course,
opening a lot of offices
in areas around the US,
what do you think--
what would you
recommend or suggest
that we could do to
help be a positive force
in the local communities
in those areas?
RAJ CHETTY: I mean, I
think one thing we find--
so let's go back to the
inventors data for a minute.
So one of the
interesting patterns,
which I didn't
discuss, is if you
ask, what is driving these sharp
differences in innovation rates
across low and high income
families, a lot of it
seems to be about exposure.
So let me give you
the following example.
If we take two kids who
currently live in Boston,
and let's say they're at MIT.
And one of those kids
grew up in Silicon Valley,
and one of those kids
grew up, let's say,
in Minneapolis, which has
a lot of medical device
manufacturers.
We see that the kid who
grew up in Minneapolis
is much more likely
to go on to have
a patent in medical devices.
And the kid who grew
up in Silicon Valley
is much more likely to go
on to patent in computers.
More generally, the chances
that you become an inventor,
and the field in
which you innovate,
are greatly influenced by the
area in which you grow up,
and the the environment
in which you grew up.
And that operates in
very specific ways.
So when women grow
up in areas where
there are a lot of female
inventors in a given field,
they're much more likely to go
on to innovate in that field.
But if there are more male
inventors in that field,
it has no impact at all on
women's innovation rates.
And so all of that,
to me, suggests
that what matters is exposure.
And so if you think about
a company like Google
going into a new area,
of course there's
going to be a lot of
attraction of top talent
from other places.
But thinking about
how you develop
programs so that you
connect with the kids
in local schools--
you know, you might
have mentoring programs
or other things that provide
that sort of exposure
in a scalable way--
I actually think, designed
properly and tested carefully
with these types
of data, could have
quite a substantial impact.
HAL VARIAN: And we have done
some programs like that.
I was talking,
actually, at my bank,
and the teller said,
oh my gosh, my kid's
taking your computer
course from Google.
I never even knew there was
one, but we've been doing it
for several years.
RAJ CHETTY: Maybe
evaluating the impacts
of those things, figuring
out how to scale them,
would be quite interesting.
HAL VARIAN: Yeah, that can
have a really big impact,
I think, from Saturday
morning programs,
after school
programs, and so on.
One thing that we've
been looking at a lot
lately is future work issues.
And as you know, there's a
lot of issues about changing
demographics, and
composition of work,
what kind of job opportunities
are going to be available.
Has any of your work shed
light on some of those issues?
Or is that something
that's for the future?
RAJ CHETTY: Let
me say two things
on that-- one that relates
to our work, and just
a broader reaction.
So one of the things
we find in our work
is, what seems to predict
longer term success,
especially in the
current economy,
is not just technical skills.
I gave you an example with
math test scores, for instance.
But more broadly, if you look
at so-called non cognitive
skills--
so, measures of social
skills, how well do you
get along with others,
discipline, and so forth--
those are actually
more predictive
in the general
population of longer term
success than technical skills.
And I think that's going to
be especially relevant going
forward, as man
is, in some sense
competing with the machine,
it's in the more social kind
of nebulous dimension--
interacting with
people, and so forth--
where I think there are
big returns to be had.
And so figuring
out how you provide
those skills, in addition to
technical skills-- or some work
showing that it's really the
combination of those two things
that's critical for
success in the long run.
So I think thinking
about how we--
education is not typically
structured with that in mind.
Traditional systems
of education are
about teaching a certain
set of technical knowledge.
I think figuring
out how we adapt
to that is very important.
My broader reaction
is, people, I think,
are rightly concerned
about the future of work.
But I would note that
100 years ago, people
were also extremely concerned
about the future of work,
and thought that
once we had cars
and all these other machines
there'd be nothing to do.
And I think humans
are inventive people,
and we will figure
out other things
to do as the economy evolves.
HAL VARIAN: Yeah, another
important point is that
the economic dynacism we saw
in the '60s and '70s from
the entry of men and women
into the labor force--
baby boomers and women who
were busy being able to take up
opportunities outside
the traditional jobs--
that's all running
in reverse now.
The baby boomers are
retiring, and women's
participation the labor force
has leveled out, or even
gone down a little bit.
So what we're
seeing is a society,
in most developed
countries, that's
really aging very rapidly.
And you'd expect to see
maybe less dynamic behavior.
Might see less support for
education, for example,
from that political change.
RAJ CHETTY: Possibly, possibly.
HAL VARIAN: And there are
big changes, geographically,
between what's happening,
let's say in Utah and Nevada,
and what's happening in
Wisconsin and Michigan.
RAJ CHETTY: Absolutely.
HAL VARIAN: So quite
interesting areas.
AUDIENCE: Thanks so much for
sharing beautiful insights
based on the data.
You talked about red
pockets in blue areas,
like the Central
District in Seattle,
or Oakland in California.
What does the picture look
like in the Carolinas, Georgia,
Alabama that are largely red?
What's the notion of moving
to a better neighborhood
five miles down the road?
RAJ CHETTY: Yeah, yeah.
AUDIENCE: What
does it look like?
RAJ CHETTY: Good question.
I could show you a map,
or you could look yourself
by going to this website,
at a place like Charlotte
or Atlanta.
And you would see very much
the same picture, actually,
that I showed you in Seattle.
Which is what I think is
the encouraging thing.
So when we've done
this work, kind
of zooming in over time to
narrower geographies as we've
had better data.
So we put out a study
comparing, basically,
cities at the metro area
level about four years ago,
and Charlotte was ranked
50th out of 50 in that list.
And they were both
unhappy about that,
but also motivated to try
to do something about it.
But at that point,
all we could say
is, Salt Lake City looks
much better than Charlotte.
Which was kind of
interesting, but what do you
do about that, right?
What's more interesting, I
think now, in what we've put out
a couple months ago, is
that you see that even
within Charlotte, there are lots
of places that actually look
like the best places in
Seattle, or Iowa, et cetera,
even within Charlotte itself.
So it's not entirely
a sea of red.
And I think that's
extremely important,
because it shows
people in Charlotte you
can look within your
community to figure out
how you potentially expand
those opportunities elsewhere.
HAL VARIAN: What about
the Dory question here,
on who becomes an
inventor in America?
This company's
demographic disparity--
what would you
recommend to bridge
this gap between the
socioeconomic issues
that impact innovation and
what we're trying to do here?
RAJ CHETTY: I mean, I
think it comes back--
some of it can be done at
the level of a company.
I think some of it comes
back to social issues
that the government and the
broader public has to tackle.
Right?
So what I will say--
looking at this question on
disparities in demographics,
one thing that comes to mind
is, if you don't do anything,
these things are changing,
but at an extremely slow rate.
So we see, for instance, if
you look at gender disparities
in innovation, 14% of patents
in the US currently go to women,
and that's changing at a rate
of a quarter of a percentage
point per year.
It's increasing at a rate
of a quarter of a percentage
point per year, so if
you just do the math,
that means it's going
to take another 140
years to reach gender parity at
the current rate of progress.
Right?
So the question is, how
do you accelerate that?
At some level I feel like it's
kind of a chicken and the egg
problem, where if there were
more women in innovation
in companies like
Google, I think
more girls would be inspired
to pursue those careers.
So the question is how you
get that cycle started.
I think some of it comes back
to some of the broader factors
that I was talking about
on other dimensions,
thinking about
racial disparities,
socioeconomic
disparities, schools,
the segregation in
our neighborhoods,
the lack of contact between
low and high income people,
things like that.
HAL VARIAN: I would be
very interested to see
what happens to schools
in New York, where
Amazon is moving in.
RAJ CHETTY: Yeah.
HAL VARIAN: There'll
be a proliferation
of private schools, of course.
There'll be a proliferation
of charter schools.
And, of course, the
New York educational
schools, which are
pretty good compared
to the national average.
Question here.
AUDIENCE: Hi, thanks so much
for coming to speak with us.
One thing that I
thought was really
compelling about your
talk was the focus
on from big data
to hyperlocal data,
and how we can begin
exploring those kinds
of interesting
topics, to figure out
ways to change our
economic mobility.
At Google I'm talking strategy
analytics in the human capital
side, so it's also
interesting from there.
But outside of work I run a
nonprofit called Consult Your
Community, that helps
college students work
with small business owners to
kind of focus on this space.
So my question for
both of you is,
what's the role in local
nonprofits and community
builders in helping
accelerate this change
from a different angle?
RAJ CHETTY: So I think
local nonprofits,
as you can see
with these data, I
think they can play
a huge role, given
that the scale of the problem
appears to be fairly local.
What I think would be
really useful to do,
and what we're trying
to do with our group,
Opportunity Insights,
with these data is,
connect with lots of nonprofits
like Big Brothers Big Sisters,
other groups like that
that are doing things
in mentoring or racial issues,
criminal justice, et cetera,
to try to evaluate more
systematically what
has the most significant
impacts in the long run.
Right?
I feel like there is a bit
of a disconnect between lots
of people who are doing really
good work on the ground, where
they have a sense that
this might be working,
but how do we scale that?
How do we think about,
in a cost effective way,
which aspects of different
programs are successful?
I think these sorts of data can
be useful in figuring that out.
And there's a disconnect
between those people
on the ground and
researchers who
may not be as aware of
the various programs
that people are
trying, and so forth.
So one of the things
we're trying to do
is bring those things
together, and hopefully
have more to say about
it in the coming years.
HAL VARIAN: What about this
question on the correlation
causation issue?
An alternative
hypothesis might be,
parents who are really
interested in the kids
might move earlier.
RAJ CHETTY: Yup.
HAL VARIAN: And by
the way, as you know,
there are studies of
immigration, where immigrants
tend to be highly
motivated people who
make decisions and move.
And is it really the
immigration per se,
or is it those characteristics
of being highly motivated?
RAJ CHETTY: So this
is a great question--
gets at kind of the heart
of the statistical issues
in the paper.
This is referring to the graph
I showed where the earlier
you move to Normandy
Park, in that example,
versus the Central
District, you appear
to have better
outcomes in adulthood.
So natural concern
you might have,
maybe the people who moved
when their kids are younger
are more motivated,
they're more educated,
maybe they're different
in various ways.
That's why their kids
earn more in adulthood,
not because they're growing
up in Normandy Park-- that
would be a natural confound
we would worry about.
Give you a sense of how we can
deal with that kind of issue.
So with the amount
of data you have,
you can compare siblings
within the same family.
So the way we would talk about
that in a regression context
is put in family fixed effects,
so you're only comparing kids
within the same family.
And so effectively what
you're doing there is asking,
suppose I move to Normandy
Park with a six-year-old
and a 12-year-old.
I now compare their outcomes.
What is remarkable is you get
exactly the same chart back
if you only rely on
within-family comparisons.
So in other words,
your younger kid
does better than your
older kid exactly
in proportion to the age
gap between the two kids.
So if you think about,
could it be a confound,
that eliminates a
lot of possibilities
that it's about
different families who
are moving when their kids are
young versus old, et cetera.
And so we do a bunch of other
things along those lines,
including actually studying
a pure randomized experiment
called the moving to
opportunity experiment, where
housing vouchers were
randomly assigned,
and we show you that
you get this pattern.
So I think we're quite
convinced that this really
seems to be a causal effect.
HAL VARIAN: Excellent.
Jackie.
AUDIENCE: Yeah,
I'm wondering what
you think the role is
of government policy
in creating some of these low
opportunity neighborhoods.
So for example, a
lot of, I think,
high concentrated
poverty communities
are housing projects
where government policy
requires you not
to have two parents
and requires there to be this
high percentage of exactly
the opposite of
all the things you
said are important-- low social
capital, and all of that.
So the role of government
policy and/or market policy--
for example, redlining districts
and not allowing people to
move to opportunity.
RAJ CHETTY: Yeah.
Yeah, I think
government policies--
inadvertently perhaps,
and historically perhaps
by design--
have led to tremendous
segregation that have basically
made it hard for people
to access opportunities.
Let me give you two
concrete examples of that
that we are thinking about.
So as I was saying earlier,
we spend $45 billion a year
on affordable housing in the US.
You would think the
intention of that
is to try to provide people
access to housing that will
give them better opportunities.
In practice, I
think unfortunately,
much of that
expenditure is working
in the opposite direction.
So I gave you one example with
the housing vouchers, where,
for whatever
reason, families are
using these vouchers to live in
pretty low opportunity areas,
thereby basically exacerbating
the problem of a lack
of intergenerational mobility.
A different example of that,
which gets to government policy
design, something called the
Low Income Housing Tax Credit,
where we spend about
$6 billion a year
giving developers tax credits
to develop affordable housing.
But the way that
program is structured
is, we give you
bigger incentives
if you develop housing
in high poverty, what
end up being relatively
low opportunity areas.
So we're basically
concentrating poverty further.
If you overlay where these
light tech developments are
on our maps, they're all in
very low opportunity places.
And so I think that is, unless
you think those developments
are going to change the level
of opportunity in that area--
which is something
one can look into--
you might actually be
exacerbating the problem
through the design
of that policy.
Another example which will
be familiar for folks here
in Silicon Valley is zoning and
the ability to build housing.
Right?
So ultimately, you can
move people around,
but supply is a
fundamental constraint.
Like, if you can't build any
housing in the Silicon Valley
area, that's going
to prevent people
from being able to live in
places where their kids would
do very well.
There are serious political
economy problems there,
where if you already own
a house in Silicon Valley
you might not want to vote for
more building, and so forth.
But I think figuring out how
you get around the zoning
restrictions so you have
more density of development
and more mixed
income communities
is another area where
one can have huge impact.
HAL VARIAN: Let
me-- actually, we
talked about this first
question here already,
before the meeting.
But why don't we go to this one?
Susan Popkin's research
suggests adverse effects
for children due to
the disruption that's
caused by moving.
RAJ CHETTY: Yeah.
HAL VARIAN: How do you view that
in terms of this prescription?
RAJ CHETTY: Yeah, so the way
I would think about the moving
idea is not that we're going
to uproot families that
are happily living
in a place and ask
them to move somewhere else.
The way this actually
works-- for instance,
the Seattle experiment I
was describing to you--
is we take a set
of families that
have come into the
housing authority,
they're looking to move.
20% of low income families
move anyway every year.
And I think about it as kind
of redirecting the flows.
You've decided to move.
You're going to move somewhere.
Let's give you the
information and support
to potentially move to a place
where your kids will thrive.
Not let's go find somebody
who doesn't want to move
and encourage them to move.
So that's the way I think
about this issue, kind
of in a narrow sense.
But the broader question is, how
can you revitalize communities?
Which, of course,
is the big issue.
And some of it, you might
think about the factors
that I was describing,
related to changing
the quality of schools, trying
to have better teachers.
You think about things like
social capital and family
structure, those things
might be very important,
but how do we change them?
That's a really
difficult question.
So the best I can say there
is, we are trying to now use
these data to study
various community
revitalization efforts that have
been conducted over the past 20
years or so by the government,
by local nonprofits,
by developers.
We're building a huge
database of all such efforts
around the United States.
And then evaluate, with
this longitudinal data,
what are the characteristics
of the things that seem to have
worked and things that haven't?
And that's our hope of
getting a scientific answer
to that question.
HAL VARIAN: Excellent.
Well, our time is up, but
thank you so much for coming.
[APPLAUSE]
RAJ CHETTY: Thank you.
