like many immigrants my parents came to the United States in search of the
the United States in search of the
the United States in search of the American Dream which is a complicated
American Dream which is a complicated
American Dream which is a complicated that means different things to different
that means different things to different
that means different things to different people but I’m going to distill it to a
people but I’m going to distill it to a
people but I’m going to distill it to a simple statistic that we can measure
simple statistic that we can measure
simple statistic that we can measure systematically in the data which are the
systematically in the data which are the
systematically in the data which are the odds that a child born to parents in the
odds that a child born to parents in the
odds that a child born to parents in the bottom fifth of the income distribution
bottom fifth of the income distribution
bottom fifth of the income distribution reaches the top fifth of the income
reaches the top fifth of the income
reaches the top fifth of the income distribution so think of the classic
distribution so think of the classic
distribution so think of the classic Horatio Alger version of the American
Horatio Alger version of the American
Horatio Alger version of the American dream
dream
dream so how common is that in the United
so how common is that in the United
so how common is that in the United States versus other developed countries
States versus other developed countries
States versus other developed countries around the world in the u.s. seven and a
around the world in the u.s. seven and a
around the world in the u.s. seven and a half percent of children who are born to
half percent of children who are born to
half percent of children who are born to parents in the bottom fifth make the
parents in the bottom fifth make the
parents in the bottom fifth make the leap all the way to the top fifth that
leap all the way to the top fifth that
leap all the way to the top fifth that compares with nine percent in the United
compares with nine percent in the United
compares with nine percent in the United Kingdom eleven point seven percent in
Kingdom eleven point seven percent in
Kingdom eleven point seven percent in Denmark and thirteen and a half percent
Denmark and thirteen and a half percent
Denmark and thirteen and a half percent in Canada now initially when people look
in Canada now initially when people look
in Canada now initially when people look at these statistics they sometimes react
at these statistics they sometimes react
at these statistics they sometimes react by saying oh even in Canada it looks
by saying oh even in Canada it looks
by saying oh even in Canada it looks like your odds of success aren’t all
like your odds of success aren’t all
like your odds of success aren’t all that high right only thirteen and a half
that high right only thirteen and a half
that high right only thirteen and a half percent of kids make it to the top
percent of kids make it to the top
percent of kids make it to the top starting from the bottom but you have to
starting from the bottom but you have to
starting from the bottom but you have to remember of course that no matter what
remember of course that no matter what
remember of course that no matter what you do you can’t have more than twenty
you do you can’t have more than twenty
you do you can’t have more than twenty percent of people in the top twenty
percent of people in the top twenty
percent of people in the top twenty percent right and so the maximum value
percent right and so the maximum value
percent right and so the maximum value that the statistic can take is plausibly
that the statistic can take is plausibly
that the statistic can take is plausibly twenty percent if you had a society
twenty percent if you had a society
twenty percent if you had a society where your parents played no role at all
where your parents played no role at all
where your parents played no role at all in determining your outcomes you’d
in determining your outcomes you’d
in determining your outcomes you’d expect 1/5 of kids to rise from the
expect 1/5 of kids to rise from the
expect 1/5 of kids to rise from the bottom 20% to the top 20% so relative to
bottom 20% to the top 20% so relative to
bottom 20% to the top 20% so relative to that benchmark max of a twenty percent
that benchmark max of a twenty percent
that benchmark max of a twenty percent rate these are actually quite large
rate these are actually quite large
rate these are actually quite large differences in rates of upward mobility
differences in rates of upward mobility
differences in rates of upward mobility across countries one way I think about
across countries one way I think about
across countries one way I think about it is that your chances of achieving the
it is that your chances of achieving the
it is that your chances of achieving the American Dream are almost two times
American Dream are almost two times
American Dream are almost two times higher if you’re growing up in Canada
higher if you’re growing up in Canada
higher if you’re growing up in Canada than in the United States now these
than in the United States now these
than in the United States now these differences across countries have
differences across countries have
differences across countries have attracted a lot of attention and have
attracted a lot of attention and have
attracted a lot of attention and have been the focus of much policy discussion
been the focus of much policy discussion
been the focus of much policy discussion a lot of worry that the u.s. is no
a lot of worry that the u.s. is no
a lot of worry that the u.s. is no longer a land of opportunity contrary to
longer a land of opportunity contrary to
longer a land of opportunity contrary to its traditional billing but what I’m
its traditional billing but what I’m
its traditional billing but what I’m going to focus on in this talk today is
going to focus on in this talk today is
going to focus on in this talk today is that upward mobility actually varies
that upward mobility actually varies
that upward mobility actually varies even more within the United States and I
even more within the United States and I
even more within the United States and I think we can learn a lot from that in
think we can learn a lot from that in
think we can learn a lot from that in recent work with my colleagues we
recent work with my colleagues we
recent work with my colleagues we calculate upward mobility for every
calculate upward mobility for every
calculate upward mobility for every metro and rural
metro and rural
metro and rural the US and we do that using anonymous
the US and we do that using anonymous
the US and we do that using anonymous earnings records drawn from tax and
earnings records drawn from tax and
earnings records drawn from tax and Social Security databases on 10 million
Social Security databases on 10 million
Social Security databases on 10 million children born between 1980 and 1982 so
children born between 1980 and 1982 so
children born between 1980 and 1982 so basically all kids born in America
basically all kids born in America
basically all kids born in America between 1980 and 1982 and the analysis
between 1980 and 1982 and the analysis
between 1980 and 1982 and the analysis that I’m going to show you is an example
that I’m going to show you is an example
that I’m going to show you is an example of a very important broader trend in
of a very important broader trend in
of a very important broader trend in economics and social science which is
economics and social science which is
economics and social science which is the use of big data to solve important
the use of big data to solve important
the use of big data to solve important social problems so much as you all hear
social problems so much as you all hear
social problems so much as you all hear about big data being used in the private
about big data being used in the private
about big data being used in the private sector and companies like Google and
sector and companies like Google and
sector and companies like Google and Amazon to provide better products
Amazon to provide better products
Amazon to provide better products likewise our vision is that we can use
likewise our vision is that we can use
likewise our vision is that we can use these data to solve some of the most
these data to solve some of the most
these data to solve some of the most important social challenges of our time
important social challenges of our time
important social challenges of our time so using this data from drawing from tax
so using this data from drawing from tax
so using this data from drawing from tax records we construct this map here which
records we construct this map here which
records we construct this map here which shows you the geography of upward
shows you the geography of upward
shows you the geography of upward mobility within the United States what
mobility within the United States what
mobility within the United States what we do is divide the u.s. into seven
we do is divide the u.s. into seven
we do is divide the u.s. into seven hundred and forty different metro and
hundred and forty different metro and
hundred and forty different metro and rural areas and in each of those areas
rural areas and in each of those areas
rural areas and in each of those areas we take the set of kids who grow up in
we take the set of kids who grow up in
we take the set of kids who grow up in that area and look at the same statistic
that area and look at the same statistic
that area and look at the same statistic that I started out with what fraction of
that I started out with what fraction of
that I started out with what fraction of the children who start out in a
the children who start out in a
the children who start out in a low-income family in the bottom 20% of
low-income family in the bottom 20% of
low-income family in the bottom 20% of the income distribution make the leap to
the income distribution make the leap to
the income distribution make the leap to the top 20% of the income distribution
the top 20% of the income distribution
the top 20% of the income distribution the map is colored so that lighter
the map is colored so that lighter
the map is colored so that lighter colored areas represent areas with
colored areas represent areas with
colored areas represent areas with higher levels of upward mobility greater
higher levels of upward mobility greater
higher levels of upward mobility greater rates of achieving the American dream
rates of achieving the American dream
rates of achieving the American dream you can see when you look at this map
you can see when you look at this map
you can see when you look at this map that there’s an incredible spectrum in
that there’s an incredible spectrum in
that there’s an incredible spectrum in rates of upward mobility within America
rates of upward mobility within America
rates of upward mobility within America in the center of the country in places
in the center of the country in places
in the center of the country in places like Iowa for example rates of upward
like Iowa for example rates of upward
like Iowa for example rates of upward mobility exceeds 16 percent higher than
mobility exceeds 16 percent higher than
mobility exceeds 16 percent higher than the numbers we saw for Denmark and for
the numbers we saw for Denmark and for
the numbers we saw for Denmark and for Canada at the other end of the spectrum
Canada at the other end of the spectrum
Canada at the other end of the spectrum if you look at places like Atlanta or
if you look at places like Atlanta or
if you look at places like Atlanta or Charlotte you see numbers below four and
Charlotte you see numbers below four and
Charlotte you see numbers below four and a half percent lower than any country
a half percent lower than any country
a half percent lower than any country for which we currently have data so even
for which we currently have data so even
for which we currently have data so even within America there are some places
within America there are some places
within America there are some places that are truly lands of opportunity and
that are truly lands of opportunity and
that are truly lands of opportunity and there are other places that are better
there are other places that are better
there are other places that are better described as lands of persistent poverty
described as lands of persistent poverty
described as lands of persistent poverty now in this big map you naturally your
now in this big map you naturally your
now in this big map you naturally your eyes gravitate towards the broad
eyes gravitate towards the broad
eyes gravitate towards the broad regional variation the Midwest looks
regional variation the Midwest looks
regional variation the Midwest looks good the southeast looks much worse etc
good the southeast looks much worse etc
good the southeast looks much worse etc but if you zoom into narrower
but if you zoom into narrower
but if you zoom into narrower geographies you see that there’s quite a
geographies you see that there’s quite a
geographies you see that there’s quite a bit of variation even across relatively
bit of variation even across relatively
bit of variation even across relatively nearby area so here were zoom
nearby area so here were zoom
nearby area so here were zoom into the bay area and looking at the
into the bay area and looking at the
into the bay area and looking at the data across counties within the bay area
data across counties within the bay area
data across counties within the bay area and you could see that if you’re growing
and you could see that if you’re growing
and you could see that if you’re growing up in the 1980s in San Francisco or in
up in the 1980s in San Francisco or in
up in the 1980s in San Francisco or in San Mateo or Santa Clara in a low-income
San Mateo or Santa Clara in a low-income
San Mateo or Santa Clara in a low-income family you had really good chances of
family you had really good chances of
family you had really good chances of succeeding you’d be something like 18
succeeding you’d be something like 18
succeeding you’d be something like 18 percent probability of making it to the
percent probability of making it to the
percent probability of making it to the top 20% which relative to that 20%
top 20% which relative to that 20%
top 20% which relative to that 20% maximum I was talking about it’s really
maximum I was talking about it’s really
maximum I was talking about it’s really a remarkable rate of upward mobility in
a remarkable rate of upward mobility in
a remarkable rate of upward mobility in contrast if you go across the Bay Bridge
contrast if you go across the Bay Bridge
contrast if you go across the Bay Bridge to Oakland you see that the probability
to Oakland you see that the probability
to Oakland you see that the probability of rising up Falls by nearly a factor of
of rising up Falls by nearly a factor of
of rising up Falls by nearly a factor of two so even within relatively small
two so even within relatively small
two so even within relatively small geographies there are substantial
geographies there are substantial
geographies there are substantial differences in children’s chances of
differences in children’s chances of
differences in children’s chances of succeeding so naturally the question of
succeeding so naturally the question of
succeeding so naturally the question of interest to us as academics and to
interest to us as academics and to
interest to us as academics and to policymakers is to ask why upward
policymakers is to ask why upward
policymakers is to ask why upward mobility vary so much across areas and
mobility vary so much across areas and
mobility vary so much across areas and what ultimately we might be able to do
what ultimately we might be able to do
what ultimately we might be able to do from a policy perspective to increase
from a policy perspective to increase
from a policy perspective to increase rates of upward mobility throughout the
rates of upward mobility throughout the
rates of upward mobility throughout the US so the first step in our analysis of
US so the first step in our analysis of
US so the first step in our analysis of that question as to establish that much
that question as to establish that much
that question as to establish that much of this difference in upward mobility
of this difference in upward mobility
of this difference in upward mobility across areas is caused by differences in
across areas is caused by differences in
across areas is caused by differences in childhood environment we demonstrate
childhood environment we demonstrate
childhood environment we demonstrate that by studying five million families
that by studying five million families
that by studying five million families that move between areas in the United
that move between areas in the United
that move between areas in the United States rather than going to the details
States rather than going to the details
States rather than going to the details of the analysis let me give you a simple
of the analysis let me give you a simple
of the analysis let me give you a simple example to show you how this works so
example to show you how this works so
example to show you how this works so let’s take a set of families that start
let’s take a set of families that start
let’s take a set of families that start out in Oakland and to pick a number
out in Oakland and to pick a number
out in Oakland and to pick a number suppose if you’re growing up in a
suppose if you’re growing up in a
suppose if you’re growing up in a low-income family in Oakland when you’re
low-income family in Oakland when you’re
low-income family in Oakland when you’re 30 years old you have an average
30 years old you have an average
30 years old you have an average earnings of $30,000 if you grow up in
earnings of $30,000 if you grow up in
earnings of $30,000 if you grow up in Oakland from birth now let’s consider a
Oakland from birth now let’s consider a
Oakland from birth now let’s consider a set of families that move from Oakland
set of families that move from Oakland
set of families that move from Oakland to San Francisco where as we saw kids
to San Francisco where as we saw kids
to San Francisco where as we saw kids and low-income families appear to have
and low-income families appear to have
and low-income families appear to have better outcomes so again to pick a round
better outcomes so again to pick a round
better outcomes so again to pick a round number suppose if you grow up from birth
number suppose if you grow up from birth
number suppose if you grow up from birth in San Francisco you earned $40,000 on
in San Francisco you earned $40,000 on
in San Francisco you earned $40,000 on average when you’re 30 years old so now
average when you’re 30 years old so now
average when you’re 30 years old so now consider a family that moves from
consider a family that moves from
consider a family that moves from Oakland to San Francisco with a child
Oakland to San Francisco with a child
Oakland to San Francisco with a child depending upon the age of their child so
depending upon the age of their child so
depending upon the age of their child so let’s start with families who move when
let’s start with families who move when
let’s start with families who move when their child is exactly nine years old
their child is exactly nine years old
their child is exactly nine years old which happens to be the earliest age we
which happens to be the earliest age we
which happens to be the earliest age we can look at and currently available data
can look at and currently available data
can look at and currently available data so if you move at age nine what we’re
so if you move at age nine what we’re
so if you move at age nine what we’re going to do is then track that child
going to do is then track that child
going to do is then track that child forward twenty-one years and the
forward twenty-one years and the
forward twenty-one years and the we have to look at how much that child
we have to look at how much that child
we have to look at how much that child is earning at age 30 and what we see is
is earning at age 30 and what we see is
is earning at age 30 and what we see is that this child on average ends up about
that this child on average ends up about
that this child on average ends up about halfway between the kids who grew up in
halfway between the kids who grew up in
halfway between the kids who grew up in Oakland from birth and the kids who grow
Oakland from birth and the kids who grow
Oakland from birth and the kids who grow up in San Francisco from birth that is
up in San Francisco from birth that is
up in San Francisco from birth that is that child is earning about $35,000 on
that child is earning about $35,000 on
that child is earning about $35,000 on average when we look at their incomes at
average when we look at their incomes at
average when we look at their incomes at age 30 now let’s repeat that analysis
age 30 now let’s repeat that analysis
age 30 now let’s repeat that analysis for kids who moved when they were 10 11
for kids who moved when they were 10 11
for kids who moved when they were 10 11 12 13 and so on and what you see is a
12 13 and so on and what you see is a
12 13 and so on and what you see is a very clear declining pattern the later
very clear declining pattern the later
very clear declining pattern the later you make that move from Oakland to San
you make that move from Oakland to San
you make that move from Oakland to San Francisco the less of the game your
Francisco the less of the game your
Francisco the less of the game your child gets if you move once you’re in
child gets if you move once you’re in
child gets if you move once you’re in your early 20s you get essentially no
your early 20s you get essentially no
your early 20s you get essentially no gain at all and if you move after that
gain at all and if you move after that
gain at all and if you move after that point there’s no impact whatsoever so
point there’s no impact whatsoever so
point there’s no impact whatsoever so what you see in this chart is that where
what you see in this chart is that where
what you see in this chart is that where you grow up really matters place matters
you grow up really matters place matters
you grow up really matters place matters if you take a given child and move that
if you take a given child and move that
if you take a given child and move that child from San Francisco to Oakland you
child from San Francisco to Oakland you
child from San Francisco to Oakland you see really meaningful changes in that
see really meaningful changes in that
see really meaningful changes in that child’s long-term outcomes and second
child’s long-term outcomes and second
child’s long-term outcomes and second you see that it’s really child and
you see that it’s really child and
you see that it’s really child and environment that appears to be critical
environment that appears to be critical
environment that appears to be critical right moving when you’re an adult
right moving when you’re an adult
right moving when you’re an adult doesn’t do a whole lot for you it’s
doesn’t do a whole lot for you it’s
doesn’t do a whole lot for you it’s moving when you’re a kid and
moving when you’re a kid and
moving when you’re a kid and particularly moving in younger ages that
particularly moving in younger ages that
particularly moving in younger ages that has a lot of impact now naturally the
has a lot of impact now naturally the
has a lot of impact now naturally the next question is to ask okay so we think
next question is to ask okay so we think
next question is to ask okay so we think childhood environment really matters in
childhood environment really matters in
childhood environment really matters in in determining kids long-term success so
in determining kids long-term success so
in determining kids long-term success so what is it about places like San
what is it about places like San
what is it about places like San Francisco or the Bay Area in general
Francisco or the Bay Area in general
Francisco or the Bay Area in general that generate really good outcomes
that generate really good outcomes
that generate really good outcomes relative to places like Atlanta where we
relative to places like Atlanta where we
relative to places like Atlanta where we see much lower levels of upward mobility
see much lower levels of upward mobility
see much lower levels of upward mobility we’ve looked at a variety of factors
we’ve looked at a variety of factors
we’ve looked at a variety of factors that correlate with these differences
that correlate with these differences
that correlate with these differences and rates of upward mobility across
and rates of upward mobility across
and rates of upward mobility across areas that I’ve been showing you I’m
areas that I’ve been showing you I’m
areas that I’ve been showing you I’m gonna show here in the interest of time
gonna show here in the interest of time
gonna show here in the interest of time the five strongest correlations that
the five strongest correlations that
the five strongest correlations that we’ve identified the first is
we’ve identified the first is
we’ve identified the first is segregation we find that places that are
segregation we find that places that are
segregation we find that places that are more residentially segregated by race or
more residentially segregated by race or
more residentially segregated by race or by income tend to have much lower levels
by income tend to have much lower levels
by income tend to have much lower levels of upward mobility now this pattern is
of upward mobility now this pattern is
of upward mobility now this pattern is so clear that you can just see it
so clear that you can just see it
so clear that you can just see it visually so let me give you a couple of
visually so let me give you a couple of
visually so let me give you a couple of examples so this map here depicts racial
examples so this map here depicts racial
examples so this map here depicts racial segregation in Atlanta the way it’s
segregation in Atlanta the way it’s
segregation in Atlanta the way it’s constructed is that each person in
constructed is that each person in
constructed is that each person in Atlanta is represented by a dot and the
Atlanta is represented by a dot and the
Atlanta is represented by a dot and the dots are colored such that whites are
dots are colored such that whites are
dots are colored such that whites are blue blacks are green
blue blacks are green
blue blacks are green Asians are red and Hispanics or orange
Asians are red and Hispanics or orange
Asians are red and Hispanics or orange you can see immediately that Atlanta is
you can see immediately that Atlanta is
you can see immediately that Atlanta is an incredibly segregated City the blue
an incredibly segregated City the blue
an incredibly segregated City the blue dots are in a completely different part
dots are in a completely different part
dots are in a completely different part of the city relative to the green dots
of the city relative to the green dots
of the city relative to the green dots the blacks and whites live in totally
the blacks and whites live in totally
the blacks and whites live in totally different parts of the city now cities
different parts of the city now cities
different parts of the city now cities that look like Atlanta in terms of
that look like Atlanta in terms of
that look like Atlanta in terms of racial or income segregation tend to
racial or income segregation tend to
racial or income segregation tend to have the lowest levels of upward
have the lowest levels of upward
have the lowest levels of upward mobility in our data compare that with
mobility in our data compare that with
mobility in our data compare that with Sacramento which has the same minority
Sacramento which has the same minority
Sacramento which has the same minority fares Atlanta that’s the same fraction
fares Atlanta that’s the same fraction
fares Atlanta that’s the same fraction of blacks and Hispanics as Atlanta you
of blacks and Hispanics as Atlanta you
of blacks and Hispanics as Atlanta you can see immediately that Sacramento is a
can see immediately that Sacramento is a
can see immediately that Sacramento is a much more integrated City the colors
much more integrated City the colors
much more integrated City the colors these dots are much more interspersed
these dots are much more interspersed
these dots are much more interspersed right and corresponding to that
right and corresponding to that
right and corresponding to that Sacramento and cities that look like it
Sacramento and cities that look like it
Sacramento and cities that look like it have much higher rates of upward
have much higher rates of upward
have much higher rates of upward mobility
mobility
mobility so that’s the first robust pattern we
so that’s the first robust pattern we
so that’s the first robust pattern we find residential segregation perhaps
find residential segregation perhaps
find residential segregation perhaps because of a lack of exposure to role
because of a lack of exposure to role
because of a lack of exposure to role models or friends or people who are
models or friends or people who are
models or friends or people who are going to help you get better jobs being
going to help you get better jobs being
going to help you get better jobs being in a more segregated area whatever the
in a more segregated area whatever the
in a more segregated area whatever the mechanism is strongly negatively
mechanism is strongly negatively
mechanism is strongly negatively associated with upward mobility we look
associated with upward mobility we look
associated with upward mobility we look at a number of other factors now just
at a number of other factors now just
at a number of other factors now just summarize these more quickly we find
summarize these more quickly we find
summarize these more quickly we find that places with more income inequality
that places with more income inequality
that places with more income inequality a smaller middle class tend to have
a smaller middle class tend to have
a smaller middle class tend to have lower levels of upward mobility we find
lower levels of upward mobility we find
lower levels of upward mobility we find that places with more stable family
that places with more stable family
that places with more stable family structures that is more two-parent
structures that is more two-parent
structures that is more two-parent families tend to have higher levels of
families tend to have higher levels of
families tend to have higher levels of upward mobility and related to that
upward mobility and related to that
upward mobility and related to that places with more social capital so this
places with more social capital so this
places with more social capital so this is the idea of whether someone else will
is the idea of whether someone else will
is the idea of whether someone else will help you out even if you’re not doing
help you out even if you’re not doing
help you out even if you’re not doing well places with more religious
well places with more religious
well places with more religious participation more civic engagement
participation more civic engagement
participation more civic engagement those sorts of places tend to have
those sorts of places tend to have
those sorts of places tend to have higher levels of upward mobility and
higher levels of upward mobility and
higher levels of upward mobility and then finally as you might expect
then finally as you might expect
then finally as you might expect intuitively places with better public
intuitively places with better public
intuitively places with better public schools tend to have much higher levels
schools tend to have much higher levels
schools tend to have much higher levels of upward mobility as well so this gives
of upward mobility as well so this gives
of upward mobility as well so this gives you a sense of what we think is driving
you a sense of what we think is driving
you a sense of what we think is driving some of these sharp differences in rates
some of these sharp differences in rates
some of these sharp differences in rates of achieving the American Dream across
of achieving the American Dream across
of achieving the American Dream across areas what I want to do in the final
areas what I want to do in the final
areas what I want to do in the final couple of minutes is present a different
couple of minutes is present a different
couple of minutes is present a different perspective on these issues of upward
perspective on these issues of upward
perspective on these issues of upward mobility so the traditional argument for
mobility so the traditional argument for
mobility so the traditional argument for greater social mobility think the reason
greater social mobility think the reason
greater social mobility think the reason that a lot of people are interested in
that a lot of people are interested in
that a lot of people are interested in these issues at the moment in the United
these issues at the moment in the United
these issues at the moment in the United States is based on principles of justice
States is based on principles of justice
States is based on principles of justice the idea that everyone should have a
the idea that everyone should have a
the idea that everyone should have a shot at the
shot at the
shot at the American dream no matter their family
American dream no matter their family
American dream no matter their family background but what I want to show you
background but what I want to show you
background but what I want to show you here is that improving opportunities for
here is that improving opportunities for
here is that improving opportunities for upward mobility even if you’re not
upward mobility even if you’re not
upward mobility even if you’re not concerned about justice and just want to
concerned about justice and just want to
concerned about justice and just want to maximize economic growth and GDP it
maximize economic growth and GDP it
maximize economic growth and GDP it might still be of interest to think
might still be of interest to think
might still be of interest to think about how to increase opportunities for
about how to increase opportunities for
about how to increase opportunities for upward mobility to illustrate that I’m
upward mobility to illustrate that I’m
upward mobility to illustrate that I’m gonna focus on one specific pathway to
gonna focus on one specific pathway to
gonna focus on one specific pathway to upward mobility which is innovation a
upward mobility which is innovation a
upward mobility which is innovation a pathway that’s particularly relevant I
pathway that’s particularly relevant I
pathway that’s particularly relevant I think here in Silicon Valley in this
think here in Silicon Valley in this
think here in Silicon Valley in this study that I’m going to describe we
study that I’m going to describe we
study that I’m going to describe we linked data on the universe of patent
linked data on the universe of patent
linked data on the universe of patent holders in the United States to the tax
holders in the United States to the tax
holders in the United States to the tax records that I was describing earlier so
records that I was describing earlier so
records that I was describing earlier so that we can study the lives of inventors
that we can study the lives of inventors
that we can study the lives of inventors so we can ask wherein mentors in America
so we can ask wherein mentors in America
so we can ask wherein mentors in America come from and how ultimately we might be
come from and how ultimately we might be
come from and how ultimately we might be able to get more of them I’m gonna start
able to get more of them I’m gonna start
able to get more of them I’m gonna start with this chart here which shows you the
with this chart here which shows you the
with this chart here which shows you the probability of becoming an inventor
probability of becoming an inventor
probability of becoming an inventor versus parent income the way this is
versus parent income the way this is
versus parent income the way this is constructed is on the horizontal x-axis
constructed is on the horizontal x-axis
constructed is on the horizontal x-axis as parent income percentile there are
as parent income percentile there are
as parent income percentile there are hundred dots here corresponding to each
hundred dots here corresponding to each
hundred dots here corresponding to each percentile the parent income
percentile the parent income
percentile the parent income distribution and on the y-axis is the
distribution and on the y-axis is the
distribution and on the y-axis is the number of kids who go on to become
number of kids who go on to become
number of kids who go on to become inventors that is have a patent by their
inventors that is have a patent by their
inventors that is have a patent by their mid-30s you can see that there’s an
mid-30s you can see that there’s an
mid-30s you can see that there’s an incredibly strong relationship between
incredibly strong relationship between
incredibly strong relationship between your parents income and your probability
your parents income and your probability
your parents income and your probability of going on to become an inventor if you
of going on to become an inventor if you
of going on to become an inventor if you happen to be born to parents in the top
happen to be born to parents in the top
happen to be born to parents in the top 1% of the income distribution you’re 10
1% of the income distribution you’re 10
1% of the income distribution you’re 10 times as likely to have a patent as if
times as likely to have a patent as if
times as likely to have a patent as if you happen to be born to parents at the
you happen to be born to parents at the
you happen to be born to parents at the median of the income distribution in the
median of the income distribution in the
median of the income distribution in the US so why is that one possibility is
US so why is that one possibility is
US so why is that one possibility is that it’s about the factors that I’ve
that it’s about the factors that I’ve
that it’s about the factors that I’ve been discussing here differences in
been discussing here differences in
been discussing here differences in childhood environment schools resources
childhood environment schools resources
childhood environment schools resources while you’re growing up maybe high
while you’re growing up maybe high
while you’re growing up maybe high income kids have much greater access to
income kids have much greater access to
income kids have much greater access to all of those things relative to low
all of those things relative to low
all of those things relative to low income kids and that’s what’s driving
income kids and that’s what’s driving
income kids and that’s what’s driving this innovation gap a different
this innovation gap a different
this innovation gap a different explanation is that this is about
explanation is that this is about
explanation is that this is about differences in ability presumably the
differences in ability presumably the
differences in ability presumably the parents who got to the top 1% of the
parents who got to the top 1% of the
parents who got to the top 1% of the income distribution were quite talented
income distribution were quite talented
income distribution were quite talented and maybe that’s why their own kids are
and maybe that’s why their own kids are
and maybe that’s why their own kids are more likely to become inventors than be
more likely to become inventors than be
more likely to become inventors than be successful themselves discriminate
successful themselves discriminate
successful themselves discriminate between those two explanations we bring
between those two explanations we bring
between those two explanations we bring in data on test scores of kids early in
in data on test scores of kids early in
in data on test scores of kids early in childhood as a measure of ability
childhood as a measure of ability
childhood as a measure of ability relatively early on and construct this
relatively early on and construct this
relatively early on and construct this chart here
chart here
chart here which shows you your the fraction of
which shows you your the fraction of
which shows you your the fraction of kids who go on to become inventors
kids who go on to become inventors
kids who go on to become inventors versus third grade math test scores so
versus third grade math test scores so
versus third grade math test scores so each dot here represents five percent of
each dot here represents five percent of
each dot here represents five percent of the test score distribution and what you
the test score distribution and what you
the test score distribution and what you can see is if you’re below something
can see is if you’re below something
can see is if you’re below something like the 85th percentile of your third
like the 85th percentile of your third
like the 85th percentile of your third grade math class
grade math class
grade math class odds are you’re probably not going to go
odds are you’re probably not going to go
odds are you’re probably not going to go on to become an inventor as measured by
on to become an inventor as measured by
on to become an inventor as measured by having a patent but if you’re in the
having a patent but if you’re in the
having a patent but if you’re in the upper tail of your third grade math
upper tail of your third grade math
upper tail of your third grade math class particularly at the very top your
class particularly at the very top your
class particularly at the very top your probability of becoming an inventor
probability of becoming an inventor
probability of becoming an inventor really shoots up now what’s interesting
really shoots up now what’s interesting
really shoots up now what’s interesting most relevant for the purposes of this
most relevant for the purposes of this
most relevant for the purposes of this talk is if we now cut this data looking
talk is if we now cut this data looking
talk is if we now cut this data looking at kids from low-income and high-income
at kids from low-income and high-income
at kids from low-income and high-income families separately so again look at the
families separately so again look at the
families separately so again look at the fraction of kids who go on to become
fraction of kids who go on to become
fraction of kids who go on to become inventors
inventors
inventors versus their third grade math test
versus their third grade math test
versus their third grade math test scores but now look at kids from
scores but now look at kids from
scores but now look at kids from relatively high income families in the
relatively high income families in the
relatively high income families in the red above the median and low-income
red above the median and low-income
red above the median and low-income families in the blue you see a very
families in the blue you see a very
families in the blue you see a very striking pattern which is that high
striking pattern which is that high
striking pattern which is that high ability kids these kids were at the top
ability kids these kids were at the top
ability kids these kids were at the top of their third grade math class are much
of their third grade math class are much
of their third grade math class are much more likely to become in mentors if
more likely to become in mentors if
more likely to become in mentors if they’re from high-income families if
they’re from high-income families if
they’re from high-income families if you’re from a low-income family and
you’re from a low-income family and
you’re from a low-income family and you’re at the top of your third grade
you’re at the top of your third grade
you’re at the top of your third grade math class your probability of becoming
math class your probability of becoming
math class your probability of becoming an inventor doesn’t look all that much
an inventor doesn’t look all that much
an inventor doesn’t look all that much higher than the rest of the kids in the
higher than the rest of the kids in the
higher than the rest of the kids in the class so to put it differently these
class so to put it differently these
class so to put it differently these data suggests that in America in order
data suggests that in America in order
data suggests that in America in order to become an inventor you need two
to become an inventor you need two
to become an inventor you need two things you need to be smart as measured
things you need to be smart as measured
things you need to be smart as measured by your test scores for instance early
by your test scores for instance early
by your test scores for instance early in childhood and you need to be from a
in childhood and you need to be from a
in childhood and you need to be from a rich family and that I think gives you a
rich family and that I think gives you a
rich family and that I think gives you a very different perspective on issues of
very different perspective on issues of
very different perspective on issues of equality of opportunity it suggests that
equality of opportunity it suggests that
equality of opportunity it suggests that if we can bring more of these kids from
if we can bring more of these kids from
if we can bring more of these kids from low-income families who are doing really
low-income families who are doing really
low-income families who are doing really well early on in school through the
well early on in school through the
well early on in school through the innovation pipeline that would benefit
innovation pipeline that would benefit
innovation pipeline that would benefit not only them in terms of greater
not only them in terms of greater
not only them in terms of greater opportunities for upward mobility but it
opportunities for upward mobility but it
opportunities for upward mobility but it would also benefit the rest of us by
would also benefit the rest of us by
would also benefit the rest of us by having more people who might discover
having more people who might discover
having more people who might discover the next blockbuster drug or develop the
the next blockbuster drug or develop the
the next blockbuster drug or develop the next iPhone in order to give you a sense
next iPhone in order to give you a sense
next iPhone in order to give you a sense to link back to the geography issues
to link back to the geography issues
to link back to the geography issues that I started out but I’m going to
that I started out but I’m going to
that I started out but I’m going to close with with this map here which
close with with this map here which
close with with this map here which shows you the origins of inventors in
shows you the origins of inventors in
shows you the origins of inventors in America this gives you a sense of what’s
America this gives you a sense of what’s
America this gives you a sense of what’s driving that innovation gap so this map
driving that innovation gap so this map
driving that innovation gap so this map shows the fraction of kids who go on to
shows the fraction of kids who go on to
shows the fraction of kids who go on to become inventor
become inventor
become inventor by where they grew up again these 740
by where they grew up again these 740
by where they grew up again these 740 Metro and rural areas and the map is
Metro and rural areas and the map is
Metro and rural areas and the map is colored so that darker red colors here
colored so that darker red colors here
colored so that darker red colors here represent areas that produce more
represent areas that produce more
represent areas that produce more inventors what you can see is places
inventors what you can see is places
inventors what you can see is places like the Bay Area or the Northeast or
like the Bay Area or the Northeast or
like the Bay Area or the Northeast or Austin Texas if you focus here tend to
Austin Texas if you focus here tend to
Austin Texas if you focus here tend to be much more likely to produce kids who
be much more likely to produce kids who
be much more likely to produce kids who go on to become inventors than other
go on to become inventors than other
go on to become inventors than other parts of the country and that echo is a
parts of the country and that echo is a
parts of the country and that echo is a broader pattern which is that exposure
broader pattern which is that exposure
broader pattern which is that exposure to innovation while you’re growing up
to innovation while you’re growing up
to innovation while you’re growing up really influences your own likelihood of
really influences your own likelihood of
really influences your own likelihood of becoming an inventor and this turns out
becoming an inventor and this turns out
becoming an inventor and this turns out to be a pattern that holds in a very
to be a pattern that holds in a very
to be a pattern that holds in a very specific way so the type of innovation
specific way so the type of innovation
specific way so the type of innovation you end up doing in adulthood is greatly
you end up doing in adulthood is greatly
you end up doing in adulthood is greatly determined by the area in which you grow
determined by the area in which you grow
determined by the area in which you grow up let me give you an example so take
up let me give you an example so take
up let me give you an example so take two kids who are let’s say are currently
two kids who are let’s say are currently
two kids who are let’s say are currently in Boston and say one grew up in Silicon
in Boston and say one grew up in Silicon
in Boston and say one grew up in Silicon Valley like many of you and one grew up
Valley like many of you and one grew up
Valley like many of you and one grew up in Minneapolis Silicon Valley has a lot
in Minneapolis Silicon Valley has a lot
in Minneapolis Silicon Valley has a lot of computer innovation maybe Minneapolis
of computer innovation maybe Minneapolis
of computer innovation maybe Minneapolis happens to have a lot of medical device
happens to have a lot of medical device
happens to have a lot of medical device manufacturers it turns out that if you
manufacturers it turns out that if you
manufacturers it turns out that if you look at these two kids who are currently
look at these two kids who are currently
look at these two kids who are currently in Boston the kid who grew up in
in Boston the kid who grew up in
in Boston the kid who grew up in Minneapolis is more likely to have a
Minneapolis is more likely to have a
Minneapolis is more likely to have a patent in medical devices and the kid
patent in medical devices and the kid
patent in medical devices and the kid who grew up in Silicon Valley is more
who grew up in Silicon Valley is more
who grew up in Silicon Valley is more likely to have a patent in computers so
likely to have a patent in computers so
likely to have a patent in computers so in a very specific way children seem to
in a very specific way children seem to
in a very specific way children seem to be greatly influenced by the exact
be greatly influenced by the exact
be greatly influenced by the exact environment which they grow up and I
environment which they grow up and I
environment which they grow up and I think much of the innovation gap is
think much of the innovation gap is
think much of the innovation gap is explained by the fact that kids from
explained by the fact that kids from
explained by the fact that kids from lower income backgrounds don’t have the
lower income backgrounds don’t have the
lower income backgrounds don’t have the internship opportunities the network’s
internship opportunities the network’s
internship opportunities the network’s the connections that lead them to to
the connections that lead them to to
the connections that lead them to to become in mentors down the road and so I
become in mentors down the road and so I
become in mentors down the road and so I think that is potentially an empowering
think that is potentially an empowering
think that is potentially an empowering message for many of us here in Silicon
message for many of us here in Silicon
message for many of us here in Silicon Valley because it shows that we can
Valley because it shows that we can
Valley because it shows that we can ourselves have important effects on in
ourselves have important effects on in
ourselves have important effects on in the innovation gap and on equality of
the innovation gap and on equality of
the innovation gap and on equality of opportunity by giving kids from
opportunity by giving kids from
opportunity by giving kids from disadvantaged backgrounds better
disadvantaged backgrounds better
disadvantaged backgrounds better opportunities and I think we would all
opportunities and I think we would all
opportunities and I think we would all benefit greatly from doing that so I’ll
benefit greatly from doing that so I’ll
benefit greatly from doing that so I’ll stop there thanks very much
”
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