improving equality of opportunity · the american dream? chance that a child born to parents in the...
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Improving Equality of Opportunity
New Insights from Big Data
Raj Chetty
Harvard University
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Chance that a child born to parents in the bottom fifth of the income
distribution reaches the top fifth:
The American Dream?
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Chance that a child born to parents in the bottom fifth of the income
distribution reaches the top fifth:
USA 7.5% Chetty, Hendren, Kline, Saez 2014
UK 9.0% Blanden and Machin 2008
11.7% Denmark Boserup, Kopczuk, and Kreiner 2013
13.5% Canada Corak and Heisz 1999
The American Dream?
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The American Dream?
Chance that a child born to parents in the bottom fifth of the income
distribution reaches the top fifth:
USA 7.5% Chetty, Hendren, Kline, Saez 2014
UK 9.0% Blanden and Machin 2008
11.7% Denmark Boserup, Kopczuk, and Kreiner 2013
13.5% Canada Corak and Heisz 1999
Chances of achieving
the “American Dream”
are almost two times
higher in Canada
than in the U.S.
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50
60
70
80
90
100 P
ct. o
f C
hild
ren
Ea
rnin
g m
ore
th
an
th
eir P
are
nts
1940 1950 1960 1970 1980
Child's Year of Birth
The Fading American Dream Percent of Children Earning More than Their Parents, by Year of Birth
Source: Chetty, Grusky, Hell, Hendren, Manduca, Narang 2017
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Empirical evidence on the determinants of economic mobility across generations has been limited because of a lack of longitudinal data
Most empirical work on inequality has used cross-sectional data to study poverty or income differences within a single generation
This talk presents an overview of recent research on economic mobility using longitudinal administrative data (based on work with John Friedman, Nathan Hendren, and many others)
Trace the roots of outcomes such as poverty and incarceration back to the environment in
which people grew up
Focus here on variation across neighborhoods as a lens to understand determinants of opportunity
How Can We Increase Upward Mobility?
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1
2
3
The Geography of Opportunity
Causal Effects of Neighborhoods
Data and Methods
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1
2
3
The Geography of Opportunity
Causal Effects of Neighborhoods
Data and Methods
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Data sources: Census data (2000, 2010, ACS) covering U.S. population linked to federal income tax returns from 1989-2015
Link children to parents based on dependent claiming on tax returns
Target sample: Children in 1978-83 birth cohorts who were born in the U.S. or are authorized immigrants who came to the U.S. in childhood
Analysis sample: 20.5 million children, 96% coverage rate of target sample
Data Sources and Sample Definitions
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Parents’ pre-tax household incomes: mean Adjusted Gross Income from 1994-2000, assigning non-filers zeros
Children’s pre-tax incomes measured in 2014-15 (ages 31-37)
To mitigate lifecycle bias, focus on percentile ranks in national distribution:
Rank children relative to others in their birth cohort and parents relative to other parents
Income Definitions
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0
20
4
0
60
8
0
10
0
Ch
ildre
n’s
Me
an
Hh
old
. In
c.
Ra
nk (
Ag
es 3
1-3
7)
0 20 40 60 80 100
Parent Household Income Rank
Intergenerational Mobility in the United States
Mean Child Household Income Rank vs. Parent Household Income Rank
($22K) ($43K) ($69K) ($105K) ($1.5M)
Source: Chetty, Friedman, Hendren, Jones, Porter (2018)
Predicted Value Given Parents at 25th Percentile
= 41st Percentile = $31,900
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1
2
3
The Geography of Opportunity
Causal Effects of Neighborhoods
Data and Methods
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Begin with a descriptive characterization of the geography of opportunity: how rates of upward income mobility vary across areas
Why is this descriptive analysis useful?
– Many policies target areas based on characteristics such as the poverty rates
– Tax policies (e.g., Opportunity zones), local services (e.g., pre-school programs), …
For such “tagging” applications, observed outcomes are of direct interest in standard optimal tax models [Akerlof 1978]
– Isolating causal effects of neighborhoods not necessarily relevant
The Geography of Opportunity and Policy Targeting
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Note: Blue = More Upward Mobility, Red = Less Upward Mobility
Source: The Opportunity Atlas: Chetty, Friedman, Hendren, Jones, Porter 2019
> $44.8k
$33.7k
< $26.8k
Atlanta
$26.6k
Washington DC
$33.9k
San Francisco
Bay Area
$37.2k
Seattle
$35.2k Salt Lake City $37.2k
Cleveland
$29.4k
Los Angeles
$34.3k
Dubuque
$45.5k
New York City $35.4k
The Geography of Upward Mobility in the United States
Average Household Income for Children with Parents Earning $27,000 (25th percentile)
Boston $36.8k
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Black Men White Men
Boston
$28k
Charlotte
$21k
Charlotte
$35k
Newark
$26k
Seattle
$24k
$25k $17k $35k
Newark
$48k
Note: Blue = More Upward Mobility, Red = Less Upward Mobility
Source: Chetty, Hendren, Jones, Porter 2018
Boston
$40k
Seattle
$37k
Two Americas: The Geography of Upward Mobility for Black vs. White Men
Average Household Income for Men with Parents Earning $27,000 (25th percentile)
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Boston
$27k
Charlotte
$21k
Charlotte
$19k
Newark
$25k
Seattle
$22k
$20k $16k $26k
Newark
$28k
Note: Blue = More Upward Mobility, Red = Less Upward Mobility
Source: Chetty, Hendren, Jones, Porter 2018
Boston
$26k
Seattle
$21k
Cleveland
$21k
Cincinnati
$19k
Cleveland
$21k
Cincinnati
$21k
Black Women White Women
The Geography of Upward Mobility for Black vs. White Women
Average Household Income for Women with Parents Earning $27,000 (25th percentile)
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Income Mobility for Black vs. White Men Raised in High-Income Families
Source: Chetty, Hendren, Jones, Porter 2018; New York Times 2018
Black men
White men
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> $44.8k
$33.7k
< $26.8k
Atlanta
$26.6k
Washington DC
$33.9k
San Francisco
Bay Area
$37.2k
Seattle
$35.2k Salt Lake City $37.2k
Cleveland
$29.4k
Los Angeles
$34.3k
Dubuque
$45.5k
New York City $35.4k
The Geography of Upward Mobility in the United States
Average Household Income for Children with Parents Earning $27,000 (25th percentile)
Boston $36.8k
Note: Blue = More Upward Mobility, Red = Less Upward Mobility
Source: The Opportunity Atlas: Chetty, Friedman, Hendren, Jones, Porter 2019
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Number of Jobs Within 5 Miles
Job Growth 2004-2013
0 0.2 0.4 0.6 0.8
Magnitude of Race-Controlled Signal Correlation
Correlations between Tract-Level Covariates and Household Income Rank
Race-Adjusted, Parent Income at 25th Percentile
Positive Negative
High-Paying Jobs Within 5 Miles
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New York
Los Angeles
Chicago
Philadelphia Dallas
Miami
Washington
Houston
Detroit
Boston
Atlanta
San Francisco
Riverside
Seattle
Minneapolis
San Diego
Baltimore
Pittsburgh
Tampa
Denver
Cleveland
Cincinnati
Portland
Kansas City
Sacramento
Charlotte
San Jose
San Antonio
Phoenix
St. Louis
$26K
$30K
$34K
$38K
0 20 40 60
Average Incom
e at A
ge 3
5 of C
hildren
who G
rew
up in Low
-Incom
e Fam
ilies
Job Growth Rate (%) from 1990-2010
High mobility,
low growth
Low mobility,
high growth
High mobility,
high growth
Low mobility,
low growth
Upward Mobility vs. Job Growth in the 30 Largest Metro Areas
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Number of Jobs Within 5 Miles
Job Growth 2004-2013
2000 Employment Rate
0 0.2 0.4 0.6 0.8
Magnitude of Race-Controlled Signal Correlation
Correlations between Tract-Level Covariates and Household Income Rank
Race-Adjusted, Parent Income at 25th Percentile
Positive Negative
High-Paying Jobs Within 5 Miles
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Share Above Poverty Line
Mean Household Income
Mean 3rd Grade Math Score
Share College Grad.
0 0.2 0.4 0.6 0.8
Magnitude of Race-Controlled Signal Correlation
Correlations between Tract-Level Covariates and Household Income Rank
Race-Adjusted, Parent Income at 25th Percentile
Positive Negative
Number of Jobs Within 5 Miles
Job Growth 2004-2013
2000 Employment Rate
High-Paying Jobs Within 5 Miles
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Coefficient at 0: -0.314 (0.007)
Sum of Coefficients 1-10: -0.129 (0.009) -0.3
-0
.2
-0.1
0.0
Re
gre
ssio
n C
oe
ffic
ien
t
0 1 (1.9)
2 3 (3.0)
4 5 (4.0)
6 7 (4.8)
8 9 (5.6)
10
Neighbor Number (Median Distance in Miles)
Spatial Decay of Correlation with Tract-Level Poverty Rate
Mean Child Household Income Rank (Parents p=25), White Children
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-0.3
-0
.2
-0.1
0.0
Re
gre
ssio
n C
oe
ffic
ien
t
0 1 (1.9)
2 3 (3.0)
4 5 (4.0)
6 7 (4.8)
8 9 (5.6)
10
Neighbor Number (Median Distance in Miles)
Spatial Decay of Correlation with Tract-Level Poverty Rate
Mean Child Household Income Rank (Parents p=25), White Children
Poverty rates in neighboring tracts have little predictive power
conditional on poverty rate in own tract
Coefficient at 0: -0.314 (0.007)
Sum of Coefficients 1-10: -0.129 (0.009)
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Coefficient at 0: -0.057 (0.001)
Sum of Coefficients 1-40: -0.224 (0.014)
-0.0
6 -
0.0
5 -
0.0
4 -
0.0
3 -
0.0
2 -
0.0
1 0.0
0
0.0
1
Regre
ssio
n C
oeffic
ient
0 20 40 (0.6)
60 80 (0.9)
100 120 (1.1)
140 160 (1.3)
180 200
Neighbor Number (Median Distance in Miles)
Spatial Decay of Correlation with Block-Level Poverty Rate
Mean Child Household Income Rank (Parents p=25), White Children
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Share Single Parent Households
0 0.2 0.4 0.6 0.8
Magnitude of Race-Controlled Signal Correlation
Correlations between Tract-Level Covariates and Household Income Rank
Race-Adjusted, Parent Income at 25th Percentile
Positive Negative
Share Above Poverty Line
Mean Household Income
Mean 3rd Grade Math Score
Share College Grad.
Number of Jobs Within 5 Miles
Job Growth 2004-2013
2000 Employment Rate
High-Paying Jobs Within 5 Miles
Census Return Rate (“Social Capital”)
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Share Single Parent Households
Census Return Rate (“Social Capital”)
Share Black
Share Hispanic
0 0.2 0.4 0.6 0.8
Magnitude of Race-Controlled Signal Correlation
Correlations between Tract-Level Covariates and Household Income Rank
Race-Adjusted, Parent Income at 25th Percentile
Positive Negative
Share Above Poverty Line
Mean Household Income
Mean 3rd Grade Math Score
Share College Grad.
Number of Jobs Within 5 Miles
Job Growth 2004-2013
2000 Employment Rate
High-Paying Jobs Within 5 Miles
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Share Single Parent Households
Share Black
Share Hispanic
0 0.2 0.4 0.6 0.8
Magnitude of Race-Controlled Signal Correlation
Correlations between Tract-Level Covariates and Household Income Rank
Race-Adjusted, Parent Income at 25th Percentile
Positive Negative
Share Above Poverty Line
Mean Household Income
Mean 3rd Grade Math Score
Share College Grad.
Number of Jobs Within 5 Miles
Job Growth 2004-2013
2000 Employment Rate
High-Paying Jobs Within 5 Miles
R-Squared
of All Covars. = 0.504
Census Return Rate (“Social Capital”)
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Share Single Parent Households
Share Black
Share Hispanic
Population Density
0 0.2 0.4 0.6 0.8
Magnitude of Race-Controlled Signal Correlation
Correlations between Tract-Level Covariates and Household Income Rank
Race-Adjusted, Parent Income at 25th Percentile
Positive Negative
Share Above Poverty Line
Mean Household Income
Mean 3rd Grade Math Score
Share College Grad.
Number of Jobs Within 5 Miles
Job Growth 2004-2013
2000 Employment Rate
High-Paying Jobs Within 5 Miles
Census Return Rate (“Social Capital”)
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Do Cities Offer Greater Opportunities for Upward Mobility?
Average Income for White Children with Parents Earning $25,000 in North Carolina
C H A R L OT T E
W I N S TO N - S A L E M
R A L E I G H
D U R H A M
< 29.5 ($20k)
44.6 ($36k)
> 64.3 ($63k)
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WAT E R L O O
C E D A R R A P I D S
D AV E N P O R T
D E S M O I N E S
Do Cities Offer Greater Opportunities for Upward Mobility?
Average Income for White Children with Parents Earning $25,000 in Iowa
< 29.5 ($20k)
44.6 ($36k)
> 64.3 ($63k)
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Illustrative Application: Currently Designated Opportunity Zones in Los Angeles County
< 31.4 ($22k)
43.7 (35k)
> 59.4 ($55k)
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Hypothetical Opportunity Zones using Upward Mobility Estimates
< 31.4 ($22k)
43.7 (35k)
> 59.4 ($55k)
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1
2
3
The Geography of Opportunity
Causal Effects of Neighborhoods
Data and Methods
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Where should a family seeking to improve their children’s outcomes live?
Answer matters both to individual families and potentially for policy design
– Ex: Many affordable housing programs (e.g., Housing Choice Vouchers) have explicit goal of helping low-income families access “higher opportunity” areas
For these questions, critical to understand whether observational variation is driven by causal effects of place or selection
Neighborhood Choice and Causal Effects of Place
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Identify causal effects using two research designs:
1. Moving-to-Opportunity (MTO) Experiment: Compare observational predictions to treatment effects of MTO experiment on children’s earnings [Chetty, Hendren, Katz 2016]
2. Movers Quasi-Experiment: Analyze outcomes of children who move at different ages across all tracts [Chetty and Hendren 2018]
Identifying Causal Effects of Place
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Ida B. Wells Homes
Robert Taylor Homes
Stateway Gardens
Moving To Opportunity Experiment: Origin (Control Group) Locations in Chicago
= Control = Section 8 = Experimental
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Moving To Opportunity Experiment: Origin and Destination Locations in Chicago
Calumet Heights
Cottage Grove Heights
Riverdale
Oakland
Washington Park
Grand Crossing
= Control = Section 8 = Experimental
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$5
,00
0
$8
,00
0
$11
,00
0
Me
an
In
div
. E
arn
ing
s in
MT
O (
with
site
FE
)
$7,000 $9,000 $11,000 $13,000
Mean Indiv. Earnings for Children with Parents at p=10 in Opportunity Atlas (with site FE)
Chicago
= Control = Section 8 = Experimental
Earnings of Young Children in MTO Experiment vs. Observational Predictions from
Opportunity Atlas
Chetty, Hendren, and Katz (2016, Online Appendix Table 7, Panel B)
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Correlation = 0.60
Slope = 0.71 (0.26)
$5
,00
0
$8
,00
0
$11
,00
0
Me
an
In
div
. E
arn
ing
s in
MT
O (
with
site
FE
)
$7,000 $9,000 $11,000 $13,000
Mean Indiv. Earnings for Children with Parents at p=10 in Opportunity Atlas (with site FE)
Baltimore Boston Chicago LA NY
= Control
= Section 8 = Experimental
Chetty, Hendren, and Katz (2016, Online Appendix Table 7, Panel B)
Earnings of Young Children in MTO Experiment vs. Observational Predictions from
Opportunity Atlas
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MTO experiment shows that observational estimates predict causal
effects of moving in a small set of neighborhoods
Now extend this approach to all areas using a quasi-experimental
design in observational data
Quasi-Experimental Estimates
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To begin, consider families who move across Census tracts when child
is exactly 5 years old
Regress child’s income rank in adulthood 𝑦𝑖 on mean rank of children
with same parental income level in destination tract:
𝑦𝑖 = α𝑞𝑜 + 𝑏𝑚𝑦 𝑝𝑑 + η𝑖
Include parent decile (q) by origin (o) fixed effects to identify 𝑏𝑚 purely
from differences in destinations
Estimating Exposure Effects in Observational Data
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Slope: 0.815
(0.031)
45
5
0
55
6
0
Me
an
Ch
ild R
an
k a
t A
ge
24
-5 0 5 10
Predicted Diff. in Child Rank Based on Permanent Residents in Dest. vs. Orig.
Movers’ Income Ranks vs. Mean Ranks of Children in Destination
For Children Who Move at Age 5
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0.2
0
.4
0.6
0
.8
1.0
Coeffic
ient on O
bserv
ational O
utc
om
e in D
estination
0 5 10 15 20 25 30
Age of Child When Parents Move
Childhood Exposure Effects on Household Income Rank at Age 24
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0.2
0
.4
0.6
0
.8
1.0
Coeffic
ient on O
bserv
ational O
utc
om
e in D
estination
0 5 10 15 20 25 30
Age of Child When Parents Move
Childhood Exposure Effects on Household Income Rank at Age 24
Selection Effect
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δ = 0.346
0.2
0
.4
0.6
0
.8
1.0
Coeffic
ient on O
bserv
ational O
utc
om
e in D
estination
0 5 10 15 20 25 30
Age of Child When Parents Move
Childhood Exposure Effects on Household Income Rank at Age 24
Ident. Assumption: Selection effect constant across ages
Shape before age 23 reflects causal effects of exposure
Selection Effect
Slope (Age>23): -0.008 (0.005)
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δ = 0.346
0.2
0
.4
0.6
0
.8
1.0
Coeffic
ient on O
bserv
ational O
utc
om
e in D
estination
0 5 10 15 20 25 30
Age of Child When Parents Move
Childhood Exposure Effects on Household Income Rank at Age 24
Ident. Assumption: Selection effect constant across ages
Shape before age 23 reflects causal effects of exposure
Selection Effect
Slope (Age>23): -0.008 (0.005)
Slope (Age<=23): -0.025 (0.002)
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Use two approaches to evaluate validity of key assumption, following
Chetty and Hendren (2018):
1. Sibling comparisons to control for family fixed effects
2. Outcome-based placebo tests exploiting heterogeneity in place effects by gender, quantile, and outcome
– Ex: moving to a place where boys have high earnings son improves in proportion to exposure but daughter does not
Identifying Causal Exposure Effects
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Outcome: Child Household Income Rank at Age 24
Males Females
(1) (2)
Prediction for Males -0.025 -0.003
(0.002) (0.002)
Prediction for Females -0.001 -0.026
(0.003) (0.003)
Num. of Obs. 1,146,000 1,082,000
Note: Standard errors in parentheses
Gender-Specific Childhood Exposure Effects on Household Income Rank
Regression Estimates Based on One-Time Movers Across Tracts
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Childhood Exposure Effects Around the World
United States
Source: Chetty, Friedman, Hendren, Jones, Porter (2018)
Australia
Source: Deutscher (2018)
Montreal, Canada
Source: Laliberté (2018)
MTO: Baltimore, Boston,
Chicago, LA, NYC
Source: Chetty, Hendren, Katz (AER 2016)
Chicago Public Housing
Demolitions
Source: Chyn (AER 2018)
Denmark
Source: Faurschou (2018)
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Moving at birth from tract at 25th percentile of distribution of upward mobility to a tract at 75th percentile within county $206,000 gain in lifetime earnings
Two paths to improving neighborhoods in which children in low-income families grow up:
1. Reduce segregation by helping families move to higher opportunity areas
2. Place-based investments to improve outcomes in low-opportunity areas
Improving Childhood Environments
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Feasibility of moving to opportunity approach relies on being able to find affordable housing in high-opportunity neighborhoods
How does the housing market price the amenity of better outcomes for children?
Moving to Opportunity
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Hyde Park
Northbrook
0
20
4
0
60
8
0
Me
an C
hild
Househ
old
Incom
e R
ank
Giv
en P
are
nts
at 25
th P
erc
entile
0 500 1000 1500 2000
Median Two-Bedroom Rent in 1990 (2015 $)
Alsip
The Price of Opportunity in Chicago
Children’s Mean Income Ranks in Adulthood vs. Median Rents in Chicago, by Tract
Correlation = 0.50
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Uptown
Evergreen
Alsip /
Marionette
Ida B. Wells Homes
Robert Taylor Homes
Stateway Gardens
Opportunity Bargain Neighborhoods in Chicago
= Control = Section 8 = Experimental
= Opp. Bargains
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$5,0
00
$8,0
00
$11,0
00
$1
4,0
00
Me
an
In
div
. E
arn
ing
s in
MT
O (
with
site
FE
)
$7,000 $10,000 $13,000 $16,000 $19,000
Mean Indiv. Earnings for Children with Parents at p=10 in Opportunity Atlas (with site FE)
Predicted Impacts of Moving to Opportunity Bargain Neighborhoods in Chicago
= Control = Section 8 = Experimental: Poverty Rate-Based Targeting = Opp. Bargain: Outcome-Based Targeting
Creating Moves to Opportunity RCT
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Randomized trial to help families with vouchers move to “opportunity bargain” areas using three approaches:
Information + financial assistance
Landlord recruitment
Brokerage services
Creating Moves to Opportunity
in Seattle
Bergman, Chetty, DeLuca, Hendren, Katz, Palmer (in progress)
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Moving to opportunity can be helpful in reducing segregation, but ultimately is not a fully scalable approach
In parallel, important to invest in low-opportunity places
Many place-based efforts focus on the labor market (e.g., tax credits for employers)
Our results call for a place-based focus on human-capital development instead
We do not yet know which place-based investments (schools, mentoring programs, crime reduction, physical infrastructure) are most effective
Currently studying impacts of historical place-based policies on prior residents using
longitudinal data
Place-Based Investments
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Traditional interest in equality of opportunity is based on principles of justice
But improving opportunities for upward mobility can also increase economic growth
To illustrate, focus on innovation
Study the lives of 750,000 patent holders in the U.S. by linking universe of patent applications
to tax data [Bell, Chetty, Jaravel, Petkova, van Reenen 2018]
Equality of Opportunity and Economic Growth
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0
2
4
6
8
0 20 40 60 80 100
Patent Rates vs. Parent Income Percentile
No
. o
f In
ve
nto
rs p
er
Th
ou
sa
nd
Ch
ildre
n
Parent Household Income Percentile
Patent rate for children
with parents in top 1%:
8.3 per 1,000
Patent rate for children
with parents below median:
0.85 per 1,000
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0
1
2
3
4
5
-2 -1 0 1 2
3rd Grade Math Test Score (Standard Deviations Relative to Mean)
Patent Rates vs. 3rd Grade Math Test Scores
90th Percentile
No
. o
f In
ve
nto
rs p
er
Th
ou
sa
nd
Ch
ildre
n
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0
2
4
6
8
-2 -1 0 1 2 3rd Grade Math Test Score (Standard Deviations Relative to Mean)
Par. Inc. Below 80th Percentile Par. Inc. Above 80th Percentile
Patent Rates vs. 3rd Grade Math Test Scores
for Children with Low vs. High Income Parents
No
. o
f In
ve
nto
rs p
er
Th
ou
sa
nd
Ch
ildre
n
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2
4
6
8
-2 -1 0 1 2
High-ability children much more
likely to become inventors if they
are from high-income families
0
3rd Grade Math Test Score (Standard Deviations Relative to Mean)
Patent Rates vs. 3rd Grade Math Test Scores
for Children with Low vs. High Income Parents
Par. Inc. Below 80th Percentile Par. Inc. Above 80th Percentile
No
. o
f In
ve
nto
rs p
er
Th
ou
sa
nd
Ch
ildre
n
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Inventors per
1000 Children
Insufficient
Data
Minneapolis
4.9 Madison
4.3
Detroit
3.8
San Jose
5.4
San Francisco
3.8
>3.1
<0.4
1.5
The Origins of Inventors in America
Patent Rates per 1000 Children by Area where Child Grew Up
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Lost Einsteins
If women, minorities,
and children from
low-income families
invent at the same
rate as high-income
white men, the
number of inventors
in America would
quadruple 4x
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1. Publicly available data: Local area statistics discussed here are all publicly available and can be used to study a variety of questions
Supporting Future Work on Equality of Opportunity
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1. Publicly available data: Local area statistics discussed here are all publicly available and can be used to study a variety of questions
2. Global network: Supporting other researchers who are constructing and analyzing analogous statistics in other countries
Supporting Future Work on Equality of Opportunity
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Source: Asher and Novosad (2018)
Note: Figure shows mean educational rank attained by sons born to fathers born in bottom half of education distribution
The Geography of Intergenerational Mobility in India
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1. Publicly available data: Local area statistics discussed here are all publicly available and can be used to study a variety of questions
2. Global network: Supporting other researchers who are constructing and analyzing analogous statistics in other countries
3. Training the next generation of researchers and policy makers
Supporting Future Work on Equality of Opportunity
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Online Course: Using Big Data to Solve Economic and Social Problems
www.opportunityinsights.org/course