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Social Mobility: A Progress Report
James HeckmanINET; HCEO
2017 INET Plenary ConferenceEdinburgh, ScotlandOctober 22nd, 2017
Heckman Social Mobility
Heckman Social Mobility
Early Childhood InterventionsThe Early Childhood Interventions Network (ECI) investigates the early origins of inequality and its lifetime consequences.
Network Leaders:Pia Britto | Flavio Cunha |James J. Heckman | Petra Todd
Inequality: Measurement, Interpretation, & PolicyThe Inequality: Measurement, Interpretation, and Policy Network (MIP) studies policies designed to reduce inequality and boost individual fl ourishing.
Network Leaders:Robert H. Dugger | Steven N. Durlauf |Scott Duke Kominers | Richard V. Reeves
Health InequalityThe Health Inequality Network (HI) unifi es several disciplines into a comprehensive framework for understanding health disparities over the lifecycle.
Network Leaders:Christopher Kuzawa |Burton Singer
Identity and PersonalityThe Identity and Personality Network (IP) studies the reciprocal relationship between individual diff erences and economic, social, and health outcomes.
Network Leaders:Angela Duckworth | Armin Falk | Joseph Kable | Tim Kautz | Rachel Kranton
MarketsThe Markets Network (M) investigates human capital fi nancing over the lifecycle.
Network LeadersDean Corbae |Lance Lochner | Mariacristina De Nardi
Family InequalityThe Family Inequality Network (FI) focuses on the interactions among family members to understand the well-being of children and their parents.
Network Leaders:Pierre-André Chiappori |Flavio Cunha | Nezih Guner
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Two Graphs that Dominate Current Discussions of SocialMobility
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Figure 1: Intergenerational Mobility and Inequality: The Great GatsbyCurve
lnY1| {z }income of
child
= ↵+ �|{z}IGE
lnY0| {z }income ofparent
+"
� ", Mobility #
Note: Data points for Italy and the United Kingdom overlap.
Heckman Social Mobility
Figure 2: The Geography of Upward Mobility in the United StatesThe Geography of Upward Mobility in the United States
Chances of Reaching the Top Fifth Starting from the Bottom Fifth by Metro Area
San Jose 12.9%
Salt Lake City 10.8% Atlanta 4.5%
Washington DC 11.0%
Charlotte 4.4%
Denver 8.7%
Note: Lighter Color = More Upward Mobility Download Statistics for Your Area at www.equality-of-opportunity.org
Boston 10.4%
Minneapolis 8.5% Chicago
6.5%
Source: Chetty (2016)Note: The measure of P(Child in Q5—Parent in Q1) derived from within-CZ OLS regressions of child income rank againstparent income rank.
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How to Interpret Any of These Relationships?
What Policies (If Any) Should Be Adopted to PromoteSocial Mobility? To Reduce Inequality?
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Direction of Causality for Gatsby Curve
• Inequality ") � " ?
• � ") inequality "?• Limited access to markets ) both � " and inequality "?
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Understanding the Sources of Inequality and SocialImmobility is Essential for Devising E↵ective Policies
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Family? Schools? Neighborhoods? Peers?
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Which Measure of Mobility to Use?
• Rank (positional) Mobility? (and in what distribution?)
• Absolute Mobility (child doing better than parent)?
• Mobility Within a Lifetime?
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Recent Cohorts Doing Worse Than Previous Ones:E↵ects Concentrated Among Younger Entrants Within
Cohorts
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Figure 3: Percent of Children Earning More than their Parents By ParentIncome Percentile
1940
1950
1960
1970
1980
0
20
40
60
80
100
0 20 40 60 80 100 Parent Income Percentile (conditional on positive income)
By Parent Income Percentile P
ct. o
f Chi
ldre
n E
arni
ng m
ore
than
thei
r Par
ents
Source: Chetty et al. (2017)
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Figure 4: Mean Rates of Absolute Mobility (Probability Children DoBetter Than Parents) by Cohort
50
60
70
80
90
100
1940 1950 1960 1970 1980
Child's Birth Cohort
Pct
. of C
hild
ren
Ear
ning
mor
e th
an th
eir P
aren
ts
Source: Chetty et al. (2017)
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Figure 5: Rising intergenerational elasticities (�)
Close Link Between Rise in Relative Wages of Skilled Labor and theIGE
50
Figure 1. Rising intergenerational elasticities
0
0.1
0.2
0.3
0.4
0.5
0.6
0
0.5
1
1.5
2
1950 1960 1970 1980 1990 2000
The 90‐10 Wage Gap and the IGE
90‐10 IGE
0
0.1
0.2
0.3
0.4
0.5
0.6
00.050.1
0.150.2
0.250.3
0.350.4
0.450.5
0.550.6
1950 1960 1970 1980 1990 2000
The Income Share of Top 10% and the IGE
Top 10 IGE
Source: Aaronson and Mazumder (2008)
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Figure 5: Rising intergenerational elasticities (�)
51
Source: Aaronson and Mazumder (2008)
0
0.1
0.2
0.3
0.4
0.5
0.6
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
1950 1960 1970 1980 1990 2000
The Return to College and the IGE
Returns to College IGE
Source: Aaronson and Mazumder (2008)
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Figure 6: Median Lifetime Income by Cohort and Gender
Source: Guvenen et al., 2017. “Lifetime Incomes in the United States over Six Decades.”
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Figure 7: Median Lifetime Income by Cohort (Across Males and Females)
Source: Guvenen et al., 2017. “Lifetime Incomes in the United States over Six Decades.”
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Figure 8: Age Profiles of Cross-Sectional Inequality, by Cohort
(a) Std Dev. of logs, Men (b) Std Dev. of logs, Women
(c) P90-10 ratio, Men (d) P90-10 ratio, Women
Figure 15: Age Profiles of Cross-Sectional Inequality, by Cohort
circles), 35 (blue squares), 45 (green triangles), and 55 (gray diamond) for each cohort.Cohorts that entered after 1983 have only partial life-cycle data, so not all data points areavailable for them. For every fifth cohort, the figure also plots the entire age profile.
Initial inequality for men (at age 25) has increased substantially – by about 30 log points– from a value of 0.55 for the 1968 cohort to 0.85 for the 2011 cohort. For comparison, recallfrom Figure 13a that the standard deviation of log income for men (of all ages) rose from0.64 to 0.96 from the 1957 cross section to the 2011 cross section, for a total of 32 log points.
37
Source: Guvenen et al., 2017. “Lifetime Incomes in the United States over Six Decades.”
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Growth in Inequality is in Early Adult Years Across Cohorts
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Figure 9: Qualified Military Available (QMA) Population, 17-24 YearsOld (2013)
Qualified Military Available (QMA) Population17‐24 years olds
Not Qualified to Serve: 71%Medical (including Overweight and Mental Health) 28% , Overlapping Reasons 31%, Drugs 8%, Conduct 1%, Dependents 2%, Aptitude 2%
Qualified but not available due to college enrollment: 12%
Qualified and Available but score < 30th on the AFQT: 4%
QMA I‐IIIB: 13%
Source: DoD QMA Study 2013
Source: DoD QMA Study (2013).
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Figure 10: Qualified Military Available (QMA): 2013 Estimates
Qualified HSDG I-IIIA 2% Qualified College
Grad I-IV 4% (IV =.3%)
Qualified Non-HSDG I-IIIA & HSDG IIIB
5%
Qualified Non-HSDG IIIB-IV & HSDG IV
6% (IV =3.4%)
Qualified College Enrolled I-IV
12%
Medical DQ Only (Includes Overweight
& Mental Health) 28%
Drugs DQ Only 8% Conduct DQ Only 1%
Dependents DQ Only 2% Aptitude DQ Only
2%
Medical & Drugs 3%
Drugs & Overweight 2%
Med, Drugs & MH 2% Drugs & Conduct 1%
Other Overlapping DQ 23%
Disqualified for Multiple Reasons
31%
(IV = 2%)
QMA: 17%
(5.8 million)
QMA I-IIIB: 13%
(4.4 million)
29% are eligible
to serve (9.6 million)
Source: DoD Qualified Military Available (QMA) Study 2013. Youth ages 17-24.Note: Percentages may not sum due to rounding.
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What are the Sources of Inequality and Immobility?
I. Taxes and transfers?
II. Skills? Skill Premia? (supply-based policy)
III. Macroeconomic trends and policies?
IV. Interactions?
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Role of Taxes and Transfers in Post Tax-Transfer Outcomes
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Figure 11: Inequality (Gini Coecient) of Market Income and Disposable(Net) Income in the OECD Area, Working-Age Persons, 2014
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Sources of Growth in Inequality
Figure 12: OECD Inequality: Demographic changes were less importantthan labour market trends in explaining changes in household earningsdistribution – Skills play an important role
-40 -20 0 20 40 60 80 100 120 140
-19% 42% 17% 11% 11% 39%
Percentage contribution
Percentage contributions to changes in household earnings inequality, OECD average,mid-1980s to mid-2000s
Men’s earnings disparity
Women’s employment Men’s employment
Assortative mating
Household structure
Residual
Note: Working-age population living in a household with a working-age head. Household earnings are calculated as the sumof earnings from all household members, corrected for di↵erences in household size with an equivalence scale (square root ofhousehold size). Percentage contributions of estimated factors were calculated with a decomposition method which relies onthe imposition of specific counterfactuals such as: “What would the distribution of earnings have been in recent year ifworkers’ attributes had remained at their early year level?”Source: Chapter 5, Figure 5.9, OECD (2013).
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Figure 13: Estimated Average Annual Percentage Change in VariousInequality Measures Accounted for by Factor Components, US 1979–2007
Gini P90/P10Actual 0.4 0.82
Household Structure 23% 33%Men's Employment 5% 5%Men's Earning Disparity 73% 50%Women's Employment -25% -22%Women's Earning Disparity 20% 29%Assortative Mating 10% 11%Other -5% -6%
Note: Household Structure: Marriage Rate, Men’s Employment: Male Head Employment, Men’s Earning Disparity: Malehead earnings distribution, Women’s Employment: Female Head Employment, Women’s Earning Disparity: Female headearnings distribution, Assortative Mating: Spouses’ earnings correlation.Source: Larrimore, Je↵. “Accounting for United States household income inequality trends: The changing importance ofhousehold structure and male and female labor earnings inequality.” Review of Income and Wealth. 60.4 (2014): 683-701.
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Fostering Skills to Promote Social Mobility and ReduceInequality?
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A Comprehensive Approach to Skills-Oriented Social Policy:E�cient Redistribution to Promote Mobility Within and
Across Generations
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Modern Approach Recognizes:
(1) Fundamental importance of skills in modern economies
(2) Multiplicity of skills
(3) The multiple sources producing skills(a) Schools(b) Families(c) Neighborhoods and peers(d) Firms
(4) The importance of supporting and incentivizing all of thesesources of skill
(5) Recent knowledge on e↵ective targeting of skills
(6) Great need for evaluations accounting for costs and benefitsmeasured in terms of social opportunity costs
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A Skills-based Policy Tackles Many Aspects of Poverty,Inequality, and Social Mobility
A Unified Approach to Policy
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Avoids Fragmented Solutions
• Current policy discussions have a fragmented quality.
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Solves Problems As They Arise“The Squeaky Wheel Gets the Grease”
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Is Prevention E�cient? How Well Can We Target?
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Evidence on the E↵ectiveness of Early Targeting to PromoteSkills (Including Character Skills)
• 80% of adult social problems regarding health, healthybehaviors, crime and poverty are due to 20% of the population.
• Reliable indicators of these problems by age 5(Caspi et al., 2016).
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Childhood Forecasting of a Small Segment of the Population with Large Economic Burden
Caspi, Moffitt, et al. (2017) Nature Human Behaviour
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The Pareto Principle
20% of the ActorsAccount for 80% of the Results.Vilfredo Pareto, 1848-1923
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Social Welfare Benefit Months
20% of Cohort Members = 80% of Total Social Welfare Benefit Months
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Link to Additional Caspi et al. Slides
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The High-need/High-cost Group in 3 or more sectors:How many health/social services do they use?
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Small Footprint of cohort members never in any high-cost group:
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Childhood Risk Factors to Describe High-cost Actor Groups:
Composites across ages 3, 5, 7, 9, 11
• IQ
• Self-control
• SES (socio-economic status)
• Maltreatment
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Adam Smith Wrong: People at Age 8 Are Vastly Di↵erentin Skills
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• 20% of people contribute 80% of social/health problems.
• A high-need/high-cost population segment uses ~half of resources in multiple sectors.
• Most high-need/high-cost people in this segment share risk factors in the first decade of life;
• Prediction is stronger than thought; AUC approaches .90.
• Brain integrity in the first years of life is important.
Seen in this way, early-life risks seem important enough to warrant investment in early-years preventions.
Summary of findings
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Exploit Understanding That Skill Deficits Are An ImportantSource of Many Social Problems
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Skill Development
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The Importance of Cognition and Character
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(a) Major advances have occurred in understanding which humancapacities matter for success in life.
(b) Cognitive ability as measured by IQ and achievement tests isimportant.
(c) So are the socio-emotional skills – sometimes called charactertraits or personality traits:
• Motivation• Sociability; ability to workwith others
• Attention
• Self Regulation• Self Esteem• Ability to defer gratification• Health and Mental Health
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• Beyond PISA scores
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Link to Report PDFhttp://tinyurl.com/OECD-Report-2014
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Cognitive and Socioemotional Skills Determine:
(a) Crime
(b) Earnings
(c) Health and healthy behaviors
(d) Civic participation
(e) Educational attainment
(f) Teenage pregnancy
(g) Trust
(h) Human agency and self-esteem
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Skill Gaps Open Up Early
• Gaps in skills across socioeconomic groups open up very early:• Persist strongly for cognitive skills• Less strongly for noncognitive skills
• Skills are not set in stone at birth—but they solidify as peopleage. They have genetic components.
• Skills evolve and can be shaped in substantial part byinvestments and environments.
Heckman Social Mobility
Figure 14: Mean Achievement Test Scores by Age by Maternal Education
!
Dropout
Dropout
Source: Brodsky, Gunn et al.
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Figure 15: Gaps throughout life, by mother’s level of education, Denmark
Figure 1: Gaps throughout life, by mother’s level of education
6.5
77.
58
8.5
9Se
lvre
gule
ring
og s
amar
bejd
e (g
ns.)
3,0 3,5 4,0 4,5 5,0 5,5Alder (halve år)
Ingen Erhvervsfag.Videregående
Selvregulering og samarbejde efter mors uddannelse
Age: 0 ys 0 ys 3-5 ys 8-14 ys 25 ys 30 ys 40 ys 40-50 ys 54 ys 60 ys
Outcome: Birth Not Score for Test scores No Years Wage Not In the Alive
weight admitted selfregulation Danish criminal of ear- contac- labor
to neo- in national tests conviction school- nings ted a force
natal ward ing hospital
Unit: Gram Fraction Rating Test score Fraction Years 1.000DKK Fraction Fraction Fraction
Note: Figure shows average outcomes by mother’s highest completed education. In the figures with three levels, mother’s education is defined as:BLUE, only compulsory schooling; RED, high school; GREEN, college
In the figures with five levels, mother’s education is defined as: BLUE, only compulsory schooling; PINK, vocational; GREEN, high school; RED, college;
YELLOW, master or phd degree
Age: 0 yrs 0 yrs 3–5 yrs
Outcome: Birth weight Not admitted to Score for self-neo-natal ward regulation
Unit: Gram Fraction Rating
Heckman Social Mobility
Figure 15: Gaps throughout life, by mother’s level of education,Denmark, Cont’d
Figure 1: Gaps throughout life, by mother’s level of education
Age: 0 ys 0 ys 3-5 ys 8-14 ys 25 ys 30 ys 40 ys 40-50 ys 54 ys 60 ys
Outcome: Birth Not Score for Test scores No Years Wage Not In the Alive
weight admitted selfregulation Danish criminal of ear- contac- labor
to neo- in national tests conviction school- nings ted a force
natal ward ing hospital
Unit: Gram Fraction Rating Test score Fraction Years 1.000DKK Fraction Fraction Fraction
Note: Figure shows average outcomes by mother’s highest completed education. In the figures with three levels, mother’s education is defined as:BLUE, only compulsory schooling; RED, high school; GREEN, college
In the figures with five levels, mother’s education is defined as: BLUE, only compulsory schooling; PINK, vocational; GREEN, high school; RED, college;
YELLOW, master or phd degree
Age: 8–14 yrs 25 yrs 30 yrs
Outcome: Test scores, Danish No criminal Years ofin national tests conviction schooling
Unit: Test score Fraction Years
Heckman Social Mobility
Figure 15: Gaps throughout life, by mother’s level of education,Denmark, Cont’d
Figure 1: Gaps throughout life, by mother’s level of education
Age: 0 ys 0 ys 3-5 ys 8-14 ys 25 ys 30 ys 40 ys 40-50 ys 54 ys 60 ys
Outcome: Birth Not Score for Test scores No Years Wage Not In the Alive
weight admitted selfregulation Danish criminal of ear- contac- labor
to neo- in national tests conviction school- nings ted a force
natal ward ing hospital
Unit: Gram Fraction Rating Test score Fraction Years 1.000DKK Fraction Fraction Fraction
Note: Figure shows average outcomes by mother’s highest completed education. In the figures with three levels, mother’s education is defined as:BLUE, only compulsory schooling; RED, high school; GREEN, college
In the figures with five levels, mother’s education is defined as: BLUE, only compulsory schooling; PINK, vocational; GREEN, high school; RED, college;
YELLOW, master or phd degree
Age: 40 yrs 40–50 yrs 54 yrs 60 yrs
Outcome: Wage earnings Not contacted In the labor Alivea hospital force
Unit: 1.000DKK Fraction Fraction Fraction
Heckman Social Mobility
How to Interpret This Evidence
• Evidence on the early emergence of gaps leaves open the question ofwhich aspects of families are responsible for producing these gaps.
• Genes? Eugenics?
• Parenting and family investment decisions?
• Family environments? Neighborhood, peer, and sorting e↵ects?
• The evidence from a large body of research demonstrates animportant role for investments and family and communityenvironments in determining adult capacities above and beyond therole of the family in transmitting genes.
• The quality of home environments by family type is highlypredictive of child success.
• Home environments can be strengthened in a voluntary fashion.
Heckman Social Mobility
Genes, Biological Embedding of Experience,and Gene-Environment Interactions
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Genes Do Not Explain Time Series Trends or IntercountryDi↵erences
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Link to Image of DNA Methylation
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Family Environments and Child Outcomes
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Hart & Risley, 1995
• In the USA, children enter school with “meaningful di↵erences”in vocabulary knowledge.
1. Emergence of the ProblemIn a typical hour, the average child hears:
Family Actual Di↵erences in Quantity Actual Di↵erences in QualityStatus of Words Heard of Words HeardWelfare 616 words 5 a�rmatives, 11 prohibitions
Working Class 1,251 words 12 a�rmatives, 7 prohibitionsProfessional 2,153 words 32 a�rmatives, 5 prohibitions
2. Cumulative Vocabulary at Age 3
Cumulative Vocabulary at Age 3Children from welfare families: 500 wordsChildren from working class families: 700 wordsChildren from professional families: 1,100 words
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Child Home Environments are Compromised:A Growing Trend World-wide
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Figure 16: Children Under 18 Living in Single Parent Households byMarital Status of Parent
Note: Parents are defined as the head of the household. Children are defined as individuals under 18, living in the household,and the child of the head of household. Children who have been married or are not living with their parents are excluded fromthe calculation. Separated parents are included in “Married, Spouse Absent” Category.Source: IPUMS March CPS 1976-2016.
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Figure 17: Proportion of Live Births Outside Marriage
0
10
20
30
40
50
601960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
Proportion of Live Births Outside Marriage
United Kingdom United States Scotland
Source: Eurostat, CDC and National record of SchotlandSource: Eurostat, CDC and National record of Scotland.
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Figure 18: Share of births outside of marriage, 1970a, 1990b and 2014 orlatest available yearc — Proportion (%) of all births where the mother’smarital status at the time of birth is other than marriedb
0102030405060708090
100%
2014 1995 1970
Source: OECD Family Database
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Consequences of Cohabitation
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Figure 19: Self-Regulation and Cooperation by Family Status
Source: ’Daycare of the Future’, Bleses and Jensen (2017)
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Figure 20: Vocabulary by Family Status
Source: ’Daycare of the Future’, Bleses and Jensen (2017)
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Link to Additional Figures
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Figure 21: Empathy by Family Status
Source: ’Daycare of the Future’, Bleses and Jensen (2017)
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These Relationships Remain Strong Even After Controllingfor Parental Income and Education and Other Measures of
Skills
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Link to Additional Figures (Children from Denmark)
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Is Family Influence Just About Money?
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Alms to the Poor? The Traditional Approach
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Great Society Programs Tried This to End IntergenerationalPoverty
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Figure 22: Trends in the Intergenerational Correlation of WelfareParticipation
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Intergen
erational Elasticity
Year
Trends in the Intergenerational Elasticity of AFDC/TANF, Other Welfare, SSI, and Food Stamps
Source: Hartley et al. 2016Note: Welfare participation includes AFDC/TANF, SSI, Food Stamps and Other Welfare.
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Welfare Subsidized Poverty Enclaves – Detached The Poorfrom Society
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The Dynamics of Skill Formation:Two Notions of Complementarity
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Static Complementarity
• The productivity of investment greater for the more capable.• High returns for more capable people: Matthew E↵ect• Does this justify social Darwinism?• On grounds of economic e�ciency, should we invest primarilyin the most capable?
• Answer: It depends on where in the stage of the lifecycle we consider the investment.
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Dynamic Complementarity
• If we invest today in the base capabilities of disadvantagedyoung children, there is a huge return.
• Makes downstream investment more productive.
• No necessary tradeo↵ between equality and e�ciencygoals.
• Augmenting this investment by public infrastructure andschools gives agency to people and enhances economic andsocial functioning.
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• Both processes are at work.
• No necessary contradiction.
• Investing early creates the skill base that makes laterinvestment productive.
• E↵ective targeting.
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Skills Beget Skills
Social-emotional Skills Cognitive Skills, Health
Cognitive Skills, Noncognitive Skills
Cognitive Skills Produce better health practices;produce more motivation; greaterperception of rewards.
Outcomes: increased productivity, higher income, better health,more family investment, upward mobility, reduced social costs.
Health
(sit still; pay attention; engage in learning; open to experience)
(fewer lost school days; ability to concentrate)
(child better understands and controls its environment)
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Figure 23: Life Cycle Developmental Framework
Prenatal ParentalEnvironments
Perinatal ParentalEnvironments
Investment:Parenting and
Preschool
ParentalEnvironments Parenting and
Preschool
Parenting, Schooling, and Workplace OJT
Parental, Social, andEconomic Environments
Parental and Governmental Prenatal
InvestmentFetal Endowments
Skills
Skills
Adult Skills
Childhood Skills(personality, cognition,
and health)
PRENATAL
BIRTH
ADULTHOOD
EARLY
CHILDHOOD 0-3
LATER
CHILDHOOD
Family and Economic Environments
Adult Education and Workplace OJT
Adult Skill ADULTHOOD
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Modern Understanding of the Dynamics of Skill FormationCauses Us to Rethink Traditional Distinctions in Philosophy
and Political Science
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Raises Question of How and When Merit Acquired?Merit vs. Chance vs. E↵ort Distinctions Currently Used in
Philosophy and Political Science Literature Are Without MuchEmpirical Content
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50% of Inequality in Lifetime Earnings Due to Factors inPlace by Age 18Cunha et al. (2005)
• John Roemer (2017) Reports a Similar Estimate
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Powerful Evidence For E↵ectiveness of TargetedInterventions Across the Life Cycle
• Contradicts The Eugenics Argument
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Perry Preschool Project
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Starts at Age 32 hrs a Day – Two Years 10% Rate of Return Per Dollar
Invested
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Enriches Home Lives of Children Outside of Childcare CenterKeeps Parental Engagement Active Long After the Children Leave
Pre-K
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Parental response to Perry Preschool Program after 1 year experience oftreatment:
1020
3040
5060
Prop
ortio
n
−.015 −.01 −.005 0 .005 .01 .015Belief in Importance of Parenting
Control Treatment
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Intergenerational E↵ects of Perry Program
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Selected Outcomes for All Children of the Perry Participants
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Completedhigh school
In goodhealth
Employedfull-time
Neversuspended
Neverarrested
P .0849 P .0624 P .0548 P .0347 P .0792
Par
ticip
ant-l
evel
ave
rage
of c
hild
ren'
s ou
tcom
es
Control group's mean Treatment effect (difference-in-means)
P: Worst-case randomization test-based exact p-value
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Selected Outcomes for All Children of the Male Participants
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Neversuspended
Neverarrested
P .0290 P .0459
Par
ticip
ant-l
evel
ave
rage
of c
hild
ren'
s ou
tcom
es
Control group's mean Treatment effect (difference-in-means)
P: Worst-case randomization test-based exact p-value
Heckman Social Mobility
Selected Outcomes for Male Children of the Perry Participants
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Attendedcollege
In goodhealth
Neversuspended
Neverarrested
P .0085 P .0464 P .0546 P .0887
Par
ticip
ant-l
evel
ave
rage
of c
hild
ren'
s ou
tcom
es
Control group's mean Treatment effect (difference-in-means)
P: Worst-case randomization test-based exact p-value
Heckman Social Mobility
Selected Outcomes for Male Children of the Male Participants
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Completedcollege
In goodhealth
Neverarrested
P .0454 P .0207 P .0558
Par
ticip
ant-l
evel
ave
rage
of c
hild
ren'
s ou
tcom
es
Control group's mean Treatment effect (difference-in-means)
P: Worst-case randomization test-based exact p-value
Heckman Social Mobility
Selected Outcomes for Male Children of the Female Participants
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Attendedcollege
Neversuspended
P .0205 P .0593
Par
ticip
ant-l
evel
ave
rage
of c
hild
ren'
s ou
tcom
es
Control group's mean Treatment effect (difference-in-means)
P: Worst-case randomization test-based exact p-value
Heckman Social Mobility
The Carolina Abecedarian CARE ProjectStarts at Birth
Foundation for Educare
Heckman Social Mobility
Figure 24: Abecedarian Project, Health E↵ects at Age 35 (Males)
Source: Campbell, Conti, Heckman, Moon, Pinto, Pungello, and Pan (2014).
Heckman Social Mobility
Substantial Lifetime Benefits
Figure 25: Net Present Value of Main Components of the Cost/benefitAnalysis Over the Life-cycle, ABC/CARE Males and Females
−1
0
1
2
3
4
100,
000’
s (2
014
USD
)
Program Costs
Total Benefits
∗Labor Income
Parental IncomeCrime
∗∗QALYs
Treatment vs. Next Best Significant at 10%
Per−annum Rate of Return: 13% (s.e. 5%). Benefit−cost Ratio: 5.6 (s.e. 2.39)
Heckman Social Mobility
Rate of Return:
• Overall: 13.7% per annum
• Males: 14% per annum
• Females: 10% per annum
Heckman Social Mobility
Enhances Parent-Child Engagement
Heckman Social Mobility
Home Visiting ProgramsEnhance Parent-Child Interactions
Heckman Social Mobility
The Jamaica Study:Grantham-McGregor et al.
Low Cost and E↵ective
Heckman Social Mobility
Preparing For Life (PFL, 2016)Home Visiting in Ireland – Orla Doyle
Heckman Social Mobility
Enriched Charter Schools Starting at Age 4Feature Mentoring Through Elementary School
Heckman Social Mobility
Figure 26: Achievement Test Results by Grade (UCCS)
Source: Hassrick, E. M., Raudenbush, S. W., & Rosen, L. S. (2017)
Heckman Social Mobility
Organizational Change Coupled With Substantial Mentoringand Personalized Education Account for Success of UCCS
Heckman Social Mobility
Beneficial Causal Outcomes of Education(Heckman, Humphries, and Veramendi, 2016)
1 Self-reported health
2 Voting
3 Trust
4 Employment
5 Wages
6 Participation in welfare
7 Depression
8 Self-esteem
9 Incarceration
10 Health related work limitations
11 Smoking
12 White-collar employment
Heckman Social Mobility
Strength of E↵ect Di↵ers by Grade Attained and Varies OverOutcomes
Heckman Social Mobility
Work Experience and On-the-Job Training
• Learning-by-doing (and sometimes failing) is a major source oflearning
• Learning by imitation
Heckman Social Mobility
The policies that are e↵ective for adolescents providementoring and often integrate schooling and work. At thecore of e↵ective mentoring is what is at the core of e↵ectiveparenting: attachment, interaction, and trust. E↵ectivepolicies focus on developing social and emotional skills,teaching conscientiousness.
Heckman Social Mobility
Mentoring: Age-Adjusted Parenting
Heckman Social Mobility
One Goal: Adolescent Mentoring
Heckman Social Mobility
Figure 27: Distribution of Cognitive and Non-Cognitive Skills forOneGoal Participants and Non-Participants
0.2
.4.6
Dens
ity
−4 −3 −2 −1 0 1 2 3 4Standardized Cognitive Skill Factor Score
skipKolmogorov˘Smirnov test for equality of distribution functions: Participants vs. OneGoal School Non−Participants: p−value = 0.00 Participants vs. Non−OneGoal School Non−Participants: p−value = 0.00
(a) Males
0.2
.4.6
Dens
ity
−4 −3 −2 −1 0 1 2 3 4Standardized Cognitive Skill Factor Score
skipKolmogorov˘Smirnov test for equality of distribution functions: Participants vs. OneGoal School Non−Participants: p−value = 0.00 Participants vs. Non−OneGoal School Non−Participants: p−value = 0.00
(b) FemalesCognitive Skill
0.2
.4.6
Dens
ity
−4 −3 −2 −1 0 1 2 3 4Standardized Non−Cognitive Skill Factor Score
skipKolmogorov−Smirnov test for equality of distribution functions: Participants vs. OneGoal School Non−Participants: p−value = 0.00 Participants vs. Non−OneGoal School Non−Participants: p−value = 0.00
(a) Males
0.2
.4.6
Dens
ity
−4 −3 −2 −1 0 1 2 3 4Standardized Non−Cognitive Skill Factor Score
skipKolmogorov−Smirnov test for equality of distribution functions: Participants vs. OneGoal School Non−Participants: p−value = 0.00 Participants vs. Non−OneGoal School Non−Participants: p−value = 0.00
(b) FemalesNon−Cognitive Skill
ParticipantsOneGoal School Non−ParticipantsNon−OneGoal School Non−Participants
0.2
.4.6
Dens
ity−4 −3 −2 −1 0 1 2 3 4
Standardized Cognitive Skill Factor ScoreskipKolmogorov˘Smirnov test for equality of distribution functions: Participants vs. OneGoal School Non−Participants: p−value = 0.00 Participants vs. Non−OneGoal School Non−Participants: p−value = 0.00
(a) Males
0.2
.4.6
Dens
ity
−4 −3 −2 −1 0 1 2 3 4Standardized Cognitive Skill Factor Score
skipKolmogorov˘Smirnov test for equality of distribution functions: Participants vs. OneGoal School Non−Participants: p−value = 0.00 Participants vs. Non−OneGoal School Non−Participants: p−value = 0.00
(b) FemalesCognitive Skill
0.2
.4.6
Dens
ity
−4 −3 −2 −1 0 1 2 3 4Standardized Non−Cognitive Skill Factor Score
skipKolmogorov−Smirnov test for equality of distribution functions: Participants vs. OneGoal School Non−Participants: p−value = 0.00 Participants vs. Non−OneGoal School Non−Participants: p−value = 0.00
(a) Males
0.2
.4.6
Dens
ity
−4 −3 −2 −1 0 1 2 3 4Standardized Non−Cognitive Skill Factor Score
skipKolmogorov−Smirnov test for equality of distribution functions: Participants vs. OneGoal School Non−Participants: p−value = 0.00 Participants vs. Non−OneGoal School Non−Participants: p−value = 0.00
(b) FemalesNon−Cognitive Skill
ParticipantsOneGoal School Non−ParticipantsNon−OneGoal School Non−Participants
Source: Kautz and Zanoni (2014)
Heckman Social Mobility
Figure 27: Distribution of Cognitive and Non-Cognitive Skills forOneGoal Participants and Non-Participants, Cont’d
0.2
.4.6
Dens
ity−4 −3 −2 −1 0 1 2 3 4
Standardized Cognitive Skill Factor ScoreskipKolmogorov˘Smirnov test for equality of distribution functions: Participants vs. OneGoal School Non−Participants: p−value = 0.00 Participants vs. Non−OneGoal School Non−Participants: p−value = 0.00
(a) Males
0.2
.4.6
Dens
ity
−4 −3 −2 −1 0 1 2 3 4Standardized Cognitive Skill Factor Score
skipKolmogorov˘Smirnov test for equality of distribution functions: Participants vs. OneGoal School Non−Participants: p−value = 0.00 Participants vs. Non−OneGoal School Non−Participants: p−value = 0.00
(b) FemalesCognitive Skill
0.2
.4.6
Dens
ity
−4 −3 −2 −1 0 1 2 3 4Standardized Non−Cognitive Skill Factor Score
skipKolmogorov−Smirnov test for equality of distribution functions: Participants vs. OneGoal School Non−Participants: p−value = 0.00 Participants vs. Non−OneGoal School Non−Participants: p−value = 0.00
(a) Males
0.2
.4.6
Dens
ity
−4 −3 −2 −1 0 1 2 3 4Standardized Non−Cognitive Skill Factor Score
skipKolmogorov−Smirnov test for equality of distribution functions: Participants vs. OneGoal School Non−Participants: p−value = 0.00 Participants vs. Non−OneGoal School Non−Participants: p−value = 0.00
(b) FemalesNon−Cognitive Skill
ParticipantsOneGoal School Non−ParticipantsNon−OneGoal School Non−Participants
0.2
.4.6
Dens
ity−4 −3 −2 −1 0 1 2 3 4
Standardized Cognitive Skill Factor ScoreskipKolmogorov˘Smirnov test for equality of distribution functions: Participants vs. OneGoal School Non−Participants: p−value = 0.00 Participants vs. Non−OneGoal School Non−Participants: p−value = 0.00
(a) Males
0.2
.4.6
Dens
ity
−4 −3 −2 −1 0 1 2 3 4Standardized Cognitive Skill Factor Score
skipKolmogorov˘Smirnov test for equality of distribution functions: Participants vs. OneGoal School Non−Participants: p−value = 0.00 Participants vs. Non−OneGoal School Non−Participants: p−value = 0.00
(b) FemalesCognitive Skill
0.2
.4.6
Dens
ity
−4 −3 −2 −1 0 1 2 3 4Standardized Non−Cognitive Skill Factor Score
skipKolmogorov−Smirnov test for equality of distribution functions: Participants vs. OneGoal School Non−Participants: p−value = 0.00 Participants vs. Non−OneGoal School Non−Participants: p−value = 0.00
(a) Males
0.2
.4.6
Dens
ity
−4 −3 −2 −1 0 1 2 3 4Standardized Non−Cognitive Skill Factor Score
skipKolmogorov−Smirnov test for equality of distribution functions: Participants vs. OneGoal School Non−Participants: p−value = 0.00 Participants vs. Non−OneGoal School Non−Participants: p−value = 0.00
(b) FemalesNon−Cognitive Skill
ParticipantsOneGoal School Non−ParticipantsNon−OneGoal School Non−Participants
Source: Kautz and Zanoni (2014)
Heckman Social Mobility
Figure 28: Treatment E↵ects for Main Outcomes
0.1
.2.3
Prob
abilit
y
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(a) Grad HS by Y2
0.0
5.1
.15
.2Pr
obab
ility
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(b) Not Arrested by Y3
0.1
.2.3
.4Pr
obab
ility
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(c) Enroll College Y30
.1.2
.3.4
Prob
abilit
y
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(d) Enroll 4−Year College Y3
0.1
.2.3
.4Pr
obab
ility
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(e) Complete 2 Sem College Y3
0.1
.2.3
.4.5
Prob
abilit
y
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(f) Complete 4 Sem College Y4
Effect p<0.05 (vs. 0)+/− S.E. p<0.10 (vs. 0)
0.1
.2.3
Prob
abilit
y
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(a) Grad HS by Y2
0.0
5.1
.15
.2Pr
obab
ility
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(b) Not Arrested by Y3
0.1
.2.3
.4Pr
obab
ility
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(c) Enroll College Y3
0.1
.2.3
.4Pr
obab
ility
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(d) Enroll 4−Year College Y3
0.1
.2.3
.4Pr
obab
ility
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(e) Complete 2 Sem College Y3
0.1
.2.3
.4.5
Prob
abilit
y
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(f) Complete 4 Sem College Y4
Effect p<0.05 (vs. 0)+/− S.E. p<0.10 (vs. 0)
Source: Kautz and Zanoni (2014)
Heckman Social Mobility
Figure 28: Treatment E↵ects for Main Outcomes, Cont’d
0.1
.2.3
Prob
abilit
y
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(a) Grad HS by Y2
0.0
5.1
.15
.2Pr
obab
ility
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(b) Not Arrested by Y3
0.1
.2.3
.4Pr
obab
ility
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(c) Enroll College Y3
0.1
.2.3
.4Pr
obab
ility
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(d) Enroll 4−Year College Y3
0.1
.2.3
.4Pr
obab
ility
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(e) Complete 2 Sem College Y3
0.1
.2.3
.4.5
Prob
abilit
y
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(f) Complete 4 Sem College Y4
Effect p<0.05 (vs. 0)+/− S.E. p<0.10 (vs. 0)
0.1
.2.3
Prob
abilit
y
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(a) Grad HS by Y2
0.0
5.1
.15
.2Pr
obab
ility
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(b) Not Arrested by Y3
0.1
.2.3
.4Pr
obab
ility
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(c) Enroll College Y3
0.1
.2.3
.4Pr
obab
ility
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(d) Enroll 4−Year College Y3
0.1
.2.3
.4Pr
obab
ility
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(e) Complete 2 Sem College Y3
0.1
.2.3
.4.5
Prob
abilit
y
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(f) Complete 4 Sem College Y4
Effect p<0.05 (vs. 0)+/− S.E. p<0.10 (vs. 0)
Source: Kautz and Zanoni (2014)
Heckman Social Mobility
Figure 28: Treatment E↵ects for Main Outcomes, Cont’d
0.1
.2.3
Prob
abilit
y
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(a) Grad HS by Y2
0.0
5.1
.15
.2Pr
obab
ility
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(b) Not Arrested by Y3
0.1
.2.3
.4Pr
obab
ility
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(c) Enroll College Y3
0.1
.2.3
.4Pr
obab
ility
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(d) Enroll 4−Year College Y3
0.1
.2.3
.4Pr
obab
ility
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(e) Complete 2 Sem College Y3
0.1
.2.3
.4.5
Prob
abilit
y
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(f) Complete 4 Sem College Y4
Effect p<0.05 (vs. 0)+/− S.E. p<0.10 (vs. 0)
0.1
.2.3
Prob
abilit
y
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(a) Grad HS by Y2
0.0
5.1
.15
.2Pr
obab
ility
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(b) Not Arrested by Y3
0.1
.2.3
.4Pr
obab
ility
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(c) Enroll College Y3
0.1
.2.3
.4Pr
obab
ility
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(d) Enroll 4−Year College Y3
0.1
.2.3
.4Pr
obab
ility
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(e) Complete 2 Sem College Y3
0.1
.2.3
.4.5
Prob
abilit
y
Males Females
BasicDemographics
+CogSkill
+Non−CogSkill
BasicDemographics
+CogSkill
+Non−CogSkill
(f) Complete 4 Sem College Y4
Effect p<0.05 (vs. 0)+/− S.E. p<0.10 (vs. 0)
Source: Kautz and Zanoni (2014)
Heckman Social Mobility
Universal Ingredient in E↵ective Interventions that ProduceSkills:
Parenting – Mentoring – Love
Heckman Social Mobility
Power of Place?
Heckman Social Mobility
Figure 29: The Geography of Upward Mobility in the United StatesThe Geography of Upward Mobility in the United States
Chances of Reaching the Top Fifth Starting from the Bottom Fifth by Metro Area
San Jose 12.9%
Salt Lake City 10.8% Atlanta 4.5%
Washington DC 11.0%
Charlotte 4.4%
Denver 8.7%
Note: Lighter Color = More Upward Mobility Download Statistics for Your Area at www.equality-of-opportunity.org
Boston 10.4%
Minneapolis 8.5% Chicago
6.5%
Source: Chetty (2016).Heckman Social Mobility
Figure 30: Causal E↵ects of Growing up in Di↵erent Counties onEarnings in Adulthood
Note: Lighter colors represent areas where children from low-income families earn more as adults
For Children in Low-Income (25th Percentile) Families in the Washington DC Area
Charles
Baltimore
DC
Hartford
Source: Chetty (2016)
Heckman Social Mobility
Figure 31: The Geography of Intergenerational Mobility
FIGURE VI: The Geography of Intergenerational Mobility
A. Absolute Upward Mobility: Mean Child Rank for Parents at 25th Percentile (r̄25) by CZ
B. Relative Mobility: Rank-Rank Slopes (r̄100 � r̄0)/100 by CZ
Notes: These figures present heat maps of our two baseline measures of intergenerational mobility by commuting zone (CZ).Both figures are based on the core sample (1980-82 birth cohorts) and baseline family income definitions for parents andchildren. Children are assigned to commuting zones based on the location of their parents (when the child was claimed asa dependent), irrespective of where they live as adults. In each CZ, we regress child income rank on a constant and parentincome rank. Using the regression estimates, we define absolute upward mobility (r̄25) as the intercept + 25�(rank-rankslope), which corresponds to the predicted child rank given parent income at the 25th percentile (see Figure V). We definerelative mobility as the rank-rank slope; the di�erence between the outcomes of the child from the richest and poorest familyis 100 times this coe�cient (r̄100 � r̄0). The maps are constructed by grouping CZs into ten deciles and shading the areas sothat lighter colors correspond to higher absolute mobility (Panel A) and lower rank-rank slopes (Panel B). Areas with fewerthan 250 children in the core sample, for which we have inadequate data to estimate mobility, are shaded with the cross-hatchpattern. In Panel B, we report the unweighted and population-weighted correlation coe�cients between relative mobility andabsolute mobility across CZs. The CZ-level statistics underlying these figures are reported in Online Data Table V.
Source: Chetty et al. (2014)
Heckman Social Mobility
Figure 32: The Geography of College Attendance by Parent IncomeGradients
ONLINE APPENDIX FIGURE VIIThe Geography of College Attendance by Parent Income Gradients
A. Slope of College Attendance-Parent Rank Gradients by CZ
B. College Attendance Rates for Children with Parents at the 25th Percentile by CZ
Notes: To construct these figures, we regress an indicator for college attendance on parent income rank (in the nationaldistribution) for each CZ separately. College attendance is defined by the presence of a 1098-T form filed by a college onbehalf of the student. We use the core sample (1980-82 birth cohorts) and baseline family income definitions for parents.Children are assigned to commuting zones based on the location of their parents (when the child was claimed as a dependent),irrespective of where they live as adults. In Panel A, we map the slope coe�cients on the college attendance indicator fromthe CZ-level regressions. Panel B maps the fitted values from the regressions at parent rank 25. The maps are constructedby grouping CZs into ten deciles and shading the areas so that lighter colors correspond to higher mobility (smaller slopesin Panel A and higher fitted values in Panel B). Areas with fewer that 250 children in the core sample, for which we haveinadequate data to estimate mobility, are shaded with the cross-hatch pattern. We report the unweighted and population-weighted correlation coe�cients across CZs between these mobility measures and the baseline measures in Figure VI. TheCZ-level statistics underlying these figures are reported in Online Data Table V.
Source: Chetty et al. (2014)
Heckman Social Mobility
Figure 33: The Geography of Teenage Birth by Parent Income Gradients
ONLINE APPENDIX FIGURE IXThe Geography of Teenage Birth by Parent Income Gradients
A. Slope of Teenage Birth-Parent Rank Gradients by CZ
B. Teenage Birth Rates for Children with Parents at the 25th Percentile by CZ
Notes: To construct these figures, we regress an indicator for teenage birth on parent income rank (in the national distribution)for each CZ separately. Teenage birth is defined as ever claiming a dependent child who was born while the mother was aged13-19. We use female children in the core sample (1980-82 birth cohorts) and baseline family income definitions for parents.Children are assigned to commuting zones based on the location of their parents (when the child was claimed as a dependent),irrespective of where they live as adults. In Panel A, we map the slope coe�cient on the teenage birth indicator from the CZ-level regressions. Panel B maps the fitted values from these regressions at parent income rank 25. The maps are constructedby grouping CZs into ten deciles and shading the areas so that lighter colors correspond to smaller slopes (in magnitudes)in Panel A and smaller fitted values in Panel B. Areas with fewer that 250 female children in the core sample, for which wehave inadequate data to estimate mobility measures, are shaded with the cross-hatch pattern. We report the unweighted andpopulation-weighted correlation coe�cients across CZs between these mobility measures and the baseline measures in FigureVI. The CZ-level statistics underlying these figures are reported in Online Data Table V.
Source: Chetty et al. (2014)
Heckman Social Mobility
What Aspects of Place Account for These Correlations?Family? Schools? Peers? Social Norms?
Heckman Social Mobility
Determinants of Correlations Not Yet Known
Heckman Social Mobility
Figure 34: Alternative Measures of Upward Mobility
ONLINE APPENDIX FIGURE VIAlternative Measures of Upward Mobility
A. Absolute Upward Mobility Adjusted for Local Cost-of-Living B. Probability of Reaching Top Quintile from Bottom Quintile
C. Fraction of Children Above Poverty Line Given Parents at 25th Percentile
Notes: These figures present heat maps for alternative measures of upward income mobility. Children are assigned to commut-ing zones based on the location of their parents (when the child was claimed as a dependent), irrespective of where they liveas adults. All panels use baseline family income definitions for parents. Panels A and C use the core sample (1980-82 birthcohorts) and panel B uses the 1980-85 birth cohorts. Panel A replicates Figure VIa, adjusting for di�erences in cost-of-livingacross areas. To construct this figure, we first deflate parent income by a cost-of-living index (COLI) for the parent’s CZ whenhe/she claims the child as a dependent and child income by a COLI for the child’s CZ in 2012. We then compute parentand child ranks using the resulting real income measures and replicate the procedure in Figure VIa exactly. The COLI isconstructed using data from the ACCRA price index combined with information on housing values and other variables asdescribed in Appendix A. Panel B presents a heat map of the probability that a child reaches the top quintile of the nationalfamily income distribution for children conditional on having parents in the bottom quintile of the family income distributionfor parents. These probabilities are taken directly from Online Data Table VI. Panel C shows the fitted values at parentrank 25 from a regression of an indicator for child family income being above the poverty line on parent income rank (seeAppendix F for details). The maps are constructed by grouping CZs into ten deciles and shading the areas so that lighter colorscorrespond to higher mobility. Areas with fewer that 250 children in the core sample (or the 1980-85 cohorts for Panel B), forwhich we have inadequate data to estimate mobility, are shaded with the cross-hatch pattern. We report the unweighted andpopulation-weighted correlation coe�cient across CZs between these mobility measures and the baseline measure in FigureVIa. The CZ-level statistics underlying Panels A and C are reported in Online Data Table V.
Source: Chetty et al. (2014)
Heckman Social Mobility
Figure 35: The Geography of Teenage Birth by Parent Income Gradients
ONLINE APPENDIX FIGURE IXThe Geography of Teenage Birth by Parent Income Gradients
A. Slope of Teenage Birth-Parent Rank Gradients by CZ
B. Teenage Birth Rates for Children with Parents at the 25th Percentile by CZ
Notes: To construct these figures, we regress an indicator for teenage birth on parent income rank (in the national distribution)for each CZ separately. Teenage birth is defined as ever claiming a dependent child who was born while the mother was aged13-19. We use female children in the core sample (1980-82 birth cohorts) and baseline family income definitions for parents.Children are assigned to commuting zones based on the location of their parents (when the child was claimed as a dependent),irrespective of where they live as adults. In Panel A, we map the slope coe�cient on the teenage birth indicator from the CZ-level regressions. Panel B maps the fitted values from these regressions at parent income rank 25. The maps are constructedby grouping CZs into ten deciles and shading the areas so that lighter colors correspond to smaller slopes (in magnitudes)in Panel A and smaller fitted values in Panel B. Areas with fewer that 250 female children in the core sample, for which wehave inadequate data to estimate mobility measures, are shaded with the cross-hatch pattern. We report the unweighted andpopulation-weighted correlation coe�cients across CZs between these mobility measures and the baseline measures in FigureVI. The CZ-level statistics underlying these figures are reported in Online Data Table V.
Source: Chetty et al. (2014)
Heckman Social Mobility
Figure 36: Trends in family income segregation, by race
56
Figure 6. Trends in family income segregation, by race
Source: Bischoff and Reardon (2014); authors’ tabulations of data from U.S. Census (1970-2000) and American Community Survey (2005- 2011). Averages include all metropolitan areas with at least 500,000 residents in 2007 and at least 10,000 families of a given race in each year 1970-2009 (or each year 1980-2009 for Hispanics). This includes 116 metropolitan areas for the trends in total and white income segregation, 65 metropolitan areas for the trends in income segregation among black families, and 37 metropolitan areas for the trends in income segregation among Hispanic families. Note: the averages presented here are unweighted. The trends are very similar if metropolitan areas are weighted by the population of the group of interest.
Source: Bischo↵ and Reardon (2014)Notes: Authors’ tabulations of data from U.S. Census (1970-2000) and American Community Survey (2005- 2011). Averagesinclude all metropolitan areas with at least 500,000 residents in 2007 and at least 10,000 families of a given race in each year1970-2009 (or each year 1980-2009 for Hispanics). This includes 116 metropolitan areas for the trends in total and whiteincome segregation, 65 metropolitan areas for the trends in income segregation among black families, and 37 metropolitanareas for the trends in income segregation among Hispanic families. Note: the averages presented here are unweighted. Thetrends are very similar if metropolitan areas are weighted by the population of the group of interest.
Heckman Social Mobility
Figure 37: Spatial variation in per capita public school expenditure
57
Figure 7. Spatial variation in per capita public school expenditure
Note: 2014 per pupil expenditure, in dollars. Source: NCES.
Source: NCES.Note: 2014 per pupil expenditure, in dollars.
Heckman Social Mobility
Figure 38: Exposure to violent crime
59
Figure 9. Exposure to violent crime
Note: Violent crimes per thousand people, 2012. Source: Uniform Crime Reporting Program.
Source: Uniform Crime Reporting Program.Note: Violent crimes per thousand people, 2012.
Heckman Social Mobility
Interventions That Shift Children Across Places:The Impacts of Neighborhoods on Economic Opportunity
MTO (2016)
Heckman Social Mobility
0%
20%
40
%
60%
80
%
100%
10 15 20 25 30 Age of Child when Parents Move
Effects of Moving to a Different Neighborhood
on a Child’s Income in Adulthood by Age at Move P
erce
ntag
e G
ain
from
Mov
ing
to a
Bet
ter A
rea
Boston
Chicago
Source: Chetty (2016)
Heckman Social Mobility
Figure 39: Impacts of MTO on Children Below Age 13 at RandomAssignment (Age 24-28)
5000
70
00
9000
11
000
1300
0 15
000
1700
0
Control Section 8 Experimental Voucher
Indi
vidu
al In
com
e at
Age
≥ 2
4 ($
)
Indi
vidu
al In
com
e at
Age
≥ 2
4 ($
)
Individual Earnings (ITT)
$12,380 $12,894 $11,270
p = 0.101 p = 0.014
Source: Chetty et al. (2015)
Heckman Social Mobility
Figure 40: Impacts of MTO on Children Below Age 13 at RandomAssignment
0 5
10
15
20
1800
0 19
000
2000
0 21
000
2200
0
(a) College Attendance (ITT) (b) College Quality (ITT)
Control Section 8
Control Section 8
Experimental Voucher
Experimental Voucher
Col
lege
Atte
ndan
ce, A
ges
18-2
0 (%
)
Mea
n C
olle
ge Q
ualit
y, A
ges
18-2
0 ($
) 16.5% 17.5% 19.0%
p = 0.028 p = 0.435
$20,915 $21,547 $21,601
p = 0.014 p = 0.003
Source: Chetty et al. (2015)
Heckman Social Mobility
Figure 41: Impacts of MTO on Children Below Age 13 at RandomAssignment
15
17
19
21
23
25
Zip
Pov
erty
Sha
re (%
)
0 12
.5
25
37.5
50
B
irth
with
no
Fath
er o
n B
irth
Cer
tific
ate
(%)
(a) ZIP Poverty Share in Adulthood (ITT) (b) Birth with no Father Present (ITT)
Females Only
33.0% 31.7% 28.2% 23.8% 22.4% 22.2%
p = 0.008 p = 0.047 p = 0.610 p = 0.042
Control Section 8
Control Section 8
Experimental Voucher
Experimental Voucher
Source: Chetty et al. (2015)
Heckman Social Mobility
Figure 42: Impacts of MTO on Children Age 13-18 at RandomAssignment
5000
70
00
9000
11
000
1300
0 15
000
1700
0
Control Section 8
Experimental Voucher
Indi
vidu
al In
com
e at
Age
≥ 2
4 ($
)
Individual Earnings (ITT)
$15,882 $14,749 $14,915
p = 0.259 p = 0.219
Source: Chetty et al. (2015)
Heckman Social Mobility
Figure 43: Impacts of MTO on Children Age 13-18 at RandomAssignment
0 5
10
15
20
1800
0 19
000
2000
0 21
000
2200
0
(a) College Attendance (ITT) (b) College Quality (ITT)
Control Section 8
Control Section 8
Experimental Voucher
Experimental Voucher
15.6% 12.6% 11.4%
p = 0.013 p = 0.091
$21,638 $21,041 $20,755
p = 0.168 p = 0.022
Col
lege
Atte
ndan
ce, A
ges
18-2
0 (%
)
Mea
n C
olle
ge Q
ualit
y, A
ges
18-2
0 ($
)
Source: Chetty et al. (2015)
Heckman Social Mobility
Figure 44: Impacts of MTO on Children Age 13-18 at RandomAssignment
15
17
19
21
23
25
Zip
Pov
erty
Sha
re (%
)
0 12
.5
25
37.5
50
B
irth
No
Fath
er P
rese
nt (%
)
23.6% 22.7% 23.1%
p = 0.418 p = 0.184 p = 0.857 p = 0.242
(a) ZIP Poverty Share in Adulthood (ITT) (b) Birth with no Father Present (ITT) Females Only
Control Section 8
Control Section 8
Experimental Voucher
Experimental Voucher
41.4% 40.7% 45.6%
Source: Chetty et al. (2015)
Heckman Social Mobility
Sources of These E↵ects are UnclearWhat Is It About Neighborhoods That Produce the
Geographic Correlations?
(a) Schools?
(b) Parents?
(c) Peers?
(d) Group norms?
Heckman Social Mobility
General Equilibrium E↵ects Not Accounted For(Recall response to bussing in 1960s and 1970s vacated entire
neighborhoods)
Heckman Social Mobility
Analytical Models of Neighborhood E↵ectsDurlauf and Sheshadri (2017)
1. Labor market outcomes for adults are determined by the humancapital that they accumulate earlier in life.
2. Human capital accumulation is, along important dimensions,socially determined. Local public finance of education createsdependence between the income distribution of a school district andthe per capita expenditure on each student in the community.Social interactions, ranging from peer e↵ects to role models toformation of personal identity, create a distinct relationship betweenthe communities in which children develop and the skills they bringto the labor market.
Heckman Social Mobility
3. In choosing a neighborhood, incentives exist for parents to prefermore a✏uent neighbors. Other incentives exist to prefer largercommunities. These incentives interact to determine the extent towhich communities are segregated by income in equilibrium.Permanent segregation of descendants of the most and leasta✏uent families is possible even though there are no poverty trapsor a✏uence traps, as conventionally defined.
4. Greater cross-sectional inequality of income increases the degree ofsegregation of neighborhoods. The greater the segregation thegreater are the disparities in human capital between children frommore and less a✏uent families, which creates the Great GatsbyCurve.
Heckman Social Mobility
Putting It All Together: Redistribution and Importance ofIncentives
A Case Study of Denmark/U.S.
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Denmark the Garden of Eden?
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Figure 45: Intergenerational Mobility and Inequality: The Great GatsbyCurve
lnY1| {z }income of
child
= ↵+ �|{z}IGE
lnY0| {z }income ofparent
+"
� ", Mobility #
Heckman Social Mobility
Denmark Spends Generously on Public EducationEqualizes Expenditure By Design
Heckman Social Mobility
Produces Better Test Score Distributions than U.S.
Heckman Social Mobility
Figure 46: Percentage of Students at Each Proficiency Level, PISA 2003
(a) Mathematics Scale (b) Reading Scale
10.2
15.5
23.9 23.8
16.6
8
2
4.7
10.7
20.6
26.2
21.9
11.8
4.1
0
5
10
15
20
25
30
Below level 1 Level 1 Level 2 Level 3 Level 4 Level 5 Level 6
Percentage of Students at Each Proficiency Level on the Mathematics Scale, PISA 2003
United States Denmark
Source : OECD2003 Learning for Tomorrow’s World ,First Results from PISA 2003
6.5
12.9
22.7
27.8
20.8
9.3
4.6
11.9
24.9
33.4
20
5.2
0
5
10
15
20
25
30
35
40
Below level 1 Level 1 Level 2 Level 3 Level 4 Level 5
Percentage of Students at Each Proficiency Level on the Reading Scale, PISA 2003
United States Denmark
Source : OECD2003 Learning for Tomorrow’s World ,First Results from PISA 2003
Source: OECD (2003) Learning for Tomorrow’s World, First Results from PISA (2003).
Heckman Social Mobility
• Nonetheless, there are steep gradients of children’seducation in parental education, income, and wealth inboth the U.S. & Denmark.
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Figure 47: Language Test Scores in Grade 2–8, by Mother’s Education
Source: Beuchert & Nandrup (2016).
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Figure 48: Intergenerational Educational Mobility and Inequality
YOS|{z}years ofschooling
child
= ↵+ �|{z}IGE
education
YOS|{z}years ofschoolingparents
+"
IGE
of S
choo
ling
Source: Setzler (2015).
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Strong Sorting by Family Background Status
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Scandinavia invests heavily in child development and booststhe test scores of the disadvantaged (though not to fullequality), but undermines these beneficial e↵ects byproviding weak labor market incentives.
Heckman Social Mobility
Returns to skills
Percent increase in hourly wages for a standard deviation increase in numeracy
0
5
10
15
20
25
SWE FIN DNK BELᵇ ITA NLD NOR FRA CZE AUS AUT KOR POL CAN IRL SVK ESP EST JPN DEU GBR ᵇ USA
Coefficients on numeracy scores from country-specific OLS regressions of log hourly wages on proficiency scores standardised at the country level
Heckman Social Mobility
Tax and Transfer Policy the Main Engine of ScandinavianReduced Inequality and Enhanced Social Mobility
Heckman Social Mobility
Summary
Heckman Social Mobility
• What can we say about the Inequality and Social Mobility?
• What are the facts? What are the causes?
• What are e↵ective social policies?
• Skills are important.
• But what are e↵ective strategies for shaping skills?
• At what age and with what interventions?
• Early years are important in shaping skills, but not the full story.
• Interventions in adolescence and adulthood are e↵ect.
• Neighborhoods play a role, but which aspects remain to besorted out.
• Love, mentoring and care matter.
Heckman Social Mobility
• Incentives built into tax and transfer policy: can underminee↵ective policies.
• More generally, labor market rewards and structure play animportant role.
• Role for macro policy and policies that encourage firms to hireand mentor workers (macro growth becoming more unevenlydistributed).
Heckman Social Mobility
Traditional Redistribution Less E↵ective Than Policies ThatPromote and Reward Skills
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Redistribution is Ine↵ective for Promoting in the Long-RunSocial Mobility
With Improper Incentive Can Cause Harm
Heckman Social Mobility
Thank You For YourAttention
Heckman Social Mobility
Early Childhood InterventionsThe Early Childhood Interventions Network (ECI) investigates the early origins of inequality and its lifetime consequences.
Network Leaders:Pia Britto | Flavio Cunha |James J. Heckman | Petra Todd
Inequality: Measurement, Interpretation, & PolicyThe Inequality: Measurement, Interpretation, and Policy Network (MIP) studies policies designed to reduce inequality and boost individual fl ourishing.
Network Leaders:Robert H. Dugger | Steven N. Durlauf |Scott Duke Kominers | Richard V. Reeves
Health InequalityThe Health Inequality Network (HI) unifi es several disciplines into a comprehensive framework for understanding health disparities over the lifecycle.
Network Leaders:Christopher Kuzawa |Burton Singer
Identity and PersonalityThe Identity and Personality Network (IP) studies the reciprocal relationship between individual diff erences and economic, social, and health outcomes.
Network Leaders:Angela Duckworth | Armin Falk | Joseph Kable | Tim Kautz | Rachel Kranton
MarketsThe Markets Network (M) investigates human capital fi nancing over the lifecycle.
Network LeadersDean Corbae |Lance Lochner | Mariacristina De Nardi
Family InequalityThe Family Inequality Network (FI) focuses on the interactions among family members to understand the well-being of children and their parents.
Network Leaders:Pierre-André Chiappori |Flavio Cunha | Nezih Guner
Additional Caspi et al. Slides
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Cigarette Smoking Pack-Years
20% of Cohort Members = 68% of Total Tobacco Smoking Pack-Years
Heckman Social Mobility
Prescription Drug Fills
20% of Cohort Members = 89% of Total Prescription Drug Fills
Heckman Social Mobility
Hospital Bed-nights
20% of Cohort Members = 77% of Total Hospital Bed-Nights
Heckman Social Mobility
Excess Weight in Kilograms
20% of Cohort Members = 98% of Total Excess Obese Kilograms
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Criminal Court Convictions
20% of Cohort Members = 97% of Total Criminal Court Convictions
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Return to main text
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Additional Doyle (2016) Slides
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Table 1: Cognitive Development
Preparing for Life (Doyle et al., 2016).*IPW-adjusted permutation tests with 100,000 replications controlling forgender. One tailed (right-sided) test.
Heckman Social Mobility
Table 2: Language Development
Preparing for Life (Doyle et al., 2016).*IPW-adjusted permutation tests with 100,000 replications controlling forgender. One tailed (right-sided) test.
Heckman Social Mobility
Table 3: Approaches to Learning
Preparing for Life (Doyle et al., 2016).*IPW-adjusted permutation tests with 100,000 replications controlling forgender. One tailed (right-sided) test.
Heckman Social Mobility
Table 4: Physical Wellbeing
Preparing for Life (Doyle et al., 2016).*IPW-adjusted permutation tests with 100,000 replications controlling forgender. One tailed (right-sided) test.
Heckman Social Mobility
Figure 49: Distribution of BAS GCA Cognitive Scores at School Entry
Heckman Social Mobility
Figure 50: Percentage of Children Scoring Above and Below Average inVerbal Ability At School Entry
xv
Executive Summary
At school entry?
By school entry, the PFL programme had a signifi cant and large impact on children’s overall verbal ability, their expressive and receptive language skills, and their communication and emerging literacy skills. This means that the children who received the high treatment supports were better able to use and understand language and had better skills for reading and writing. The programme did not improve children’s basic or advanced literacy skills.
Verbal Ability Below Average
Perc
enta
ge o
f Chi
ldre
n
Verbal Ability Above Average
High TreatmentLow Treatment
0
5
10
15
20
25
30
35
40
50
45
26%
46%
25%
8%
“What’s easy about school?” “Ahm, my letters and I could read on my own now.”
“What’s hard about school?” “Ahhh, tricky words..They are words that are tricky, but they don’t trick us.” PFL Child in Junior Infants
Figure ES.5 - Percentage of Children Scoring Above and Below Average in Verbal Ability
The PFL programme made limited improvements to children’s language development up to 48 months. Children who received the high treatment supports had better emergent literacy skills at 24 months and better communication skills at 36 months. The programme did not improve children’s expressive or receptive language skills during the programme.
During the programme?
Did PFL improve children’s language development…
Source: PFL Evaluation Team at the UCD Geary Institute for Public Policy (2016).
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Figure 51: Mean Scores of Children on Ability to Manage Attention TaskAt School Entry
xvi
Preparing for Life: Early Childhood InterventionDid Preparing for Life Improve Children’s School Readiness?
By school entry, the PFL programme had some impact on how children approached learning. Children who received the high treatment supports were better able to manage their attention, yet the programme did not change their general approaches to learning, interest in school subjects, keenness to explore new things, or their ability to control impulsive behaviour.
At school entry?
Ability to Manage Attention Score
Mea
n Sc
ore
High TreatmentLow Treatment
15
16
17
18
19
20
21
22
23
24
25
Figure ES.6 - Mean Scores of Children on Ability to Manage Attention Task
The PFL programme improved children’s approaches to learning from 36 months onwards. This means that the children who received the high treatment supports were more likely to explore their world and learn with toys.
During the programme?
Did PFL improve children’s approaches to learning…
“What will Riley the rabbit like about school?” “He’ll like to work...Because you get to colour in...You learn and you get to colour and play and you get to go out into the yard….I like colouring and I like going out to the yard...”
PFL Child in Junior Infants
Source: PFL Evaluation Team at the UCD Geary Institute for Public Policy (2016).
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Figure 52: Behavioural Problems*
Preparing for Life (Doyle et al., 2016).*IPW-adjusted permutation tests with 100,000 replications controlling forgender. One tailed (right-sided) test.
Heckman Social Mobility
Figure 53: Percentage of Children ‘Not on Track’ on Measures of Socialand Emotional Development At School Entry
xvii
The PFL programme reduced children’s internalising and externalising behaviour problems from 24 months onwards. This means that the children who received the high treatment supports were less likely to feel negative emotions such as sadness or act out in negative ways like throwing tantrums. From 36 months onwards, the programme improved children’s positive prosocial behaviours such as sharing with others.
Executive Summary
At school entry?
During the programme?
By school entry, the PFL programme had a signifi cant impact on reducing children’s hyperactivity and inattentive behaviours and improving their social competencies and autonomy. This means that the children who received the high treatment supports were less likely to be distractible in the classroom, got on better with their classmates, and had the skills needed to be independent in the school day. The programme had no impact on children’s aggression, oppositional-defi ance, anxious behaviour, or on their prosocial, respectful behaviours according to the teacher reports.
Did PFL improve children’s social and emotional development…
Hyperactivity &Inattention
Social Competencewith Peers
Autonomy0
10
20
30
40
50
60
Perc
enta
ge o
f Chi
ldre
n
High TreatmentLow Treatment
16%
31%
25%
43%
27%
51%
“What will Riley the rabbit need to know about school?” “She will have to know to say hi in the yard….Maybe she will make some friends out in the yard I guess….Yes I really think so.”
PFL Child in Junior Infants
Figure ES.7 - Percentage of Children ‘Not on Track’ on Measures of Social and Emotional DevelopmentSource: PFL Evaluation Team at the UCD Geary Institute for Public Policy (2016).
Heckman Social Mobility
Figure 54: Protein Intake*
Preparing for Life (Doyle et al., 2016).*IPW-adjusted permutation tests with 100,000 replications controlling forgender. One tailed (right-sided) test.
Heckman Social Mobility
Figure 55: Body Mass Index at Age 4*
Preparing for Life (Doyle et al., 2016).*IPW-adjusted permutation tests with 100,000 replications controlling forgender. One tailed (right-sided) test.
Heckman Social Mobility
Figure 56: Percentage of Outpatient Children who ever visitedOutpatient Departments At School Entry
xviii
Preparing for Life: Early Childhood InterventionDid Preparing for Life Improve Children’s School Readiness?
The programme had a signifi cant impact on reducing the amount of hospital services the children used and improved how families used these services. There was a limited impact on the diagnoses children received in hospital, but children who received the high treatment supports were less likely to have to visit the hospital for urgent reasons, and were less likely to experience fractures. They were also less likely to have visited the Orthopaedics, Physiotherapy, Paediatrics, Occular, and Plastic Surgery Outpatient departments.
At school entry?
Orthopaedics Physiotherapy Paediatrics Occular Departments Plastic Surgery0
10
20
30
40
50
60
70
80
18%
38%
12%
0% 0%
15%
20%
31%
5%
75%
Perc
enta
ge o
f chi
ldre
n
High TreatmentLow Treatment
Figure ES.8 - Percentage of Outpatient Children who ever visited Outpatient Departments
The PFL programme had an impact on the children’s physical wellbeing and motor development from birth onwards. Children who received the high treatment supports were more likely to be born naturally, to be immunised, were healthier, had better diets and motor skills, were less likely to be overweight, and more likely to be toilet trained.
During the programme?
Did PFL improve children’s physical wellbeing and motor development…
“I eat healthy stuff, I eat my nanny’s apples, I eat nanny’s bananas...And I eat carrots and grapes. I don’t even eat peppers, they are too hot
PFL Child in Junior Infants
Source: PFL Evaluation Team at the UCD Geary Institute for Public Policy (2016).
Heckman Social Mobility
Figure 57: Mean Scores of Children on Physical Wellbeing and MotorDevelopment At School Entry
xix
Executive Summary
Overall, PFL achieved its aim of improving children’s school readiness. The programme had a positive and signifi cant impact on each of the fi ve domains as summarised below:
Key Results
Figure ES.10 - Key Results from the PFL Evaluation
Cognitive Development
Language Development
Approaches to Learning
Social & Emotional Development
Physical Wellbeing &Motor Development
Impacts during the programme
Cognitive improvements from 18 months onwards
High treatment children were better at combining words at 24 months
High treatment children showed better approaches to learning from 36 months
2% of high treatment children were at risk of behavioural problems compared to 17% of low treatment children at 48 months
24% of high treatment children were classifi ed as overweight compared to 41% of low treatment children at 48 months
Impacts at School Entry
10 point IQ gap between children in the high and low treatment groups
25% of high treatment children had above average verbal ability compared to 8% of low treatment children
High treatment children were better able to control their attention than low treatment children
25% of high treatment children ‘not on track’ in their social competence compared to 43% of low treatment children
High treatment children had better gross and fi ne motor skills
0
2
4
6
8
10
Gross and Fine Motor Skills Physical Independance
Mea
n Sc
ore
High TreatmentLow Treatment
Figure ES.9 - Mean Scores of Children on Physical Wellbeing and Motor Development at School Entry
By school entry, the PFL programme had a signifi cant impact on children’s gross and fi ne motor skills and their physical independence. The programme had no impact on children’s physical readiness for the school day.
Source: PFL Evaluation Team at the UCD Geary Institute for Public Policy (2016).
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Return to main text
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DNA Methylation
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Figure 58: DNA Methylation and Histone Acetylation Patterns in Youngand Old TwinsMethylation patterns in young and old twins
Manel Esteller
Source: Fraga, Ballestar et al. (2005)
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Return to main text
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Figure 59: Print Concepts by Family Status
Source: ’Daycare of the Future’, Bleses and Jensen (2017)
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Figure 60: Rhyme by Family Status
Source: ’Daycare of the Future’, Bleses and Jensen (2017)
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Return to main text
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Table 5: Estimated coe�cients from regressions of child outcomes onfamily status, controlling for age and mothers education. Sample of 3-5year old children from Denmark.
TEAM TEAM SEAM SEAMGeometry Numbers Empathy Self-Regulation
& Cooperation
Cohabitating couple -0.064 -0.332*** -0.445*** -0.252**Single -0.125* -0.405*** -0.712*** -0.649***
(0.072) (0.130) (0.166) (0.116)
Controls
Age intervals X X X XMother’s education X X X X
Observations 5218 5196 5571 5572
Notes: Child outcomes: mathematical skills and socio-emotional skills. Married couple is reference category. Standard errorsin parentheses * p < 0.10, ** p < 0.05, *** p < 0.01.Source: ‘Daycare of the Future,’ Bleses and Jensen (2017).
Heckman Social Mobility
Table 6: Estimated coe�cients from regressions of child outcomes onfamily status, controlling for age and mothers education. Sample of 3-5year old children from Denmark.
Language Language Language LanguageRhyme Print Concepts Vocabulary Comprehension
Cohabitating couple 0.003 -0.466*** -0.333** -0.098(0.107) (0.151) (0.163) (0.088)Single -0.350*** -0.209 -0.206 -0.100
(0.124) (0.169) (0.187) (0.102)
Controls
Age intervals X X X XMother’s education X X X X
Observations 4284 3003 4803 4933
Notes: Child outcomes: language skills (four subscales). Married couple is reference category. Standard errors in parentheses* p < 0.10, ** p < 0.05, *** p < 0.01.Source: ‘Daycare of the Future,’ Bleses and Jensen (2017).
Heckman Social Mobility
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Heckman Social Mobility