The Skills Problem and Inequality

The Skills Problem and Inequality

James HeckmanUniversity of Chicago

University College Dublin

Inequality and the Challenge of Employment (Axica, Forum)INET in Berlin: Rethinking Economics and Politics

Axica Conference Center & Federal Foreign Office, BerlinApril 14, 2012

James Heckman The Skills Problem and Inequality

Income — Average real after-tax household income.


2.0

2.5

3.0

3.5

4.0

4.5

5.0

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

Canada France Germany Italy Japan United Kingdom United States

Canada

United Kingdom

Italy

Germany

Japan

France

United States

Trends in Wage Dispersion (D9/D1), OECD (G7) countries, 1980‐2008

Source: OECD Earnings Database; Note: Wage dispersion: D9/D1 ratios of full‐time earnings calculated as the ratio of the upper bound value of the 9th decile to the upper bound value of the 1st decile.


Real, Composition-Adjusted Log Weekly Wages for Full-Time Full-Year Workers: U.S. MalesReal, Composition-Adjusted Log Weekly Wages for Full-Time

Full-Year Workers Males

0.2

.4.6

Com

posi

tion-

Adju

sted

Rea

l Log

Wee

kly

Wag

es

1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008Year

HSD HSGSMC CLGGTC

Source: Recreated from Acemoglu and Autor, 2011

People withPost-BA Degrees

College Graduates

People withSome College

High SchoolGraduates

High SchoolDropouts

Source: Recreated from Acemoglu and Autor, 2011


The Decline of the American Blue-Collar Middle Class








0

0.05

0.1

0.15

0.2

0.25

Share of youth not in education, employment or training (NEET) Ages 15‐24, EU, 2007

Women

Men

Source: European Comission. LFS anonymised microdata set. DG EMPL calculations.


50

Share of NEET Youth, OECD Countries 2007As a percentage of population in the age group

40

50

30

20

0

10

0

ethe

rland

sDe

nmark

Norway

uxem

bourg

Sloven

iawitzerland

Australia

chRe

public

Austria

Ireland

Swed

enFinland

Canada

ewZealand

Daverage*

France

Portugal

Estonia

Belgium

itedStates

Hungary

Spain

Greece

dKingdo

mPo

land

akRe

public

Italy

Brazil

Israel

Turkey

Ne

Lu S

Czec Ne

*OECD

Un

United

Slova

Youths aged between 20 and 24 Youths aged between 15 and 19


Share of youth in education, employment or inactivity by age 2006

UKN h l d


80%

100%

UK

80%

100%Netherlands

20%

40%

60%

20%

40%

60%

0%

15

100% 100%FranceGermany

0%

15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

60%

80%

100%

60%

80%

100%

0%

20%

40%

0%

20%

40%

0%

15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

0%

15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

Source: Source: OECD (2008e), Employment Outlook 2008.

UKN h l d


80%

100%

UK

80%

100%Netherlands

20%

40%

60%

20%

40%

60%

0%

15


0%

15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

60%

80%

100%

60%

80%

100%

0%

20%

40%

0%

20%

40%

0%

15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

0%

15 16 17 18 19 20 21 22 23 24 25 26 27 28 29





UKN h l d


80%

100%

UK

80%

100%Netherlands

20%

40%

60%

20%

40%

60%

0%


0%

15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

60%

80%

100%

60%

80%

100%

0%

20%

40%

0%

20%

40%

0%

15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

0%

15 16 17 18 19 20 21 22 23 24 25 26 27 28 29


15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

UKN h l d


80%

100%

UK

80%

100%Netherlands

20%

40%

60%

20%

40%

60%

0%

15


0%

15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

60%

80%

100%

60%

80%

100%

0%

20%

40%

0%

20%

40%

0%

15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

0%

15 16 17 18 19 20 21 22 23 24 25 26 27 28 29




Probability of Employment by Age 30 - Males

2 4 6 8 100

0.2

0.4

0.6

0.8

1ii. By Decile of Cognitive Factor

Decile

Prob

abilit

y and

Conf

idenc

e Inte

rval (2

.75-97

.5%)

2 4 6 8 100

0.2

0.4

0.6

0.8

1iii. By Decile of Non-Cognitive Factor

Decile

Note: This figure plots the probability of a given behavior associated with moving up in one ability distribution for someoneafter averaging out the other distribution. For example, the lines with markers show the effect of increasing noncognitiveability after integrating out the cognitive ability.

Source: Heckman, Stixrud, and Urzua (2006).


Ever been in jail by age 30, by ability (males)

Polarization

Argument

Skills

Evidence

Critical and Sensitive Periods

Environment

Intuitive

Estimates

Illustration

Summary

.15

.05

.10

.00

Cognitive

0 – 20 21 – 40 41 – 60 61 – 80 81 – 100

Prob

abili

ty

Percentile

Note: This �gure plots the probability of a given behavior associated with moving up in one ability distribution for someone after integrating out the other distribution. F or example, the lines with mark ers show the e�ect of increasing noncognitive ability after integrating the cognitive ability.

Ever Been in Jail by Age 30, by Ability (Males)





Ever been in jail by age 30, by ability (males)

Polarization

Argument

Skills

Evidence


Environment

Intuitive

Estimates

Illustration

Summary

.15

.05

.10

.00

PersonalityCognitive

0 – 20 21 – 40 41 – 60 61 – 80 81 – 100

Prob

abili

ty

Percentile


Ever Been in Jail by Age 30, by Ability (Males)





Probability of being single with children (females)

Polarization

Argument

Skills

Evidence


Environment

Intuitive

Estimates

Illustration

Summary

Probability of Being Single With Children (Females)

.10

.08

.04

.06

.02

Cognitive

0 – 20 21 – 40 41 – 60 61 – 80 81 – 100

Prob

abili

ty

Percentile






Probability of being single with children (females)

Polarization

Argument

Skills

Evidence


Environment

Intuitive

Estimates

Illustration

Summary

Probability of Being Single With Children (Females)

.08

.04

.06

.02

.10

PersonalityCognitive

0 – 20 21 – 40 41 – 60 61 – 80 81 – 100

Prob

abili

ty

Percentile






The Effect of Cognitive and Personality Endowments

Participated in 2006 Election

Personality

Source: Heckman, Humphries, Urzua and Veramendi (2011).


The Probability of Educational Decisions, by Endowment Levels

Dropping from Secondary School vs. Graduating

Personality



Probability of Graduating from High School - By Cognitive and Noncognitive Skill Decile

Personality



Probability of being a 4-year college graduate by age 30

1 2 3 4 5 6 7 8 9 100

0.2

0.4

0.6

0.8

1ii. By Decile of Cognitive Factor

Decile

Pro

bab

ility

an

dC

on

fiden

ce I

nte

rval

(2.

5-97

.5%

)

Notes: The data are simulated from the estimates of the model and our NLSY79 sample. We use the standard convention that higher deciles are associated with higher values of the variable.The confidence intervals are computed using bootstrapping (200 draws).

1 2 3 4 5 6 7 8 9 100

0.2

0.4

0.6

0.8

1iii. By Decile of

Decile

Personality




Cognitive Ability by Educational Status

Uncerti�ed Dropouts GEDs

Secondary SchoolGraduates

Males: Distribution of Cognitive Skills


Post-Secondary Educational Attainment Across Education Groups Through Age 40 (NLSY79) —Males


Distribution of Noncognitive Skill

Uncerti�ed Dropouts Secondary School

Graduates

GEDs

Males: Distribution of Noncognitive Skills


Differences in College Entry Proportions Between Minorities and Whites, Mid-1990s

Black-White Hispanic-White

Actual -0.12 -0.14

Adjusted 0.16 0.15

Source: Stephen V. Cameron and James J. Heckman, “The Dynamics of Educational Attainment for Black, Hispanic, and WhiteMales,” Journal of Political Economy 109 (3) (2001).


Differences in College Entry Proportions Between Minorities and Whites, Mid-1990s

Black-White Hispanic-White

Actual -0.12 -0.14

Adjusted 0.16 0.15

Source: Stephen V. Cameron and James J. Heckman, “The Dynamics of Educational Attainment for Black, Hispanic, and WhiteMales,” Journal of Political Economy 109 (3) (2001).


Shortfalls in Hourly Wages for Blacks and Hispanics in the Last Twenty Years: Actual Disparity andDisparity Adjusted for Ability

Males Females

Actual Adjusted

Black -25%

Hispanic -15%

∗Denotes not statistically significant from zero, that is, the adjusted gap is likely to arise from chance. Source: Author’s calculationsfrom the National Longitudinal Survey of Youth. For details, see the Web appendix athttp://jenni.uchicago.edu/understanding_b-w_gap/. The wages are adjusted for age.


http://jenni.uchicago.edu/understanding_b-w_gap/


Males Females

Actual Adjusted Adjusted

Black -25% -6%

Hispanic -15% 3%*





Males Females

Actual Adjusted Actual Adjusted

Black -25% -6% -17%

Hispanic -15% 3%* -7%





Males Females

Actual Adjusted Actual Adjusted

Black -25% -6% -17% 12%

Hispanic -15% 3%* -7% 17%




Trend in mean by age for cognitive score by maternal education

0.5

1M

ean

co

gn

itiv

e sc

ore

3 5 8 18Age (years)

College grad

Each score standardized within observed sample. Using all observations and assuming data missing at random. Source:Brooks-Gunn et al. (2006).



0.5

1M

ean

co

gn

itiv

e sc

ore

3 5 8 18Age (years)

College grad Some college




0.5

1M

ean

co

gn

itiv

e sc

ore

3 5 8 18Age (years)

College grad Some college HS Grad




0.5

1M

ean

co

gn

itiv

e sc

ore

3 5 8 18Age (years)

College grad Some college HS Grad Less than HS



0.05

0.10

0.15

0.20

0.25

0.30

Prop

ortio

n of

Chi

ldre

n in

Fam

ily T

ype

1975 1980 1985 1990 1995 2000 2005 2010Year

Divorced Married, Spouse AbsentWidowed Never Married/Single

Children Under 18 Living in Single Parent Households by Marital Status of Parent

Source: March CPS 1976‐2010 ; Note: Source: March CPS 1976‐2010. 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 from the calculation. Separated parents are included in “Married, Spouse Absent” Category


Source: Center for Disease Control and Prevention; Note: For the period 1940‐1950 on 1940 and 1950 birth rates are presented; Age of mother 15‐44


0 10 20 30 40 50 60 70

SwitzerlandItaly

CanadaMalta

LithuaniaSlovak Republic

LuxembourgSpain

GermanyIreland

PortugalCzech RepublicUnited States

AustriaHungaryFinland

NetherlandsLatvia

BelgiumUnited Kingdom

DenmarkNew Zealand

BulgariaFrance

SloveniaSwedenNorwayIceland

2008 1970

Source: Eurostat (2010), United Nations Statistical Division (2010) and National Statistical Offices Note: * Data refers to 2007 for Japan, Italy, Ireland, Australia, the United States, Belgium and New Zealand; 2006 for Korea; 2005 for Canada. The proportion of births out of wedlock is calculated as the percentage of all children born to parents who are not married (nor living in a legal partnership), occurring during that year .

Proportion of Births out of Wedlock in Selected OECD Countries 1970 and 2008*


The Proportion of Sole‐Parent Families in all Households with Children, latest year*

0

5

10

15

20

25

30

35

40

45

Latvia

Estonia

United States

Czech Re

public

United Kingdo

mSlovak Rep

ublic

Austria

Hungary

Poland

Canada

Lithuania

Finland

Iceland

Sloven

iaIre

land

New

Zealand

Norway

OEC

D‐29

France

Swed

enLuxembo

urg

Romania

Spain

Denm

ark

Japan

Germany

Italy

Bulgaria

Greece

Australia

Nethe

rland

sPo

rtugal

Belgium

Switzerland

Korea

Cyprus

Mexico

Source: OECD Family Database; Note: * Data concern 1999 for France; 2000: Estonia, Finland, Korea, Latvia, Switzerland, Turkey, and the United States; 2006‐7: Austria, Belgium, Bulgaria, Cyprus, Czech republic, Denmark, Greece, Hungary, Italy, Lithuania, Luxembourg, the Netherlands, Norway, Portugal, Slovak republic, Spain, and the United Kingdom; 2002: Ireland, Poland, Romania, Slovenia, and Sweden; 2005: Iceland, Mexico,


Mothers’ Speech and Child Vocabulary






Hart & Risley, 1995

Children enter school with ”meaningful differences” in vocabulary knowledge.

1. Cumulative Vocabulary Experiences

Family Words heard Words heard in a Words heard in a Word heard inStatus per hour 100-hour week 5,200 hour year 4 years

Welfare 616 62,000 3 million 13 million

Working Class 1,251 125,000 6 million 26 million

Professional 2,153 215,000 11 million 45 million

2. Cumulative Vocabulary at Age 3

Cumulative Vocabulary at Age 3

Children from welfare families: 500 words

Children from working class families: 700 words

Children from professional families: 1,100 words


Hart & Risley, 1995

Children enter school with ”meaningful differences” in vocabulary knowledge.

1. Cumulative Vocabulary Experiences

Family Words heard Words heard in a Words heard in a Word heard inStatus per hour 100-hour week 5,200 hour year 4 years

Welfare 616 62,000 3 million 13 million

Working Class 1,251 125,000 6 million 26 million

Professional 2,153 215,000 11 million 45 million

2. Cumulative Vocabulary at Age 3

Cumulative Vocabulary at Age 3

Children from welfare families: 500 words

Children from working class families: 700 words

Children from professional families: 1,100 words


Perry preschool program: IQ, by age and treatment group

79.6

95.5 94.9

91.3 91.7

88.1 87.7

85

75

80

85

90

95

100

IQ

4 5 6 7 8 9 10EntryAge

Treatment Group

Source: Perry Preschool Program. IQ measured on the Stanford Binet Intelligence Scale (Terman & Merrill, 1960).Test was administered at program entry and each of the ages indicated.


Perry preschool program: IQ, by age and treatment group

79.6

95.5 94.9

91.3 91.7

88.1 87.7

85

78.5

83.3 83.5

86.3 87.1 86.9 86.884.6

75

80

85

90

95

100

IQ

4 5 6 7 8 9 10EntryAge

Treatment Group Control Group

Source: Perry Preschool Program. IQ measured on the Stanford Binet Intelligence Scale (Terman & Merrill, 1960).Test was administered at program entry and each of the ages indicated.


Decompositions of Treatment Effects on Outcomes

.136

.062

.071

.071 .557

.161

.088

.144

.246

.114

.013

# f i d 40 ( )

# of adult arrests (misd.+fel.) , age 27 (‐)

# of felony arrests, age 27 (‐)

# of misdemeanor arrests, age 27 (‐)

CAT total⁽¹⁾, age 14 (+)

.077

.086

.056

.204

.149

.403

0% 20% 40% 60% 80% 100%

# of lifetime arrests, age 40 (‐)

# of adult arrests (misd.+fel.), age 40 (‐)

# of felony arrests, age 40 (‐)

# of misdemeanor arrests, age 40 (‐)

Cognitive Factor Externalizing Behavior Academic Motivation Other Factors

Notes: The total treatment effect is normalized to 100%. One-sided p-values are shown above each component in each outcome. “(+)” and “(-)” denotepositive and negative total treatment effects. “CAT total” denotes California Achievement Test total score.


Skills Enhance Each Other: Technology of Skill Formation

1

1

Personality and Social Skills Cognitive Skills

(sit still; pay attention; engage in learning; open to experience)

Skills Cross Foster Each Other



1

1



Health Cognitive Skills

(fewer lost school days; ability to concentrate)




1

1



Health Cognitive Skills

(fewer lost school days; ability to concentrate)

Cognitive Skills Produce better health

practices; produce more

motivation; greater

perception of rewards.

(child better understands and controls its environment)

Outcomes increase productivity, higher income

better health, more family investment

upward mobility, reduced social costs



Disparities by Education (Post-compulsory Education)

Note: Conti and Heckman (2010). Author’s calculations using BCS70.


Disparities by Education (Post-compulsory Education)

Note: Conti and Heckman (2010). Author’s calculations using BCS70.


Returns to a Unit Euro Invested

Source: Heckman (2008).











PredistributionNot

Redistribution


Documents

The Skills Problem and Inequality