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Observations Abilities/Outcomes Model
Extract for “The Technology of Skill Formation”
by James J. Heckman and Flavio Cunha. Published in the American Economic Review
(2007). Volume 97, Issue 2, pp.31–47.
James J. HeckmanUniversity of Chicago
AEA Continuing Education ProgramASSA Course: Microeconomics of Life Course Inequality
San Francisco, CA, January 5-7, 2016
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 1 / 76
Observations Abilities/Outcomes Model
Cunha and Heckman, 2007, and later work
This paper uses simple economic models of skill formation toorganize a large body of evidence on the development of skills inchildren in economics, psychology, education and neuroscience.
The existing theoretical literature on child development ineconomics treats childhood as a single period (see, e.g., Gary S.Becker and Nigel Tomes, 1986; Roland Benabou, 2000; S. RaoAiyagari et al., 2002).
The implicit assumption in this approach is that inputs into theproduction of skills at different stages of childhood are perfectsubstitutes.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 2 / 76
Observations Abilities/Outcomes Model
To account for a large body of evidence, it is important to buildmodels of skill formation with multiple stages of childhood,where inputs at different stages are complements and wherethere is self-productivity of investment.
In order to rationalize the evidence, it is also important torecognize three distinct credit constraints operating on thefamily and its children.
1 The inability of a child to choose its parents. This is thefundamental constraint imposed by the accident of birth.
2 The inability of parents to borrow against their children’sfuture income to finance investments in them.
3 The inability of parents to borrow against their own income tofinance investments in their children.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 3 / 76
Observations Abilities/Outcomes Model
A model that is faithful to the evidence must recognize:
a Parental influences are key factors governing childdevelopment;
b Early childhood investments must be distinguished from latechildhood investments;
c An equity-efficiency trade-off exists for late investments, butnot for early investments;
d Abilities are created, not solely inherited, and are multiple invariety;
e The traditional ability-skills dichotomy is misleading. Bothskills and abilities are created; and
f The “nature versus nurture” distinction is obsolete.g Gene-environment or epigenetic interactions are important.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 4 / 76
Observations Abilities/Outcomes Model
These insights change the way we interpret evidence and designpolicy about investing in children.
Point (a) is emphasized in many papers.
Point (b) is ignored in models that consider only one period ofchildhood investment.
Points (c), (d) and (e) have received scant attention in theformal literature on child investment.
Point (f) is ignored in the literature that partitions the varianceof child outcomes into components due to nature andcomponents due to nurture.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 5 / 76
Observations Abilities/Outcomes Model
Observations About Human Diversity and HumanDevelopment and Some Facts Our Model Explains
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 6 / 76
Observations Abilities/Outcomes Model
Three Observations
The first observation is that ability matters.
A large number of empirical studies document that cognitiveability is a powerful determinant of wages, schooling,participation in crime and success in many aspects of social andeconomic life.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 7 / 76
Observations Abilities/Outcomes Model
Second observation: Abilities are multiple in nature.
Noncognitive abilities (perseverance, motivation, timepreference, risk aversion, self-esteem, self-control, preferencesfor leisure) have direct effects on wages (controlling forschooling), schooling, teenage pregnancy, smoking, crime,performance on achievement tests and many other aspects ofsocial and economic life.
See, e.g., Samuel Bowles and Herb Gintis (1976); SamuelBowles et al. (2001); Lex Borghans et al. (2006); Heckman etal. (2006).
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 8 / 76
Observations Abilities/Outcomes Model
Some Evidence on the Importance of Cognitive andNoncognitive Skills
Recent research has shown that earnings, employment, laborforce experience, college attendance, teenage pregnancy,participation in risky activities, compliance with healthprotocols and participation in crime strongly depend oncognitive and noncognitive abilities.
Noncognitive abilities mean socioemotional regulation, timepreference, personality factors and ability to work with others.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 9 / 76
Observations Abilities/Outcomes Model
Much public policy discussion is devoted to cognitive scores or“smarts.”
Many governments and public policy officials focus onachievement on a test score at a certain age to measure successor failure in schools.
Yet an emerging body of evidence shows that, as is intuitivelyobvious and commonsensical, much more than smarts arerequired.
Motivation,Sociability; ability to work with others,Attention,Self Regulation,Self Esteem,Time Preference,Health and Mental Health.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 10 / 76
Observations Abilities/Outcomes Model
The importance of noncognitive skills, for example, tends to beunderrated in current policy discussions because they are noteasily measured.
A lot of recent evidence shows that the workplace isincreasingly oriented towards a greater valuation of socialinteraction and sociability.
Evidence from the GED program (Heckman and Rubinstein,2001).
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 11 / 76
Observations Abilities/Outcomes Model
The GED program is a second chance program given tosecondary school dropouts in the U.S. and Canada.
Participation in the GED program is growing. Currently 20% ofU.S. high school “graduates” are dropouts who exam certify.
GEDs are required to pass a test of cognitive abilities.
Level relatively low–at the grade 8 to grade 10 level.
Test is successful in its own terms.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 12 / 76
Observations Abilities/Outcomes Model
Density of age adjusted AFQT scores, GED recipients and high schoolgraduates with twelve years of schooling
White Males White Females(a) White males
0
5
10
15
20
25
30
-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5
(b) White females
0
5
10
15
20
25
30
35
-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5
HS graduates
GEDs
Source: Heckman, Hsee and Rubinstein (2001)
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 13 / 76
Observations Abilities/Outcomes Model
Density of age adjusted AFQT scores, GED recipients and high schoolgraduates with twelve years of schooling
Black Males Black Females
0
5
10
15
20
25
30
35
-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5
HS graduates
GEDs
0
5
10
15
20
25
30
35
40
-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5
Source: Heckman, Hsee and Rubinstein (2001)
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 14 / 76
Observations Abilities/Outcomes Model
Density of age adjusted AFQT scores, GED recipients and high schoolgraduates with twelve years of schooling
Hispanic Males Hispanic FemalesHS graduates
GEDs
0
5
10
15
20
25
30
-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5
0
5
10
15
20
25
30
35
-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5
Source: Heckman, Hsee and Rubinstein (2001)
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 15 / 76
Observations Abilities/Outcomes Model
Yet GEDs earn at the rate of high school dropouts.
GEDs are as “smart” as ordinary high school graduates.
They lack noncognitive skills.
The GEDs are the wise guys who can’t finish anything.
Most branches of the U.S. military recognize this in theirrecruiting strategies.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 16 / 76
Observations Abilities/Outcomes Model
There is a lot of evidence that both cognitive and noncognitiveskills are important.
Both cognitive and noncognitive skills matter in a variety ofaspects of life.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 17 / 76
Observations Abilities/Outcomes Model
Cognitive and noncognitive ability are important determinantsof schooling and socioeconomic success.
In the U.S. and many countries around the world, schoolinggaps across ethnic and income groups have more to do withability deficits than family finances in the school-going years.
Those with higher abilities of both types are more likely to takepost-school company job training, to participate in preventionprograms; less likely be obese; have greater health and mentalhealth.
Look at effects of both cognitive and noncognitive skills onmany measures of social performance.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 18 / 76
Observations Abilities/Outcomes Model
Ever been in jail by age 30, by ability (males)
Pro
bab
ility
Percentile
.15
.10
.05
.0
0 - 20 21 - 40 41 - 60 61 - 80 81 - 100
Cognitive Noncognitive
Note: This figure plots the probability of a given behavior associated with moving upin one ability distribution for someone after integrating out the other distribution. Forexample, the lines with markers show the effect of increasing noncognitive ability afterintegrating the cognitive ability.
Source: Heckman, Stixrud, and Urzua (2006).
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 19 / 76
Observations Abilities/Outcomes Model
Probability of being single with children (females)
Pro
bab
ility
Percentile
.10
.08
.06
.04
.02
0 - 20 21 - 40 41 - 60 61 - 80 81 - 100
Cognitive Noncognitive
Note: This figure plots the probability of a given behavior associated with moving upin one ability distribution for someone after integrating out the other distribution. Forexample, the lines with markers show the effect of increasing noncognitive ability afterintegrating the cognitive ability.
Source: Heckman, Stixrud, and Urzua (2006).by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 20 / 76
Observations Abilities/Outcomes Model
Probability of being a high school dropout by age 30 (males)
24
68
10
24
68
10
0
0.2
0.4
0.6
0.8
1
Decile of Noncognitive
Figure 1A. Probability of Being a High School Dropout by Age 30 - Malesi. By Decile of Cognitive and Noncognitive Factors
Decile of Cognitive
Pro
bab
ility
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 Noncognitive Factor
Decile
24
68
10
24
68
10
0
0.2
0.4
0.6
0.8
1
Decile of Noncognitive
Figure 1A. Probability of Being a High School Dropout by Age 30 - Malesi. By Decile of Cognitive and Noncognitive Factors
Decile of Cognitive
Pro
bab
ility
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 Noncognitive Factor
Decile
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 21 / 76
Observations Abilities/Outcomes Model
Probability of being a 4-year college graduate by age 30 (males)
24
68
10
123456789100
0.2
0.4
0.6
0.8
1
Decile of Noncognitive
Figure 1C. Probability of Being a 4-yr College Graduate by Age 30 - Malesi. By Decile of Cognitive and Noncognitive Factors
Decile of Cognitive
Pro
bab
ility
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 Noncognitive Factor
Decile
24
68
10
123456789100
0.2
0.4
0.6
0.8
1
Decile of Noncognitive
Figure 1C. Probability of Being a 4-yr College Graduate by Age 30 - Malesi. By Decile of Cognitive and Noncognitive Factors
Decile of Cognitive
Pro
bab
ility
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 Noncognitive Factor
Decile
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 22 / 76
Observations Abilities/Outcomes Model
Probability of daily smoking by age 18 (males)
24
68
10
24
68
10
0
0.2
0.4
0.6
0.8
1
Decile of Noncognitive
Figure 1F. Probability Of Daily Smoking By Age 18 - Malesi. By Decile of Cognitive and Noncognitive Factor
Decile of Cognitive
Pro
bab
ility
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 Noncognitive Factor
Decile
24
68
10
24
68
10
0
0.2
0.4
0.6
0.8
1
Decile of Noncognitive
Figure 1F. Probability Of Daily Smoking By Age 18 - Malesi. By Decile of Cognitive and Noncognitive Factor
Decile of Cognitive
Pro
bab
ility
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 Noncognitive Factor
Decile
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 23 / 76
Observations Abilities/Outcomes Model
Mean log wages by age 30 (males)
24
68
10
123456789102
2.2
2.4
2.6
2.8
3
Decile of Non-Cognitive
Mean Log Wages by Age 30 - Males i. By Decile of Cognitive and Non-Cognitive Factors
Decile of Cognitive
Lo
g W
ages
2 4 6 8 102
2.5
3i. By Decile of Cognitive Factor
Decile
Lo
g W
ages
an
dC
on
fid
ence
In
terv
al (
2.75
-97.
5%)
2 4 6 8 102
2.5
3ii. By Decile of Non-Cognitive Factor
Decile
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 withhigher values of the variable. The confidence intervals are computed using bootstrapping (50 draws).
Mean Log Wages by Age 30 - Males
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 24 / 76
Observations Abilities/Outcomes Model
Controlling for ability, minorities are more likely to attendcollege than others despite their lower family incomes(Cameron and Heckman, 2001).
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 25 / 76
Observations Abilities/Outcomes Model
A third observation is that the “nature versus nurture”distinction is obsolete.
The modern literature on epigenetic expression teaches us thatthe sharp distinction between acquired skills and ability featuredin the early human capital literature is not tenable.
Additive “nature” and “nurture” models, while traditional andstill used in many studies of heritability and family influence,mischaracterize how ability is manifested.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 26 / 76
Observations Abilities/Outcomes Model
Abilities are produced, and gene expression is governed byenvironmental conditions (Eric Turkheimer et al., 2003).
Measured abilities are susceptible to environmental influences,including in utero experiences, and also have geneticcomponents.
These factors interact to produce abilities that have both agenetic and an acquired character and the modified genes areheritable. Genes and environment cannot be meaningfullyparsed by traditional linear models that assign variance to eachcomponent, even though it is traditional to do so.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 27 / 76
Observations Abilities/Outcomes Model
Six Facts We Explain
First, ability gaps between individuals and across socioeconomicgroups open up at early ages, for both cognitive andnoncognitive skills.
Adjusting for family background by regression analysis reducesthese gaps.
Experimental manipulations of early environments (Perry,Abecedarian et al.) show that these effects are causal.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 28 / 76
Observations Abilities/Outcomes Model
Figure 1: Children of NLSY Average Standardized Score PIAT Math byPermanent Income Quartile Figure 1
Children of NLSYAverage Standardized Score
PIAT Math b Permanent Income uartile
-0.8000
-0.6000
-0.4000
-0.2000
0.0000
0.2000
0.4000
0.6000
0.8000
5 6 7 8 9 10 11 12 13 14
Age
Bottom Quartile Second Quartile Third Quartile Top Quartile
Source: Full Sample of CNLSY
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 29 / 76
Observations Abilities/Outcomes Model
Figure D0: Trend in Mean Cognitive Score by Maternal Education
Source: Brooks-Gunn et al., (2006).by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 30 / 76
Observations Abilities/Outcomes Model
The dramatic results on the importance of the early years increating differences among children shown in the previous grapharise if “Bayley scores” are used as a measure of cognition atage 1.
As Michael Lewis and Harry McGurk (1972) point out, this isillegitimate since the Bayley score tests other aspects of childdevelopment in addition to cognition.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 31 / 76
Observations Abilities/Outcomes Model
Figure D00: Children of NLSY Average Standardized ScorePeabody Picture Vocabulary Test by Permanent Income QuartileFigure 1
Children of NLSYAverage Standardized Score
^
-0.8000
-0.6000
-0.4000
-0.2000
0.0000
0.2000
0.4000
0.6000
0.8000
3 4 5 6 7 8 9 10 11 12 13 14
Age
Bottom Quartile Second Quartile Third Quartile Top Quartile
Source: Full Sample of CNLSY
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 32 / 76
Observations Abilities/Outcomes Model
Figure D1a. Average percentile rank on PIAT-Math score, by incomequartile
Average Percentile Rank on PIAT-Math Score, by Income Quartile
Age
Scor
e pe
rcen
tile
Highest income quartileThird income quartileSecond income quartileLowest income quartile
10 126 8
65
60
55
50
45
40
35
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 33 / 76
Observations Abilities/Outcomes Model
Figure D1b. Adjusted average PIAT-Math score percentiles, by incomequartile
D . Residualized Average PIAT-Math Score Percentiles by Income Quartile*
10 12
Age* Residualized on maternal education, maternal AFQT (corrected for the effect of schooling) and broken
home at each age
Scor
e pe
rcen
tile
Highest income quartileThird income quartileSecond income quartileLowest income quartile
65
60
55
50
45
40
356 8
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 34 / 76
Observations Abilities/Outcomes Model
Figure D2a. Average percentile rank on PIAT-Math score, by race
Average Percentile Rank on PIAT-Math Score, by Race
Age
Scor
e pe
rcen
tile
Hispanic Black White
10 126 8
65
60
55
50
45
40
35
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 35 / 76
Observations Abilities/Outcomes Model
Figure D2b. Adjusted average PIAT-Math score percentiles, by race
F . Residualized Average PIATM Score Percentile by Race*
Age* Residualized on maternal education, maternal AFQT (corrected for the effect of schooling) and broken
home at each age
Hispanic Black White
10 126 8
65
60
55
50
45
40
35
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 36 / 76
Observations Abilities/Outcomes Model
Figure D3a. Average percentile rank on anti-social behavior score, byincome quartile
G . Average Percentile Rank on Anti-Social Score, by Income Quartile*
Age
Scor
e pe
rcen
tile
Highest income quartileThird income quartileSecond income quartileLowest income quartile
10 126 84
55
50
45
40
35
25
20
30
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 37 / 76
Observations Abilities/Outcomes Model
Figure D3b. Adjusted average anti-social behavior score percentile, byincome quartile
J. Residualized Average Anti-Social Score Percentile by Income Quartile*
Age* Residualized on maternal education, maternal AFQT (corrected for the effect of schooling) and broken
home at each age
Scor
e pe
rcen
tile
10 126 84
Highest income quartileThird income quartileSecond income quartileLowest income quartile
55
50
45
40
35
25
20
30
60
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 38 / 76
Observations Abilities/Outcomes Model
Figure D4a. Average percentile rank on anti-social behavior score, by race
I . Average Percentile Rank on Anti-Social Score, by Race
Age
Scor
e pe
rcen
tile
Hispanic Black White
10 126 84
55
50
45
40
35
30
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 39 / 76
Observations Abilities/Outcomes Model
Figure D4b. Adjusted average anti-social behavior score percentile, by race
L . Residualized Average Anti-Social Score Percentile by Race*
Age* Residualized on maternal education, maternal AFQT (corrected for the effect of schooling) and broken
home at each age
Scor
e pe
rcen
tile
10 126 84
Hispanic Black White
55
50
45
40
35
30
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 40 / 76
Observations Abilities/Outcomes Model
Figure D5a. Early Childhood Longitudinal Study (ECLS)ReadingEarly Childhood Longitudinal Study (ECLS)
(a) Reading
10
20
30
40
50
60
70
55 65 75 85
Age (months)
Rea
ding
Sco
re
Low povertyHigh povery
Figure D5a
Source: Raudenbush (2006)Source: Raudenbush (2006)
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 41 / 76
Observations Abilities/Outcomes Model
Figure D5b. Mean trajectories, high and low priority schools (ECLS)Math Mean trajectories, high and low poverty schools (ECLS)
(b) Math
10
20
30
40
50
60
55 65 75 85
Age (months)
Mat
h Sc
ore
Low povertyHigh poverty
Figure D5b
Source: Raudenbush (2006)Source: Raudenbush (2006)
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 42 / 76
Observations Abilities/Outcomes Model
Figure D6a. Average Trajectories, Grades 1–3, high and low poverty schools(Sustaining Effects Study)ReadingAverage trajectories, Grades 1-3, high and low poverty schools (Sustaining Effects Study)
(a) Reading
350
400
450
500
550
70 75 80 85 90 95
Age (months)
Rea
ding
Sco
re
Low povertyHigh Poverty
Figure D6a
Source: Raudenbush (2006)Source: Raudenbush (2006)
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 43 / 76
Observations Abilities/Outcomes Model
Figure D6b. Average Trajectories, Grades 1–3, high and low povertyschools (Sustaining Effects Study)Math
Average trajectories, Grades 1-3, high and low poverty schools (Sustaining Effects Study)
(b) Math
350
400
450
500
550
70 75 80 85 90 95
Age (months)
Mat
h Sc
ore
Low povertyHigh poverty
Figure D6b
Source: Raudenbush (2006)Source: Raudenbush (2006)
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 44 / 76
Observations Abilities/Outcomes Model
Figure D7a. Average achievement trajectories, grades 8–12, (NELS 88)Science
Average achievement trajectories, Grades 8-12 (NELS 88).(a) Science
15
20
25
30
8 10 12
Grade
Scie
nce
Scor
e
Low povertyHigh poverty
Figure D7a
Source: Raudenbush (2006)
Source: Raudenbush (2006)
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 45 / 76
Observations Abilities/Outcomes Model
Figure D7b. Average achievement trajectories, grades 8–12, (NELS 88)MathAverage achievement trajectories, Grades 8-12 (NELS 88).
(b) Math
30
35
40
45
50
55
60
8 10 12
Grade
Mat
h Sc
ore
Low povertyHigh poverty
Figure D7b
Source: Raudenbush (2006)Source: Raudenbush (2006)
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 46 / 76
Observations Abilities/Outcomes Model
Figure D8a. Growth as a function of student social background: ECLSReading
Growth as a function of student social background: ECLS
(a) Reading
10
20
30
40
50
60
70
55 65 75 85
Age (months)
Rea
ding
Sco
re
Hi SESLow SES
Figure D8a
Source: Raudenbush (2006)Source: Raudenbush (2006)
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 47 / 76
Observations Abilities/Outcomes Model
Figure D8b. Growth as a function of student social background: ECLSMath
Growth as a function of student social background: ECLS
(b) Math
10
20
30
40
50
60
70
55 65 75 85
Age (months)
Mat
h Sc
ore
Hi SESLow SES
Figure D8b
Source: Raudenbush (2006)Source: Raudenbush (2006)
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 48 / 76
Observations Abilities/Outcomes Model
Figure D9a. Growth as a function of school poverty for poor children:sustaining effects dataReading
Growth as a Function of School Poverty for Poor Children: Sustaining Effects Data
(a) Reading
350
400
450
500
550
70 75 80 85 90 95
Age (months)
Rea
ding
Sco
re
Low povertyHigh poverty
Figure D9a
Source: Raudenbush (2006)Source: Raudenbush (2006)
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 49 / 76
Observations Abilities/Outcomes Model
Figure D9b. Growth as a function of school poverty for poor children:sustaining effects dataMath
Growth as a Function of School Poverty for Poor Children: Sustaining Effects Data
(b) Math
350
400
450
500
550
70 75 80 85 90 95
Age (months)
Mat
h Sc
ore
Low PovertyHigh poverty
Figure D9b
Source: Raudenbush (2006)Source: Raudenbush (2006)
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 50 / 76
Observations Abilities/Outcomes Model
Schooling quality and school resources have relatively smalleffects on ability deficits and only marginally account for anydivergence by age across children from different socioeconomicgroups in test scores.
See Heckman, Larenas et al. (2004) and Raudenbush (2006).
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 51 / 76
Observations Abilities/Outcomes Model
Gaps also emerge in health. These appear to be divergentwith age, at least in the U.S.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 52 / 76
Observations Abilities/Outcomes Model
Health and income for children and adults, U.S. National Health Interview Survey
1986-1995. From Case, A., Lubotsky, D. & Paxson, C. (2002), American
Economic Review, Vol. 92, 1308-1334.
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by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 53 / 76
Observations Abilities/Outcomes Model
Second, in both animal and human species, there is compellingevidence of critical and sensitive periods in the development ofthe child.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 54 / 76
Observations Abilities/Outcomes Model
Second language learning
200
220
240
260
280
Native 3-7 8-19 11-17 17 -22 23-28 31-39
Second Language Learning������ 6
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 55 / 76
Observations Abilities/Outcomes Model
The later remediation is given to a disadvantaged child, the lesseffective it is.
A substantial body of evidence suggests that returns toadolescent education for the most disadvantaged and less ableare lower than the returns for the more advantaged.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 56 / 76
Observations Abilities/Outcomes Model
The economic returns to adolescent intervensions — jobtraining, high school graduation, and college attendance — arelower for less able persons.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 57 / 76
Observations Abilities/Outcomes Model
Table 1. Return to one year of college for individuals at different percentilesof the math test score distributionWhite males from high school and beyond
Table 2.4Return to one year of college for individuals
at different percentiles of the math test score distributionWhite males from High School and Beyond
5% 25% 50% 75% 95%Average return in the population 0.1121 0.1374 0.1606 0.1831 0.2101
(0.0400) (0.0328) (0.0357) (0.0458) (0.0622)Return for those who attend college 0.1640 0.1893 0.2125 0.2350 0.2621
(0.0503) (0.0582) (0.0676) (0.0801) (0.0962)Return for those who do not attend college 0.0702 0.0954 0.1187 0.1411 0.1682
(0.0536) (0.0385) (0.0298) (0.0305) (0.0425)Return for those at the margin 0.1203 0.1456 0.1689 0.1913 0.2184
(0.0364) (0.0300) (0.0345) (0.0453) (0.0631)Wages are measured in 1991 by dividing annual earnings by hours worked per weekmultiplied by 52. The math test score is and average of two 10th grade math test scores.There are no dropouts in the sample and the schooling variable is binary (high school - college).The gross returns to college are divided by 3.5 (average difference in years of schoolingbetween high school graduates that go to college and high school graduates that do not in asample of white males in the NLSY). To construct the numbers in the table we proceed in twosteps. First we compute the marginal treatment effect using the method of local instrumentalvariables as in Carneiro, Heckman and Vytlacil (2001). The parameters in the table aredifferent weighted averages of the marginal treatment effect. Therefore, in the second stepwe compute the appropriate weight for each parameter and use it to construct a weightedaverage of the m arginal treatment effect ( see also Carneiro, 2002). Individuals at t he marginare indifferent between attending college or not.
Source: Carneiro and Heckman (2003)
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Observations Abilities/Outcomes Model
Third, despite the low returns to interventions targeted towarddisadvantaged adolescents, the empirical literature shows higheconomic returns for remedial investments in youngdisadvantaged children.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 59 / 76
Observations Abilities/Outcomes Model
Fourth, if early investment in disadvantaged children is notfollowed up by later investment, its effect tends to weaken atlater ages.
Currie and Thomas (1995) document a decline in theperformance of minority Head Start participants after theyleave the program.
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Observations Abilities/Outcomes Model
Fifth, the effects of credit constraints on the child’s adultoutcomes depend on the age at which they bind for the child’sfamily.
Controlling for cognitive ability, under meritocratic policiescurrently in place in American society, family income during thechild’s college-going years plays only a minor role indetermining child college participation.
Holding ability fixed, minorities are more likely to attend collegethan others despite their lower family incomes.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 61 / 76
Observations Abilities/Outcomes Model
Carneiro and Heckman present evidence for the United Statesthat only a small fraction (at most 8%) of the families ofadolescents are credit constrained in making their collegedecisions.
This evidence is supported in research by Cameron and Taber(2004) and Stinebrickner and Stinebrickner (2006).
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 62 / 76
Observations Abilities/Outcomes Model
There is some evidence that credit constraints operating in theearly years have effects on adult ability and schooling outcomes,but there is not full agreement in the literature on themagnitude of the effect (Duncan and Brooks-Gunn, 1997; Dahland Lochner, 2004; Duncan and Ariel Kalil, 2006; Carneiro andHeckman, 2003).
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 63 / 76
Observations Abilities/Outcomes Model
The empirically important market failures in the life cycle ofskill formation in contemporary American society are theinability of children to buy their parents or the lifetimeresources that parents provide.
It is not credit constraints facing families seeking to secureloans for a child’s education when the child is an adolescent.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 64 / 76
Observations Abilities/Outcomes Model
Sixth, socioemotional (noncognitive) skills foster cognitive skillsand are an important product of successful families andsuccessful interventions in disadvantaged families.
The Perry Preschool Program, which was evaluated by randomassignment, did not boost participant adult IQ but enhancedperformance of participants in a number of dimensions,including elevated scores on achievement tests, employmentand reduced participation in a variety of social pathologies.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 65 / 76
Observations Abilities/Outcomes Model
Figure D10a. 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.
Perry Preschool Program: IQ, by Age and Treatment GroupFigure 2a
16by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 66 / 76
Observations Abilities/Outcomes Model
Figure D10b. Perry Preschool Program: educational effects, by treatmentgroup
45%
66%
15%
49%
34%
15%
0% 10% 20% 30% 40% 50% 60% 70%
On Time Grad.from HS
High Achievementat Age 14*
SpecialEducation
Source: Barnett (2004).Notes: *High achievement defined as performance at or above the lowest 10th percentile on the California AchievementTest (1970).
Perry Preschool Program: Educational Effects, by Treatment GroupFigure 7B
Treatment Control
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 67 / 76
Observations Abilities/Outcomes Model
Figure D10c. Perry Preschool Program: economic effects at age 27, bytreatment group
14%
29%
13%
36%
7%
29%
0% 10% 20% 30% 40%
Never on Welfareas Adult*
Own Home
Earn +$2,000Monthly
Source: Barnett (2004). *Updated through Age 40 using recent Perry Preschool Program data, derived from self reportand all available state records.
Perry Preschool Program: Economic Effects at Age 27, by Treatment GroupFigure 7C
Treatment Control
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Observations Abilities/Outcomes Model
Figure D10d. Perry Preschool Program: arrests per person before age 40,by treatment group
1.2 3.9 .4
2.1 6.7 .6
0 2 4 6 8 10
Treatment
Control
Source: Perry Preschool Program. Juvenile arrests are defined as arrests prior to age 19.
Perry Preschool Program: Arrests per Person before Age 40, by Treatment GroupFigure 7D
Felony Misdemeanor Juvenile
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 69 / 76
Observations Abilities/Outcomes Model
Table D1. Economic benefits and costs
Perry Chicago CPC
Child Care 986 1,916
Earnings 40,537 32,099
K-12 9,184 5,634
College/Adult -782 -644
Crime 94,065 15,329
Welfare 355 546
FG Earnings 6,181 4,894
Abuse/Neglect 0 344
Total Benefits 150,525 60,117
Total Costs 16,514 7,738
Net Present Value 134,011 52,380
Benefits-To-Costs Ratio 9.11 7.77
Table D1 Economic Benefits And Costs
Notes: All values discounted at 3% and are in $2004. Numbers differ
slightly from earlier estimates because FG Earnings for Perry and
Chicago were estimated using the ratio of FG Earnings Effect to
Earnings Effect (about 15%) that was found in Abecedarian
Source: Barnett, 2004.Source: Barnett (2004)
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Observations Abilities/Outcomes Model
A Model of Skill Formation
Agents possess a vector of abilities at each age.
These abilities (or skills) are multiple in nature and range frompure cognitive abilities (e.g. IQ) to noncognitive abilities(patience, self control, temperament, risk aversion, timepreference).
These abilities are used with different weights in different tasksin the labor market and in social life more generally.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 71 / 76
Observations Abilities/Outcomes Model
The human skill formation process is governed by a multistagetechnology.
Each stage corresponds to a period in the life cycle of a child.
Like earlier work by Ben-Porath (1967), we use a productionfunction to determine the relationship between inputs and theoutput of skill.
Unlike Ben-Porath, in our models qualitatively different inputscan be used at different stages and the technologies can bedifferent at different stages of child development.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 72 / 76
Observations Abilities/Outcomes Model
Ben-Porath focuses on adult investments where time and itsopportunity cost play important roles.
For child investments, parents make decisions and childopportunity costs are less relevant.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 73 / 76
Observations Abilities/Outcomes Model
The outputs at each stage in our technology are the levels ofeach skill achieved at that stage.
Some stages of the lifecycle may be more productive inproducing some skills than other stages, and some inputs maybe more productive at some stages than at other stages.
Those stages that are more effective in producing certain skillsare called “sensitive periods” for the acquisition of those skills.
If one stage alone is effective in producing a skill (or ability), itis called a “critical period” for that skill.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 74 / 76
Observations Abilities/Outcomes Model
The skills produced at one stage augment the skills attained atlater stages. This effect is termed self-productivity.
Skills are self-reinforcing and cross-fertilizing.
A second key feature of skill formation is dynamiccomplementarity.
Skills produced at one stage raise the productivity ofinvestment at subsequent stages. Complementarity implies thatlevels of skill investments at different ages bolster each other.They are synergistic.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 75 / 76
Observations Abilities/Outcomes Model
Together, dynamic complementarity and self-productivityproduce multiplier effects which are the mechanisms throughwhich skills beget skills and abilities beget abilities.
Dynamic complementarity, self-productivity of human capital,and multiplier effects imply an equity-efficiency trade-off forlate child investments but not for early investments.
These concepts, embedded in alternative market settings,explain the six facts previously listed.
by James J. Heckman and Flavio Cunha. Published in the American Economic Review (2007). Volume 97, Issue 2, pp.31–47. [4mm]James J. Heckman University of ChicagoExtract for “The Technology of Skill Formation” 76 / 76