tc
FT
Gregory Veramendi
Northwestern University
o
no
Sergio Urzúa
Northwestern University
and IZA
-d
James J. Heckman
University of Chicago,
University College Dublin
Cowles Foundation, Yale University
and the American Bar Foundation
ite
THE EFFECTS OF SCHOOLING ON LABOR MARKET AND
HEALTH OUTCOMES ∗
DR
A
First version: October 10, 2009
This version: January 28, 2010
Abstract
IM
IN
AR
Y
We estimate a sequential model of educational choice with unobserved heterogeneity. We
investigate the effect of educational choices on labor market and health outcomes. We posit two
unobserved endowments (cognitive and socio-emotional abilities) that explain the correlation
among outcomes in addition schooling. The model estimates each outcome separately for each
schooling level, allowing us to generate counter-factual outcomes. We then simulate the model to
estimate treatment effects for individuals of different ability levels. In this framework, we analyze
the impact of education on health and labor market outcomes when responses to treatment vary
among observationally identical persons and agents select into the treatment on the basis of their
responses. Our findings confirm the effects of early cognitive and socio-emotional abilities on
schooling choices, labor market outcomes and we provide new evidence on their effects on adult
health. We find that, in general, education produces gains on labor market and health outcomes.
Finally, observationally equivalent individuals receive different gains depending on their ability
levels.
PR
EL
Keywords: health, education, early endowments, factor models, treatment effects
JEL codes:
∗
James Heckman: Department of Economics, University of Chicago, 1126 East 59th Street, Chicago, IL 60637;
phone, 773-702-0634; fax, 773-702-8490; email, jjh@uchicago.edu. Sergio Urzúa, Department of Economics and Institute for Policy Research, Northwestern University, Handerson Hall, 2001 Sheridan Road, Evanston, IL 60208;
Phone, 847-491-8213; email, s-urzua@northwestern.edu. Gregory Veramendi, Department of Economics, Northwestern University, Handerson Hall, 2001 Sheridan Road, Evanston, IL 60208; Phone, 847-491-8211; email, gveramendi@northwestern.edu. The website for this paper is http://CCCC/.
1
Introduction
The increase in inequality in income and wealth in the United States over the past 25 years
most of the twentieth century. One contribution to this is the increasing wage premium paid
tc
to highly educated individuals. This is often referred to as skill-biased technical change. One
ite
has reversed the steady progress toward greater equality that had been underway throughout
important question is what is being refered to by skill. Are they endowments of early childhood
no
investments by parents or something taught to students while in school. If there is a causal
effect from education, what would the right policy be?
Without experimental data in which educational attainment is exogenously determined one
o
is forced to deal with the potential endogeneity associated with each educational decision. As
-d
a consequence of the educational decision made by the agent, any comparison of outcomes
across individuals experiencing the two final schooling levels (treatment or control) would be
FT
subject to important qualifications. Without further considerations, these comparisons could
not be interpreted as indicative of the causal effects of the treatment on adult outcomes. This is
DR
A
the standard problem of causal inference (Heckman and Vytlacil, 2007a). Previous studies have
dealt with this problem using extensive sets of controls, fixed effect regressions, and instrumental
variables. Our approach combines all these previous efforts in a single empirical strategy. We do
so by estimating a model with endogenous outcomes and unobserved heterogeneity (Heckman
and Vytlacil, 2007a; Heckman, Urzua, and Vytlacil, 2006). Additionally, and following the
AR
Y
recent development in the literature, we interpret unobserved heterogeneity as (latent) abilities
(Carneiro, Hansen, and Heckman, 2003; Heckman, Stixrud, and Urzua, 2006; Urzúa, 2008).
Brief Literature Review
IN
2
IM
The importance of human capital has long been recognized in labor economics (Becker, 1964).
More educated individuals have better performance in terms of labor market outcomes (Card,
1999). However, the importance of human capital has not been exclusively restricted to its
PR
EL
effects on the labor market. The positive correlation between health and schooling is one of
the most well-established findings in the social sciences (Kolata, 2007) as well as the the fact
that health gaps among education groups have been rising over the years (Meara, Richards,
and Cutler, 2008). See Grossman (2000) and Grossman (2006) for comprehensive reviews of the
literature.
However, the identification of the causal effect of education is a complex task. Observed
2
correlations are not necessarily informative of causal effects. For example, the positive correlation between health and schooling could be due to either a causal relationship from schooling to
health (Grossman, 1972, 2008; Grossman and Kaestner, 1997; Cutler and Lleras-Muney, 2007)
a variety of methods, ranging from experimental or quasi-experimental methods to estimation
tc
of structural economic models, to obtain estimates of the causal effects of education on labor
market outcomes (Card, 2001; Willis and Rosen, 1979) and health (Adams, 2002; Arendt, 2005;
no
Lleras-Muney, 2005; Silles, 2009; Spasojevic, 2003; Arkes, 2003; Auld and Sidhu, 2005).
In this paper, we combine these different approaches. We first postulate a model of sequen-
o
tial schooling decisions in which individuals decide based on their observed and unobserved
-d
characteristics. Our model shares the features of a long tradition of structural models for the
analysis of schooling choices (Willis and Rosen, 1979; Hauser, 1993; Keane and Wolpin, 1997;
Cameron and Heckman, 1998, 2001). The identification of our model however, depends on the
FT
availability of information not contaminated by the sorting of individuals into schooling levels.
We follow Heckman, Stixrud, and Urzua (2006) and Carneiro, Hansen, and Heckman (2003),
DR
A
and not only use this information to identify the distribution of the unobserved components
affecting schooling decisions (and potentially, labor market productivity and health status) but
also to link these components to unobserved abilities (or endowments). Specifically, we follow
the analysis of Heckman, Stixrud, and Urzua (2006) and Urzúa (2008) and postulate a model
AR
Y
of multiple abilities. Furthermore, we extend this previous work by allowing the abilities to be
correlated. We assume individuals are endowed with cognitive and socio-emotional abilities and
that these abilities are variables determining schooling attainment, labor market productivity
and health status.
IN
Therefore, our analysis also relates to growing literature documenting the impact of cognition
on health (Grossman, 1975; Shakotko, Edwards, and Grossman, 1982; Hartog and Oosterbeek,
IM
1998; Elias, 2004; Auld and Sidhu, 2005; Kenkel, Lillard, and Mathios, 2006; Cutler and LlerasMuney, 2007; Kaestner, 2008; Whalley and Deary, 2001; Gottfredson and Deary, 2004; Deary,
PR
EL
2009) and labor market outcomes (Cawley, Conneely, Heckman, and Vytlacil, 1997; Herrnstein
and Murray, 1994; Neal and Johnson, 1996; Carneiro and Heckman, 2002; Glewwe, 2002) as well
as of socio-emotional development on health and labor market outcomes (Hampson and Friedman, 2008; ?; Heckman, Stixrud, and Urzua, 2006; Cutler and Lleras-Muney, 2007; Roberts,
Kuncel, Shiner, Caspi, and Goldberg, 2007).
Armed with this framework we investigate the role of schooling choices and abilities on labor
1
Observed correlations could be also attributed to time or risk preferences (Fuchs, 1982).
3
ite
or from health to schooling (Conti, Heckman, and Urzúa, 2009).1 Social scientists have utilized
market and health outcomes.
Model for measuring the returns to schooling
Once agents make their educational decisions, all of their adult outcomes are observed con-
tc
ditional on those decisions. Thus, observed and unobserved characteristics drive the agent’s
ite
3
decision process. To the extent that these unobserved components correlate with unobservables
no
determining the individual’s outcomes, we need to control for the potential consequences of
selection when comparing outcomes across the treatment and control groups. We deal with the
o
selection problem by studying models of potential outcomes to get the counterfactuals. Impor-
-d
tantly, we allow the unobserved components determining these variables to be correlated across
regimes and with the agent’s educational decision.
The model’s outline is as follows:
FT
• The model begins by describing the latent cognitive and social-emotional endowments
of the agents before they have made any educational decisions. The endowments are
DR
A
interpreted as the results of parental investments. The cognitive endowment is measured
from achievement tests, while the social-emotional endowment measurement is based on
the risky and social behaviors of the agent.
• Agents then make sequential decisions to determine their final schooling level based on
AR
Y
their family background and latent abilities.
• Finally, we model adult labor market and health outcomes separately by schooling level,
and calculate the treatment effects of each schooling decision controlling for observed
Factor model for Latent Endowments
IM
3.1
IN
background and latent abilities.
We describe a measurement system for identification of the latent factors (θiC , θiSE ). Let θiC and
PR
EL
θiSE denote the levels of cognitive and social-emotional abilities, respectively. We assume that
the latent factors can be correlated, and are described by a mixture of bivariate normals. The
latent factors are identified by a set of tests and early behavioral outcomes. These measures allow
the unobserved factors to be interpreted as early cognitive and socio-emotional endowments.
The cognitive factor is identified from the Armed Forces Vocational Aptitude Battery (ASVAB).
We posit a linear measurement system to identify the cognitive measure from the set of five
4
ASVAB tests (Tijs ) at schooling level s:
Tijs = Xijs βjs + αjs θiC + eijs
Although, the role of cognitive ability has been studied in the economic literature for some
tc
time, the role played by socio-emotional ability is more recently being considered. Psychologist
ite
(1)
have been studying personality traits for a long time and have, in the last 15 years, come to
no
a consensus on a taxonomy called the Big Five John, Robins, and Pervin (2008). This is an
organizing framework that provides a taxonomy for categorizing most of the disparate field of
personality traits into 5 categories. The five traits are Extraversion, Agreeableness, Conscien-
-d
o
tiousness, Neuroticism, and Openness. Psychologists have found that Conscientiousness and
Agreeableness plays an important role in educational and labor market outcomes. Ideally, we
would like to isolate these traits as our socio-emotional endowment.
FT
Unfortunately, most economic surveys do not include good questions that test for personality type. We leverage the literature to identify behaviors that have strong correlations with
DR
A
conscientiousness and agreeableness and later educational and adult outcomes.
The paper Gullone and Moore (2000) studies the relationship between personality traits
and adolescent risk-behavior. The authors identify different categories of risk behaviors, and we
would like to focus on what they call rebellious and reckless risk-taking. Examples of rebellious
risk-taking are smoking, drinking, and staying out late. Examples of reckless risk-taking are
AR
Y
drinking and driving, having unprotected sex, and speeding. Both of these behaviors had
highly negative correlations with conscientiousness. Duckworth and Urzúa (2009) study the
relationship between personality and the number of arrests between 14 and 17 years old, and they
IN
find it is correlated with conscientiousness, agreeableness and IQ. Finally, two papers (Morris,
2003; Borghans, Ter Weel, and Weinberg, 2007) find significant correlations between belonging
IM
to a club in high school, and later educational and labor force outcomes. Our identification
strategy will rely on their work to use early adolescent risky, adolescent illegal behaviors and
PR
EL
belonging to a club in high school as measures of the social-emotional factor.
Based on the aforementioned literature, we identify 7 behaviors that fall into the categories of
either rebellious or reckless risk-taking. They are participation in minor illegal activity in 1979
(vandalism, shoplifting, petty theft, robbery, fraud and fencing), participation in major illegal
activity in 1979 (auto theft, breaking/entering private property and grand theft), participation
in violent crime in 1979 (fighting, assault and aggravated assault), tried marijuana before age
15, daily smoking before age 15, regular drinking before age and any intercourse before age 15.
5
In the case of the variables measured in 1979 we also control for the potential effect of schooling
on the the risky behavior. On the other hand, as proxy for a positive socio-emotional dimension
we use “Member of Clubs During High School”. This is a binary variable that takes a value of
school: youth organizations, hobby, student government, yearbook and newspaper, performance
tc
art, and other clubs; and zero otherwise.
no
Let the latent utility, Wil , for each outcome, l, be defined by:
Wil = Xil βl + αlSE θiSE − νil
(2)
-d
o
where Xil , θiSE , and νil have analagous definitions as above. Since Wil is unobserved we assume that νil has a mean-zero unit variance Normal distribution in order to identify the model
parameters. We can define a binary variable, Ril :
(3)
DR
A
FT
⎧
⎨ 1 if Wil ≥ 0
Ril =
⎩ 0 otherwise
The identification of the factor model follows the identification strategies in Karsten T. Hansen
(2004) and Carneiro, Hansen, and Heckman (2003).
Model of Educational Attainment
AR
Y
3.2
Following Cameron and Heckman (2001), each agent i will make schooling decisions based on
a sequential choice model. The choices available to the agent will be limited by their earlier
schooling decisions.
IN
Table 1 and Figure 1 describe the five possible educational choices and their conditional
structure.
IM
We model the conditional schooling choice using a latent index structure. Let the reward
(psychic and monetary) to agent i from making educational choice j be represented by the latent
PR
EL
utility:
Iij = Xij βjS + αjS θi − νij
(4)
where Xij is a vector of the observed constraint and expectation variables relevant to schooling
decision j, θi = (θiC , θiSE ) are the latent endowments which are mean zero random variables.
The latent factors θi are unobserved to the econometrician, but are known to the agent. This
is the source of the essential heterogeniety. νij represents an idiosyncratic error term such that
6
ite
one if the individual was a member of at least one of the following organizations during high
νij ⊥
⊥ (Xij , θi ) and is independent across agents. νij is assumed to have a mean-zero unit
variance Normal distribution.
⎧
⎨ 1 if Iij ≥ 0
Dij =
⎩ 0 otherwise
tc
(5)
model. Conditioning on θ,
Pr(Iij ≥ 0 | Za,s , θ, Dij−1 )
=
Φ(Xij βjS + αjS θi )
o
=
-d
Pr(Dij = 1 | Xi,j , θ, Dij−1 )
no
These assumptions allow us to write down the probability of making choice j as a probit
(6)
(7)
where Dij−1 are the past decisions taken by agent i. So we can write down the probability of
FT
any sequence of life cycle schooling histories, Di , given the observed variables and θ as
[Pr(Dij = 1 | Xi,j , θi , Dij−1 )]Dij [Pr(Dij = 0 | Xi,j , θi , Dij−1 )]1−Dij
(8)
DR
A
j∈Ci
where Ci is the set of decision nodes that individual i has visited.
Finally, let Fis be an indicator variable for agents with final schooling s. So for example, Fi5
AR
Y
is the indicator variable for college graduate:
Fi5
⎧
⎨ 1 if Di1 = Di3 = Di4 = 1
=
⎩ 0 otherwise
(9)
Labor market and behavioral outcomes
IN
3.2.1
IM
We allow for the possibility of a causal effect from education, whether it be due to separate
labor markets for different education levels (skills would be priced/valued differently in different
markets), or experiences and information affecting preferences and behavior. Let s be the
PR
EL
ite
We can define the binary outcome variable, Dij :
schooling level attained by individual i, and k denote the outcome.
• Continuous Outcomes
Continous outcomes are given by a linear-in-the-parameters specification:
C C
SE SE
θi + αsk
θi + νisk
Yisk = Xisk βsk + αsk
7
(10)
Again, Xisk is the vector of observed controls relevant for outcome k and (θiC , θiSE ) are
the latent factors for individual i. νisk represents an idiosyncratic error term such that
⊥ (Xisk , θiC , θiSE ). In the case of wages νisk is estimated using a mean-zero mixture
νisk ⊥
ite
of three normals. For all other outcomes νisk is assumed to have a mean-zero Normal
Yik =
tc
distribution. Equations 10 and 9 can be used to define observed outcome Yik :
Fis Yisk
(11)
no
s
• Discrete Outcomes
o
A number of the outcomes are binary. We model the binary outcomes using a latent index
is given by a linear-in-the-parameters specification:
-d
structure. Let Visj denote the latent utility associated with outcome j. The latent utility
FT
C C
SE SE
θi + αsj
θi + νisj b
Visj = Xisj βsj + αsj
(12)
DR
A
where Xisj , (θiC , θiSE ), and νisj have analagous definitions to the continuous outcome
case. Since Visj is unobserved we assume that νisj has a mean-zero unit variance Normal
distribution in order to identify the model parameters. We can define a binary outcome
variable, Bisj :
AR
Y
Bisj
⎧
⎨ 1 if Visj ≥ 0
=
⎩ 0 otherwise
(13)
Then we can write the observed outcome as in the continous case:
Fis Bisj
(14)
IN
s
Estimation strategy
IM
4
Bij =
PR
EL
I estimate this model in two stages. The latent endowments and schooling decisions can be
estimated in the first stage, and adult outcomes can be estimated in the second stage using the
estimates from the first stage. I can do this since we assume the factor structure is the only
component that provides correlations across outcomes (conditional on Xi )) and the identification
8
of the factors comes strictly from the schooling and measurement system. So the likelihood is
L =
f (Yi , Bi , Di , Ti , Ri |Xi )
=
f (Yi , Bi |Di Xi θ)f (Di , Ti , Ri |Xi θ)f (θ)dθ
i
tc
⊥ Xi , that the outcomes are independent
where the last two steps are justified by the fact that θi ⊥
So for the first stage, the sample likelihood is represented by:
i
f (Di , Ti , Ri |Xi , z C , z SE )dFθC ,θSE (z C , z SE ).
(15)
o
(θ C ,θ SE )∈Θ
-d
no
once we condition on θi and Xi .
L1 =
where we integrate over the distributions of the latent factors. The goal of the first stage is to
get estimators for fˆ(Di , Ti , Ri |Xi θi ) and fˆ(θi ).
i
(θ C ,θ SE )∈Θ
f (Yi , Bi |Di , Xi , z C , z SE )fˆ(Di , Ti , Ri |Xi , z C , z SE )dF̂θC ,θSE (z C , z SE )
DR
A
L =
FT
Then in the second stage we will use the estimates found from the first stage,
2
(16)
Since Yi , Bi are independent from the first stage conditional on Xi , θi , Di , this will give us a
consistent estimate of the parameters in the adult outcome models.
AR
Y
Each stage will be estimated using maximum-likelihood technique.
4.1
Treatment Effects
Once the model is estimated then the estimates can be used to learn about the causal effect of
IN
education and ability. Since our model deals with the estimation of counter-factual outcomes
we can generate average and distributional treatment effects. This is useful, since we can learn
IM
how changing education will affect people of different ability levels, and allow us to understand
the effectiveness of policy for different parts of the population.
PR
EL
Traditionally in the literature treatment effects have been defined as differences in the out-
comes for two final schooling levels. While this is informative on the gains an individual receives
from going from one final schooling level to another, it is not clear how one would implement a
policy to achieve this when the decisions are fundamentally sequential. For example, consider
the gain in going from GED to four-year college graduate. Many policies would have to be enacted in order to encourage this transition. One would need to discourage GED and encourage
9
ite
i
high school graduation, then one would need to encourage college enrollment and then college
graduation. Which of these policies have the highest returns? Are they all necessary?
Another approach that is being presented is to define the treatment effect based on the
effects at each binary decision node. For example, we could estimate the treatment effect for
tc
D1 or going from high school drop-out to high school graduate. But once you are a high school
ite
decisions made by the individuals. Our schooling model lends itself to clearly estimate treatment
graduate, you have the option of going to college and even graduating from college, as well as
no
having the option of getting the GED and going to college if you drop out. All of these schooling
decisions can be considered in the expectation of each outcome for each decision.
o
We will consider both of these definitions of the treatment effect. The first, traditional in
-d
the literature, will be the gains from switching between final schooling levels. The gains will be
calculated relative to a high school drop out. In this way we can compare our results with other
methods used in the literature. In addition, we will estimate treatment effects for each sequential
FT
decision node. This method will take into account future options and will be informative for
4.1.1
DR
A
policy makers.
Gains from changing final schooling levels
Let Y0 be defined as the outcome for the final schooling level of a high school dropout and Y1
is the final schooling level being studied. The average treatment effect in this case is measured
AR
Y
in the full population.
ΔAT E ≡
Eε (Y1 − Y0 |X = x, θ = f )dFX,θ (x, f )
(17)
IN
where Eε is the expectation over idiosyncratic shocks to outcome Y .
The average effect of the treatment on the treated is measured only for those who attain the
PR
EL
IM
final schooling being studied (j).:
Δ
TT
≡
Eε (Y1 − Y0 |X = x, θ = f, Fj = 1)dFX,θ|Fj =1 (x, f ).
(18)
and the average effect of the treatment on the untreated is measured only for those who are
high school dropouts (j = 1):
ΔT T ≡
Eε (Y1 − Y0 |X = x, θ = f, F1 = 1)dFX,θ|F1 =1 (x, f ).
10
(19)
4.1.2
Treatment effect of educational decisions
Let the person-specific treatment effect for an individual changing his decision at decision node
ite
Dj be defined as
tc
Δj [Y |X = x, θ = f, j ∈ C] ≡ ED (Y S (Dj = 1)|X = x, θ = f, j ∈ C)−ED (Y S (Dj = 0)|X = x, θ = f, j ∈ C)
where the expectation is over future educational choices and the outcome is indexed by schooling.
(j ∈ C, where C is the set of decision nodes that an individual has visited).
-d
ΔAT E ≡
o
The average treatment effect then is
no
In this case we estimate the treatment effects for those who reach the relevant decision node
Eε (Δj [Y |X = x, θ = f, j ∈ C])dFX,θ|j∈C (x, f )
(20)
FT
where Eε is the expectation over idiosyncratic shocks to outcome Y .
The average effect of the treatment on the treated:
≡
Eε (Δj [Y |X = x, θ = f, Ij ≥ 0, j ∈ C])dFX,θ|Ij ≥0,j∈C (x, f ).
DR
A
Δ
TT
(21)
and the average effect of the treatment on the untreated:
Eε (Δj [Y |X = x, θ = f, Ij < 0, j ∈ C])dFX,θ|Ij <0,j∈C (x, f ).
(22)
AR
Y
ΔT U T ≡
Finally, the average marginal treatment effect is the average effect of participating in the
ΔAM T E ≡ Eε (Δj [Y |X = x, |Ij | < εS , j ∈ C]).
(23)
IM
IN
treatment for individuals who are at the margin of indifference between participating or not:
Data and Estimation Strategy
PR
EL
5
The 1979 National Longitudinal Survey of Youth (NLSY79) is a survey of nationally representative sample of men and women born in the years 1957-64, and so the respondents were ages
14-22 when first interviewed in 1979. It performs surveys on a annual or biennual basis about
labor force activity. It also contains a vast about of information about the other aspects of
the respondents lives, such as educational achievement, marital status, fertility, participation
in crime, income, assets, health, alcohol and substance abuse, and scores on achievement and
11
psychological tests. We use the core sample of males, which, after removing observations with
missing covariates, contains 2242 observations.
Outcomes
5.1.1
tc
We consider a number of labor market and behavioral outcomes conditional on schooling levels.
Schooling Levels.
no
We consider five different transitions and six final schooling levels. The transitions are (i)
enrolled in high school deciding between graduating from high school and dropping out from high
o
school, (ii) high school dropouts deciding whether or not to get the GED, (iii) GED recipients
-d
deciding whether or not to enroll in college, (iv) high school graduates deciding whether or not
to enroll in college, and (v) college students deciding whether to graduate from college or to
FT
drop out before getting the degree. Consequently, the final schooling levels are (i) high school
dropout, (ii) GED, (iii) GED with some college, (iv) High school graduate, (v) some college and
(vi) four year college degree. As final schooling level we utilize the information available at age
5.1.2
DR
A
30.2
Labor Market Outcomes.
Following the analysis of Heckman, Stixrud, and Urzua (2006), we consider labor market out-
AR
Y
comes at age 30. We analyze (log) wages, labor market participation and the probability of
a white-collar occupation. Following Keane and Wolpin (1997), we denote as white-collar occupations (i) professional, technical, and kindred; (ii) managers, officials, and proprietors; (iii)
IN
sales workers; (iv) farmers and farm managers; and (v) clerical and kindred. For (log) wages we
use linear regression models by schooling level. For labor market participation and white-collar
IM
occupation we use binary decision models by schooling levels. As previously explained, our
PR
EL
factor model controls for the selection into schooling levels.
5.1.3
Physical Health and Healthy Behaviors.
As proxy for healthy behavior we use the binary variable “Regular Exercise” in 2006. Specifically,
regular exercise is based on the following question “How often do you do vigorous activities
for at least 10 minutes that cause heavy sweating or large increases in breathing or heart
rate?”. We define “Regular Exercise” equal to one if the answer is at least once per week. As
2
ite
5.1
A negligible fraction of individuals changes schooling level after age 30.
12
measures of physical health we utilize Body Mass Index during adulthood (in 2006), an obesity
indicator based on BMI and the PCS-12 scale (measure of physical health). BMI is calculated
as BMI=(Weight in Ponds * 703)/(Height in inches)2 , and the obesity indicator takes a value
Component Summary obtained from SF-12. SF-12 is a 12-question health survey designed by
tc
John Ware of the New England Medical Center Hospital (Ware, Kosinski, and Keller, 1996).3
ite
of one if the BMI is 30 and above, and zero otherwise. Finally, the PCS-12 scale is the Physical
The MCS-12 is designed to provide a measure of the respondents mental and physical health
no
irrespective of their proclivity to use formal health services. Respondents with a score above
(below) 50 have better (worse) health than the typical person in the general U.S. population.
o
Each one-point difference above or below 50 corresponds to a one-tenth of a standard deviation.
-d
For example, a person with a score of 30 is two standard deviations away from the mean. We
standardized the SF-12 score to have mean zero and variance one in the overall population.
Mental Health.
FT
5.1.4
We study Pearlin’s “Personal Mastery Scale” (collected in 1992), Rosenberg’s Self-esteem scale
DR
A
(collected in 2006), the Mental Component Summary or MCS-12 (age 40), and The Center for
Epidemiologic Studies Depression Scale (CES-D) (Age 40). Pearlins “Personal Mastery Scale”
consists of 7 items which are answered on a 4-point (4 strongly agree, 3 agree, 2 disagree, 1
strongly disagree) scale and has been shown to exhibit reasonable internal reliability (Seeman,
AR
Y
1991) and good construct validity (see Pearlin et al, 1981). The items are “there is really no
way i can solve some of the problems i have”, “sometimes i feel that i’m being pushed around in
life” , “i have little control over the things that happen to me”, “i can do just about anything
i really set my mind to”, “i often feel helpless in dealing with the problems of life”, “what
IN
happens to me in the future mostly depends on me”, “there is little i can do to change many
3
IM
of the important things in my life”. We form the scale summing the scores from the items, and
PR
EL
The questions in SF-12 includes:“In general, would you say your health is Excellent (1), Very Good (2), Good
(3), Fair (4) or Poor (5)”, “The following items are activities you might do during a typical day. Does your health
limit you in these activites? [1] Moderate activities, such as moving a table, pushing a vacuum cleaner, bowling or
playing golf? [2] Climbing several flights of stairs? [3] Accomplished less than you would like? [4] Were limited in the
kind of work or other activities?”, “During the past 4 weeks, have you had any of the following problems with your
work or other regular daily activities as a result of any emotional problems (such as feeling depressed or anxious)?
(Please answer YES or NO for each question). [1] Accomplished less than you would like? [2] Didn’t do work or
other activities as carefully as usual?”, “During the past 4 weeks, how much did pain interfere with your normal work
(including both work outside of the home and housework)?”, “The next questions are about how you feel and how
things have been with you during the past 4 weeks. (For each question, please give the one answer that comes closest
to the way you have been feeling). How often during the past 4 weeks: [1] have you felt calm and peaceful? , [2] Did
you have a lot of energy?, [3] Have you felt down-hearted and blue?”, “During the past 4 weeks, how much of the
time has your physical health or emotional problems interfered with your social activities (like visiting with friends,
relatives, etc.)?”
13
standardizing the scores to have mean 0 and variance 1 in the overall population. The numbers
in this table represents the estimated coefficients and Std. Errors associated with the linear
regression models on the set of controls presented in rows. Each column contains the results
Rosenberg’s Self-Esteem Scale consists of 11 items which are answered on a 4-point (4
tc
strongly agree, 3 agree, 2 disagree, 1 strongly disagree). The items are “I feel that I’m a
ite
obtained for a particular schooling level.
person of worth, at least on equal basis with others”, “I feel that I have a number of good
no
qualities” , “All in all, I am inclined to feel that I am a failure”, “I am able to do things as well
as most other people”, “I feel I do not have much to be proud of”, “I take a positive attitude
o
toward myself”, “On the whole, I am satisfied with myself”, “I wish I could have more respect
-d
for myself” , “I certainly feel useless at times”, “At times I think I am no good at all”. We
form the scale summing the scores from the items, and standardizing the scores to have mean
0 and variance 1 in the overall population. The numbers in this table represents the estimated
FT
coefficients and Std. Errors associated with the linear regression models on the set of controls
presented in rows. Each column contains the results obtained for a particular schooling level.
DR
A
The MCS-12 scale is the Mental Component Summary (measures mental health) obtained
from SF-12. SF-12 is a 12-question health survey designed by John Ware of the New England
Medical Center Hospital (Ware, Kosinski, and Keller, 1996). The MCS-12 is designed to provide
a measure of the respondents mental health irrespective of their proclivity to use formal health
AR
Y
services. Respondents with a score above (below) 50 have better (worse) health than the typical
person in the general U.S. population. Each one-point difference above or below 50 corresponds
to a one-tenth of a standard deviation. For example, a person with a score of 30 is two standard
deviations away from the mean. We standardized the SF-12 score to have mean zero and
IN
variance one in the overall population.
CES-D is one of the most common screening tests for helping an individual to determine his
IM
or her depression quotient. This scale measures symptoms of depression, discriminates between
clinically depressed individuals and others, and is highly correlated with other depression rating
PR
EL
scales (see Radloff, 1977; Ross and Mirowsky, 1989). We form the scale summing the scores
from the items: “I did not feel like eating; my appetite was poor”, “I had trouble keeping my
mind on what I was doing”, “I felt depressed”, “I felt that everything I did was an effort”,
“My sleep was restless”, “I felt sad” and “I could not get going”. For each items the potential
answers are: “0 Rarely/None of the time/1 Day”, “1 Some/A little of the time/1-2 Days”, “2
Occasionally/Moderate amount of the time/3-4 Days”, “3 Most/All of the time/5-7 Days”. We
standardized the scores to have mean 0 and variance 1 in the overall population. The numbers
14
in this table represents the estimated coefficients and Std. Errors associated with the linear
regression models of CES-D on the set of controls presented in rows. Each column contains the
5.2
Measurement System
tc
The set of cognitive measures include the ASVAB components utilized to generate the Armed
ite
results obtained for a particular schooling level.
Forces Qualification Test (AFQT) score.4 Specifically, we consider the scores from Arithmetic
no
Reasoning, Coding Speed, Paragraph Comprehension, World Knowledge, and Numerical Operations. For each test we estimate a separate model and we control for the effect of schooling at
o
the time of the tests using the method developed in Hansen, Heckman, and Mullen (2004).
-d
The set of socio-emotional includes early indicators of risky behaviors as well as an indicator
of socialization during high school. Among the risky behaviors we consider participation in
FT
minor illegal activity in 1979 (vandalism, shoplifting, petty theft, robbery, fraud and fencing),
participation in major illegal activity in 1979 (auto theft, breaking/entering private property and
grand theft), participation in violent crime in 1979 (fighting, assault and aggravated assault),
DR
A
tried marijuana before age 15, daily smoking before age 15, regular drinking before age and
any intercourse before age 15. In the case of the variables measured in 1979 we also control for
the potential effect of schooling on the the risky behavior. On the other hand, as proxy for a
positive socio-emotional dimension we use “Member of Clubs During High School”. This is a
AR
Y
binary variable that takes a value of one if the individual was a member of at least one of the
following organizations during high school: youth organizations, hobby, student government,
yearbook and newspaper, performance art, and other clubs; and zero otherwise.
Exogenous observed characteristics
IN
5.3
IM
The set of exogenous characteristics used as covariates in the outcome equations for ilegal
activity, early risky behavior, Pearlin test scores, Rosenberg test scores and ASVAB test scores
PR
EL
are dummies for race, living in an urban area at 14, living in the south at 14, living in a broken
home at 14, and then the number of siblings, mother’s education, father’s education, family
income in 1979, and age in 1980 as a continuous cohort variable, except for the ASVAB test
scores, which have individual cohort dummies.
The set of exogenous characteristics used as covariates in the outcome equations for wages,
labor market participation, and employed in white collar job are dummies for race, rural area
4
The AFQT scores are usually interpreted as proxies for cognitive ability (Herrnstein and Murray, 1994).
15
at 30, region of residence in the United States at 30, and local unemployment at age 30 and age
in 1980 as a continuous cohort variable.
Finally, the set of exogenous characteristics used as covariates in the schooling choice models
broken home at 14, and then the number of siblings, mother’s education, father’s education,
tc
family income in 1979, and age in 1979 as a continuous cohort variable. In addition, wages and
ite
are dummies for race, living in an urban area at 14, living in the south at 14, living in a
unemployment for different schooling outcomes and the local cost of college and taking the GED
o
Estimation Results
-d
6
no
test are included in their respective latent utilities.
Figure 2 presents the joint and marginal distributions of cognitive and socio-emotional endowments. The associated parameters are presented at the bottom of the page. Our estimates
6.1
DR
A
reject the hypothesis of normally distributed factors.
FT
suggest a positive and statistically significant correlation between the factors. Likewise, we
The Effect of Cognitive and Socio-emotional Endowments on School-
ing Decision, Labor Market and Health Outcomes
Table 3 presents the results from the schooling choice model. Tables 4 to 8 contain the results
AR
Y
for the measurement system identifying the socio-emotional endowment. Tables 9 to 11 contain
the results for the measurement system identifying the cognitive endowment.
Tables 12, 13 and 14 display the estimates associated with the labor market participation,
white-collar occupation, and (log) wages, respectively. Figures 6, 7, and 8 complement the
IN
results from these tables presenting a graphic analysis of the effects of endowments on labor
market participation, white-collar occupation, and (log) wages, respectively.
IM
Table 15, 16, 17 and 18 display the results for obesity, regular exercise, BMI and physical
health scale (PCS-12), respectively. Figures 9, 10, and 11 and 12 complement the results
PR
EL
from these tables presenting a graphic analysis of the effects of endowments on obesity, regular
exercise, BMI and physical health scale (PCS-12), respectively.
Table 19, 20, 21 and 22 display the results for Pearlin’s “Personal Mastery Scale”, self-
esteem, mental health (MCS-12), and depression (CES-D), respectively. Figures 13, 14, and 15
and 16 complement the results from these tables presenting a graphic analysis of the effects of
endowments on Pearlin’s “Personal Mastery Scale”, self-esteem, mental health (MCS-12), and
depression (CES-D), respectively.
16
Findings:
1. Measurement system: We find that the cognitive loadings are significant on the cognitive
2. Labor Market Outcomes: We find that cognitive loadings are significant in labor market
participation, white collar employment, and wages for all schooling levels. While social-
tc
emotional loading is significant for white collar employment for high school dropouts.
ite
measures and the social-emotional loadings are significant on the social-emotional measures
no
3. Physical Health Outcomes: We find that there isn’t significant evidence of loading on
exercise, BMI, and obesity. We do find some evidence for cognitive loadings on PCS-12
o
for lower educational levels, and social-emotional for High School Graduates.
-d
4. Mental Health Outcomes: We find significant cognitive loadings on Pearlin, Self-Esteem
and depression for lower schooling levels, while PCS-12 does not have significant cognitive loadings. There is some evidence for social-emotional loadings on PCS-12 for higher
FT
education levels as well as self-esteem for GEDs and depression for GEDs, high school
6.2
DR
A
graduates and some college.
Sorting into schooling level
Since the model is highly nonlinear and multidimensional, the best way to understand the results
are to simulate it. This is done by randomly drawing the exogenous regressors from the data,
AR
Y
randomly drawing factors from the estimated factor distributions, and then simulating the different outcomes. The first outcome is to understand how individuals sort into different schooling
levels. Figure 3 shows the distribution of the factors by chosen schooling level. It seems that
individuals sort by cognitive ability into increasing schooling levels. The only exception is GED,
IN
which have a very similar distribution to high school graduates. On the other hand, the sorting
by the socio-emotional factor is not so straight-forward. GEDs have a lower socio-emotional
IM
distribution than any other schooling level, including high school dropouts. Likewise, those
completing only some college have a lower socio-emotional distribution than high school gradu-
PR
EL
ates. This sorting demonstrates the role that conscientiousness plays in educational attainment
and the ability to complete tasks.
Each panel in Figure 4 presents the fraction of individuals by deciles of cognitive and socio-
emotional endowments for a particular final schooling level. The deciles are computed uing the
overall marginal distributions (instead o the joint distributions).
Figure 5 repeats the analysis of Figure 4 but now focusing on the sorting into schooling levels
by decision node.
17
6.3
Goodness of Fit
Tables 23, 24, 25, 26, and 27 show the the goodness of fit for the various outcomes and mea-
Hypothesis is Model=Data. For continuous outcomes the equality of the model and data distributions are tested using a two-sample Kolmogorov-Smirnov test. Whereas we would expect the
tc
model to do very well for the discrete outcomes, it is difficult to model non-normal outcomes
ite
surement systems. Goodness of fit for discrete outcomes is tested using a χ2 test where the Null
with the normality assumptions we made above. Since we allowed the error term for wages to
no
take on a mixture of three normals approximation, the model does a decent job reproducing
the wage data. Especially for outcomes that are not truly continuous like the Rosenberg and
o
Pearlin tests, we don’t expect the model to pass a Kolmogorov-Smirnov test. Nevertheless, at
-d
least in terms of the first and second moments, the model does a good job of reproducing the
6.4
FT
data.
Treatment Effects: Comparison of Outcomes for different final
DR
A
schooling levels
We now compare the outcomes from a particular schooling level j with those associated with
the HS dropout status. In other words, we use HS dropout as our baseline comparison group.
Tables 28, 29 and 30 display the estimated treatment effects associated with the labor market
participation, white-collar occupation, and (log) wages, respectively. Table 31, 32, 33 and 34
AR
Y
display the treatment effects for obesity, regular exercise, BMI and physical health scale (PCS12), respectively. Table 35, 36, 37 and 38 display the results for Pearlin’s “Personal Mastery
Scale”, self-esteem, mental health (MCS-12), and depression (CES-D), respectively. For each the
IN
outcomes, the numbers under the column “Observed” displays the observed differences in the
data. The column “ATE” displays the average treatment effect obtained from the comparison
IM
of the outcomes associated with a particular schooling level j relative to the HS dropout status.
ATE is computed using the overall population. The column “TT” displays the average treatment
PR
EL
effects associated with a particular schooling level j relative to the HS dropout status but
computed from those individuals selecting j as their final schooling decision. Finally, the column
“TUT” displays the average treatment effects associated with a particular schooling level j
relative to the HS dropout status but computed only for those individuals selecting “HS dropout”
as their final schooling decision.
Findings:
1. In general the difference are much larger when you do not control for observables and
18
latent abilities. There is significant heterogeneity in the gains from school as can be seen
from the large differences seen between ATE, TT, and TUT in most outcomes.
2. In most cases the gain from education is increasing with the school level, even after con-
3. Labor Market Outcomes: GED with some college are less likely to have a job, but once
tc
they have a job they gain more in earnings and are more likely to have white collar
employment. As expected four-year college graduate has the largest gains in terms of
no
earnings and likelihood of white collage employment.
4. Physical Health Outcomes: Higher educational levels are more likely to exercise and have
-d
o
better PCS-12 measures compared to high school dropouts, except for college with GED.
Most educational levels are more likely to be obese, except for 4-year college graduates
and college with GED.
FT
5. Mental Health Outcomes: In almost all outcomes, the observed gain is much large than
the ATE. In general, educational attainment causes large gains in the Pearlin Mastery
DR
A
Scale and MCS-12 measure. In general self-esteem is increasing with education, and in
most cases education decreases depression. It is interesting that TT for any level of college
is either zero or positive. This consistent with the literature on depression and high ability
people.
Treatment Effects: Pairwise Comparing by Decision Node
AR
Y
6.5
After discussing the treatment effects obtained for the analysis of final schooling levels, we now
analyze the treatment effects by decision node.
IN
Tables 39, 40 and 41 display the estimated treatment effects associated with the labor market
participation, white-collar occupation, and (log) wages, respectively. Table 42, 43, 44 and 45
IM
display the treatment effects for obesity, regular exercise, BMI and physical health scale (PCS12), respectively. Table 45, 46, 47 and 48 display the results for Pearlin’s “Personal Mastery
PR
EL
Scale”, self-esteem, mental health (MCS-12), and depression (CES-D), respectively.
Each table presents the average effects of education on the outcome of interest. The effects
are presented in the different panels in each table and they are defined as the differences in
the outcome associated with two schooling levels (not necessarily final or terminal schooling
levels). For each panel, let Y0 and Y1 denotes the outcomes associated with schooling levels
0 and 1, respectively. Let Y0 and Y1 denotes the outcomes associated with schooling levels
0 and 1, respectively. Importantly, each schooling level might provide the option to pursuing
19
ite
trolling for ability.
higher schooling levels. Only final schooling levels do not provide the option. In this table,
final schooling levels are highlighted using bold letters. For each pairs of schooling levels 0
and 1, the column ATE presents E(Y1 − Y0 ) computed for those who reach the decision node
D = 1 indicates the transition to the higher schooling level available at the node. Likewise,
tc
TUT column presents E(Y1 − Y0 |D = O) where D = 0 indicates the transition to the lower
ite
involving a decision between levels 0 and 1. The TT column presents E(Y1 − Y0 |D = 1) where
schooling level. Finally, AMTE presents the average (Y1 −Y0 ) for those indifferent between 0 and
no
1. The table also presents the estimated treatment effects conditional upon endowment levels.
The high (low) ability group is defined as those individuals with cognitive and socio-emotional
-d
fraction of individuals with low and high ability levels visiting each node.
o
endowment above (below) the overall median. Finally, for each decision node we display the
Figures 17, 18, and 19 complement the results for labor market outcomes. Figure 20, 21,
22, 23 complement the results for physical health and healthy behavior. Figure 24, 25, 26, 27
FT
complement the results for mental health.
Each figure analyzes the average effects of education on the outcome of interest. For a
DR
A
particular outcome, the effect is defined as the difference in the outcomes associated with two
schooling levels (not necessarily final or terminal schooling levels). For each pairwise comparison
of outcomes, let Y0 and Y1 denotes the outcomes associated with schooling levels 0 and 1,
respectively. Importantly, each schooling level might provide the option to pursuing higher
AR
Y
schooling levels. Final schooling levels do not allow for further options. Notice that in the
figures final schooling levels are highlighted using bold letters. For each pair of schooling levels
0 and 1, the first figure (top) presents E(Y1 −Y0 |dC , dSE ) where dC and dSE denote the cognitive
and socio-emotional deciles computed from the marginal distributions of cognitive and socio-
IN
emotional endowments. E(Y1 − Y0 |dC , dSE ) is computed for those who reach the decision node
involving a decision between levels 0 and 1. The second figure (bottom left) presents E(Y1 −
IM
Y0 |dC ) so that the socio-emotional factor is integrated out. The bars in this figure displays,
for a given decile of cognitive endowment, the fraction of individuals visiting the node leading
PR
EL
to the educational decision involving levels 0 and 1. The last figure (bottom right) presents
E(Y1 − Y0 |dSE ) as well as, for a given decile of socio-emotional endowment, the fraction of
individuals visiting the node leading to the educational decision involving levels 0 and 1.
Our results indicate:
1. Labor Market Outcomes: Graduating for high school significantly increases the probability
of labor force participation, while further education does not have an impact. Getting the
20
GED hurts your chances of employment. As expected there are large gains from college in
the probability of white collar employment and it is increasing with ability, while GED’s
have smaller gains. Although in general higher educational attainment results in larger
college degree.
tc
2. Physical Health Outcomes: Again college attainment reduces obesity and BMI, and on
ite
gains in wagre, low ability individuals gain very little from getting a GED or a four-year
average high school graduation and GED have small effects. There are significant differ-
no
ences between high ability and low ability types, with low ability individuals being more
likely to be obese and have higher BMI at lower educational levels, but are less likely to be
o
obese and have lower BMI if they are a four-year college graduate. GEDs and college with
-d
GED are less likely to exercise, where the effect is larger for high ability people. The other
education levels are more likely to exercise. Finally, high school graduation and college
FT
increase the PCS-12 measure, more so for the low ability types. The opposite is the case
for the GED’s, where the PCS-12 is on average worsened by GED and college with GED
attainment, with large differences between high and low ability types.
DR
A
3. Mental Health Outcomes: In general, educational attainment increases an individuals
Pearlin score, but the effect is much larger for the low ability types. Likewise self-esteem
is improved for choosing college attainment, but it is again much larger for low ability
types. There are small improvements in the MCS-12 measure for lower educational levels,
AR
Y
but they become slightly negative for college attainment. The college with GED has a large
negative impact on the MCS-12 measure. In general, increasing educational attainment
results in less depression, where again most of the effect is coming from the low ability
IN
types. The GED is the exception where the high ability types have less depression. It is
IM
also interesting that college with GED are more likely to be depressed than GEDs.
7
Conclusions
PR
EL
We have estimated a model of educational choices, labor market and health outcomes with
unobserved heterogeneity. In this framework, and following Heckman and Vytlacil (2007a,b),
we analyze the impact of education on health and labor market outcomes when responses to
treatment vary among observationally identical persons and agents select into the treatment on
the basis of their responses.
We posit two latent factors that determine most of the correlation among outcomes outside
of schooling. Our results indicate that there is strong sorting into schooling level according to
21
latent abilities. We estimate treatment effects for many adult outcomes, and find that the causal
effect of schooling can be quite different for individuals of different ability levels.
In general the difference in outcomes are much larger when you do not control for observables
from the large differences seen between ATE, TT, and TUT in most outcomes. In most cases
tc
the gain from education is increasing with the school level, even after controlling for ability. In
many of the outcomes we find very different effects for high and low ability people, indicating
no
that there is some amount of essential heterogeneity.
As expected, there are still significant gains in labor market outcomes from graduating high
o
school and going to college, and the gains are larger for high ability people. The benefit of
-d
the GED is not as clear-cut. Compared to high school dropouts, GEDs are less likely to be
employed, earn higher wages and are more likely to hold a white collar job, and the high ability
types have larger gains. But when considering the GEDs that go to college, the transition going
FT
into college has much greater benefits for the low ability types.
College attainment reduces obesity and BMI, and on average high school graduation and
DR
A
GED have small effects. There are significant differences between high ability and low ability
types, with low ability individuals being more likely to be obese and have higher BMI at lower
educational levels, but are less likely to be obese and have lower BMI if they are a four-year
college graduate. Those graduating from high school and going to college are more likely to
AR
Y
exercise regularly, while GEDs and college with GED are less likely to exercise, where the effect
is larger for high ability people. Finally, high school graduation and college increase the PCS-12
measure, more so for the low ability types. The opposite is the case for the GED’s, where the
PCS-12 is on average worsened by GED and college with GED attainment, with large differences
IN
between high and low ability types.
In general, educational attainment increases an individuals Pearlin score, but the effect is
IM
much larger for the low ability types. Likewise self-esteem is improved by college attainment,
and it is much larger for low ability types. In general, increasing educational attainment results
PR
EL
in less depression, where again most of the effect is coming from the low ability types. The GED
is the exception where the high ability types have less depression.
22
ite
and latent abilities. There is significant heterogeneity in the gains from education as can be seen
References
Adams, S. J. (2002): “Educational Attainment and Health: Evidence from a Sample of Older
Arendt, J. N. (2005): “Does education cause better health? A panel data analysis using
tc
school reforms for identification,” Economics of Education Review, 24(2), 149–160.
no
Arkes, J. (2003): “Does Schooling Improve Adult Health?,” Working Paper.
Auld, M. C., and N. Sidhu (2005): “Schooling, Cognitive Ability and Health,” Health Eco-
o
nomics, 14(10), 1019–1034.
-d
Becker, G. S. (1964): Human Capital: A Theoretical and Empirical Analysis, with Special
Reference to Education. National Bureau of Economic Research, distributed by Columbia
FT
University Press, New York.
Borghans, L., B. Ter Weel, and B. Weinberg (2007): Interpersonal styles and labor
DR
A
market outcomes. National Bureau of Economic Research Cambridge, Mass., USA.
Cameron, S. V., and J. J. Heckman (1998): “Life Cycle Schooling and Dynamic Selection Bias: Models and Evidence for Five Cohorts of American Males,” Journal of Political
AR
Y
Economy, 106(2), 262–333.
(2001): “The Dynamics of Educational Attainment for Black, Hispanic, and White
Males,” Journal of Political Economy, 109(3), 455–99.
Card, D. (1999): “The Causal Effect of Education on Earnings,” in Handbook of Labor Eco-
IM
IN
nomics, ed. by O. Ashenfelter, and D. Card, vol. 5, pp. 1801–1863. North-Holland, New
York.
(2001): “Estimating the Return to Schooling: Progress on Some Persistent Econometric
PR
EL
Problems,” Econometrica, 69(5), 1127–1160.
Carneiro, P., K. Hansen, and J. J. Heckman (2003): “Estimating Distributions of Treatment Effects with an Application to the Returns to Schooling and Measurement of the Effects
of Uncertainty on College Choice,” International Economic Review, 44(2), 361–422.
Carneiro, P., and J. J. Heckman (2002): “The Evidence on Credit Constraints in PostSecondary Schooling,” Economic Journal, 112(482), 705–734.
23
ite
Adults,” Education Economics, 10(1), 97–109.
Cawley, J., K. Conneely, J. J. Heckman, and E. J. Vytlacil (1997): “Cognitive Ability,
Wages and Meritocracy,” in Intelligence, Genes, and Success : Scientists Respond to The Bell
Curve, ed. by B. Devlin, S. Fienberg, D. Resnick, and K. Roeder, pp. 179–192. Springer, New
Conti, G., J. J. Heckman, and S. Urzúa (2009): “Early Endowments, Education and
tc
Health,” Working Paper.
no
Cutler, D. M., and A. Lleras-Muney (2007): “Understanding Differences in Health Behaviors by Education,” Unpublished working paper.
o
Deary, I. (2009): “Introduction to the special issue on cognitive epidemiology,” Intelligence,
-d
37(6), 517–519.
Duckworth, A., and S. Urzúa (2009): “Determinants of Success in Early Adulthood: Com-
FT
paring the Effects of Intelligence and Big Five Personality Trait,” Working Paper.
Elias, J. (2004): “The effects of ability and family background on non-monetary returns to
DR
A
education,” University of Chicago mimeograph, November.
Fuchs, V. R. (1982): Time Preference and Health: An Exploratory Study. National Bureau of
Economic Research Cambridge, Mass., USA.
AR
Y
Glewwe, P. (2002): “Schools and Skills in Developing Countries: Education Policies and
Socioeconomic Outcomes,” Journal of Economic Literature, 40(2), 436–482.
Gottfredson, L. S., and I. J. Deary (2004): “Intelligence Predicts Health and Longevity,
IN
but Why?,” Current Directions in Psychological Science, 13(1), 1–4.
Grossman, M. (1972): “On the Concept of Health Capital and the Demand for Health,”
IM
Journal of Political Economy, 80(2), 223–255.
(1975): “The Correlation Between Health and Schooling,” in Household Production
PR
EL
ite
York.
and Consumption, ed. by N. E. Terleckyj, pp. 147–211. Columbia University Press, New York.
(2000): “The Human Capital Model,” in Handbook of Health Economics, ed. by A. J.
Culyer, and J. P. Newhouse, vol. 1, pp. 347–408. Elsevier, Amsterdam.
(2006): “Education and Nonmarket Outcomes,” Handbook of the Economics of Edu-
cation, p. 577.
24
Grossman, M. (2008): “The Relationship Between Health and Schooling,” Eastern Economic
Journal, 34, 281–292.
cial Benefits of Education, ed. by J. R. Behrman, and N. Stacey, pp. 69–124. University of
tc
Michigan Press, Ann Arbor, MI.
Gullone, E., and S. Moore (2000): “Adolescent risk-taking and the five-factor model of
no
personality,” Journal of Adolescence, 23(4), 393–407.
Hampson, S. E., and H. S. Friedman (2008): “Personality and health: A lifespan perspec-
-d
o
tive,” in The handbook of personality: Theory and research, ed. by O. P. John, R. Robins,
and L. Pervin, pp. 770–794. Guilford, New York, third edn.
Hansen, K. T., J. J. Heckman, and K. J. Mullen (2004): “The Effect of Schooling and
FT
Ability on Achievement Test Scores,” Journal of Econometrics, 121(1-2), 39–98.
Hartog, J., and H. Oosterbeek (1998): “Health, wealth and happiness: why pursue a
DR
A
higher education?,” Economics of Education Review, 17(3), 245–256.
Hauser, R. M. (1993): “Trends in College Entry Among Whites, Blacks, and Hispanics,” in
Studies of Supply and Demand in Higher Education, ed. by C. Clotfelter, and M. Rothschild,
AR
Y
pp. 61–104. University of Chicago Press, Chicago.
Heckman, J. J., J. Stixrud, and S. Urzua (2006): “The Effects of Cognitive and Noncognitive Abilities on Labor Market Outcomes and Social Behavior,” Journal of Labor Economics,
IN
24(3), 411–482.
Heckman, J. J., S. Urzua, and E. J. Vytlacil (2006): “Understanding Instrumental
IM
Variables in Models with Essential Heterogeneity,” Review of Economics and Statistics, 88(3),
389–432.
PR
EL
Heckman, J. J., and E. J. Vytlacil (2007a): “Econometric Evaluation of Social Programs,
Part I: Causal Models, Structural Models and Econometric Policy Evaluation,” in Handbook of Econometrics, ed. by J. Heckman, and E. Leamer, vol. 6B, pp. 4779–4874. Elsevier,
Amsterdam.
(2007b): “Econometric Evaluation of Social Programs, Part II: Using the Marginal
Treatment Effect to Organize Alternative Economic Estimators to Evaluate Social Programs
25
ite
Grossman, M., and R. Kaestner (1997): “Effects of Education on Health,” in The So-
and to Forecast Their Effects in New Environments,” in Handbook of Econometrics, ed. by
J. Heckman, and E. Leamer, vol. 6B, pp. 4875–5144. Elsevier, Amsterdam.
Structure in American Life. Free Press, New York.
tc
John, O., R. W. Robins, and L. A. Pervin (eds.) (2008): Handbook of personality: Theory
and research. Guilford Press., New York, NY, second edn.
no
Kaestner, R. (2008): “Adolescent Cognitive and Non-cognitive Correlates of Adult Health,”
Unpublished manuscript, Institute of Government and Public Affairs, University of Illinois at
-d
o
Chicago, Department of Economics.
Karsten T. Hansen, James J. Heckman, K. J. M. (2004): “The effect of schooling and
FT
ability on achievement test scores,” Journal of Econometrics, 121(1-2), 39–98.
Keane, M. P., and K. I. Wolpin (1997): “The Career Decisions of Young Men,” Journal of
DR
A
Political Economy, 105(3), 473–522.
Kenkel, D., D. Lillard, and A. Mathios (2006): “The Roles of High School Completion
and GED Receipt in Smoking and Obesity,” Journal of Labor Economics, 24(3), 635–660.
Kolata, G. (2007): “A surprising secret to a long life: Stay in school,” .
AR
Y
Lleras-Muney, A. (2005): “The Relationship Between Education and Adult Mortality in the
United States,” Review of Economic Studies, 72(1), 189–221.
Meara, E. R., S. Richards, and D. M. Cutler (2008): “The Gap Gets Bigger: Changes
IN
in Mortality and Life Expectancy, by Education, 1981-2000,” Health Affairs, 27(2), 350–360.
IM
Morris, P. (2003): “Club participation: Effects on future household earning potential,” J.
Legal Econ., 13, 45.
PR
EL
Neal, D. A., and W. R. Johnson (1996): “The Role of Premarket Factors in Black-White
Wage Differences,” Journal of Political Economy, 104(5), 869–895.
Radloff, L. (1977): “The CES-D Scale: A Self Report Depression Scale for Research in the
General Population.,” Applied Psychological Measurement, 1, 385–401.
Roberts, B. W., N. R. Kuncel, R. L. Shiner, A. Caspi, and L. R. Goldberg (2007):
“The power of personality: The comparative validity of personality traits, socioeconomic
26
ite
Herrnstein, R. J., and C. A. Murray (1994): The Bell Curve: Intelligence and Class
status, and cognitive ability for predicting important life outcomes,” Perspectives in Psychological Science, Forthcoming.
and Problem Solving - or Support and Talking?,” Journal of Health and Social Behavior, 30,
tc
206–209.
Shakotko, R. A., L. N. Edwards, and M. Grossman (1982): “An Exploration of the
no
Dynamic Relationship Between Health and Cognitive Development in Adolescence,” .
Silles, M. A. (2009): “The causal effect of education on health: Evidence from the United
-d
o
Kingdom,” Economics of Education Review, 28(1), 122–128.
Spasojevic, J. (2003): “Effects of Education on Adult Health in Sweden: Results from a
FT
Natural Experiment,” New York: City University of New York Graduate Center.
Urzúa, S. (2008): “Racial Labor Market Gaps: The Role of Abilities and Schooling Choices,”
DR
A
Forthcoming, Journal of Human Resources.
Ware, J., M. Kosinski, and S. Keller (1996): “Club participation: Effects on future
household earning potential,” Medical Care, 34, 220–233.
Whalley, L., and I. Deary (2001): “Longitudinal cohort study of childhood IQ and survival
AR
Y
up to age 76,” British Medical Journal, 322(7290), 819.
Willis, R. J., and S. Rosen (1979): “Education and Self-Selection,” Journal of Political
PR
EL
IM
IN
Economy, 87(5, Part 2), S7–S36.
27
ite
Ross, C. E., and J. Mirowsky (1989): “Explaining the Social Patterns of Depression: Control
ite
Conditional on
none
Di1 = 0
Di1 = 1
Di3 = 1
no
Decision=0
High School Dropout
High School Dropout
High School Graduate
Some College
PR
EL
IM
IN
AR
Y
DR
A
FT
-d
o
Di1
Di2
Di3
Di4
Decision=1
High School Graduate
Get GED
Attend College
Graduate 4-yr college
28
tc
Table 1: Summary of Decisions
29
IN
IM
-0.79
Cognitive
Measure
-0.03
41
78
9
-d
-1.22
-1.52
-3.04
-3.02
o
Socio-emotional
Measure
-1.85
FT
0.56
0.19
-0.40
DR
A
22
%
Simulated
100
no
0.19
0.08
-0.07
-0.28
Cognitive
Endowment
0.00
0.31
0.22
-0.86
-0.79
Socio-emotional
Endowment
0.00
tc
ite
Notes: The Cognitive Measure is the standardized average over arithmetic reasoning, coding speed, word knowledge, numerical operations
and paragraph comprehension. The socio-emotional scale is computed as the sum of the following eight binary variables: participation in
minor illegal activity in 1979, participation in major illegal activity in 1979, participation in violent crime in 1979, tried marijuana before
age 15, daily smoking before age 15, regular drinking before age and any intercourse before age 15. This gives a range between 0 and 8 for
this variable. We then reverse the scale so that it ranges (-8,0) and add a one if the individual was a member of a club during high school.
In this way the socio-emotional variable ranges between -8 and 1. The endowments are simulated from our model. The simulated data
(Model) contains one million observations generated from the Model’s estimates. The actual data (Actual) contains 2242 observations
from the NLSY79 sample of Males.
40
77
9
23
%
Actual
100
AR
Y
Enrolled in HS
(going to HSG or HSD)
HS Dropout
(going to HSD or GED)
GED
(going to GED or College)
HS Graduate
(going to HSG or College)
Enrolled in College
(going to Some College or 4YCollege)
Initial Node
Table 2: Schooling Transitions, Average Cognitive and Socio-emotional Measures, and Cognitive and Socio-emotional Endowments
PR
EL
30
FT
DR
A
HSGraduate
β
StdEr.
0.146
0.11
0.657
0.136
-0.013
0.04
0.159
0.067
-0.108 0.077
-0.055 0.009
0.095
0.003
0.123
0.003
0.009
0.001
0.021
0.002
-3.378 0.034
-0.193 0.003
2.836
0.481
0.191
0.002
-4.778 0.598
1.122
0.088
0.374
0.062
-d
o
0.116
0.082
tc
1.069
0.505
College Graduate
β
StdEr.
0.085
0.173
0.261
0.216
0.012
0.055
-0.102
0.091
-0.315
0.112
-0.027
0.014
0.081
0.004
0.099
0.004
0.013
0.002
0.02
0.002
-2.412
0.048
-0.151
0.003
0.044
0.847
0.074
0.002
4.59
1.461
0.004
0.022
no
Some College
β
StdEr.
0.146
0.11
0.657
0.136
-0.013
0.04
0.159
0.067
-0.108 0.077
-0.055 0.009
0.095
0.003
0.123
0.003
0.009
0.001
0.021
0.002
-3.378 0.034
-0.193 0.003
2.836
0.481
0.191
0.002
-4.778 0.598
1.122
0.088
0.374
0.062
GED+College
β
StdEr.
-0.753 0.449
0.245
0.619
0.073
0.141
0.064
0.201
0.116
0.201
-0.131 0.037
0.156
0.011
0.099
0.011
-0.04
0.007
0.064
0.007
-1.933 0.127
0.269
0.01
5.029
1.763
-0.386 0.009
-8.598 2.246
0.639
0.324
0.007
0.171
ite
Notes: (a) The schooling levels are the final schoolings observed in the data; (b) The variable is measured when the individual was 17
years old.
HSDropout
β
StdEr.
0.159
0.094
0.544
0.129
-0.277 0.043
-0.349 0.062
-0.517 0.067
-0.04
0.009
0.119
0.003
0.074
0.003
0.022
0.002
0.047
0.002
-1.304 0.037
0.201
0.003
-3.123 0.307
-0.208 0.003
1.69
0.517
0.962
0.088
0.826
0.063
GED
β
StdEr.
0.011
0.142
0.181
0.199
0.016
0.069
0.085
0.096
-0.227 0.097
0.005
0.013
0.055
0.006
0.072
0.006
0.022
0.004
-0.074 0.003
-0.232 0.061
0.001
0.002
1.515
0.148
-0.069
0.08
Table 3: Estimates for Schooling Choice Model(a)
AR
Y
IN
IM
Black
Hispanics
Urban Area (14)
South (14)
Broken Home
Number of Siblings
Mother’s Education
Father’s Education
Family Income
Age in 1980
Intercept
Local wage of hsd(b)
Local unemp. of hsd(b)
Local wage of hsg(b)
Local unemp. of hsg(b)
Local wage of some college(b)
Local unemp. of some college(b)
Local wage of college graduates(b)
Local unemp. of college graduates(b)
Tuition at local 4-yr college(b)
GED Cost
Cognitive
Socio-emotional
Variable
PR
EL
tc
no
Std. Error
0.084
0.113
0.033
0.053
0.059
0.008
0.002
0.002
0.001
0.001
0.029
0.048
o
β
0.268
0.547
-0.098
0.085
-0.168
-0.04
0.074
0.062
0.003
-0.005
-1.726
0
0.382
-d
Variable
Black
Hispanic
Urban Area (14)
South (14)
Broken Home
Number of Siblings
Mother’s Education
Father’s Education
Family Income
Age
Intercept
Cognitive
Socio-emotional
ite
Table 4: Measurement System
Estimates for “Member of Clubs During High School”
DR
A
FT
Notes: The variable “Member of Clubs During High School” is a binary variable taking a value of
one if the individual was a member of at least one of the following organizations during high school:
youth organizations, hobby, student government, yearbook and newspaper, performance art, and
other clubs, and zero otherwise.
AR
Y
Table 5: Measurement System
Estimates for “Participation in Violent Crime during 1979”, by Schooling at the Time of the Test
Violent Crime
PR
EL
IM
IN
Black
Hispanic
Urban Area (14)
South (14)
Broken Home
Number of Siblings
Mother’s Education
Father’s Education
Family Income
Age
Intercept
Cognitive
Socio-emotional
<12 yrs
β
Std. Error
-0.213
0.112
-0.369
0.145
0.184
0.047
-0.074
0.07
0.249
0.079
0.007
0.01
0.022
0.003
-0.036
0.003
-0.006
0.002
-0.137
0.002
3.061
0.041
0
-0.774
0.075
β
0.188
-0.028
0.229
-0.04
0.272
0.028
-0.02
-0.023
0
-0.102
2.481
0
-0.863
12 yrs
Std. Error
0.174
0.218
0.062
0.105
0.124
0.014
0.005
0.004
0.002
0.003
0.054
0.113
>12 yrs
β
Std. Error
-0.51
0.473
-2.502
0.542
-0.986
0.145
-0.642
0.263
0.606
0.311
0.014
0.035
-0.183
0.01
0.019
0.009
-0.008
0.005
-0.089
0.006
5.542
0.131
0
-2.707
0.339
Notes: The variable “Participation in Violent Crime” takes a value of one if the individual participated in any of the following criminal activities: Fighting, Assault and Aggravated Assault.
31
tc
>12 yrs
β
Std. Error
-0.869
0.458
-2.136
0.509
-0.762
0.141
-0.28
0.265
0.42
0.302
0.017
0.035
-0.181
0.01
-0.01
0.009
0.008
0.005
0.028
0.006
3.258
0.128
0
-2.651
0.269
no
12 yrs
Std. Error
0.158
0.212
0.059
0.099
0.115
0.013
0.004
0.004
0.002
0.002
0.051
0.094
o
β
-0.074
-0.057
0.191
-0.26
-0.021
0.099
0.012
0.01
0.002
-0.147
2.5
0
-0.524
FT
Black
Hispanic
Urban Area (14)
South (14)
Broken Home
Number of Siblings
Mother’s Education
Father’s Education
Family Income
Age
Intercept
Cognitive
Socio-emotional
<12 yrs
β
Std. Error
0.111
0.109
-0.125
0.142
0.006
0.046
-0.208
0.068
0.279
0.075
-0.003
0.009
-0.024
0.003
0.029
0.003
0.007
0.002
-0.088
0.002
1.649
0.039
0
-0.688
0.07
-d
Minor
ite
Table 6: Measurement System
Estimates for “Participation in a Minor Illegal Activity during 1979”, by Schooling at the Time of
the Test
DR
A
Notes: The variable “Participation in Minor Crime” takes a value of one if the individual participated in any of the following criminal activities: Vandalism, Shoplifting, Petty Theft, Robbery,
Fraud and Fencing; and zero otherwise.
Major
AR
Y
Table 7: Measurement System
Estimates for “Participation in a Major Illegal Activity during 1979”, by Schooling at the Time of
the Test
PR
EL
IM
IN
Black
Hispanic
Urban Area (14)
South (14)
Broken Home
Number of Siblings
Mother’s Education
Father’s Education
Family Income
Age
Intercept
Cognitive
Socio-emotional
<12 yrs
β
Std. Error
-0.191
0.126
-0.078
0.158
0.223
0.05
-0.158
0.079
0.148
0.081
0.001
0.011
-0.006
0.004
0.017
0.004
-0.003
0.002
-0.081
0.002
0.363
0.044
0
-0.874
0.072
β
-0.375
-0.24
0.114
-0.014
0.028
0.089
0.03
-0.004
0
-0.061
-0.565
0
-0.735
12 yrs
Std. Error
0.234
0.291
0.075
0.129
0.152
0.016
0.005
0.005
0.002
0.003
0.066
0.112
>12 yrs
β
Std. Error
-0.74
0.512
-1.114
0.571
-0.747
0.166
-0.511
0.279
0.812
0.321
0.081
0.04
0.004
0.011
-0.11
0.011
0.023
0.005
0.241
0.007
-5.047
0.147
0
-2.096
0.326
Notes: The variable “Participation in Major Crime” takes a value of one if the individual participated in any of the following criminal activities: Auto Theft, Breaking/Entering and Grand
Theft.
32
ite
tc
no
o
Regular Drinking
β
Std. Error
-0.292
0.103
-0.014
0.127
0.118
0.04
0.066
0.062
0.284
0.067
0.035
0.008
-0.004
0.003
-0.003
0.003
-0.001
0.001
-0.023
0.002
-0.774
0.035
0
-0.817
0.056
FT
Daily Smoking
β
Std. Error
-0.417
0.109
-0.517
0.146
0.146
0.043
-0.033
0.067
0.511
0.07
0.043
0.009
-0.034
0.003
-0.042
0.003
-0.003
0.002
0.032
0.002
-1.135
0.038
0
-1.041
0.062
DR
A
Black
Hispanic
Urban Area (14)
South (14)
Broken Home
Number of Siblings
Mother’s Education
Father’s Education
Family Income
Age
Intercept
Cognitive
Socio-emotional
Tried Marijuana
β
Std. Error
-0.421
0.099
-0.214
0.125
0.343
0.038
-0.153
0.061
0.541
0.066
0.04
0.008
0.007
0.003
-0.012
0.003
0.001
0.001
-0.108
0.002
1.131
0.034
0
-1.108
0.067
AR
Y
Variable
-d
Table 8: Measurement System
Estimates for “Early Risky Behaviors” (Before Age 15)
Any Intercourse
β
Std. Error
0.693
0.092
0.004
0.134
0.251
0.044
0.117
0.066
0.463
0.069
0.016
0.009
-0.037
0.003
-0.03
0.003
-0.004
0.002
-0.011
0.002
-0.747
0.039
0
-1
-
PR
EL
IM
IN
Notes: In each case, the dependent variable takes a value of one if the individual has reported the
behavior before age 15, and zero otherwise.
33
34
-d
o
no
Numerical
Operatons
β
Std. Error
-0.342
0.043
0.106
0.058
-0.099
0.019
-0.055
0.028
-0.106
0.029
0.001
0.004
0.067
0.001
0.056
0.001
0.014
0.001
-1.517
0.016
-0.242
0.088
-0.166
0.079
-0.167
0.067
-0.21
0.069
-0.198
0.039
-0.096
0.029
-0.233
0.03
1.362
0.036
0
0.457
0.013
Coding
Speed
β
Std. Error
-0.492
0.055
0.031
0.074
-0.021
0.024
-0.091
0.036
-0.061
0.038
-0.009
0.005
0.037
0.002
0.025
0.002
0.013
0.001
-1.208
0.021
0.145
0.113
0.066
0.102
0.057
0.087
-0.166
0.087
-0.117
0.051
-0.013
0.037
-0.21
0.039
1
0
0.638
0.015
tc
ite
Notes: The numbers in this table represents the estimated coefficients and Std. Errors associated with the linear regression models of
each cognitive test score (column) on the set of controls presented in rows.
FT
Paragraph
Comprehension
β
Std. Error
-0.525
0.054
0.007
0.072
-0.146
0.023
-0.124
0.034
-0.102
0.036
-0.026
0.005
0.078
0.002
0.057
0.002
0.011
0.001
-1.352
0.02
-0.099
0.109
-0.093
0.099
-0.213
0.083
-0.518
0.085
-0.463
0.049
-0.35
0.036
-0.589
0.038
1.536
0.045
0
0.582
0.016
DR
A
Word
Knowledge
β
Std. Error
-0.674
0.05
-0.027
0.067
-0.139
0.022
-0.182
0.032
-0.05
0.034
-0.029
0.004
0.082
0.002
0.053
0.002
0.011
0.001
-1.372
0.019
0.049
0.102
-0.098
0.092
-0.205
0.078
-0.389
0.079
-0.397
0.046
-0.208
0.034
-0.442
0.035
1.344
0.042
0
0.554
0.014
AR
Y
IN
Arithmetic
Reasoning
β
Std. Error
-0.533
0.044
-0.101
0.058
-0.034
0.019
-0.102
0.028
-0.117
0.03
0
0.004
0.059
0.001
0.043
0.001
0.015
0.001
-1.099
0.017
-0.177
0.089
-0.18
0.08
-0.236
0.068
-0.358
0.07
-0.366
0.04
-0.256
0.03
-0.417
0.031
1.456
0.036
0
0.452
0.013
Table 9: Measurement System
Cognitive Test Scores - < 12 Years of Education at the time of the test
IM
Black
Hispanic
Urban Area (14)
South (14)
Broken Home
Number of Siblings
Mother’s Education
Father’s Education
Family Income
Intercept
d58
d59
d60
d61
d62
d63
d64
Cognitive
Socio-emotional
Std. Error
Variables
PR
EL
35
-d
o
Numerical
Operatons
β
Std. Error
-0.354
0.059
0.004
0.081
0.001
0.022
-0.162
0.035
-0.2
0.042
-0.028
0.005
0.035
0.002
0.062
0.002
0.003
0.001
-0.988
0.019
0.04
0.057
-0.074
0.051
-0.053
0.043
0.114
0.042
0.171
0.039
-0.423
0.146
1.515
0.047
0
0.484
0.015
Coding
Speed
β
Std. Error
-0.685
0.075
0.11
0.104
-0.092
0.028
-0.182
0.045
0.075
0.053
-0.001
0.006
0.027
0.002
0.035
0.002
0.006
0.001
-0.612
0.024
-0.203
0.072
-0.052
0.065
-0.212
0.056
-0.247
0.054
-0.278
0.05
-0.496
0.187
1.052
0.06
0
0.68
0.018
no
tc
ite
Notes: The numbers in this table represents the estimated coefficients and Std. Errors associated with the linear regression models of
each cognitive test score (column) on the set of controls presented in rows.
FT
Paragraph
Comprehension
β
Std. Error
-0.707
0.063
0.137
0.087
-0.119
0.023
-0.1
0.038
-0.15
0.044
-0.043
0.005
0.035
0.002
0.05
0.002
0.002
0.001
-0.324
0.02
-0.166
0.06
-0.357
0.054
-0.312
0.046
-0.317
0.045
-0.304
0.041
-0.455
0.156
1.419
0.05
0
0.536
0.015
DR
A
Word
Knowledge
β
Std. Error
-0.905
0.061
-0.078
0.084
-0.082
0.023
-0.109
0.036
-0.12
0.043
-0.057
0.005
0.037
0.002
0.049
0.002
0.003
0.001
-0.181
0.019
-0.096
0.058
-0.336
0.053
-0.359
0.045
-0.426
0.043
-0.428
0.04
-0.627
0.151
1.3
0.048
0
0.523
0.015
AR
Y
IN
Table 10: Measurement System
Cognitive Test Scores - 12 Years of Education at the time of the test
Arithmetic
Reasoning
β
Std. Error
-0.806
0.059
-0.165
0.082
-0.101
0.022
-0.157
0.036
-0.155
0.042
-0.001
0.005
0.04
0.002
0.048
0.002
0.003
0.001
-0.347
0.019
-0.07
0.057
-0.299
0.051
-0.319
0.043
-0.321
0.042
-0.33
0.039
-0.437
0.147
1.655
0.047
0
0.468
0.016
IM
Black
Hispanic
Urban Area (14)
South (14)
Broken Home
Number of Siblings
Mother’s Education
Father’s Education
Family Income
Intercept
d58
d59
d60
d61
d62
d63
Cognitive
Socio-emotional
Std. Error
Variables
PR
EL
36
-d
o
Numerical
Operatons
β
Std. Error
-1.062
0.071
-0.637
0.085
-0.02
0.023
-0.029
0.042
-0.209
0.053
-0.022
0.006
0.009
0.002
0.024
0.002
0.003
0.001
0.374
0.021
-0.036
0.045
0.035
0.048
1.721
0.049
0
0.402
0.016
Coding
Speed
β
Std. Error
-0.822
0.116
-0.155
0.137
0.08
0.038
-0.003
0.068
-0.084
0.087
-0.055
0.01
0.038
0.003
-0.016
0.002
0.001
0.001
0.195
0.034
0.103
0.074
-0.041
0.076
0.791
0.08
0
0.693
0.024
no
tc
ite
Notes: The numbers in this table represents the estimated coefficients and Std. Errors associated with the linear regression models of
each cognitive test score (column) on the set of controls presented in rows.
FT
Paragraph
Comprehension
β
Std. Error
-0.54
0.075
-0.437
0.088
-0.043
0.024
0.032
0.044
-0.173
0.056
-0.032
0.006
0.003
0.002
-0.002
0.002
0.002
0.001
0.627
0.022
0.069
0.048
0.031
0.05
0.745
0.052
0
0.446
0.016
DR
A
Word
Knowledge
β
Std. Error
-0.754
0.071
-0.484
0.084
-0.003
0.023
-0.023
0.042
-0.03
0.053
-0.031
0.006
0.006
0.002
0.019
0.001
0
0.001
0.44
0.021
0.074
0.046
0.061
0.047
0.73
0.049
0
0.423
0.015
AR
Y
Arithmetic
Reasoning
β
Std. Error
-1.234
0.048
-0.817
0.059
-0.014
0.017
-0.049
0.03
-0.036
0.036
-0.023
0.004
0.03
0.001
0.013
0.001
-0.001
0.001
0.291
0.015
0.003
0.03
0.069
0.035
1.774
0.031
0
0.224
0.015
IN
Table 11: Measurement System
Cognitive Test Scores - >12 Years of Education at the time of the test
IM
Black
Hispanic
Urban Area (14)
South (14)
Broken Home
Number of Siblings
Mother’s Education
Father’s Education
Family Income
Intercept
d58
d59
Cognitive
Socio-emotional
Std. Error
Variables
PR
EL
37
AR
Y
HSDropout
β
Std. Error
-1.229
0.231
-0.457
0.318
0.7
0.186
-0.409
0.272
-0.265
0.232
-0.904
0.267
0.013
0.023
0.125
0.011
-0.916
0.208
0.677
0.316
0.052
0.15
β
-0.069
-0.313
-0.335
-0.113
0.325
-0.728
-0.023
0.006
1.875
1.14
0.18
FT
Some College
β
Std. Error
-0.718
0.367
-0.384
0.297
0.108
0.437
-0.277
0.168
-0.072
0.281
-0.335
0.218
-0.063
0.248
0.026
0.022
0.06
0.009
0.771
0.17
0.196
0.273
-0.093
0.175
-d
HSGraduate
β
Std. Error
-0.678
0.204
0.14
0.387
-0.024
0.137
0.718
0.326
-0.13
0.175
0.129
0.238
-0.054
0.017
0.008
0.007
2.146
0.145
0.47
0.248
-0.149
0.146
DR
A
GED
Std. Error
0.323
0.527
0.261
0.372
0.409
0.358
0.031
0.016
0.304
0.735
0.413
College Graduate
β
Std. Error
-0.083
0.447
0.285
0.171
0.106
0.244
0.038
0.217
0.767
0.406
-0.137
0.022
-0.025
0.009
2.713
0.17
0.658
0.31
0.208
0.193
o
no
tc
ite
Notes: The numbers in this table represents the estimated coefficients and Std. Errors associated with binary choice models of labor
market participation on the set of controls presented in rows. Each column contains the results obtained for a particular schooling level.
GED
Black
Hispanic
Urban
Northeast
Northcentral
West
Unemployment Rate
Cohort
Intercept
Cognitive
Socio-emotional
Variables
IN
IM
Table 12: Outcome Model: Labor Market Participation at Age 30 by Schooling Level
PR
EL
38
β
-0.106
-0.152
0.601
0.123
0.56
0.366
-0.067
-0.088
0.583
0.748
-0.026
AR
Y
HSDropout
β
Std. Error
-0.635
0.351
-0.461
0.372
-0.112
0.267
0.682
0.293
-0.241
0.328
0.58
0.3
-0.027
0.051
-0.053
0.047
0.398
1.053
0.373
0.247
0.335
0.144
FT
o
Some College
β
Std. Error
0.473
0.328
-0.406
0.242
0.331
0.277
0.188
0.18
0.135
0.2
0.032
0.186
-0.144
0.195
-0.074
0.031
0.017
0.025
-0.001
0.552
0.312
0.173
0.003
0.111
-d
HSGraduate
β
Std. Error
-0.509
0.183
0.157
0.196
0.206
0.12
-0.134
0.152
-0.258
0.135
-0.149
0.173
-0.007
0.023
0.009
0.024
-0.651
0.541
0.368
0.126
0.073
0.088
DR
A
GED
Std. Error
0.327
0.496
0.349
0.404
0.299
0.357
0.072
0.059
1.326
0.384
0.151
Table 13: Outcome Model: White Collar Employment (Age 30)
IN
IM
College Graduate
β
Std. Error
-0.359
0.296
0.087
0.443
0.261
0.144
0.026
0.187
-0.098
0.165
-0.218
0.205
-0.028
0.021
-0.007
0.008
1.043
0.166
0.933
0.217
0.058
0.129
no
tc
ite
Notes: White-collar occupations are (i) professional, technical, and kindred; (ii) managers, officials, and proprietors; (iii) sales workers;
(iv) farmers and farm managers; and (v) clerical and kindred. The numbers in this table represents the estimated coefficients and Std.
Errors associated with binary model for whether or not the individual works in a white-collar occupation at age 30 on the set of controls
presented in rows. Each column contains the results obtained for a particular schooling level.
dged
black
hisp
crural
northeast
northcentral
west
Unemployment Rate
Cohort
Intercept
Cognitive
Socio-emotional
Variable
PR
EL
39
AR
Y
HSDropout
β
Std. Error
-0.208
0.063
-0.261
0.073
0.055
0.053
0.195
0.067
-0.051
0.056
0.027
0.068
-0.008
0.01
0.006
0.01
2.249
0.237
0.11
0.068
-0.071
0.032
0.333
0.016
β
-0.254
-0.111
0.055
0.207
0.063
0.181
0.005
0.05
1.374
0.16
0.01
0.394
FT
Some College
β
Std. Error
0.116
0.109
-0.323
0.081
-0.227
0.093
0.11
0.063
0.073
0.071
0.018
0.064
0.039
0.066
-0.024
0.009
0.015
0.011
2.483
0.232
-0.014
0.059
-0.022
0.038
0.439
0.017
-d
HSGraduate
β
Std. Error
-0.296
0.05
-0.066
0.061
0.116
0.035
0.149
0.045
0.078
0.039
0.147
0.051
-0.004
0.007
0.027
0.007
1.938
0.149
0.174
0.038
-0.068
0.025
0.397
0.01
DR
A
GED
Std. Error
0.089
0.132
0.097
0.107
0.089
0.106
0.014
0.017
0.377
0.102
0.051
0.024
-0.146
-0.132
0.174
0.21
0.032
0.039
0.011
0.008
2.404
0.32
0.005
0.396
0.078
0.108
0.058
0.052
0.048
0.059
0.009
0.009
0.195
0.058
0.037
0.013
College Graduate
β
Std. Error
o
no
tc
ite
Notes: The numbers in this table represents the estimated coefficients and Std. Errors associated with the linear regression models of
(log) wages on the set of controls presented in rows. Each column contains the results obtained for a particular schooling level.
GED
Black
Hispanic
Urban
Northeast
Northcentral
West
Unemployment Rate
Cohort
Intercept
Cognitive
Socio-emotional
Std. Error
Variables
IN
IM
Table 14: Outcome Model: Estimates for (Log) Wages at Age 30 by Schooling Level
PR
EL
40
HSDropout
β
Std. Error
0.049
0.233
0.297
0.284
-0.221
0.224
0.251
0.178
0.006
0.192
-0.036
0.036
-0.058
0.075
-0.026
0.059
0
0.013
0.043
0.033
-0.283
0.782
-0.22
0.271
0.352
0.135
β
0.318
0.919
-0.124
-0.49
0.17
-0.075
0.014
0.013
0.008
0.047
-1.35
0.079
0.095
FT
DR
A
GED
Std. Error
0.327
0.516
0.301
0.274
0.273
0.072
0.052
0.043
0.015
0.041
1.152
0.343
0.167
Some College
β
Std. Error
-0.819
0.44
0.502
0.267
0.646
0.311
0.305
0.177
0.032
0.168
-0.195
0.2
-0.046
0.044
0.043
0.036
-0.002
0.028
-0.006
0.007
0.027
0.031
-1.396
0.733
-0.096
0.188
-0.124
0.128
-d
HSGraduate
β
Std. Error
-0.059
0.178
0.08
0.222
-0.112
0.117
0.019
0.122
0.043
0.133
0.008
0.024
0.006
0.027
0.015
0.02
0.002
0.006
0.049
0.02
-1.535
0.501
-0.274
0.131
0.115
0.091
Table 15: Outcome Model: Obesity (Age 40)
AR
Y
IN
IM
College Graduate
β
Std. Error
0.799
0.292
0.027
0.449
0.072
0.194
0.025
0.176
0.182
0.218
0.037
0.042
-0.023
0.042
-0.019
0.031
0.001
0.005
0.01
0.044
-0.653
1.188
-0.281
0.257
0.202
0.176
o
no
tc
ite
Notes: Obesity has been more precisely defined as a BMI of 30 and above. The numbers in this table represents the estimated coefficients
and Std. Errors associated with binary models of obesity (age 40) on the set of controls presented in rows. Each column contains the
results obtained for a particular schooling level.
GED
Black
Hispanic
Urban Area (14)
South (14)
Broken Home
Number of Siblings
Mother’s Education
Father’s Education
Family Income
Cohort
Intercept
Cognitive
Socio-emotional
Variable
PR
EL
41
β
0.246
0.551
-0.272
0.626
-0.152
-0.052
0.077
0.037
-0.017
-0.054
0.605
0.074
0.169
FT
o
no
Some College
β
Std. Error
-0.72
0.357
-0.459
0.266
-0.387
0.297
0.002
0.161
0.284
0.175
-0.161
0.193
0.026
0.037
0.056
0.017
-0.003
0.015
0.001
0.006
-0.004
0.011
0.181
0.224
-0.354
0.202
-0.039
0.135
-d
HSGraduate
β
Std. Error
-0.056
0.176
-0.157
0.215
-0.08
0.113
0.253
0.123
-0.164
0.134
-0.002
0.022
0.026
0.015
-0.013
0.014
-0.002
0.005
-0.057
0.01
1.618
0.194
0.141
0.138
0.038
0.093
DR
A
GED
Std. Error
0.306
0.436
0.272
0.23
0.27
0.044
0.032
0.029
0.014
0.022
0.414
0.349
0.185
AR
Y
IN
Table 16: Outcome Model: Regular Exercise during Adulthood
HSDropout
β
Std. Error
-0.622
0.222
-0.438
0.282
0.051
0.185
0.044
0.168
-0.03
0.178
0.039
0.03
0.012
0.025
0.021
0.023
-0.01
0.01
-0.036
0.014
0.962
0.291
0.169
0.303
0.004
0.132
IM
College Graduate
β
Std. Error
-0.027
0.33
-0.79
0.414
-0.333
0.156
0.151
0.181
0.265
0.228
-0.002
0.034
0.099
0.013
-0.009
0.012
0.007
0.004
0.016
0.01
-0.533
0.192
-0.075
0.216
0.112
0.135
tc
ite
Notes: The binary variable “Regular Exercise” takes a value of one if the individual reports exercising on a regular base by 2006, and zero
otherwise. Specifically, regular exercise is based on the following question “How often do you do vigorous activities for at least 10 minutes
that cause heavy sweating or large increases in breathing or heart rate?”. The numbers in this table represent the estimated coefficients
and Std. Errors associated with binary models of regular exercise on the set of controls presented in rows. Each column contains the
results obtained for a particular schooling level.
GED
Black
Hispanic
Urban Area (14)
South (14)
Broken Home
Number of Siblings
Mother’s Education
Father’s Education
Family Income
Cohort
Intercept
Cognitive
Socio-emotional
Variable
PR
EL
42
β
0.633
2.906
-0.62
-1.468
-0.799
-0.274
0.03
0.051
-0.012
0.296
24.927
0.523
1.201
4.499
FT
Some College
β
Std. Error
-1.762
1.537
1.349
1.166
2.583
1.357
-0.215
0.736
-0.094
0.704
-0.87
0.811
-0.091
0.175
0.261
0.165
-0.024
0.12
-0.004
0.029
-0.022
0.142
26.994
3.31
-0.034
0.785
0.03
0.531
5.417
0.222
-d
HSGraduate
β
Std. Error
-0.617
0.685
1.157
0.876
0.088
0.456
0.117
0.478
-0.313
0.52
-0.008
0.095
0.031
0.112
0.011
0.083
-0.013
0.022
0.187
0.091
25.317
2.255
-0.54
0.515
0.441
0.348
4.99
0.14
DR
A
GED
Std. Error
1.071
1.783
1.051
0.882
0.987
0.181
0.202
0.159
0.057
0.199
4.799
1.229
0.605
0.29
AR
Y
IN
Table 17: Outcome Model: Body Mass Index during Adulthood
HSDropout
β
Std. Error
0.168
0.963
3.289
1.272
-0.786
0.901
0.813
0.773
0.363
0.792
-0.229
0.152
-0.158
0.179
-0.303
0.152
-0.023
0.051
0.13
0.177
31.975
4.035
-0.006
1.356
0.453
0.519
5.586
0.279
IM
College Graduate
β
Std. Error
2.292
0.828
-0.159
1.164
0.118
0.491
0.498
0.448
0.676
0.567
0.027
0.102
-0.033
0.097
-0.063
0.074
0.005
0.013
0.024
0.086
27.733
2.205
-0.874
0.603
0.391
0.387
3.654
0.131
o
no
tc
ite
Notes: BMI is calculated as BMI=(Weight in Ponds * 703)/(Height in inches)2 . We use the 2006 BMI variable from the NLSY79. The
numbers in this table represents the estimated coefficients and Std. Errors associated with the linear regression models on the set of
controls presented in rows. Each column contains the results obtained for a particular schooling level.
GED
Black
Hispanic
Urban Area (14)
South (14)
Broken Home
Number of Siblings
Mother’s Education
Father’s Education
Family Income
Cohort
Intercept
Cognitive
Socio-emotional
Variance
Variable
PR
EL
43
FT
o
no
Some College
β
Std. Error
-0.328
0.209
-0.199
0.171
0.166
0.208
-0.117
0.11
0.025
0.104
0.074
0.119
-0.051
0.025
0.017
0.025
-0.014
0.017
0.008
0.004
0.029
0.021
-0.415
0.492
0.063
0.114
0.012
0.077
0.845
0.033
-d
HSGraduate
β
Std. Error
0.064
0.11
-0.09
0.136
0.025
0.071
0.006
0.075
-0.054
0.082
-0.021
0.015
0.034
0.018
0.008
0.013
-0.002
0.003
0.013
0.014
-0.565
0.357
0.087
0.08
0.137
0.057
0.824
0.022
DR
A
GED
Std. Error
0.232
0.352
0.216
0.186
0.207
0.038
0.044
0.033
0.011
0.041
1.005
0.259
0.137
0.061
AR
Y
HSDropout
β
Std. Error
-0.212
0.199
0.144
0.266
0.081
0.185
0.04
0.157
-0.065
0.159
-0.046
0.03
0.028
0.037
0.013
0.031
0.013
0.01
-0.029
0.035
0.003
0.819
0.427
0.261
0.009
0.106
1.19
0.055
β
0.279
0.218
0.145
0.101
-0.065
0.023
0
0.019
-0.003
-0.05
0.448
0.462
0.048
1.016
Table 18: Outcome Model: Physical Health at Age 40 (PCS-12)
IN
IM
College Graduate
β
Std. Error
-0.171
0.107
0.171
0.136
0.05
0.061
-0.115
0.055
-0.022
0.072
0.013
0.013
0.022
0.012
-0.014
0.009
0.001
0.002
-0.011
0.01
0.431
0.268
0.061
0.074
-0.057
0.048
0.478
0.016
tc
ite
Notes: The PCS-12 scale is the Physical Component Summary (measures physical health) obtained from SF-12. SF-12 is a 12-question
health survey designed by John Ware of the New England Medical Center Hospital (Ware, Kosinski, and Keller, 1996). The MCS-12 is
designed to provide a measure of the respondents mental and physical health irrespective of their proclivity to use formal health services.
Respondents with a score above (below) 50 have better (worse) health than the typical person in the general U.S. population. Each
one-point difference above or below 50 corresponds to a one-tenth of a standard deviation. For example, a person with a score of 30 is two
standard deviations away from the mean. We standardized the SF-12 score to have mean zero and variance one in the overall population.
GED
Black
Hispanic
Urban Area (14)
South (14)
Broken Home
Number of Siblings
Mother’s Education
Father’s Education
Family Income
Cohort
Intercept
Cognitive
Socio-emotional
Std. Error
Variable
PR
EL
44
β
0.276
0.01
-0.199
-0.217
0.036
0.016
0.046
0.002
0.021
-0.04
0.014
0.542
0.026
0.908
FT
HSGraduate
β
Std. Error
-0.066
0.118
0.063
0.148
-0.024
0.077
-0.067
0.081
0.061
0.09
-0.008
0.016
0.003
0.019
0.019
0.014
0.002
0.004
-0.038
0.016
0.479
0.384
0.405
0.087
0.029
0.06
0.929
0.024
DR
A
GED
Std. Error
0.193
0.304
0.187
0.161
0.173
0.033
0.038
0.029
0.008
0.035
0.882
0.212
0.107
0.052
AR
Y
IN
HSDropout
β
Std. Error
-0.005
0.14
0.153
0.178
0.161
0.128
-0.069
0.109
-0.053
0.11
-0.013
0.021
0.068
0.025
-0.003
0.022
0.006
0.007
-0.051
0.025
-0.149
0.56
0.228
0.172
0.029
0.071
0.87
0.035
IM
Some College
β
Std. Error
0.21
0.229
-0.042
0.182
-0.092
0.217
-0.051
0.122
-0.171
0.115
-0.178
0.132
-0.012
0.026
0
0.027
0.007
0.019
-0.002
0.005
-0.042
0.023
1.164
0.542
0.107
0.129
-0.004
0.084
0.982
0.036
Table 19: Outcome Model: Pearlin’s “Personal Mastery Scale”
College Graduate
β
Std. Error
0.218
0.18
-0.067
0.25
-0.015
0.109
-0.12
0.097
0.059
0.129
0.004
0.023
0.019
0.021
-0.011
0.016
0.002
0.003
-0.032
0.019
0.891
0.471
-0.081
0.131
-0.018
0.087
0.91
0.029
-d
o
no
tc
ite
Notes: Pearlins “Personal Mastery Scale” consists of 7 items which are answered on a 4-point (4 strongly agree, 3 agree, 2 disagree, 1
strongly disagree) scale and has been shown to exhibit reasonable internal reliability (Seeman, 1991) and good construct validity (see
Pearlin et al, 1981). The items are “there is really no way i can solve some of the problems i have”, “sometimes i feel that i’m being
pushed around in life” , “i have little control over the things that happen to me”, “i can do just about anything i really set my mind to”,
“i often feel helpless in dealing with the problems of life”, “what happens to me in the future mostly depends on me”, “there is little i
can do to change many of the important things in my life”. We form the scale summing the scores from the items, and standardizing the
scores to have mean 0 and variance 1 in the overall population. The numbers in this table represents the estimated coefficients and Std.
Errors associated with the linear regression models on the set of controls presented in rows. Each column contains the results obtained
for a particular schooling level.
dged
Black
Hispanic
Urban Area (14)
South (14)
Broken Home
Number of Siblings
Mother’s Education
Father’s Education
Family Income
Cohort
Intercept
Cognitive
Socio-emotional
Std. Error
Variable
PR
EL
45
0.006
0.019
-0.136
-0.069
0.033
-0.011
0.048
0.032
0.004
-0.091
1.011
0.669
0.056
0.912
0.326
-0.41
0.026
-0.247
-0.139
-0.057
-0.039
-0.02
0.002
0.041
0.297
0.424
0.368
0.993
β
AR
Y
0.168
0.216
0.153
0.132
0.134
0.026
0.031
0.025
0.009
0.03
0.681
0.217
0.091
0.045
HSDropout
β
Std. Error
o
no
0.216
0.297
0.126
0.115
0.145
0.026
0.025
0.019
0.003
0.022
0.57
0.156
0.101
0.034
College Graduate
β
Std. Error
0.196
0.062
0.112
-0.088
-0.184
-0.022
0.024
-0.011
-0.008
-0.03
1.035
0.073
-0.045
0.933
tc
Some College
β
Std. Error
-0.063
0.284
0.272
0.203
0.334
0.24
-0.207
0.128
-0.171
0.124
0.222
0.143
-0.007
0.03
0.012
0.029
-0.003
0.02
0.002
0.005
0.036
0.025
-0.474
0.571
0.175
0.135
0.023
0.09
0.928
0.039
-d
0.122
0.164
0.082
0.085
0.093
0.017
0.021
0.015
0.004
0.016
0.406
0.092
0.063
0.025
FT
-0.139
0.123
-0.121
0.054
0.06
-0.016
0.036
0.015
0.003
-0.005
-0.433
0.541
0.009
0.889
HSGraduate
β
Std. Error
DR
A
0.245
0.427
0.234
0.197
0.224
0.041
0.046
0.036
0.013
0.044
1.068
0.275
0.132
0.066
GED
Std. Error
Table 20: Outcome Model: Rosenberg’s Self-Esteem Scale
IN
IM
ite
Notes: Rosenberg’s Self-Esteem Scale consists of 11 items which are answered on a 4-point (4 strongly agree, 3 agree, 2 disagree, 1 strongly
disagree). The items are “I feel that I’m a person of worth, at least on equal basis with others”, “I feel that I have a number of good
qualities” , “All in all, I am inclined to feel that I am a failure”, “I am able to do things as well as most other people”, “I feel I do not
have much to be proud of”, “I take a positive attitude toward myself”, “On the whole, I am satisfied with myself”, “I wish I could have
more respect for myself” , “I certainly feel useless at times”, “At times I think I am no good at all”. We form the scale summing the
scores from the items, and standardizing the scores to have mean 0 and variance 1 in the overall population. The numbers in this table
represents the estimated coefficients and Std. Errors associated with the linear regression models on the set of controls presented in rows.
Each column contains the results obtained for a particular schooling level.
GED
Black
Hispanic
Urban Area (14)
South (14)
Broken Home
Number of Siblings
Mother’s Education
Father’s Education
Family Income
Age 80
Intercept
Cognitive
Socio-emotional
Std. Error
Variable
PR
EL
46
FT
o
no
Some College
β
Std. Error
-0.169
0.2
-0.096
0.165
0.177
0.2
-0.05
0.106
-0.058
0.1
-0.225
0.115
0.016
0.024
0.034
0.024
-0.017
0.017
-0.001
0.004
-0.002
0.02
0.132
0.475
-0.113
0.111
0.207
0.074
0.808
0.032
-d
HSGraduate
β
Std. Error
-0.007
0.109
0.034
0.134
-0.079
0.07
0.01
0.074
-0.221
0.081
0
0.015
0.009
0.018
0.005
0.013
0.001
0.003
0
0.014
0.125
0.352
0.076
0.079
0.118
0.055
0.815
0.022
DR
A
GED
Std. Error
0.242
0.366
0.224
0.193
0.215
0.04
0.045
0.035
0.011
0.043
1.046
0.271
0.136
0.063
AR
Y
HSDropout
β
Std. Error
-0.095
0.198
0.438
0.265
-0.033
0.185
-0.045
0.156
-0.025
0.159
-0.044
0.03
0.012
0.037
-0.009
0.031
0.01
0.01
-0.007
0.035
0.143
0.817
0.145
0.261
0.009
0.11
1.188
0.055
β
0.407
-0.367
0.02
-0.009
-0.028
0.043
-0.021
0.047
-0.003
0.061
-1.401
0.418
0.138
1.058
Table 21: Outcome Model: Mental Health at Age 40 (MCS-12)
IN
IM
College Graduate
β
Std. Error
0.346
0.174
-0.179
0.22
-0.063
0.099
0.014
0.089
-0.159
0.116
0.005
0.021
0
0.019
-0.015
0.015
-0.001
0.002
0.017
0.017
0.11
0.433
-0.096
0.122
0.122
0.072
0.775
0.026
tc
ite
Notes: The MCS-12 scale is the Mental Component Summary (measures mental health) obtained from SF-12. SF-12 is a 12-question
health survey designed by John Ware of the New England Medical Center Hospital (Ware, Kosinski, and Keller, 1996). The MCS-12 is
designed to provide a measure of the respondents mental and physical health irrespective of their proclivity to use formal health services.
Respondents with a score above (below) 50 have better (worse) health than the typical person in the general U.S. population. Each
one-point difference above or below 50 corresponds to a one-tenth of a standard deviation. For example, a person with a score of 30 is two
standard deviations away from the mean. We standardized the SF-12 score to have mean zero and variance one in the overall population.
dged
Black
Hispanic
Urban Area (14)
South (14)
Broken Home
Number of Siblings
Mother’s Education
Father’s Education
Family Income
Cohort
Intercept
Cognitive
Socio-emotional
1/Precision
Variable
PR
EL
47
IM
β
-0.205
-0.088
0.122
-0.09
0.063
-0.014
-0.025
-0.041
0
-0.059
1.635
-0.255
-0.215
0.953
FT
HSGraduate
β
Std. Error
0.094
0.114
0.007
0.143
0.03
0.073
0.014
0.078
0.203
0.085
0.024
0.016
-0.014
0.018
-0.022
0.014
0.006
0.004
-0.009
0.015
0.151
0.371
-0.232
0.083
-0.128
0.058
0.852
0.023
DR
A
GED
Std. Error
0.218
0.33
0.201
0.172
0.191
0.036
0.04
0.031
0.01
0.038
0.928
0.237
0.116
0.057
AR
Y
IN
HSDropout
β
Std. Error
0.085
0.21
0.001
0.285
-0.024
0.196
0.249
0.167
0.133
0.168
0.06
0.032
-0.057
0.039
0.032
0.033
-0.021
0.011
0.032
0.037
-0.363
0.863
-0.555
0.277
-0.056
0.117
1.249
0.058
Some College
β
Std. Error
0.079
0.175
0.656
0.144
0.223
0.174
0.022
0.093
-0.014
0.087
0.169
0.1
-0.006
0.021
-0.042
0.021
0.028
0.014
-0.001
0.004
0.004
0.018
-0.225
0.414
-0.14
0.096
-0.177
0.064
0.705
0.028
College Graduate
β
Std. Error
-0.173
0.143
-0.015
0.181
0.062
0.082
0.05
0.073
0.044
0.095
-0.018
0.017
-0.004
0.016
-0.011
0.012
0
0.002
-0.024
0.014
0.314
0.358
0.089
0.102
-0.091
0.062
0.635
0.022
-d
o
no
tc
ite
Notes: CES-D is one of the most common screening tests for helping an individual to determine his or her depression quotient. This
scale measures symptoms of depression, discriminates between clinically depressed individuals and others, and is highly correlated with
other depression rating scales (see Radloff, 1977; Ross and Mirowsky, 1989). We form the scale summing the scores from the items: “I
did not feel like eating; my appetite was poor”, “I had trouble keeping my mind on what I was doing”, “I felt depressed”, “I felt that
everything I did was an effort”, “My sleep was restless”, “I felt sad” and “I could not get going”. For each items the potential answers
are: “0 Rarely/None of the time/1 Day”, “1 Some/A little of the time/1-2 Days”, “2 Occasionally/Moderate amount of the time/3-4
Days”, “3 Most/All of the time/5-7 Days”. We standardized the scores to have mean 0 and variance 1 in the overall population. The
numbers in this table represents the estimated coefficients and Std. Errors associated with the linear regression models of CES-D on the
set of controls presented in rows. Each column contains the results obtained for a particular schooling level.
dged
Black
Hispanic
Urban Area (14)
South (14)
Broken Home
Number of Siblings
Mother’s Education
Father’s Education
Family Income
Cohort
Intercept
Cognitive
Socio-emotional
Std. Error
Variable
Table 22: Outcome Model: The Center for Epidemiologic Studies Depression Scale (CES-D) (Age 40)
PR
EL
ite
tc
no
o
-d
Table 23: Goodness of Fit - Schooling Choice
Model
0.13
0.07
0.01
0.37
0.18
0.23
FT
Data
0.14
0.08
0.01
0.37
0.17
0.23
DR
A
Schooling Level
High School Dropout
GED (No College)
GED with Some College
High School Graduate
Some College
College Graduate
p-value
0.51
0.81
0.93
0.95
0.54
0.88
AR
Y
Notes: The simulated data (Model) contains one million observations generated from the Model’s
estimates. The actual data (Actual) contains 2242 observations from the NLSY79 sample of Males.
(a) Goodness of fit is tested using a χ2 test where the Null Hypothesis is Model=Data.
Table 24: Goodness of Fit - Early Risky Behavior
IM
IN
Outcome
Club Member during HS
Early marijuanac
Early daily smokingc
Early drinkingc
Early intercoursec
Actual
0.38
0.34
0.19
0.19
0.16
Model
0.39
0.30
0.18
0.18
0.16
p-valuea
0.92
0.15
0.61
0.82
0.84
PR
EL
Notes: The simulated data (Model) contains one million observations generated from the Model’s
estimates. The actual data (Actual) contains 2242 observations from the NLSY79 sample of Males.
(a) Goodness of fit is tested using a χ2 test where the Null Hypothesis is Model=Data. (b) The
illegal variables are taken from the NSLY 1980 Illegal Activities Supplement. (c) Early is defined
as engaging in risky behavior before 15 years old.
48
ite
p-valuea
0.66
0.60
0.59
0.72
0.77
0.82
0.66
0.58
0.56
0.72
0.77
0.80
0.97
0.84
0.93
0.99
0.96
0.76
0.31
0.30
0.12
0.37
0.35
0.22
0.29
0.31
0.15
0.37
0.35
0.23
0.76
0.93
0.89
0.99
1.00
0.89
0.82
0.84
0.85
0.94
0.94
0.96
0.84
0.84
0.86
0.96
0.95
0.96
0.72
0.96
0.97
0.33
0.61
0.75
0.14
0.21
0.62
0.28
0.48
0.87
0.13
0.22
0.66
0.29
0.49
0.85
0.80
0.96
0.88
0.80
0.91
0.75
AR
Y
IN
IM
no
Model
FT
-d
o
Actual
DR
A
Outcome
Regular Exercise
High school dropouts
GED
GED+College
High school graduates
Some college
Four-year college graduate
Obesity
High school dropouts
GED
GED+College
High school graduates
Some college
Four-year college graduate
Participation
High school dropouts
GED
GED+College
High school graduates
Some college
Four-year college graduate
White Collar
High school dropouts
GED
GED+College
High school graduates
Some college
Four-year college graduate
tc
Table 25: Goodness of Fit - Discrete Outcomes
PR
EL
Notes: The simulated data (Model) contains one million observations generated from the Model’s
estimates. The actual data (Actual) contains 2242 observations from the NLSY79 sample of Males.
(a) Goodness of fit is tested using a χ2 test where the Null Hypothesis is Model=Data.
49
Table 26: Goodness of Fit - Continuous Outcomes
AR
Y
IN
IM
PR
EL
0.90
0.97
0.95
1.00
0.92
0.99
0.91
0.97
0.95
0.99
0.92
1.00
0.00
0.00
0.00
0.00
0.00
0.86
-0.33
-0.11
-0.06
0.24
0.40
0.10
-0.37
-0.20
-0.05
0.25
0.40
0.11
0.97
1.08
0.93
0.95
0.95
0.91
1.00
1.14
0.92
0.95
0.95
0.95
0.00
0.19
0.00
0.00
0.00
0.83
-0.11
0.08
0.19
0.23
0.18
-0.12
-0.11
0.05
0.20
0.25
0.19
-0.21
1.21
1.12
0.83
0.84
0.79
0.80
1.21
1.13
0.82
0.83
0.79
0.85
0.00
0.00
0.00
0.00
0.00
0.20
0.37
-0.03
-0.13
-0.24
-0.08
0.00
0.39
0.00
-0.14
-0.25
0.02
0.00
1.31
1.00
0.87
0.76
0.73
0.00
1.32
1.01
0.87
0.75
0.76
0.00
0.00
0.00
0.00
0.00
0.09
0.00
-0.31
-0.12
0.05
0.13
0.36
-0.14
-0.32
-0.14
0.06
0.13
0.36
-0.24
1.22
1.06
0.84
0.87
0.49
0.84
1.23
1.05
0.84
0.86
0.49
0.85
0.00
0.00
0.00
0.00
0.00
0.18
28.81
28.13
29.17
28.98
27.42
26.55
28.66
28.00
29.17
29.02
27.48
26.98
5.99
4.82
5.04
5.53
3.75
4.49
6.05
4.87
5.04
5.50
3.76
5.52
0.01
0.37
0.00
0.02
0.01
0.49
FT
-d
o
no
-0.48
-0.04
-0.03
0.24
0.40
0.44
ite
p-valuea
-0.46
-0.05
-0.04
0.24
0.40
0.42
DR
A
Pearlin
High school dropouts
GED
High school graduates
Some college
Four-year college graduate
GED+College
Rosenberg
High school dropouts
GED
High school graduates
Some college
Four-year college graduate
GED+College
Mental Health (MCS-12)
High school dropouts
GED
High school graduates
Some college
Four-year college graduate
GED+College
Depression (CES-D)
High school dropouts
GED
High school graduates
Some college
Four-year college graduate
GED+College
Physical Health (PCS-12)
High school dropouts
GED
High school graduates
Some college
Four-year college graduate
GED+College
BMI
High school dropouts
GED
High school graduates
Some college
Four-year college graduate
GED+College
Std Dev
Actual Model
tc
Average
Actual Model
Notes: The simulated data (Model) contains one million observations generated from the Model’s
estimates. The actual data (Actual) contains 2242 observations from the NLSY79 sample of Males.
(a) The equality of the Model and Data distributions are tested using a two-sample KolmogorovSmirnov test.
50
2.30
2.38
2.53
2.66
2.90
2.83
0.37
0.44
0.43
0.45
0.43
0.65
0.37
0.43
0.43
0.46
0.43
0.45
0.77
0.72
0.85
0.82
0.04
0.21
tc
2.29
2.39
2.53
2.67
2.93
2.75
p-valuea
o
Log Wages
High school dropouts
GED
High school graduates
Some college
Four-year college graduate
GED+College
Std Dev
Actual Model
no
Average
Actual Model
ite
Table 27: Goodness of Fit - Continuous Outcomes
DR
A
FT
-d
Notes: The simulated data (Model) contains one million observations generated from the Model’s
estimates. The actual data (Actual) contains 2242 observations from the NLSY79 sample of Males.
(a) The equality of the Model and Data distributions are tested using a two-sample KolmogorovSmirnov test.
AR
Y
Table 28: The Effects of Education on Labor Market Participation at age 30, by Final
Schooling Level using High School Dropouts as Baseline
IN
GED vs. HS Dropout
HS Graduate vs. HS Dropout
Some College vs. HS Dropout
Four Year College Degree vs. HS Dropout
GED with Some College vs. HS Dropout
Observed
0.02
0.12
0.11
0.13
0.03
ATE
-0.02
0.05
0.04
0.01
-0.08
TT
-0.04
0.05
0.03
0.02
-0.06
TUT
-0.08
0.11
0.11
0.00
-0.02
PR
EL
IM
Notes: Each row compares the outcomes from a particular schooling level j and the HS dropout
status. The column “Observed” displays the observed differences in the data. The column “ATE”
displays the average treatment effect obtained from the comparison of the outcomes associated with
a particular schooling level j relative to the HS dropout status. ATE is computed using the overall
population. The column “TT” displays the average treatment effects associated with a particular
schooling level j relative to the HS dropout status but computed from those individuals selecting j
as their final schooling decision. Finally, the column “TUT” displays the average treatment effects
associated with a particular schooling level j relative to the HS dropout status but computed only
for those individuals selecting “HS dropout” as their final schooling decision.
51
TT
0.06
0.06
0.24
0.54
0.49
TUT
0.01
0.10
0.29
0.51
0.47
tc
ATE
-0.01
0.06
0.24
0.54
0.42
no
GED vs. HS Dropout
HS Graduate vs. HS Dropout
Some College vs. HS Dropout
Four Year College Degree vs. HS Dropout
GED with Some College vs. HS Dropout
Observed
0.07
0.14
0.34
0.72
0.48
ite
Table 29: The Effects of Education on Probability of White-collar Employment at age 30, by
Final Schooling Level using High School Dropouts as Baseline
DR
A
FT
-d
o
Notes: Each row compares the outcomes from a particular schooling level j and the HS dropout
status. The column “Observed” displays the observed differences in the data. The column “ATE”
displays the average treatment effect obtained from the comparison of the outcomes associated with
a particular schooling level j relative to the HS dropout status. ATE is computed using the overall
population. The column “TT” displays the average treatment effects associated with a particular
schooling level j relative to the HS dropout status but computed from those individuals selecting j
as their final schooling decision. Finally, the column “TUT” displays the average treatment effects
associated with a particular schooling level j relative to the HS dropout status but computed only
for those individuals selecting “HS dropout” as their final schooling decision.
AR
Y
Table 30: The Effects of Education on (log) Wages at age 30, by Final Schooling Level using
High School Dropouts as Baseline
IM
IN
GED vs. HS Dropout
HS Graduate vs. HS Dropout
Some College vs. HS Dropout
Four Year College Degree vs. HS Dropout
GED with Some College vs. HS Dropout
Observed
0.10
0.24
0.37
0.64
0.46
ATE
0.13
0.22
0.34
0.46
0.45
TT
0.03
0.21
0.34
0.54
0.42
TUT
0.04
0.18
0.34
0.32
0.46
PR
EL
Notes: Each row compares the outcomes from a particular schooling level j and the HS dropout
status. The column “Observed” displays the observed differences in the data. The column “ATE”
displays the average treatment effect obtained from the comparison of the outcomes associated with
a particular schooling level j relative to the HS dropout status. ATE is computed using the overall
population. The column “TT” displays the average treatment effects associated with a particular
schooling level j relative to the HS dropout status but computed from those individuals selecting j
as their final schooling decision. Finally, the column “TUT” displays the average treatment effects
associated with a particular schooling level j relative to the HS dropout status but computed only
for those individuals selecting “HS dropout” as their final schooling decision.
52
TT
0.07
0.06
0.06
-0.02
-0.03
TUT
0.01
0.07
0.10
0.04
-0.14
tc
ATE
0.09
0.08
0.07
-0.01
-0.16
no
GED vs. HS Dropout
HS Graduate vs. HS Dropout
Some College vs. HS Dropout
Four Year College Degree vs. HS Dropout
GED with Some College vs. HS Dropout
Observed
-0.02
0.06
0.03
-0.09
-0.20
ite
Table 31: The Effects of Education on Probability of Being Obese during Adulthood, by Final
Schooling Level using High School Dropouts as Baseline
DR
A
FT
-d
o
Notes: Each row compares the outcomes from a particular schooling level j and the HS dropout
status. The column “Observed” displays the observed differences in the data. The column “ATE”
displays the average treatment effect obtained from the comparison of the outcomes associated with
a particular schooling level j relative to the HS dropout status. ATE is computed using the overall
population. The column “TT” displays the average treatment effects associated with a particular
schooling level j relative to the HS dropout status but computed from those individuals selecting j
as their final schooling decision. Finally, the column “TUT” displays the average treatment effects
associated with a particular schooling level j relative to the HS dropout status but computed only
for those individuals selecting “HS dropout” as their final schooling decision.
AR
Y
Table 32: The Effects of Education on Regular Exercise during Adulthood, by Final Schooling
Level using High School Dropouts as Baseline
IM
IN
GED vs. HS Dropout
HS Graduate vs. HS Dropout
Some College vs. HS Dropout
Four Year College Degree vs. HS Dropout
GED with Some College vs. HS Dropout
Observed
-0.06
0.06
0.11
0.16
-0.07
ATE
-0.05
0.03
0.08
0.07
-0.16
TT
-0.11
0.03
0.08
0.09
-0.18
TUT
-0.09
0.03
0.13
0.03
-0.11
PR
EL
Notes: Each row compares the outcomes from a particular schooling level j and the HS dropout
status. The column “Observed” displays the observed differences in the data. The column “ATE”
displays the average treatment effect obtained from the comparison of the outcomes associated with
a particular schooling level j relative to the HS dropout status. ATE is computed using the overall
population. The column “TT” displays the average treatment effects associated with a particular
schooling level j relative to the HS dropout status but computed from those individuals selecting j
as their final schooling decision. Finally, the column “TUT” displays the average treatment effects
associated with a particular schooling level j relative to the HS dropout status but computed only
for those individuals selecting “HS dropout” as their final schooling decision.
53
TT
0.13
1.09
1.29
0.52
-0.27
TUT
-0.66
0.33
-0.20
-0.18
-1.93
tc
ATE
1.75
1.30
1.07
0.10
-0.69
no
GED vs. HS Dropout
HS Graduate vs. HS Dropout
Some College vs. HS Dropout
Four Year College Degree vs. HS Dropout
GED with Some College vs. HS Dropout
Observed
-0.69
0.35
0.17
-1.40
-2.26
ite
Table 33: The Effects of Education on BMI (2006), by Final Schooling Level using High School
Dropouts as Baseline
DR
A
FT
-d
o
Notes: Each row compares the outcomes from a particular schooling level j and the HS dropout
status. The column “Observed” displays the observed differences in the data. The column “ATE”
displays the average treatment effect obtained from the comparison of the outcomes associated with
a particular schooling level j relative to the HS dropout status. ATE is computed using the overall
population. The column “TT” displays the average treatment effects associated with a particular
schooling level j relative to the HS dropout status but computed from those individuals selecting j
as their final schooling decision. Finally, the column “TUT” displays the average treatment effects
associated with a particular schooling level j relative to the HS dropout status but computed only
for those individuals selecting “HS dropout” as their final schooling decision.
AR
Y
Table 34: The Effects of Education on Physical Health (PCS-12) at Age 40 , by Final Schooling
Level using High School Dropouts as Baseline
IM
IN
GED vs. HS Dropout
HS Graduate vs. HS Dropout
Some College vs. HS Dropout
Four Year College Degree vs. HS Dropout
GED with Some College vs. HS Dropout
Observed
0.19
0.36
0.44
0.67
0.17
ATE
-0.18
-0.01
0.02
0.27
-0.31
TT
-0.08
0.05
-0.03
-0.02
-0.34
TUT
0.01
0.16
0.29
0.66
-0.04
PR
EL
Notes: Each row compares the outcomes from a particular schooling level j and the HS dropout
status. The column “Observed” displays the observed differences in the data. The column “ATE”
displays the average treatment effect obtained from the comparison of the outcomes associated with
a particular schooling level j relative to the HS dropout status. ATE is computed using the overall
population. The column “TT” displays the average treatment effects associated with a particular
schooling level j relative to the HS dropout status but computed from those individuals selecting j
as their final schooling decision. Finally, the column “TUT” displays the average treatment effects
associated with a particular schooling level j relative to the HS dropout status but computed only
for those individuals selecting “HS dropout” as their final schooling decision.
54
TT
0.27
0.21
0.39
0.39
0.64
TUT
0.14
0.23
0.60
0.95
0.81
tc
ATE
0.31
0.21
0.43
0.63
0.64
no
GED vs. HS Dropout
HS Graduate vs. HS Dropout
Some College vs. HS Dropout
Four Year College Degree vs. HS Dropout
GED with Some College vs. HS Dropout
Observed
0.41
0.42
0.70
0.86
0.88
ite
Table 35: The Effects of Education on Pearlin’s “Personal Mastery Scale” , by Final Schooling
Level using High School Dropouts as Baseline
DR
A
FT
-d
o
Notes: Each row compares the outcomes from a particular schooling level j and the HS dropout
status. The column “Observed” displays the observed differences in the data. The column “ATE”
displays the average treatment effect obtained from the comparison of the outcomes associated with
a particular schooling level j relative to the HS dropout status. ATE is computed using the overall
population. The column “TT” displays the average treatment effects associated with a particular
schooling level j relative to the HS dropout status but computed from those individuals selecting j
as their final schooling decision. Finally, the column “TUT” displays the average treatment effects
associated with a particular schooling level j relative to the HS dropout status but computed only
for those individuals selecting “HS dropout” as their final schooling decision.
AR
Y
Table 36: The Effects of Education on Self-Esteem (2006), by Final Schooling Level using High
School Dropouts as Baseline
IM
IN
GED vs. HS Dropout
HS Graduate vs. HS Dropout
Some College vs. HS Dropout
Four Year College Degree vs. HS Dropout
GED with Some College vs. HS Dropout
Observed
0.22
0.27
0.57
0.73
0.43
ATE
0.05
-0.12
0.09
0.28
0.03
TT
-0.17
-0.11
0.01
-0.09
-0.04
TUT
0.10
0.04
0.53
0.78
0.46
PR
EL
Notes: Each row compares the outcomes from a particular schooling level j and the HS dropout
status. The column “Observed” displays the observed differences in the data. The column “ATE”
displays the average treatment effect obtained from the comparison of the outcomes associated with
a particular schooling level j relative to the HS dropout status. ATE is computed using the overall
population. The column “TT” displays the average treatment effects associated with a particular
schooling level j relative to the HS dropout status but computed from those individuals selecting j
as their final schooling decision. Finally, the column “TUT” displays the average treatment effects
associated with a particular schooling level j relative to the HS dropout status but computed only
for those individuals selecting “HS dropout” as their final schooling decision.
55
TT
0.06
0.16
0.16
0.01
-0.21
TUT
0.04
0.11
0.16
0.29
-0.00
tc
ATE
0.16
0.13
0.16
0.15
-0.01
no
GED vs. HS Dropout
HS Graduate vs. HS Dropout
Some College vs. HS Dropout
Four Year College Degree vs. HS Dropout
GED with Some College vs. HS Dropout
Observed
0.20
0.31
0.35
0.30
-0.01
ite
Table 37: The Effects of Education on Mental Health (MCS-12) at Age 40, by Final Schooling
Level using High School Dropouts as Baseline
DR
A
FT
-d
o
Notes: Each row compares the outcomes from a particular schooling level j and the HS dropout
status. The column “Observed” displays the observed differences in the data. The column “ATE”
displays the average treatment effect obtained from the comparison of the outcomes associated with
a particular schooling level j relative to the HS dropout status. ATE is computed using the overall
population. The column “TT” displays the average treatment effects associated with a particular
schooling level j relative to the HS dropout status but computed from those individuals selecting j
as their final schooling decision. Finally, the column “TUT” displays the average treatment effects
associated with a particular schooling level j relative to the HS dropout status but computed only
for those individuals selecting “HS dropout” as their final schooling decision.
AR
Y
Table 38: The Effects of Education on Depression Scale (CES-D) at Age 40, by Final Schooling
Level using High School Dropouts as Baseline
IM
IN
GED vs. HS Dropout
HS Graduate vs. HS Dropout
Some College vs. HS Dropout
Four Year College Degree vs. HS Dropout
GED with Some College vs. HS Dropout
Observed
-0.40
-0.50
-0.61
-0.75
-0.45
ATE
-0.12
0.01
-0.04
-0.19
0.04
TT
-0.07
-0.02
0.00
0.15
0.06
TUT
-0.29
-0.25
-0.30
-0.68
-0.22
PR
EL
Notes: Each row compares the outcomes from a particular schooling level j and the HS dropout
status. The column “Observed” displays the observed differences in the data. The column “ATE”
displays the average treatment effect obtained from the comparison of the outcomes associated with
a particular schooling level j relative to the HS dropout status. ATE is computed using the overall
population. The column “TT” displays the average treatment effects associated with a particular
schooling level j relative to the HS dropout status but computed from those individuals selecting j
as their final schooling decision. Finally, the column “TUT” displays the average treatment effects
associated with a particular schooling level j relative to the HS dropout status but computed only
for those individuals selecting “HS dropout” as their final schooling decision.
56
ite
ATE
TT
TUT
AR
Y
0.07
0.11
0.03
0.05
0.09
0.03
0.11
0.15
0.03
0.08
0.30
0.30
0.59
0.09
-d
AMTE
-0.06
-0.10
0.01
o
no
%
-0.08
-0.11
0.01
-0.03
-0.01
-0.02
-0.01
-0.01
-0.02
-0.01
-0.01
-0.02
-0.01
-0.02
0.01
-0.03
0.02
-0.01
-0.04
0.01
-0.01
0.15
0.46
0.00
-0.03
0.02
0.48
0.13
-0.01
0.07
-0.13
-0.00
0.08
-0.11
-0.01
0.07
-0.13
FT
-0.04
-0.08
0.01
0.22
0.36
DR
A
A. Dropping from HS vs. Graduating from HS
All
Low Ability
High Ability
B. HS Dropout vs. Getting a GED
All
Low Ability
High Ability
C. HS Graduate vs. College Enrollment
All
Low Ability
High Ability
D. Some College vs. 4-year college degree
All
Low Ability
High Ability
E. GED vs. GED with some College
All
Low Ability
High Ability
tc
Table 39: The Effects of Education on Labor Force Participation (age 30), by Decision Node
PR
EL
IM
IN
Notes: Let Y0 and Y1 denotes the outcomes associated with schooling levels 0 and 1, respectively. Importantly,
each schooling level might provide the option to pursuing higher schooling levels. Only final schooling levels
do not provide the option. In this table, final schooling levels are highlighted using bold letters. For each
pairs of schooling levels 0 and 1, the column ATE presents E(Y1 − Y0 ) computed for those who reach the
decision node involving a decision between levels 0 and 1. The TT column presents E(Y1 − Y0 |D = 1) where
D = 1 indicates the transition to the higher schooling level available at the node. Likewise, TUT column
presents E(Y1 − Y0 |D = O) where D = 0 indicates the transition to the lower schooling level. Finally,
AMTE presents the average (Y1 − Y0 ) for those indifferent between 0 and 1. The table also presents the
estimated treatment effects conditional upon endowment levels. The high (low) ability group is defined as
those individuals with cognitive and socio-emotional endowment above (below) the overall median. Finally,
for each decision node we display the fraction of individuals with low and high ability levels visiting each
node.
57
ite
ATE
TT
TUT
0.30
0.30
0.17
0.12
0.23
0.19
0.14
0.24
0.11
0.10
0.16
0.59
0.09
0.08
0.08
0.05
AR
Y
0.10
o
-d
FT
AMTE
no
%
0.12
0.11
0.06
0.05
0.06
0.03
0.09
0.32
0.26
0.38
0.36
0.29
0.40
0.28
0.25
0.33
0.31
0.22
0.36
0.32
0.25
0.35
0.33
0.26
0.35
0.31
0.25
0.35
0.32
0.15
0.46
0.48
0.13
0.42
0.44
0.40
0.43
0.47
0.40
0.42
0.44
0.40
DR
A
A. Dropping from HS vs. Graduating from HS
All
Low Ability
High Ability
B. HS Dropout vs. Getting a GED
All
Low Ability
High Ability
C. HS Graduate vs. College Enrollment
All
Low Ability
High Ability
D. Some College vs. 4-year college degree
All
Low Ability
High Ability
E. GED vs. GED with some College
All
Low Ability
High Ability
tc
Table 40: The Effects of Education on the Probability of White Collar Employment (age 30),
by Decision Node
PR
EL
IM
IN
Notes: Let Y0 and Y1 denotes the outcomes associated with schooling levels 0 and 1, respectively. Importantly,
each schooling level might provide the option to pursuing higher schooling levels. Only final schooling levels
do not provide the option. In this table, final schooling levels are highlighted using bold letters. For each
pairs of schooling levels 0 and 1, the column ATE presents E(Y1 − Y0 ) computed for those who reach the
decision node involving a decision between levels 0 and 1. The TT column presents E(Y1 − Y0 |D = 1) where
D = 1 indicates the transition to the higher schooling level available at the node. Likewise, TUT column
presents E(Y1 − Y0 |D = O) where D = 0 indicates the transition to the lower schooling level. Finally,
AMTE presents the average (Y1 − Y0 ) for those indifferent between 0 and 1. The table also presents the
estimated treatment effects conditional upon endowment levels. The high (low) ability group is defined as
those individuals with cognitive and socio-emotional endowment above (below) the overall median. Finally,
for each decision node we display the fraction of individuals with low and high ability levels visiting each
node.
58
TUT
AMTE
0.20
0.18
0.22
0.20
0.18
0.22
0.19
0.18
0.19
0.18
0.30
0.30
0.08
0.04
0.20
0.08
0.04
0.21
0.09
0.59
0.09
0.08
0.04
0.20
0.20
0.18
0.24
0.22
0.17
0.26
0.18
0.18
0.20
0.18
0.22
0.36
0.19
0.02
0.28
0.21
0.03
0.28
0.15
0.01
0.26
0.17
0.39
0.45
0.27
0.39
0.46
0.30
0.39
0.45
0.27
0.48
0.13
no
-d
FT
0.15
0.46
ite
TT
tc
ATE
DR
A
A. Dropping from HS vs. Graduating from HS
All
Low Ability
High Ability
B. HS Dropout vs. Getting a GED
All
Low Ability
High Ability
C. HS Graduate vs. College Enrollment
All
Low Ability
High Ability
D. Some College vs. 4-year college degree
All
Low Ability
High Ability
E. GED vs. GED with some College
All
Low Ability
High Ability
%
o
Table 41: The Effects of Education on (log) Wages at age 30, by Decision Node
PR
EL
IM
IN
AR
Y
Notes: Let Y0 and Y1 denotes the outcomes associated with schooling levels 0 and 1, respectively. Importantly,
each schooling level might provide the option to pursuing higher schooling levels. Only final schooling levels
do not provide the option. In this table, final schooling levels are highlighted using bold letters. For each
pairs of schooling levels 0 and 1, the column ATE presents E(Y1 − Y0 ) computed for those who reach the
decision node involving a decision between levels 0 and 1. The TT column presents E(Y1 − Y0 |D = 1) where
D = 1 indicates the transition to the higher schooling level available at the node. Likewise, TUT column
presents E(Y1 − Y0 |D = O) where D = 0 indicates the transition to the lower schooling level. Finally,
AMTE presents the average (Y1 − Y0 ) for those indifferent between 0 and 1. The table also presents the
estimated treatment effects conditional upon endowment levels. The high (low) ability group is defined as
those individuals with cognitive and socio-emotional endowment above (below) the overall median. Finally,
for each decision node we display the fraction of individuals with low and high ability levels visiting each
node.
59
ite
ATE
TT
TUT
AMTE
-0.01
0.05
-0.05
0.06
0.10
-0.02
0.03
0.30
0.30
0.00
0.07
-0.05
0.02
0.06
-0.10
0.06
0.10
-0.08
-0.00
0.04
-0.12
0.05
0.59
0.09
-0.07
-0.05
-0.08
-0.09
-0.08
-0.09
-0.06
-0.04
-0.06
-0.05
0.22
0.36
-0.09
-0.15
-0.07
-0.11
-0.16
-0.08
-0.08
-0.14
-0.03
-0.09
0.15
0.46
-0.17
-0.14
-0.23
-0.18
-0.14
-0.25
-0.17
-0.14
-0.23
-0.2
0.48
0.13
AR
Y
FT
-d
o
%
DR
A
A. Dropping from HS vs. Graduating from HS
All
Low Ability
High Ability
B. HS Dropout vs. Getting a GED
All
Low Ability
High Ability
C. HS Graduate vs. College Enrollment
All
Low Ability
High Ability
D. Some College vs. 4-year college degree
All
Low Ability
High Ability
E. GED vs. GED with some College
All
Low Ability
High Ability
no
tc
Table 42: The Effects of Education on the Probability of being Obese during Adulthood, by
Decision Node
PR
EL
IM
IN
Note: Let Y0 and Y1 denotes the outcomes associated with schooling levels 0 and 1, respectively. Importantly,
each schooling level might provide the option to pursuing higher schooling levels. Only final schooling levels
do not provide the option. In this table, final schooling levels are highlighted using bold letters. For each
pairs of schooling levels 0 and 1, the column ATE presents E(Y1 − Y0 ) computed for those who reach the
decision node involving a decision between levels 0 and 1. The TT column presents E(Y1 − Y0 |D = 1) where
D = 1 indicates the transition to the higher schooling level available at the node. Likewise, TUT column
presents E(Y1 − Y0 |D = O) where D = 0 indicates the transition to the lower schooling level. Finally,
AMTE presents the average (Y1 − Y0 ) for those indifferent between 0 and 1. The table also presents the
estimated treatment effects conditional upon endowment levels. The high (low) ability group is defined as
those individuals with cognitive and socio-emotional endowment above (below) the overall median. Finally,
for each decision node we display the fraction of individuals with low and high ability levels visiting each
node.
60
ATE
TT
TUT
0.30
0.30
0.11
0.09
0.12
0.12
0.10
0.12
0.08
0.08
0.07
0.59
0.09
-0.11
-0.11
-0.09
AR
Y
ite
AMTE
no
%
-d
o
0.07
-0.10
-0.11
-0.09
-0.10
0.06
0.11
0.02
0.05
0.11
0.03
0.06
0.11
-0.00
0.04
0.03
-0.02
0.05
0.00
-0.04
0.04
-0.00
0.15
0.46
0.02
-0.03
0.05
-0.05
0.04
-0.22
-0.09
-0.01
-0.27
-0.04
0.04
-0.21
-0.22
0.48
0.13
FT
-0.12
-0.12
-0.09
0.22
0.36
DR
A
A. Dropping from HS vs. Graduating from HS
All
Low Ability
High Ability
B. HS Dropout vs. Getting a GED
All
Low Ability
High Ability
C. HS Graduate vs. College Enrollment
All
Low Ability
High Ability
D. Some College vs. 4-year college degree
All
Low Ability
High Ability
E. GED vs. GED with some College
All
Low Ability
High Ability
tc
Table 43: The Effects of Education on the Probability of Exercising Regularly during
Adulthood, by Decision Node
PR
EL
IM
IN
Note: The binary variable “Regular Exercise” takes a value of one if the individual reports exercising on
a regular base in 2006, and zero otherwise. Let Y0 and Y1 denotes the outcomes associated with schooling
levels 0 and 1, respectively. Importantly, each schooling level might provide the option to pursuing higher
schooling levels. Only final schooling levels do not provide the option. In this table, final schooling levels are
highlighted using bold letters. For each pairs of schooling levels 0 and 1, the column ATE presents E(Y1 −Y0 )
computed for those who reach the decision node involving a decision between levels 0 and 1. The TT column
presents E(Y1 − Y0 |D = 1) where D = 1 indicates the transition to the higher schooling level available at
the node. Likewise, TUT column presents E(Y1 − Y0 |D = O) where D = 0 indicates the transition to the
lower schooling level. Finally, AMTE presents the average (Y1 − Y0 ) for those indifferent between 0 and 1.
The table also presents the estimated treatment effects conditional upon endowment levels. The high (low)
ability group is defined as those individuals with cognitive and socio-emotional endowment above (below)
the overall median. Finally, for each decision node we display the fraction of individuals with low and high
ability levels visiting each node.
61
ATE
TT
TUT
0.30
0.30
-0.20
0.69
-1.14
-0.38
0.60
-1.17
0.43
0.82
-0.69
-0.44
-0.43
-0.73
0.06
0.25
-0.37
-0.78
-0.76
-1.21
-0.03
0.59
0.09
-0.90
-0.58
-1.14
-1.03
-0.68
-1.24
-0.75
-0.53
-0.94
-0.69
-1.48
-1.56
-1.44
-1.73
-1.88
-1.67
-1.15
-1.37
-0.97
-1.21
0.15
0.46
-1.17
-0.44
-2.79
-1.22
-0.44
-3.02
-1.16
-0.44
-2.75
-1.22
0.48
0.13
AR
Y
o
-d
FT
0.22
0.36
ite
AMTE
no
%
DR
A
A. Dropping from HS vs. Graduating from HS
All
Low Ability
High Ability
B. HS Dropout vs. Getting a GED
All
Low Ability
High Ability
C. HS Graduate vs. College Enrollment
All
Low Ability
High Ability
D. Some College vs. 4-year college degree
All
Low Ability
High Ability
E. GED vs. GED with some College
All
Low Ability
High Ability
tc
Table 44: The Effects of Education on BMI during Adulthood, by Decision Node
0.25
PR
EL
IM
IN
Note: Notes: BMI is calculated as BMI=(Weight in Ponds * 703)/(Height in inches)2 . Let Y0 and Y1 denotes
the outcomes associated with schooling levels 0 and 1, respectively. Importantly, each schooling level might
provide the option to pursuing higher schooling levels. Only final schooling levels do not provide the option.
In this table, final schooling levels are highlighted using bold letters. For each pairs of schooling levels 0 and
1, the column ATE presents E(Y1 − Y0 ) computed for those who reach the decision node involving a decision
between levels 0 and 1. The TT column presents E(Y1 − Y0 |D = 1) where D = 1 indicates the transition to
the higher schooling level available at the node. Likewise, TUT column presents E(Y1 − Y0 |D = O) where
D = 0 indicates the transition to the lower schooling level. Finally, AMTE presents the average (Y1 − Y0 )
for those indifferent between 0 and 1. The table also presents the estimated treatment effects conditional
upon endowment levels. The high (low) ability group is defined as those individuals with cognitive and
socio-emotional endowment above (below) the overall median. Finally, for each decision node we display the
fraction of individuals with low and high ability levels visiting each node.
62
ite
ATE
TT
TUT
0.30
0.30
0.20
0.21
0.17
0.22
0.25
0.18
0.14
0.16
0.07
-0.04
-0.11
0.18
-0.12
-0.21
0.10
0.01
-0.06
0.27
-0.05
0.59
0.09
0.09
0.13
0.06
0.08
0.12
0.06
0.10
0.14
0.07
0.10
0.19
0.21
0.18
0.17
0.18
0.17
0.22
0.23
0.21
0.21
0.15
0.46
-0.17
0.01
-0.48
-0.11
0.06
-0.31
-0.18
0.00
-0.51
-0.11
0.48
0.13
AR
Y
o
-d
FT
0.22
0.36
AMTE
no
%
DR
A
A. Dropping from HS vs. Graduating from HS
All
Low Ability
High Ability
B. HS Dropout vs. Getting a GED
All
Low Ability
High Ability
C. HS Graduate vs. College Enrollment
All
Low Ability
High Ability
D. Some College vs. 4-year college degree
All
Low Ability
High Ability
E. GED vs. GED with some College
All
Low Ability
High Ability
tc
Table 45: The Effects of Education on Physical Health at Age 40 (PCS-12), by Decision Node
0.19
PR
EL
IM
IN
Note: The PCS-12 scale is the Physical Component Summary (measures physical health). Let Y0 and Y1
denotes the outcomes associated with schooling levels 0 and 1, respectively. Importantly, each schooling
level might provide the option to pursuing higher schooling levels. Only final schooling levels do not provide
the option. In this table, final schooling levels are highlighted using bold letters. For each pairs of schooling
levels 0 and 1, the column ATE presents E(Y1 − Y0 ) computed for those who reach the decision node
involving a decision between levels 0 and 1. The TT column presents E(Y1 − Y0 |D = 1) where D = 1
indicates the transition to the higher schooling level available at the node. Likewise, TUT column presents
E(Y1 − Y0 |D = O) where D = 0 indicates the transition to the lower schooling level. Finally, AMTE presents
the average (Y1 − Y0 ) for those indifferent between 0 and 1. The table also presents the estimated treatment
effects conditional upon endowment levels. The high (low) ability group is defined as those individuals with
cognitive and socio-emotional endowment above (below) the overall median. Finally, for each decision node
we display the fraction of individuals with low and high ability levels visiting each node.
63
ite
ATE
TT
TUT
AR
Y
0.05
0.10
-0.02
0.02
0.07
-0.04
0.16
0.13
0.22
0.18
0.30
0.30
0.59
0.09
-d
AMTE
0.25
0.19
0.40
o
no
%
0.20
0.16
0.39
0.34
0.25
0.39
0.14
0.20
0.36
0.11
0.31
0.40
0.18
0.27
0.11
0.21
0.07
0.17
0.22
0.13
0.13
0.15
0.46
0.13
0.22
0.09
0.45
0.55
0.30
0.47
0.59
0.34
0.44
0.54
0.30
0.42
0.48
0.13
FT
0.32
0.25
0.41
0.22
0.36
DR
A
A. Dropping from HS vs. Graduating from HS
All
Low Ability
High Ability
B. HS Dropout vs. Getting a GED
All
Low Ability
High Ability
C. HS Graduate vs. College Enrollment
All
Low Ability
High Ability
D. Some College vs. 4-year college degree
All
Low Ability
High Ability
E. GED vs. GED with some College
All
Low Ability
High Ability
tc
Table 46: The Effects of Education on Pearlin’s “Personal Mastery Scale”, by Decision Node
PR
EL
IM
IN
Notes: Let Y0 and Y1 denotes the outcomes associated with schooling levels 0 and 1, respectively. Importantly,
each schooling level might provide the option to pursuing higher schooling levels. Only final schooling levels
do not provide the option. In this table, final schooling levels are highlighted using bold letters. For each
pairs of schooling levels 0 and 1, the column ATE presents E(Y1 − Y0 ) computed for those who reach the
decision node involving a decision between levels 0 and 1. The TT column presents E(Y1 − Y0 |D = 1) where
D = 1 indicates the transition to the higher schooling level available at the node. Likewise, TUT column
presents E(Y1 − Y0 |D = O) where D = 0 indicates the transition to the lower schooling level. Finally,
AMTE presents the average (Y1 − Y0 ) for those indifferent between 0 and 1. The table also presents the
estimated treatment effects conditional upon endowment levels. The high (low) ability group is defined as
those individuals with cognitive and socio-emotional endowment above (below) the overall median. Finally,
for each decision node we display the fraction of individuals with low and high ability levels visiting each
node.
64
ATE
TT
TUT
0.30
0.30
-0.01
0.10
-0.13
-0.04
0.06
-0.13
0.11
0.15
-0.11
0.59
0.09
0.02
-0.07
0.30
AR
Y
-d
o
0.07
0.13
0.01
0.47
-0.07
0.23
0.35
0.13
0.15
0.28
0.08
0.32
0.38
0.23
0.26
0.14
0.21
0.11
0.17
0.22
0.11
0.19
0.15
0.46
0.15
0.22
0.11
0.31
0.54
-0.20
0.38
0.59
-0.16
0.30
0.53
-0.21
0.40
0.48
0.13
FT
-0.15
-0.26
0.17
0.22
0.36
ite
AMTE
no
%
DR
A
A. Dropping from HS vs. Graduating from HS
All
Low Ability
High Ability
B. HS Dropout vs. Getting a GED
All
Low Ability
High Ability
C. HS Graduate vs. College Enrollment
All
Low Ability
High Ability
D. Some College vs. 4-year college degree
All
Low Ability
High Ability
E. GED vs. GED with some College
All
Low Ability
High Ability
tc
Table 47: The Effects of Education on Rosenberg’s Self-Esteem, by Decision Node
PR
EL
IM
IN
Notes: Let Y0 and Y1 denotes the outcomes associated with schooling levels 0 and 1, respectively. Importantly,
each schooling level might provide the option to pursuing higher schooling levels. Only final schooling levels
do not provide the option. In this table, final schooling levels are highlighted using bold letters. For each
pairs of schooling levels 0 and 1, the column ATE presents E(Y1 − Y0 ) computed for those who reach the
decision node involving a decision between levels 0 and 1. The TT column presents E(Y1 − Y0 |D = 1) where
D = 1 indicates the transition to the higher schooling level available at the node. Likewise, TUT column
presents E(Y1 − Y0 |D = O) where D = 0 indicates the transition to the lower schooling level. Finally,
AMTE presents the average (Y1 − Y0 ) for those indifferent between 0 and 1. The table also presents the
estimated treatment effects conditional upon endowment levels. The high (low) ability group is defined as
those individuals with cognitive and socio-emotional endowment above (below) the overall median. Finally,
for each decision node we display the fraction of individuals with low and high ability levels visiting each
node.
65
ite
ATE
TT
TUT
0.30
0.30
0.04
0.11
-0.06
0.03
0.13
-0.07
0.08
0.09
-0.01
0.03
-0.10
0.38
0.02
-0.13
0.35
0.03
-0.08
0.42
0.08
0.59
0.09
-0.01
0.04
-0.05
-0.05
-0.00
-0.08
0.04
0.06
0.01
0.02
-0.06
-0.05
-0.07
-0.09
-0.09
-0.08
-0.03
-0.03
-0.03
-0.04
0.15
0.46
-0.21
-0.07
-0.07
-0.25
-0.11
-0.11
-0.20
-0.06
-0.06
-0.32
0.48
0.48
AR
Y
o
-d
FT
0.22
0.36
AMTE
no
%
DR
A
A. Dropping from HS vs. Graduating from HS
All
Low Ability
High Ability
B. HS Dropout vs. Getting a GED
All
Low Ability
High Ability
C. HS Graduate vs. College Enrollment
All
Low Ability
High Ability
D. Some College vs. 4-year college degree
All
Low Ability
High Ability
E. GED vs. GED with some College
All
Low Ability
High Ability
tc
Table 48: The Effects of Education on Mental Health at Age 40 (MCS-12), by Decision Node
0.12
PR
EL
IM
IN
Note: MCS-12 scale is the Mental Component Summary (measures mental health). Let Y0 and Y1 denotes
the outcomes associated with schooling levels 0 and 1, respectively. Importantly, each schooling level might
provide the option to pursuing higher schooling levels. Only final schooling levels do not provide the option.
In this table, final schooling levels are highlighted using bold letters. For each pairs of schooling levels 0 and
1, the column ATE presents E(Y1 − Y0 ) computed for those who reach the decision node involving a decision
between levels 0 and 1. The TT column presents E(Y1 − Y0 |D = 1) where D = 1 indicates the transition to
the higher schooling level available at the node. Likewise, TUT column presents E(Y1 − Y0 |D = O) where
D = 0 indicates the transition to the lower schooling level. Finally, AMTE presents the average (Y1 − Y0 )
for those indifferent between 0 and 1. The table also presents the estimated treatment effects conditional
upon endowment levels. The high (low) ability group is defined as those individuals with cognitive and
socio-emotional endowment above (below) the overall median. Finally, for each decision node we display the
fraction of individuals with low and high ability levels visiting each node.
66
ite
ATE
TT
TUT
AMTE
-0.02
-0.10
0.08
0.01
-0.05
0.09
-0.15
-0.17
-0.04
-0.17
-0.30
-0.30
-0.19
-0.16
-0.33
-0.05
-0.01
-0.27
-0.29
-0.23
-0.41
o
-0.08
-0.59
-0.09
-0.07
-0.11
-0.02
-0.04
-0.10
0.01
-0.11
-0.12
-0.08
-0.10
-0.03
-0.18
0.04
-0.11
-0.21
-0.03
-0.06
-0.15
-0.46
-0.06
-0.20
0.02
0.10
0.05
0.21
0.10
0.07
0.28
0.10
0.04
0.20
0.39
-0.48
-0.13
AR
Y
-d
FT
-0.22
-0.36
no
%
DR
A
A. Dropping from HS vs. Graduating from HS
All
Low Ability
High Ability
B. HS Dropout vs. Getting a GED
All
Low Ability
High Ability
C. HS Graduate vs. College Enrollment
All
Low Ability
High Ability
D. Some College vs. 4-year college degree
All
Low Ability
High Ability
E. GED vs. GED with some College
All
Low Ability
High Ability
tc
Table 49: The Effects of Education on Depression at Age 40 (CES-D), by Decision Node
PR
EL
IM
IN
Note: CES-D is one of the most common screening tests for helping an individual to determine his or her
depression quotient. This scale measures symptoms of depression, discriminates between clinically depressed
individuals and others. Let Y0 and Y1 denotes the outcomes associated with schooling levels 0 and 1,
respectively. Importantly, each schooling level might provide the option to pursuing higher schooling levels.
Only final schooling levels do not provide the option. In this table, final schooling levels are highlighted
using bold letters. For each pairs of schooling levels 0 and 1, the column ATE presents E(Y1 − Y0 ) computed
for those who reach the decision node involving a decision between levels 0 and 1. The TT column presents
E(Y1 − Y0 |D = 1) where D = 1 indicates the transition to the higher schooling level available at the
node. Likewise, TUT column presents E(Y1 − Y0 |D = O) where D = 0 indicates the transition to the
lower schooling level. Finally, AMTE presents the average (Y1 − Y0 ) for those indifferent between 0 and 1.
The table also presents the estimated treatment effects conditional upon endowment levels. The high (low)
ability group is defined as those individuals with cognitive and socio-emotional endowment above (below)
the overall median. Finally, for each decision node we display the fraction of individuals with low and high
ability levels visiting each node.
67
FT
-d
o
tc
no
ite
Figure 1: Sequential model for schooling decisions.
PR
EL
IM
IN
AR
Y
DR
A
68
tc
ite
Figure 2: Distribution of Cognitive and Socio-emotional Endowments
0.5
no
0.4
0.3
0.2
0
3
So
2
cio
-d
o
0.1
1
-Em
on 0
al
-1
-2
1.5 2
0.5 1 Cognitive
0
-1 -0.5
-2 -1.5
-3
FT
oti
Distribution of Factors
0.9
0.5
DR
A
0.8
0.4
0.7
0.6
0.3
0.5
0.4
0.2
0.3
0.2
0.1
0-2
-1.5
-1
AR
Y
0.1
-0.5
0
0.5
1
1.5
0
2
Cognitive Factor
-3
-2
-1
0
1
Social-Emotional Factor
θC
∼ p1 Φ (μ1 , Σ1 ) + p2 Φ (μ2 , Σ2 ) + p3 Φ (μ3 , Σ3 )
θSE
IN
PR
EL
IM
Σ1 =
where
0.39 0.38
0.38 0.39
μ1 =
0.18
0.29
, Σ2 =
, μ2 =
0.53 0.03
0.03 0.66
−0.35
−2.26
, Σ3 =
, μ3 =
p = (0.45, 0.10, 0.45)
69
0.42 0.13
0.13 0.75
−0.10
0.22
2
3
ite
tc
-d
o
no
Figure 3: Distribution of factors by schooling level
0
DR
A
.2
.4
.6
Density
FT
.8
1
Sorting into Schooling
−1
0
1
Cognitive Factor
AR
Y
.4
.6
Density
.2
0
−2
−1
GED
Some College (GED)
HS Grad.
Some College
4yr Coll. Grad.
0
Socio−emotional Factor
IM
IN
−3
2
HS Drop
.8
1
−2
1
2
PR
EL
Note: The factors are simulated from the estimates of the model. The simulated data contain 1
million observations.
70
Figure 4: Distribution of Cognitive and Socio-emotional Endowment by Final Schooling Level
3
2
1
3
6
9 10
7 8 ognitive
of C
e
il
c
e
D
0.03
Fraction of Population
0.04
IN
0.05
0.02
0.01
PR
EL
0
10
De 9
cil 8
eo 7
fS 6 5
oc
io- 4
Em 3
oti 2 1
on
al
ite
tc
3
2
1
1
2
3
4
5
6
1
2
3
4
5
6
9 10
7 8 ognitive
of C
e
il
c
e
D
F. GED with Some College
IM
Fraction of Population
0.06
4
FT
Fraction of Population
2
5
DR
A
1
4
0.02
0.018
0.016
0.014
0.012
0.01
0.008
0.006
0.004
0.002
10
De 9
cil 8
eo 7
fS 6 5
oc
io- 4
Em 3
oti 2 1
on
al
E. Four-Year College
0.07
6 5
8 7
10 9 f Cognitive
o
Decile
D. Some College
AR
Y
Fraction of Population
C. High School Graduate
0.018
0.016
0.014
0.012
0.01
0.008
0.006
0.004
0.002
10
De 9
cil 8
eo 7
fS 6 5
oc
io- 4
Em 3
oti 2 1
on
al
no
4
o
6 5
8 7
10 9 f Cognitive
o
Decile
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
1
De 2 3
cile
4
5
of
So 6
cio 7
-Em 8
oti 910
on
al
-d
0.18
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
1
De 2 3
cile
4
of
5
So 6
cio 7
-Em 8
oti 910
on
al
B. GED
Fraction of Population
Fraction of Population
A. High School Dropout
9 10
7 8 ognitive
fC
o
e
il
c
De
0.05
0.04
0.03
0.02
0.01
0
1
2
De 3
cile 4
of 5 6
So
cio 7 8
-Em
oti 9 10
on
al
6 5
8 7 itive
9
n
g
10
o
e of C
Decil
4
3
2
Note: We define as “decile 1” the decile with the lowest values of endowments and “decile 10”
as the decile with the highest levels of endowments. The deciles are computed using the overall
marginal distributions of endowments (instead of the joint distribution). Each panel presents the
distribution of individuals by levels of endowments for a particular final schooling level.
71
1
Figure 5: Distribution of Cognitive and Socio-emotional Endowment by Decision Node
0.025
0.02
0.015
0.01
0.005
3
6
ite
5
4
3
2
0.01
0.005
0.045
0.04
0.035
0.03
0.025
0.02
0.015
0.01
0.005
0
10
De 9
cil 8
eo 7
fS 6 5
oc
io- 4
Em 3
oti 2 1
on
al
1
2
4
3
5
6
9 10
7 8 ognitive
of C
Decile
1
2
3
4
5
6
9 10
7 8 ognitive
of C
Decile
Fraction of Population
IM
IN
E. GED going to GED or Some College
PR
EL
1
D. Enrolled in College going to
Some College or College Degree
Fraction of Population
0.02
0.015
10
De 9
cil 8
eo 7
fS 6 5
oc
io- 4
Em 3
oti 2 1
on
al
6
8 7
e
10 9 of Cognitiv
e
il
c
e
D
DR
A
0.025
AR
Y
Fraction of Population
0.03
0.02
0
1
De 2 3
cile 4
5
of
So 6
cio 7
-Em 8
oti 9 10
on
al
9 10
7 8 ognitive
C
f
o
e
il
Dec
C. High School Graduate going to
HSG or Enrolled in College
0.04
o
2
5
0.1
0.08
0.06
-d
1
4
0.12
FT
10
De 9
cil 8
eo 7
fS 6 5
oc
io- 4
Em 3
oti 2 1
on
al
0.14
tc
0.03
no
0.035
B. High School Dropout going to
HSD or GED
Fraction of Population
Fraction of Population
A. Enrolled in HS going to
HSG or HSD
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
1
2
De 3 4
cile
of S 5 6
oci
7
o-E 8
mo 9 10
tion
al
6 5
8 7
e
10 9 of Cognitiv
e
Decil
4
3
2
1
Note: Each panel presents the distribution of individuals by levels of endowments for a particular decision node schooling level. We define as “decile 1” the decile with the lowest values of
endowments and “decile 10” as the decile with the highest levels of endowments. The deciles are
computed using the overall marginal distributions of endowments (instead of the joint distribution).
72 of the same height.
This explains why panel (A) does not present bars
Figure 6: The Effect of Cognitive and Socio-emotional endowments on Labor Market
Participation (age 30)
0.9
0.4
0.8
0.5
0.9
0.4
0.2
0.7
0.2
0.9
0.3
0.8
0.2
0.7
5
4
6
tc
ite
0.25
Prob. of Employed
1
2
9 10
7 8
itive
of Cogn
Decile
0.15
0.7
Prob. of Employed
1
0.6
Fraction
0.6
3
no
Prob. of Employed
1
1
Fraction
9 10
7 8
itive
of Cogn
Decile
0.8
0.3
0.7
6
0.5
0.9
o
0.5
5
4
0.4
0.8
0.3
0.1
0.7
-d
0.6
1
3
1
0.95
0.9
0.85
0.8
0.75
0.7
0.65
0.6
0.55
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Prob. of Employed
Prob. of Employed
2
Prob. of Employed
1
Fraction
Prob. of Employed
B. GED
Fraction
1
0.95
0.9
0.85
0.8
0.75
0.7
0.65
0.6
0.55
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Prob. of Employed
Prob. of Employed
Prob. of Employed
A. High School Dropout
0.2
0.05
0.1
0.1
0.6
0.6
7
8
9
10
1
2
3
4
5
6
7
Decile of Cognitive
8
9
10
1
2
3
4
Prob. of Employed
0.14
6
9 10
7 8
itive
of Cogn
Decile
0.22
Prob. of Employed
1
0.2
0.18
0.16
0.9
0.14
0.8
0.1
AR
Y
0.7
3
4
5
6
7
8
9
10
0
1
2
3
4
5
6
7
Decile of Cognitive
0.2
9
10
0.3
0.9
0.25
0.8
0.2
0.16
0.14
3
4
0.05
6
7
8
9
10
Decile of Cognitive
4
5
6
7
8
9
10
0
Decile of Socio-Emotional
0.6
0
0
6
9 10
7 8
itive
of Cogn
Decile
0.22
Prob. of Employed
0.2
1
0.18
0.16
0.9
0.14
0.12
0.1
0.08
0.7
0.06
0.04
0.6
0.02
1
2
3
4
5
6
7
8
9
10
0
0.02
1
2
3
4
5
6
7
Decile of Cognitive
8
9
10
Prob. of Employed
9 10
7 8
itive
of Cogn
Decile
0.35
Prob. of Employed
1
0.3
0.25
0.9
0.2
0.8
1
0.95
0.9
0.85
0.8
0.75
0.7
0.65
0.6
0.55
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Prob. of Employed
1
0.22
0.2
1
0.18
0.16
0.9
0.14
0.12
0.8
0.1
0.15
2
3
5
4
6
9 10
7 8
itive
of Cogn
Decile
0.8
Prob. of Employed
0.7
1
0.6
0.9
0.5
0.4
0.8
0.3
0.08
0.1
0.05
0.6
1
2
3
4
5
6
7
0
Decile of Socio-Emotional
F. GED with Some College
Fraction
Fraction
6
5
4
0.04
Decile of Socio-Emotional
0.7
5
3
0.8
0.06
0.7
0.06
0.1
0.6
2
5
4
3
0.12
0.15
0.7
1
3
2
0.18
0.7
Prob. of Employed
0.35
2
Prob. of Employed
0.4
0.22
0.9
IN
Prob. of Employed
IM
Prob. of Employed
PR
EL
1
0.45
Prob. of Employed
1
2
0.08
0.02
8
1
0.24
Prob. of Employed
1
E. Four-Year College
1
0.95
0.9
0.85
0.8
0.75
0.7
0.65
0.6
0.55
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
1
0.95
0.9
0.85
0.8
0.75
0.7
0.65
0.6
0.55
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
0.04
0.6
0.02
2
0.1
1
0.1
0.06
0.04
1
10
0.08
0.06
0.6
9
0.8
0.1
0.08
0.7
8
0.12
0.12
0.8
7
FT
0.16
5
4
Prob. of Employed
0.18
0.9
3
Fraction
0.2
2
Fraction
1
0.22
Prob. of Employed
6
D. Some College
DR
A
1
0.95
0.9
0.85
0.8
0.75
0.7
0.65
0.6
0.55
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Prob. of Employed
Prob. of Employed
Prob. of Employed
C. High School Graduate
1
5
Decile of Cognitive
Decile of Socio-Emotional
Fraction
6
8
9
10
0
Decile of Socio-Emotional
0.7
0.2
0.04
0.6
0.02
1
2
3
4
5
6
7
8
9
10
Decile of Cognitive
0
0.1
0.6
1
2
3
4
5
6
7
8
Note: For each schooling level we present three figures. The first figure (top) displays the levels of the outcome as a function
of cognitive and socio-emotional endowments. In particular, we present the average level of outcomes for different deciles of
cognitive and socio-emotional endowments. Notice that we define as “decile 1” the decile with the lowest values of endowments
and “decile 10” as the decile with the highest levels of endowments. The second figure (bottom left) displays the average levels
of endowment across deciles of cognitive endowments. The bars in this figure indicates the fraction of individuals reporting the
respective schooling level for each decile of cognitive endowment. The last figure (bottom right) mimics the structure of the
second one but now for the socio-emotional endowment.
73
9
10
Decile of Socio-Emotional
0
Fraction
5
Fraction
4
Prob. of Employed
3
0.6
0
Fraction
2
0.6
0
Prob. of Employed
1
0
Figure 7: The Effect of Cognitive and Socio-emotional endowments on Probability of
White-collar occupation (age 30)
0.6
0.5
0.4
0.6
0.3
0.4
0.2
7
8
9
10
1
2
3
4
5
6
7
Decile of Cognitive
8
9
10
1
2
3
4
0.4
4
0.8
1
0.22
Prob. of Whitecollar
0.2
0.18
0.16
0.14
0.6
0.12
0.4
AR
Y
0.04
0.2
7
8
9
10
Prob. of Whitecollar
0.16
0.14
0.6
0.4
0.04
0.2
3
4
5
6
7
8
9
10
1
2
3
4
tc
7
0.3
0.6
0.25
8
9
10
0
4
0.8
Prob. of Whitecollar
De
1
0.35
Prob. of Whitecollar
0.3
0.25
0.6
0.2
0.2
0.4
0.15
6
7
8
9
10
0.15
0.4
1
0.05
0.05
7
8
9
10
Decile of Cognitive
0.2
0.18
0.16
0.14
0.6
0.12
0.4
0.08
0.06
0.04
0.2
0.02
1
1
0.22
Prob. of Whitecollar
0.2
0.8
0.18
0.16
0.14
0.6
2
3
4
5
6
7
8
9
10
2
5
3
4
0.8
6
1
0.8
Prob. of Whitecollar
0.7
0.6
0.5
0.6
0.4
0.1
0.4
0.08
0.3
0.4
0.06
0.04
0.2
0.2
0.2
0.1
0.02
1
2
3
4
5
6
7
0
9 10
7 8
itive
of Cogn
Decile
0.12
0.1
0.2
0
0.22
Prob. of Whitecollar
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.1
0.2
1
Decile of Socio-Emotional
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
9 10
ve
7 8
Cogniti
cile of
6
9 10
7 8
itive
of Cogn
Decile
F. GED with Some College
Fraction
5
3
0.8
6
Decile of Cognitive
Prob. of Whitecollar
0.35
Fraction
0.4
2
Prob. of Whitecollar
1
5
Decile of Socio-Emotional
IN
Prob. of Whitecollar
6
0.1
0
2
5
4
0.04
0
IM
Prob. of Whitecollar
PR
EL
5
0.06
0
0.45
6
4
Decile of Socio-Emotional
3
0.08
0.02
Prob. of Whitecollar
5
3
0.12
0.02
1
2
0.18
0.02
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
0.8
0.22
0.2
E. Four-Year College
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
1
0.24
1
0.8
0.06
0.2
Decile of Cognitive
4
2
0.1
0.08
0.06
3
0.1
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.1
0.08
2
10
Fraction
5
3
0.1
1
9
0.2
8
9
10
0
Decile of Socio-Emotional
1
2
3
4
5
6
7
8
9
10
Decile of Cognitive
0
1
2
3
4
5
6
7
8
Note: For each schooling level we present three figures. The first figure (top) displays the levels of the outcome as a function
of cognitive and socio-emotional endowments. In particular, we present the average level of outcomes for different deciles of
cognitive and socio-emotional endowments. Notice that we define as “decile 1” the decile with the lowest values of endowments
and “decile 10” as the decile with the highest levels of endowments. The second figure (bottom left) displays the average levels
of endowment across deciles of cognitive endowments. The bars in this figure indicates the fraction of individuals reporting the
respective schooling level for each decile of cognitive endowment. The last figure (bottom right) mimics the structure of the
second one but now for the socio-emotional endowment.
74
9
10
Decile of Socio-Emotional
0
Fraction
0.12
1
8
Fraction
Prob. of Whitecollar
0.14
0.6
6
7
0.3
FT
0.16
5
6
0.4
0.2
0
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
10
8 9
itive
6 7
of Cogn
Decile
Prob. of Whitecollar
0.18
4
0.5
D. Some College
Fraction
0.2
3
5
DR
A
0.22
Prob. of Whitecollar
2
Fraction
Prob. of Whitecollar
1
Prob. of Whitecollar
Prob. of Whitecollar
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.8
2
0.6
Decile of Cognitive
Decile of Socio-Emotional
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
1
ite
0.05
C. High School Graduate
1
0.7
Prob. of Whitecollar
0.6
Prob. of Whitecollar
6
9 10
7 8
itive
of Cogn
Decile
0.4
Fraction
5
0
6
1
Prob. of Whitecollar
4
0
0.8
0.1
0.2
0.1
4
-d
3
0.15
0.6
0.2
0.1
2
0.2
0.4
0.2
1
Prob. of Whitecollar
0.3
0.4
0.2
0.25
1
0.8
2
Fraction
Prob. of Whitecollar
5
3
no
1
Fraction
0.8
1
o
0.4
0.6
4
Prob. of Whitecollar
Prob. of Whitecollar
0.5
5
3
Prob. of Whitecollar
0.6
0.8
2
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
10
8 9
itive
6 7
of Cogn
Decile
Fraction
Prob. of Whitecollar
1
Fraction
Prob. of Whitecollar
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
1
B. GED
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
Prob. of Whitecollar
Prob. of Whitecollar
A. High School Dropout
Figure 8: The Effect of Cognitive and Socio-emotional endowments on (log) Wages (age 30)
3
2.8
2.6
Log-Wages
3.4
0.6
0.25
0.2
0.5
0.4
3
0.4
3
0.3
2.8
0.3
2.8
3.2
2.6
0.2
2.6
2.4
0.1
2.4
0.1
2.4
7
8
9
10
0
1
2
3
4
5
6
7
Decile of Cognitive
8
9
10
2.2
0
1
2
3
4
Log-Wages
0.18
6
9 10
7 8
itive
of Cogn
Decile
0.16
0.14
2.8
8
9
10
0.2
0
1
2
3
4
5
6
7
Decile of Cognitive
8
9
10
IN
tc
7
8
9
10
2.4
0
0
1
2
3
4
5
6
7
8
9
10
0.35
Log-Wages
3.4
0.3
3.2
0.25
Fraction
5
4
0.2
2.6
0.15
2.6
10
Decile of Cognitive
0
0.08
0.06
0.04
0.02
2.2
1
2
3
4
5
6
7
8
9
10
0
Decile of Socio-Emotional
3
Log-Wages
3.4
0.2
0.18
0.14
0.12
0.1
0.15
0.08
2.6
0.05
2
3
5
4
6
9 10
7 8
itive
of Cogn
Decile
0.8
Log-Wages
3.4
0.7
3.2
0.6
3
0.5
2.8
0.4
0.16
2.8
2.4
1
0.22
3.2
3
0.05
9
0.1
2.4
2.8
0.1
8
0.14
3.2
0.2
2.8
7
0.16
2.2
2.8
2.2
0.18
0.12
0
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
10
8 9
itive
6 7
of Cogn
Decile
3
2.4
0.2
3.2
F. GED with Some College
0.25
0.1
0.22
Log-Wages
3.4
Decile of Cognitive
Decile of Socio-Emotional
0.3
3
9 10
7 8
itive
of Cogn
Decile
0.02
2.2
Log-Wages
0.35
Fraction
0.4
3
Log-Wages
0.45
2
6
2.6
0.04
2.4
1
5
4
0.06
2.4
IM
Log-Wages
6
2.8
0.08
2.6
Log-Wages
PR
EL
5
3
0.1
2.6
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
3
0.12
Log-Wages
Log-Wages
3
2.8
6
4
Decile of Socio-Emotional
0.14
2.2
5
3
0.16
E. Four-Year College
3.2
2
0.18
0.02
2.2
AR
Y
7
0.22
3.2
2.6
0.04
2.4
1
0.24
Log-Wages
3.4
2.8
0.08
0.02
2.2
4
0.1
2
3
0.06
0.04
3
1
2.8
3
0.1
2.6
0.06
2
0
3.2
0.12
0.08
2.4
0.2
0.18
3.2
3
0.1
2.6
0.22
Log-Wages
3.4
0.12
2.8
1
10
Fraction
5
4
Fraction
3
0.14
3
3.2
9
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
DR
A
0.2
Fraction
0.22
2
Log-Wages
Log-Wages
1
0.16
3.4
8
2.2
Log-Wages
6
7
2.4
3.2
5
6
2.2
2.6
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
4
5
0.2
2.4
FT
3
2.8
Log-Wages
Log-Wages
3.2
2.2
3
0.4
D. Some College
2.4
2
0.5
Decile of Cognitive
Decile of Socio-Emotional
2.6
1
0.6
3.2
0.3
0.05
C. High School Graduate
3.4
0.7
Log-Wages
3.4
0.3
2.6
0.06
0.04
2.4
0.2
2.4
0.1
0.02
2.2
1
2
3
4
5
6
7
8
9
10
0
Decile of Socio-Emotional
2.2
1
2
3
4
5
6
7
8
9
10
Decile of Cognitive
0
2.2
1
2
3
4
5
6
7
8
10
Decile of Socio-Emotional
Note: For each schooling level we present three figures. The first figure (top) displays the levels of the outcome as a function
of cognitive and socio-emotional endowments. In particular, we present the average level of outcomes for different deciles of
cognitive and socio-emotional endowments. Notice that we define as “decile 1” the decile with the lowest values of endowments
and “decile 10” as the decile with the highest levels of endowments. The second figure (bottom left) displays the average levels
of endowment across deciles of cognitive endowments. The bars in this figure indicates the fraction of individuals reporting the
respective schooling level for each decile of cognitive endowment. The last figure (bottom right) mimics the structure of the
second one but now for the socio-emotional endowment.
75
9
0
Fraction
6
9 10
7 8
itive
of Cogn
Decile
2.6
Fraction
5
6
2.8
Log-Wages
4
5
4
3
Fraction
3
3
Log-Wages
2
0.15
2
0.1
0.2
2.2
Log-Wages
3.4
3.2
2.6
2.2
1
no
9 10
7 8
itive
of Cogn
Decile
o
6
-d
2.8
5
4
Log-Wages
3
3
Fraction
0.6
0.5
2
Fraction
Log-Wages
1
Fraction
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Log-Wages
2.2
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Log-Wages
Log-Wages
2.4
2.2
3.2
1
3
2.8
2.6
2.4
3.4
3.2
Fraction
3.2
ite
B. GED
Log-Wages
Log-Wages
A. High School Dropout
Figure 9: The Effect of Cognitive and Socio-emotional endowments on Obesity during
Adulthood
0.4
0.3
0.3
0.5
0.4
0.4
0.3
ite
0.15
0.2
5
6
tc
4
9 10
7 8
itive
of Cogn
Decile
0.7
Prob. of Obesity
0.5
0.6
0.5
0.4
0.2
0.4
0.3
0.1
0.2
0.2
2
Fraction
0.2
0.4
0.3
0.3
0.2
0.25
Prob. of Obesity
0.5
3
no
0.6
Fraction
Prob. of Obesity
0.5
1
-d
0.4
5
o
0.5
4
Prob. of Obesity
Prob. of Obesity
0.6
0.5
3
Prob. of Obesity
2
Fraction
Prob. of Obesity
1
0.55
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
10
8 9
itive
6 7
of Cogn
Decile
Fraction
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Prob. of Obesity
B. GED
0.55
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
Prob. of Obesity
Prob. of Obesity
A. High School Dropout
0.3
0.2
0.2
0.05
0.1
0.1
0.1
0.1
7
8
9
10
1
2
3
4
5
6
7
Decile of Cognitive
8
9
10
1
2
3
4
Prob. of Obesity
2
0.18
0.16
0.4
3
0.14
4
5
0.2
0.18
0.16
0.4
0.14
0.3
0.1
0.1
7
8
9
10
0.24
Prob. of Obesity
0.2
0.16
0.4
1
0.2
2
3
4
5
6
7
8
9
10
0.1
0
1
2
3
4
7
8
9
10
0
Decile of Socio-Emotional
4
5
6
7
8
9
10
0.1
Prob. of Obesity
De
0.35
Prob. of Obesity
0.5
0.3
0.25
0.4
Prob. of Obesity
0.15
7
8
9
10
Decile of Cognitive
0.2
0.18
0.16
0.4
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0.1
2
3
4
5
6
7
8
9
10
0.1
0.2
1
0.2
0.18
0.16
0.4
2
3
4
5
6
9 10
7 8
itive
of Cogn
Decile
0.8
Prob. of Obesity
0.7
0.5
0.6
0.4
0.5
0.14
0.12
0.3
0.1
0.2
0.06
0.4
0.3
0.3
0.02
0.1
2
3
4
5
6
7
8
9
10
0
Decile of Socio-Emotional
0.2
0.2
0.04
0.05
1
0
Decile of Socio-Emotional
0.08
0.1
0
0.22
Prob. of Obesity
1
0.22
0.5
0.2
0.3
0.05
0.1
9 10
7 8
itive
of Cogn
Decile
0.55
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.15
0.2
6
0.5
0
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
0.25
0.2
5
F. GED with Some College
9 10
ve
7 8
Cogniti
cile of
6
4
Decile of Cognitive
Prob. of Obesity
3
5
Decile of Socio-Emotional
Fraction
Fraction
2
Prob. of Obesity
0.3
6
6
0.2
0.06
IN
Prob. of Obesity
IM
Prob. of Obesity
PR
EL
1
0.35
5
5
0.3
0.02
0.1
3
0.1
0.02
0.4
0.3
4
4
0.12
0.02
0.45
Prob. of Obesity
0.4
3
3
0.14
0.04
0
2
0.18
0.04
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
0.5
0.22
0.5
E. Four-Year College
0.55
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
1
0.04
Decile of Cognitive
2
2
0.08
0.06
0.2
0.06
1
0.1
1
0.55
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.08
AR
Y
0.2
6
10
0.3
0.1
0.08
5
9
0.12
0.12
0.3
0.22
Prob. of Obesity
0.5
Prob. of Obesity
0.2
4
8
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
10
8 9
itive
6 7
of Cogn
Decile
Fraction
0.22
Prob. of Obesity
0.5
3
7
D. Some College
DR
A
1
Fraction
Prob. of Obesity
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
2
6
FT
0.55
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
Prob. of Obesity
Prob. of Obesity
C. High School Graduate
1
5
Decile of Cognitive
Decile of Socio-Emotional
Fraction
6
1
2
3
4
5
6
7
8
9
10
Decile of Cognitive
0
0.1
0.1
1
2
3
4
5
6
7
8
Note: For each schooling level we present three figures. The first figure (top) displays the levels of the outcome as a function
of cognitive and socio-emotional endowments. In particular, we present the average level of outcomes for different deciles of
cognitive and socio-emotional endowments. Notice that we define as “decile 1” the decile with the lowest values of endowments
and “decile 10” as the decile with the highest levels of endowments. The second figure (bottom left) displays the average levels
of endowment across deciles of cognitive endowments. The bars in this figure indicates the fraction of individuals reporting the
respective schooling level for each decile of cognitive endowment. The last figure (bottom right) mimics the structure of the
second one but now for the socio-emotional endowment.
76
9
10
Decile of Socio-Emotional
0
Fraction
5
Fraction
4
Prob. of Obesity
3
0.1
0
Fraction
2
0.1
0
Prob. of Obesity
1
0
Figure 10: The Effect of Cognitive and Socio-emotional endowments on Exercising Regularly
during Adulthood
0.5
0.8
0.4
0.7
0.3
0.6
4
5
6
7
Decile
Prob. of Regular Exercise
0.6
0.9
0.5
0.8
0.4
0.7
0.3
0.6
7
8
9
10
0.4
0
1
2
3
4
5
6
7
Decile of Cognitive
8
9
10
1
2
3
4
0.8
0.16
0.14
0.7
0.12
0.1
0.6
3
4
5
6
9 10
7 8
itive
of Cogn
Decile
0.22
Prob. of Regular Exercise
0.2
0.9
0.18
0.16
0.8
0.14
0.12
0.7
0.1
0.6
0.5
0.5
0.04
0.4
7
8
9
10
ite
0.3
0.2
0.5
0.1
0.4
1
2
3
4
5
6
7
8
9
10
0
Decile of Socio-Emotional
1
2
3
4
5
6
7
0.3
0.25
0.7
0.2
0.6
0.15
4
5
6
0.5
0.4
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
8
9
10
0.22
0.14
0.7
0.12
0.1
7
8
9
10
Decile of Cognitive
0
3
4
5
6
9 10
7 8
itive
of Cogn
Decile
0.22
Prob. of Regular Exercise
0.2
0.9
0.18
0.16
0.8
0.14
0.12
0.7
0.1
0.6
0.08
0.08
0.06
0.5
0.04
0.4
0
1
2
3
4
5
6
7
8
9
10
0.04
0.02
0.4
0
1
2
3
4
5
6
7
Decile of Cognitive
Decile of Socio-Emotional
8
9
10
Prob. of Regular Exercise
9 10
7 8
itive
of Cogn
Decile
0.35
Prob. of Regular Exercise
0.9
0.3
0.8
0.25
0.2
0.7
0.15
0.6
0.9
0.8
0.7
0.6
0.5
0.4
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Prob. of Regular Exercise
0.5
1
0.22
0.9
0.2
0.18
0.8
0.16
0.14
0.7
0.12
0.1
0.6
0.08
2
3
4
5
6
9 10
7 8
itive
of Cogn
Decile
0.8
Prob. of Regular Exercise
0.9
0.7
0.6
0.8
0.5
0.7
0.4
0.3
0.6
0.06
0.5
0.04
0.05
0.2
0.5
0.1
0.02
0.4
1
2
3
4
5
6
7
0
Decile of Socio-Emotional
F. GED with Some College
0.05
0.4
2
0.16
0.1
0.1
0.5
1
0.02
Prob. of Regular Exercise
0.35
3
0.6
0.5
Fraction
0.4
Fraction
0.45
Prob. of Regular Exercise
2
Prob. of Regular Exercise
1
0.7
0.18
0.5
0.4
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
0.8
0.2
0.6
6
0.4
0.9
0.8
0.7
0.8
5
0
0.24
IN
Prob. of Regular Exercise
10
Prob. of Regular Exercise
0.8
IM
PR
EL
Prob. of Regular Exercise
0.9
0.9
4
9
0.9
E. Four-Year College
3
8
0.02
0.4
0
Decile of Cognitive
2
0.5
0.7
0.06
0.04
0.02
1
7
0.06
0.06
6
6
0.6
0.08
AR
Y
0.08
Prob. of Regular Exercise
0.22
2
Fraction
1
Fraction
0.4
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
DR
A
0.5
0.18
5
5
Prob. of Regular Exercise
0.6
0.2
4
0.6
0.8
FT
0.7
Prob. of Regular Exercise
Prob. of Regular Exercise
Prob. of Regular Exercise
0.8
Prob. of Regular Exercise
3
0.7
Prob. of Regular Exercise
0.9
D. Some College
0.9
0.9
2
9 10
7 8
itive
of Cogn
Decile
Decile of Cognitive
Decile of Socio-Emotional
C. High School Graduate
1
6
0.6
0.05
0.4
0
5
Fraction
6
0.1
0.5
0.1
4
8
9
10
0
Decile of Socio-Emotional
0.4
1
2
3
4
5
6
7
8
9
10
Decile of Cognitive
0
0.4
1
2
3
4
5
6
7
8
Note: For each schooling level we present three figures. The first figure (top) displays the levels of the outcome as a function
of cognitive and socio-emotional endowments. In particular, we present the average level of outcomes for different deciles of
cognitive and socio-emotional endowments. Notice that we define as “decile 1” the decile with the lowest values of endowments
and “decile 10” as the decile with the highest levels of endowments. The second figure (bottom left) displays the average levels
of endowment across deciles of cognitive endowments. The bars in this figure indicates the fraction of individuals reporting the
respective schooling level for each decile of cognitive endowment. The last figure (bottom right) mimics the structure of the
second one but now for the socio-emotional endowment.
77
9
10
Decile of Socio-Emotional
0
Fraction
5
0.7
Fraction
4
0.15
Prob. of Regular Exercise
3
0.8
Fraction
2
0.2
3
Prob. of Regular Exercise
1
0.25
Prob. of Regular Exercise
2
-d
0.5
1
0.9
0.2
0.1
0.4
0.4
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
0.6
0.2
0.5
0.5
o
0.6
0.9
3
Prob. of Regular Exercise
2
Fraction
Prob. of Regular Exercise
1
10
8 9
itive
of Cogn
Fraction
0.4
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
0.6
Fraction
0.5
0.7
tc
0.6
0.8
no
0.7
0.9
Fraction
0.8
Prob. of Regular Exercise
Prob. of Regular Exercise
B. GED
0.9
Prob. of Regular Exercise
Prob. of Regular Exercise
Prob. of Regular Exercise
A. High School Dropout
Figure 11: The Effect of Cognitive and Socio-emotional endowments on BMI
BMI
0.6
34
0.5
32
0.4
0.15
0.4
30
30
30
0.3
0.3
28
0.2
26
26
10
0
1
2
3
4
5
6
7
8
9
10
0
1
2
3
4
BMI
5
6
9 10
7 8
itive
of Cogn
Decile
0.22
BMI
34
28
0.08
26
0.04
5
6
7
8
9
10
24
0
1
2
3
4
5
6
7
Decile of Cognitive
8
9
10
0.3
0.25
0.15
3
4
5
6
7
8
9
10
Decile of Cognitive
6
7
8
9
10
0
Decile of Socio-Emotional
5
6
0
5
6
9 10
7 8
itive
of Cogn
Decile
0.22
BMI
0.2
0.18
0.16
32
0.14
0.12
30
0.1
28
0.08
0.06
26
0.04
0.04
0.02
24
0
1
2
3
4
5
6
7
8
9
10
0.02
24
0
1
2
3
4
5
6
7
Decile of Cognitive
BMI
9 10
7 8
itive
of Cogn
Decile
0.35
BMI
34
0.3
BMI
4
BMI
Fraction
3
4
8
9
10
33
32
31
30
29
28
27
26
25
24
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
1
0.22
BMI
34
0.2
2
3
4
5
6
9 10
7 8
itive
of Cogn
Decile
0.8
BMI
34
0.7
0.18
32
0.25
30
0.2
0.15
28
26
32
0.16
0.6
32
0.5
0.14
30
0.12
30
0.4
0.1
28
0.08
0.3
28
0.06
26
0.04
0.05
0.2
26
0.1
0.02
24
1
2
3
4
5
6
7
0
Decile of Socio-Emotional
F. GED with Some College
0.05
24
5
0.06
0.1
0.1
26
4
34
0.08
Decile of Socio-Emotional
0.2
28
3
0.1
0.35
30
2
2
3
0.12
26
Fraction
IN
BMI
IM
BMI
PR
EL
0.4
32
1
1
0.45
BMI
34
0.1
2
0.14
E. Four-Year College
33
32
31
30
29
28
27
26
25
24
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
2
0.16
0.02
AR
Y
4
1
0.18
28
0.04
0.02
3
0.2
0.06
0.06
2
0.22
30
0.08
1
0.24
BMI
0.1
0.1
1
0
33
32
31
30
29
28
27
26
25
24
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
32
0.12
30
0.12
24
10
0.14
0.14
26
9
34
0.16
32
0.16
28
0.2
0.18
0.18
30
8
FT
4
BMI
3
Fraction
0.2
32
7
0.2
24
D. Some College
DR
A
0.22
BMI
2
BMI
1
Fraction
BMI
BMI
C. High School Graduate
34
6
0.3
Decile of Cognitive
Decile of Socio-Emotional
33
32
31
30
29
28
27
26
25
24
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
5
0.4
Fraction
9
Decile of Cognitive
0.5
8
9
10
0
Decile of Socio-Emotional
24
1
2
3
4
5
6
7
8
9
10
Decile of Cognitive
0
24
1
2
3
4
5
6
7
8
10
Decile of Socio-Emotional
Note: For each schooling level we present three figures. The first figure (top) displays the levels of the outcome as a function
of cognitive and socio-emotional endowments. In particular, we present the average level of outcomes for different deciles of
cognitive and socio-emotional endowments. Notice that we define as “decile 1” the decile with the lowest values of endowments
and “decile 10” as the decile with the highest levels of endowments. The second figure (bottom left) displays the average levels
of endowment across deciles of cognitive endowments. The bars in this figure indicates the fraction of individuals reporting the
respective schooling level for each decile of cognitive endowment. The last figure (bottom right) mimics the structure of the
second one but now for the socio-emotional endowment.
78
9
0
Fraction
8
0.6
30
BMI
7
0.7
BMI
26
Fraction
6
9 10
7 8
itive
of Cogn
Decile
BMI
5
24
6
28
Fraction
4
24
5
32
-d
3
0.1
4
34
0.05
26
0.1
3
0.1
28
0.2
2
o
28
2
ite
0.2
32
1
0.25
BMI
34
0.5
32
24
1
Fraction
9 10
7 8
itive
of Cogn
Decile
tc
6
no
5
BMI
4
33
32
31
30
29
28
27
26
25
24
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Fraction
0.6
34
3
BMI
2
Fraction
1
BMI
BMI
B. GED
BMI
33
32
31
30
29
28
27
26
25
24
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Fraction
BMI
BMI
A. High School Dropout
Figure 12: The Effect of Cognitive and Socio-emotional endowments on Physical Health at age
40 (PCS-12)
-0.6
-0.6
0.6
0.6
0.5
0.2
0.4
0
0.3
-0.2
SF-12 Physical
0.4
0.4
0
7
Decile
1
0.25
0.6
SF-12 Physical
0.4
0.2
2
0.2
0.3
-0.2
-0.4
0.2
-0.4
0.2
-0.4
-0.6
0.1
-0.6
0.1
-0.6
-0.8
0
-0.8
0
-0.8
5
4
6
9 10
7 8
itive
of Cogn
Decile
0.6
0.7
SF-12 Physical
0.4
0.6
0.2
0.5
0
0.4
-0.2
0.3
-0.4
0.2
0.15
0
-0.2
3
o
0.2
6
0.1
-d
0.5
5
4
SF-12 Physical
0.6
0.4
3
Fraction
SF-12 Physical
2
-0.8
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
10
8 9
itive
of Cogn
Fraction
-0.4
1
ite
0
-0.4
Fraction
SF-12 Physical
0.2
-0.2
tc
-0.2
0.4
no
0
0.6
Fraction
0.2
SF-12 Physical
SF-12 Physical
0.4
-0.8
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
0.6
B. GED
0.6
SF-12 Physical
SF-12 Physical
A. High School Dropout
0.05
7
8
9
10
1
2
3
4
5
6
7
Decile of Cognitive
8
9
10
1
2
3
4
SF-12 Physical
0.4
0.2
0
-0.2
0.18
0.2
3
0.16
6
0.2
0.18
0.16
0.2
0.1
-0.2
-0.4
0.06
0.04
-0.6
7
8
9
10
-0.8
0
1
2
3
4
5
6
7
SF-12 Physical
0.2
9
10
0.16
-0.8
0
1
2
3
4
5
6
7
8
9
10
0
0.3
0
0.25
0.2
0.15
-0.4
SF-12 Physical
9 10
7 8
itive
of Cogn
Decile
0.6
0.35
SF-12 Physical
0.4
0.3
0.2
0.25
0
0.2
-0.2
0.15
Fraction
6
SF-12 Physical
0.35
Fraction
0.4
SF-12 Physical
0.45
5
0.05
7
8
9
10
Decile of Cognitive
0
0.6
0.22
SF-12 Physical
0.2
0.4
0.18
0.16
0.2
0.12
0.1
0.08
-0.4
0.06
0.04
-0.6
0.02
-0.8
0
1
2
3
4
5
6
7
8
9
10
0.4
0.2
0
-0.2
0.6
SF-12 Physical
1
0.22
0.2
0.4
0.18
0.2
0.16
0.14
0
2
3
5
4
6
9 10
7 8
itive
of Cogn
Decile
0.6
0.8
SF-12 Physical
0.7
0.4
0.6
0.2
0.5
0
0.12
0.1
-0.2
0.4
-0.2
0.3
0.08
-0.4
0.1
-0.6
-0.4
0.06
0.05
0.04
-0.6
-0.4
0.2
-0.6
0.1
0.02
-0.8
1
2
3
4
5
6
7
0
Decile of Socio-Emotional
0.6
0.1
-0.6
9 10
7 8
itive
of Cogn
Decile
F. GED with Some College
-0.8
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
4
6
Decile of Cognitive
Decile of Socio-Emotional
-0.6
3
5
4
0.02
-0.8
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
6
10
0.14
0.04
-0.6
-0.6
SF-12 Physical
5
9
-0.2
0.06
-0.4
-0.2
4
8
0
0.08
-0.4
2
3
0.1
-0.2
0.2
3
7
0.12
0
0.4
2
6
Decile of Socio-Emotional
0.14
0.2
1
2
0.18
0.2
IN
SF-12 Physical
0.4
IM
PR
EL
SF-12 Physical
0.6
0.22
0.4
0.02
8
1
0.24
0.6
E. Four-Year College
1
5
0
-0.4
0.04
-0.6
Decile of Cognitive
-0.8
4
0.2
-0.2
0.06
0.02
0.6
3
-0.2
0.08
AR
Y
-0.4
6
2
0.4
0
0.12
0.08
5
0.1
1
0.6
0.14
0
0.1
-0.2
0.22
SF-12 Physical
0.4
0.12
4
10
-0.8
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
9 10
7 8
itive
of Cogn
Decile
0.6
0.14
0
5
4
Fraction
0.2
3
9
-0.6
DR
A
0.22
SF-12 Physical
0.4
2
Fraction
1
SF-12 Physical
SF-12 Physical
-0.8
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
2
8
-0.4
-0.6
1
7
FT
0.6
-0.4
-0.8
6
D. Some College
SF-12 Physical
SF-12 Physical
C. High School Graduate
0.6
5
Decile of Cognitive
Decile of Socio-Emotional
Fraction
6
-0.8
8
9
10
0
Decile of Socio-Emotional
-0.8
1
2
3
4
5
6
7
8
9
10
Decile of Cognitive
0
-0.8
1
2
3
4
5
6
7
8
Note: For each schooling level we present three figures. The first figure (top) displays the levels of the outcome as a function
of cognitive and socio-emotional endowments. In particular, we present the average level of outcomes for different deciles of
cognitive and socio-emotional endowments. Notice that we define as “decile 1” the decile with the lowest values of endowments
and “decile 10” as the decile with the highest levels of endowments. The second figure (bottom left) displays the average levels
of endowment across deciles of cognitive endowments. The bars in this figure indicates the fraction of individuals reporting the
respective schooling level for each decile of cognitive endowment. The last figure (bottom right) mimics the structure of the
second one but now for the socio-emotional endowment.
79
9
10
Decile of Socio-Emotional
0
Fraction
5
Fraction
4
SF-12 Physical
3
Fraction
2
SF-12 Physical
1
0
-0.6
Figure 13: The Effect of Cognitive and Socio-emotional endowments on Pearlin’s “Personal
Mastery Scale”
0.6
0.4
0.6
0.4
0
0.2
-0.2
5
6
7
8
9
10
1
2
3
4
5
6
7
Decile of Cognitive
8
9
10
-0.6
1
2
3
4
Pearlin
1.2
0.22
0.6
0.14
0.6
0.4
0.12
0.4
0.2
0.1
0.2
0.08
3
4
5
6
7
8
9
10
-0.6
0
1
2
3
4
5
6
7
Decile of Cognitive
0.16
0.8
0.14
0.6
0.12
0.4
0.1
0.2
0.35
0.3
0.6
0.25
0.4
0.2
0.2
0.15
0
8
9
10
-0.6
4
1.2
6
0.3
0.25
0.6
0.2
0.4
0.2
0.15
0
0.1
-0.6
0
-0.6
0
8
9
10
Decile of Cognitive
tc
6
7
8
9
10
Fraction
0
2
3
4
5
6
7
6
9 10
7 8
itive
of Cogn
Decile
0.22
Pearlin
0.2
1
0.18
0.8
0.16
0.6
0.14
0.12
0.4
0.12
0.1
0.2
0.1
0.08
0
0.06
-0.2
0.04
-0.4
0.02
1
2
3
4
5
6
7
8
9
10
-0.6
0
0.02
1
2
3
4
5
6
7
8
9
10
0
Decile of Socio-Emotional
1.2
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
8
9
10
Decile of Socio-Emotional
1
0.22
Pearlin
1.2
0.2
1
0.18
0.8
0.16
0.14
2
5
3
4
1.2
0.8
0.5
0.2
0.04
-0.4
-0.6
0.02
1
2
3
4
5
6
7
8
9
10
Decile of Cognitive
0
0.7
0.6
0.4
0.1
0.06
Pearlin
0.6
0.12
0
9 10
7 8
itive
of Cogn
Decile
1
0.2
0.08
6
0.8
0.4
-0.2
1
1.2
0.06
0.6
0.05
7
5
4
0.08
Pearlin
0.35
Pearlin
1
0.8
-0.4
6
5
Decile of Socio-Emotional
3
0.14
9 10
7 8
itive
of Cogn
Decile
0.05
5
4
F. GED with Some College
-0.4
4
3
Decile of Cognitive
-0.2
3
2
0.16
Decile of Socio-Emotional
Pearlin
5
3
2
0.18
-0.4
0
0.1
2
0.1
1
0.04
0.02
-0.2
1
ite
0.2
-0.2
0.04
1
0.22
0
Fraction
0.4
2
Pearlin
0.45
Pearlin
1
Fraction
1
1.2
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
0.24
IN
Pearlin
IM
PR
EL
Pearlin
1.2
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
0.8
10
Pearlin
1.2
E. Four-Year College
1.2
9
0.06
-0.4
0.02
2
8
1
0.08
-0.2
0.04
1
0.2
0.18
AR
Y
-0.6
Pearlin
0
0.06
-0.4
9 10
7 8
itive
of Cogn
Decile
0.8
0.16
-0.2
6
1
0.18
0
7
-0.6
FT
4
Pearlin
5
3
Fraction
0.2
0.8
6
0.2
-0.4
D. Some College
DR
A
0.22
Pearlin
2
Pearlin
1
Fraction
Pearlin
Pearlin
1.2
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
1
5
0
Decile of Cognitive
Decile of Socio-Emotional
C. High School Graduate
1.2
0.3
-0.2
Fraction
4
-0.4
0
0.4
0.4
0.3
0
0.2
-0.2
0.1
-0.4
-0.6
1
2
3
4
5
6
7
8
Note: For each schooling level we present three figures. The first figure (top) displays the levels of the outcome as a function
of cognitive and socio-emotional endowments. In particular, we present the average level of outcomes for different deciles of
cognitive and socio-emotional endowments. Notice that we define as “decile 1” the decile with the lowest values of endowments
and “decile 10” as the decile with the highest levels of endowments. The second figure (bottom left) displays the average levels
of endowment across deciles of cognitive endowments. The bars in this figure indicates the fraction of individuals reporting the
respective schooling level for each decile of cognitive endowment. The last figure (bottom right) mimics the structure of the
second one but now for the socio-emotional endowment.
80
9
10
Decile of Socio-Emotional
0
Fraction
3
0.05
Pearlin
2
-0.6
0.5
0.2
Fraction
1
-0.4
0
0.6
0.6
Pearlin
-0.4
0.7
Pearlin
0
-0.2
0.1
9 10
7 8
itive
of Cogn
Decile
1
0.1
0
0.2
6
0.4
0.2
-0.2
0.1
1.2
0.15
0.4
0.3
0.2
4
0.8
Fraction
0
0.2
2
0.8
0.4
0.3
0.2
0.25
Pearlin
1.2
1
0.5
5
3
no
0.6
1
Pearlin
Pearlin
1
0.8
0.4
-0.6
9 10
7 8
itive
of Cogn
Decile
o
1.2
6
-d
4
0.5
0.6
5
3
1.2
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Fraction
Pearlin
2
Pearlin
0.6
1
0.8
B. GED
Fraction
Pearlin
1.2
1
Pearlin
1.2
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Fraction
Pearlin
Pearlin
A. High School Dropout
Figure 14: The Effect of Cognitive and Socio-emotional endowments on Self-Esteem during
Adulthood
0.5
0.2
0.8
Rosenberg
0.6
0.6
0.4
0.5
0.2
8
9
10
1
2
3
4
5
6
7
Decile of Cognitive
8
9
10
2
3
4
Rosenberg
0.16
3
5
4
6
9 10
7 8
itive
of Cogn
Decile
0.8
0.22
Rosenberg
0.6
0.2
0.18
0.4
0.14
0
0.12
0.16
-0.4
0.08
-0.4
0.06
AR
Y
0.08
0.06
-0.6
0.04
0.04
-0.8
0.02
7
8
9
10
1
2
3
4
5
6
7
Decile of Cognitive
8
9
10
0.25
5
4
-0.4
2
3
4
0.05
5
6
7
8
9
10
Decile of Cognitive
8
9
10
0
0
3
5
4
6
9 10
7 8
itive
of Cogn
Decile
0.8
0.22
Rosenberg
0.2
0.6
0.18
0.4
0.16
0.2
0.14
0
0.12
0.1
-0.4
0.08
-0.6
0.06
-0.6
0.06
-0.8
0.04
-0.8
1
2
3
4
5
Rosenberg
9 10
7 8
itive
of Cogn
Decile
0.35
Rosenberg
0.3
0.25
0
0.15
1
7
6
7
8
9
10
0.04
0.02
-1
0
1
2
3
4
5
6
7
8
9
10
0.2
0.15
0.1
-0.6
-0.8
0.05
0.8
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
-1
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Rosenberg
1
0.22
0.6
0.2
0.4
0.18
0.2
0.16
0.14
0
0.12
2
3
5
4
6
9 10
7 8
itive
of Cogn
Decile
0.8
0.8
Rosenberg
0.6
0.7
0.4
0.6
0.2
0.5
0
0.4
-0.2
0.1
-0.2
-0.4
0.08
-0.4
0.3
-0.6
0.06
-0.6
0.2
0.04
-0.8
-0.8
0.1
0.02
-1
1
2
3
4
5
6
7
0
Decile of Socio-Emotional
F. GED with Some College
0.2
-0.4
-1
6
Decile of Cognitive
0.4
-0.2
-0.8
5
0.08
Decile of Socio-Emotional
0.6
0.2
0.1
6
0.8
-0.2
-0.6
4
-0.2
-1
0
Rosenberg
0.3
0
3
0.02
Fraction
0.35
0.2
Fraction
0.4
0.4
3
Rosenberg
0.6
2
2
0.1
IN
Rosenberg
IM
Rosenberg
PR
EL
1
0.45
Rosenberg
2
Decile of Socio-Emotional
0.12
E. Four-Year College
0.8
0.1
1
0.14
0.02
-1
0
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
-1
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
0.22
0.18
-0.4
6
Rosenberg
0
0.12
1
0.2
-0.2
5
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
-1
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
0.2
0.1
4
10
0.4
-0.2
3
9
0.6
0.1
2
8
0.24
0.8
-0.2
1
7
0.16
0.2
0.14
-1
6
0.2
-1
FT
0.2
-0.8
0.3
-0.8
D. Some College
Rosenberg
0.18
-0.6
0.4
-0.6
Decile of Cognitive
Fraction
0.2
0.4
0
5
DR
A
0.22
0.6
2
Fraction
1
Rosenberg
Rosenberg
Rosenberg
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
-1
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Rosenberg
ite
o
1
Decile of Socio-Emotional
C. High School Graduate
0.8
0.5
Fraction
7
0.6
0.4
8
9
10
0
Decile of Socio-Emotional
-1
1
2
3
4
5
6
7
8
9
10
Decile of Cognitive
0
-1
1
2
3
4
5
6
7
8
Note: For each schooling level we present three figures. The first figure (top) displays the levels of the outcome as a function
of cognitive and socio-emotional endowments. In particular, we present the average level of outcomes for different deciles of
cognitive and socio-emotional endowments. Notice that we define as “decile 1” the decile with the lowest values of endowments
and “decile 10” as the decile with the highest levels of endowments. The second figure (bottom left) displays the average levels
of endowment across deciles of cognitive endowments. The bars in this figure indicates the fraction of individuals reporting the
respective schooling level for each decile of cognitive endowment. The last figure (bottom right) mimics the structure of the
second one but now for the socio-emotional endowment.
81
9
10
Decile of Socio-Emotional
0
Fraction
6
Fraction
5
Rosenberg
4
0.7
Rosenberg
0.6
0
Fraction
3
-1
Rosenberg
2
0.05
-0.8
0
-d
1
0.1
-1
0
0.8
-0.4
-0.6
-0.8
9 10
7 8
itive
of Cogn
Decile
-0.2
0.1
-0.4
0.2
-0.6
0.1
6
0
-0.2
0.3
-0.4
0.2
5
4
0.2
0
-0.2
-0.6
-1
0.2
0.4
3
0.15
0
0.3
-0.8
Rosenberg
0.6
0.4
0
-0.4
0.25
0.8
2
0.2
0.4
-0.2
1
Fraction
9 10
7 8
itive
of Cogn
Decile
tc
6
no
5
4
Fraction
3
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
-1
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Rosenberg
Rosenberg
2
Rosenberg
0.6
0.6
0.4
B. GED
Fraction
Rosenberg
1
Fraction
0.8
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
-1
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Rosenberg
Rosenberg
Rosenberg
A. High School Dropout
Figure 15: The Effect of Cognitive and Socio-emotional endowments on Mental Health at age 40
(MCS-12)
0.6
0.5
0.4
0.4
0.4
0.4
0.2
0.3
0.2
0
7
8
9
10
0.05
-0.4
1
2
3
4
5
6
7
Decile of Cognitive
8
9
10
0
1
2
3
4
0.2
0
0.22
SF-12 Mental
0.2
0.18
0.16
0.14
0.4
0.2
-0.4
-0.2
7
8
9
10
0.24
1
SF-12 Mental
0
1
2
3
4
5
6
7
Decile of Cognitive
0.2
0.18
tc
4
5
6
7
8
9
10
0
Decile of Socio-Emotional
9
10
0.35
4
0.8
0
0
0.15
1
2
3
4
5
6
7
8
9
10
SF-12 Mental
De
1
0.35
SF-12 Mental
0.3
0.25
1
-0.4
7
8
9
10
Decile of Cognitive
0
0
0.14
0.12
0.08
0.06
-0.2
0.05
3
4
5
6
7
0.08
0.06
0.04
-0.4
0.02
2
3
4
5
6
7
8
9
10
0
8
9
10
0
Decile of Socio-Emotional
2
5
6
9 10
7 8
itive
of Cogn
Decile
3
4
0.8
0.7
0.6
0.6
0.4
0.5
0.2
0.4
0
0.3
-0.2
0.2
1
0.8
SF-12 Mental
0.1
0
2
0.1
-0.2
0.16
0.2
1
0.12
1
0.18
0.15
-0.4
0.14
0
Decile of Socio-Emotional
0.2
0.6
0.2
0.05
0.16
1
0.22
SF-12 Mental
0.8
0.4
0.2
-0.2
0.18
0
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
0.6
0.1
0.2
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
0.1
-0.2
0.22
SF-12 Mental
F. GED with Some College
0.25
0.2
1
0.6
Decile of Cognitive
0.4
0.2
9 10
7 8
itive
of Cogn
Decile
0.2
0.02
Decile of Socio-Emotional
0.3
0.4
6
0.4
0.06
9 10
ve
7 8
Cogniti
cile of
6
0.8
0.08
-0.4
SF-12 Mental
0.4
5
3
4
0.04
Fraction
SF-12 Mental
0.6
Fraction
0.45
1
0.8
2
SF-12 Mental
1
5
3
0.12
-0.2
0.02
8
2
0.14
IN
SF-12 Mental
IM
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
SF-12 Mental
3
0.16
E. Four-Year College
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
0.22
0.6
0
0.04
-0.4
0.02
6
2
0.1
0.06
0.04
1
0.8
0.2
0.08
AR
Y
-0.2
5
0.1
1
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
0.4
0.1
0
0.06
PR
EL
10
0.12
0.08
4
9
Fraction
1
0.6
0.1
3
8
0.04
-0.4
0.02
1
2
3
4
5
6
7
8
9
10
Decile of Cognitive
0
0.1
-0.4
1
2
3
4
5
6
7
8
Note: For each schooling level we present three figures. The first figure (top) displays the levels of the outcome as a function
of cognitive and socio-emotional endowments. In particular, we present the average level of outcomes for different deciles of
cognitive and socio-emotional endowments. Notice that we define as “decile 1” the decile with the lowest values of endowments
and “decile 10” as the decile with the highest levels of endowments. The second figure (bottom left) displays the average levels
of endowment across deciles of cognitive endowments. The bars in this figure indicates the fraction of individuals reporting the
respective schooling level for each decile of cognitive endowment. The last figure (bottom right) mimics the structure of the
second one but now for the socio-emotional endowment.
82
9
10
Decile of Socio-Emotional
0
Fraction
0.8
0.12
2
7
Fraction
4
0.14
1
6
0.2
-0.4
SF-12 Mental
SF-12 Mental
0.4
6
0.3
FT
5
3
0.16
5
0.4
-0.2
0
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
10
8 9
itive
6 7
of Cogn
Decile
SF-12 Mental
0.18
4
0.5
D. Some College
Fraction
0.2
0.6
3
5
DR
A
0.22
SF-12 Mental
0.8
2
Fraction
SF-12 Mental
1
1
SF-12 Mental
SF-12 Mental
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
2
0.6
Decile of Cognitive
Decile of Socio-Emotional
C. High School Graduate
1
0.7
SF-12 Mental
0
Fraction
6
1
0.6
SF-12 Mental
5
9 10
7 8
itive
of Cogn
Decile
0.4
-d
4
ite
0
-0.2
-0.4
0
6
0.2
0.1
-0.4
0.8
0.15
0.2
0.1
4
0.1
0
3
0.2
0.2
-0.2
2
SF-12 Mental
0.6
0.4
0.3
0.2
-0.2
1
0.25
1
0.8
2
Fraction
SF-12 Mental
5
3
no
1
0.6
Fraction
0.8
1
o
0.5
0.6
4
SF-12 Mental
SF-12 Mental
0.6
0.8
5
3
SF-12 Mental
1
SF-12 Mental
2
Fraction
1
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
10
8 9
itive
6 7
of Cogn
Decile
Fraction
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
SF-12 Mental
B. GED
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
SF-12 Mental
SF-12 Mental
A. High School Dropout
Figure 16: The Effect of Cognitive and Socio-emotional endowments on Depression at age 40
(CES-D - Reverse Score)
0.4
-0.4
-0.6
-0.6
0.6
CESD
0.6
0.4
0.5
CESD
4
Fraction
0.6
0.4
5
3
CESD
0
0
0.3
0.3
-0.2
0.2
-0.4
-0.4
0.1
-0.6
10
0.05
0.1
-0.6
9
0.1
-0.2
0.2
-0.4
8
0
-0.6
1
2
3
4
5
6
7
Decile of Cognitive
8
9
10
0
1
2
3
4
CESD
DR
A
0.22
CESD
0.4
5
6
7
8
9
10
0
0.6
0.4
-0.2
AR
Y
8
9
10
0
1
2
3
4
5
6
7
Decile of Cognitive
9
10
-0.6
0
1
2
3
4
5
6
10
0.25
0
4
0.6
6
CESD
De
0.35
CESD
0.4
0.3
0.2
0.25
CESD
5
3
0.2
0
0.15
-0.2
-0.6
7
8
9
10
Decile of Cognitive
0.02
2
3
4
5
6
7
8
9
10
0.22
CESD
0.2
0.4
2
5
3
4
0.6
6
0
9 10
7 8
itive
of Cogn
Decile
0.2
0.16
0.8
CESD
0.7
0.4
0.6
0.2
0.5
0.14
0
0.12
0.1
-0.2
0
0.4
-0.2
0.3
-0.4
0.2
0.08
0.1
-0.4
0.05
-0.6
0
1
0.6
0.15
-0.4
0.04
1
0.18
0.2
-0.2
0.06
Decile of Socio-Emotional
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
9 10
ve
7 8
Cogniti
cile of
Fraction
Fraction
9
0
0.2
CESD
IM
8
0.35
0.3
0.08
-0.6
0.4
-0.6
0.4
0.1
0.6
-0.6
0.45
0.12
-0.4
F. GED with Some College
0
0.2
0.14
Decile of Cognitive
-0.4
CESD
7
Decile of Socio-Emotional
-0.4
0.4
0.16
-0.2
0.02
-0.2
0.6
0.18
0.04
-0.2
2
0.2
0
0.06
-0.4
0
1
0.22
CESD
0.2
0.1
0.2
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
9 10
7 8
itive
of Cogn
Decile
0.4
0.12
IN
0.4
0.6
6
0.14
E. Four-Year College
0.6
4
0.08
0.02
8
5
3
0.16
0.04
-0.6
2
0.18
-0.2
0.06
-0.4
0.02
7
0.2
0
0.08
0.04
6
0.22
0.1
0.06
-0.6
CESD
0.2
0.12
0
1
0.24
0.6
0.4
0.16
0.08
-0.4
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
0.14
0.1
-0.2
0.2
0.18
0.2
0.12
CESD
4
0.1
0.05
1
2
3
4
5
6
7
8
9
10
0
Decile of Socio-Emotional
0.06
-0.4
0.04
-0.6
0.02
1
2
3
4
5
6
7
8
9
10
Decile of Cognitive
0
0.1
-0.6
1
2
3
4
5
6
7
8
Note: For each schooling level we present three figures. The first figure (top) displays the levels of the outcome as a function
of cognitive and socio-emotional endowments. In particular, we present the average level of outcomes for different deciles of
cognitive and socio-emotional endowments. Notice that we define as “decile 1” the decile with the lowest values of endowments
and “decile 10” as the decile with the highest levels of endowments. The second figure (bottom left) displays the average levels
of endowment across deciles of cognitive endowments. The bars in this figure indicates the fraction of individuals reporting the
respective schooling level for each decile of cognitive endowment. The last figure (bottom right) mimics the structure of the
second one but now for the socio-emotional endowment.
83
9
10
Decile of Socio-Emotional
0
Fraction
0
CESD
3
Fraction
0.6
0.14
6
2
Decile of Socio-Emotional
CESD
4
0.16
5
0.1
1
CESD
0.2
PR
EL
10
Fraction
5
3
0.18
4
9
Fraction
0.2
3
8
-0.6
10
8 9
itive
6 7
of Cogn
Decile
Fraction
0.22
CESD
0.4
2
CESD
1
Fraction
0.6
2
7
0.2
-0.6
0
0
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
1
6
0.3
-0.4
-0.6
CESD
5
0.4
-0.4
-0.2
-0.4
5
0.5
0
-0.2
0.2
-0.2
4
0.6
0.2
FT
0.4
CESD
CESD
0.6
0
3
0.7
CESD
0.4
D. Some College
0.2
2
9 10
7 8
itive
of Cogn
Decile
Decile of Cognitive
Decile of Socio-Emotional
C. High School Graduate
1
0.6
6
o
0
7
4
0.15
0.4
-0.2
6
0.2
5
3
0.2
0.4
5
CESD
0.4
0.2
4
0.25
0.6
2
0.5
0.2
3
1
-d
CESD
2
Fraction
CESD
1
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
10
8 9
itive
6 7
of Cogn
Decile
no
-0.4
CESD
0
-0.2
tc
0.2
-0.2
0.6
2
0.4
0
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
1
0.6
Fraction
0.2
Fraction
0.6
ite
B. GED
CESD
CESD
A. High School Dropout
Figure 17: Average Treatment Effect of Education on Labor Market Participation, by Decision
Node and Endowment Levels
0.1
0
-0.1
0.1
0.2
Y 1 - Y 0 (Participation)
0.3
0.18
0.16
0.2
0.14
0.12
0.1
0.1
0
0.08
-0.1
0.02
6
7
8
9
10
1
2
3
4
5
6
7
8
9
-0.1
1
-0.1
4
5
6
7
8
9
10
0.1
1
2
3
4
5
6
7
8
0.16
0.1
0.14
0.12
0
5
4
0.24
Y 1 - Y 0 (Participation)
0.3
0.22
0.2
0.2
0.18
0.16
0.1
0.14
0.12
0
10
0
0.2
0.1
0
-0.1
0.06
7
8
9
10
0.25
0.1
0.2
0
0.15
0
1
2
3
4
5
6
8
9
6
9 10
7 8
itive
of Cogn
Decile
Y 1 - Y 0 (Participation)
0.3
0.25
0.2
0.2
0.1
0.15
0
-0.1
-0.2
0
10
5
4
0.05
0.05
0.02
7
Decile of Cognitive
3
0.1
-0.1
0.04
-0.2
2
0.1
0.06
AR
Y
0.02
1
0.3
0.2
0.08
-0.1
0.04
-0.2
Y 1 - Y 0 (Participation)
0.3
0.1
0.08
-0.1
Fraction
3
0.1
-0.2
1
2
3
4
5
6
7
8
9
10
0
Decile of Cognitive
Decile of Socio-Emotional
1
2
3
4
5
6
7
8
Y 1 - Y 0 (Participation)
IN
0.2
0.1
0
-0.1
-0.2
0.22
0.2
0.2
0.18
0.16
0.1
0.14
0.12
0
0.1
3
5
4
6
9 10
7 8
itive
of Cogn
Decile
Y 1 - Y 0 (Participation)
0.3
0.7
0.6
0.2
Fraction
0.24
Y 1 - Y 0 (Participation)
0.3
2
Y 1 - Y0 (Participation)
1
Fraction
Y 1 - Y0 (Participation)
IM
0.3
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
0.5
0.1
0.4
0
0.3
0.08
0.06
-0.1
0.2
-0.1
0.04
0.02
-0.2
1
2
3
4
5
6
7
8
9
10
Decile of Cognitive
0
0.1
-0.2
1
2
3
4
5
6
7
8
9
10
9
10
Decile of Socio-Emotional
E. GED vs. GED with some College
PR
EL
9
Decile of Socio-Emotional
0.3
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
9 10
7 8
itive
of Cogn
Decile
Y 1 - Y0 (Participation)
0.18
2
6
DR
A
0.22
Fraction
1
Y 1 - Y0 (Participation)
Y 1 - Y0 (Participation)
3
0.2
-0.2
0
-0.2
0.2
6
2
Y 1 - Y 0 (Participation)
0
0.24
5
0.3
FT
Y 1 - Y 0 (Participation)
0.1
Y 1 - Y 0 (Participation)
4
0.4
D. Some College vs. 4-year college degree
0.2
0.2
3
0.5
Decile of Cognitive
0.3
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
2
0.6
0.1
-0.1
0.05
Decile of Socio-Emotional
-0.2
1
0.7
Y 1 - Y 0 (Participation)
0.2
0
0.1
-0.2
C. HS Graduate vs. College Enrollment
0.3
9 10
7 8
itive
of Cogn
Decile
0.3
0.2
0
10
6
0.15
0.02
Decile of Cognitive
5
4
0
Decile of Socio-Emotional
Notes: Each panel in this figure studies the average effects of education on the outcome of interest. The effect is defined as
the differences in the outcome associated with two schooling levels (not necessarily final or terminal schooling levels). For
each panel, let Y0 and Y1 denotes the outcomes associated with schooling levels 0 and 1, respectively. Importantly, each
schooling level might provide the option to pursuing higher schooling levels. Final schooling levels do not allow for further
options. Final schooling levels are highlighted using bold letters. For each pair of schooling levels 0 and 1, the first figure
(top) presents E(Y1 − Y0 |dC , dSE ) where dC and dSE denote the cognitive and socio-emotional deciles computed from the
marginal distributions of cognitive and socio-emotional endowments. E(Y1 − Y0 |dC , dSE ) is computed for those who reach the
decision node involving a decision between levels 0 and 1. The second figure (bottom left) presents E(Y1 − Y0 |dC ) so that
the socio-emotional factor is integrated out. The bars in this85
figure displays, for a given decile of cognitive endowment, the
fraction of individuals visiting the node leading to the educational decision involving levels 0 and 1. The last figure (bottom
right) presents E(Y1 − Y0 |dSE ) as well as, for a given decile of socio-emotional endowment, the fraction of individuals visiting
the node leading to the educational decision involving levels 0 and 1.
0
Fraction
5
0.25
Fraction
4
0.3
0
0.04
-0.2
0
3
Y 1 - Y0 (Participation)
3
0.35
0.1
0.06
0.04
-0.2
0.4
2
-d
-0.1
0.45
0.2
0.08
0.06
Y 1 - Y 0 (Participation)
0.3
1
no
De
Fraction
6
5
4
o
0.12
0
3
Y 1 - Y0 (Participation)
0.14
0.1
2
0
-0.1
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
9 10
ve
7 8
Cogniti
cile of
Fraction
0.16
Fraction
0.18
2
Y 1 - Y0 (Participation)
Y 1 - Y0 (Participation)
1
0.2
Y 1 - Y 0 (Participation)
0.2
1
0.1
-0.2
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
0.3
0.2
Y 1 - Y0 (Participation)
-0.2
0.3
Fraction
0.2
tc
0.3
ite
B. HS Dropout vs. Getting a GED
Y 1 - Y 0 (Participation)
Y 1 - Y 0 (Participation)
A. Dropping from HS vs. Graduating from HS
Figure 18: Average Treatment Effect of Probability of White-collar Employment at Age 30, by
Decision Node and Endowment Levels
0.3
0.2
0.1
0.5
0.4
0.3
0.2
0.1
0
0.14
0.12
0.18
0.16
0.4
0.14
0.3
0.12
0.1
0.08
0.06
0.1
0.06
0
5
6
7
8
9
10
0
1
2
3
4
5
6
7
8
9
0
0
10
1
0.1
3
4
5
0.16
0.3
0.14
6
5
4
0.3
0.2
6
7
8
9
10
0.1
0
0
1
2
3
4
5
6
7
8
0.1
0.24
Y 1 - Y0 (Whitecollar)
0.5
0.22
0.2
0.4
0.18
0.16
0.3
0.14
0.12
0.2
10
0
0.4
0.3
0.2
0.1
8
9
10
0.2
2
3
5
4
6
9 10
7 8
itive
of Cogn
Decile
Y 1 - Y0 (Whitecollar)
0.5
0.25
0.4
0.2
0.3
0.15
0
1
2
3
4
5
6
0.15
0.2
0.1
0.1
0.1
0
0.05
0
0.04
0
0.05
0.02
7
Decile of Cognitive
0.2
0.1
0.06
0.02
7
0.25
0.3
0.08
0.1
0.04
0
0.3
0.4
0.1
AR
Y
0.06
Y 1 - Y0 (Whitecollar)
0.5
1
0.12
0.2
0.08
0.1
8
9
0
10
1
2
3
4
5
6
7
8
9
10
0
Decile of Cognitive
Decile of Socio-Emotional
1
2
3
4
5
6
7
8
Y 1 - Y 0 (Whitecollar)
IN
0.4
0.3
0.2
0.1
0
0.2
0.18
0.16
0.3
0.14
3
5
4
6
9 10
7 8
itive
of Cogn
Decile
Y 1 - Y0 (Whitecollar)
0.5
0.7
0.6
0.4
0.5
0.3
0.4
0.12
0.2
0.1
0.08
0.1
0.06
0.04
0
Fraction
0.22
0.4
2
Y 1 - Y0 (Whitecollar)
0.24
Y 1 - Y0 (Whitecollar)
0.5
1
Fraction
Y 1 - Y0 (Whitecollar)
IM
0.5
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
0.2
0.3
0.1
0.2
0.1
0
0.02
1
2
3
4
5
6
7
8
9
10
Decile of Cognitive
0
1
2
3
4
5
6
7
8
9
10
9
10
Decile of Socio-Emotional
E. GED vs. GED with some College
PR
EL
9
Decile of Socio-Emotional
0.5
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
9 10
7 8
itive
of Cogn
Decile
Fraction
3
Y 1 - Y0 (Whitecollar)
0.18
2
DR
A
0.22
Fraction
1
Y 1 - Y0 (Whitecollar)
Y 1 - Y0 (Whitecollar)
0.4
0.1
0
0.2
6
2
Y 1 - Y 0 (Whitecollar)
0.2
0.24
5
0.5
0.2
FT
Y 1 - Y 0 (Whitecollar)
0.3
Y 1 - Y0 (Whitecollar)
4
0.6
0.3
D. Some College vs. 4-year college degree
0.4
0.4
3
0.7
Y 1 - Y0 (Whitecollar)
0.4
Decile of Cognitive
0.5
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
2
9 10
7 8
itive
of Cogn
Decile
0.5
0.05
Decile of Socio-Emotional
0
1
6
0.1
C. HS Graduate vs. College Enrollment
0.5
0.15
0.1
0.02
Decile of Cognitive
5
4
0
Decile of Socio-Emotional
Notes: Each panel in this figure studies the average effects of education on the outcome of interest. The effect is defined as
the differences in the outcome associated with two schooling levels (not necessarily final or terminal schooling levels). For
each panel, let Y0 and Y1 denotes the outcomes associated with schooling levels 0 and 1, respectively. Importantly, each
schooling level might provide the option to pursuing higher schooling levels. Final schooling levels do not allow for further
options. Final schooling levels are highlighted using bold letters. For each pair of schooling levels 0 and 1, the first figure
(top) presents E(Y1 − Y0 |dC , dSE ) where dC and dSE denote the cognitive and socio-emotional deciles computed from the
marginal distributions of cognitive and socio-emotional endowments. E(Y1 − Y0 |dC , dSE ) is computed for those who reach the
decision node involving a decision between levels 0 and 1. The second figure (bottom left) presents E(Y1 − Y0 |dC ) so that
the socio-emotional factor is integrated out. The bars in this86
figure displays, for a given decile of cognitive endowment, the
fraction of individuals visiting the node leading to the educational decision involving levels 0 and 1. The last figure (bottom
right) presents E(Y1 − Y0 |dSE ) as well as, for a given decile of socio-emotional endowment, the fraction of individuals visiting
the node leading to the educational decision involving levels 0 and 1.
0
Fraction
4
0.2
Fraction
3
0.25
0.2
Y 1 - Y0 (Whitecollar)
2
0.3
0.04
0.02
1
0.35
0.3
0.08
0.1
0.04
0
0.4
0.1
0.2
0.45
0.4
3
o
0.2
Y 1 - Y0 (Whitecollar)
0.5
2
no
0.2
Y 1 - Y0 (Whitecollar)
0.5
1
-d
0.3
9 10
7 8
itive
of Cogn
Decile
Fraction
0.16
6
5
4
Y 1 - Y0 (Whitecollar)
0.18
0.4
3
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Fraction
0.2
Y 1 - Y0 (Whitecollar)
0.5
2
Fraction
1
Y 1 - Y0 (Whitecollar)
Y 1 - Y0 (Whitecollar)
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Y 1 - Y0 (Whitecollar)
0
Fraction
0.4
tc
0.5
ite
B. HS Dropout vs. Getting a GED
Y 1 - Y 0 (Whitecollar)
Y 1 - Y 0 (Whitecollar)
A. Dropping from HS vs. Graduating from HS
Figure 19: Average Treatment Effect of Education on (Log) Wages at Age 30, by Decision Node
and Endowment Levels
0.1
0.08
0.12
0.2
0.1
1
2
3
4
5
6
7
8
9
10
0.1
1
2
3
4
5
6
7
8
9
-0.1
1
6
5
4
0.22
0.2
0.5
0.18
0.4
0.16
0.3
0.14
0
6
7
8
9
10
9
10
0
tc
ite
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1
-0.2
10 9
De
cil 8
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Y 1 - Y0 (Log-wages)
0.3
0.25
0.3
0.2
0.2
0.15
0.1
0.1
1
2
3
4
5
6
7
8
1
2
3
4
5
6
2
3
5
4
6
0
9 10
7 8
itive
of Cogn
Decile
Y 1 - Y0 (Log-wages)
0.6
0.25
0.5
0.4
0.2
0.3
0.15
0.2
-0.1
0.1
0
0.05
-0.1
7
8
9
-0.2
0
10
1
2
3
4
5
6
7
8
9
10
0
-0.2
Decile of Cognitive
0.05
Decile of Socio-Emotional
1
2
3
4
5
6
7
8
0.2
0.18
0.4
0.16
0.3
0.14
0.2
0.12
3
5
4
6
9 10
7 8
itive
of Cogn
Decile
Y 1 - Y0 (Log-wages)
0.6
0.7
0.5
0.6
0.4
0.5
0.3
0.4
0.2
0.1
0.1
0.08
0
0.06
0.04
-0.1
Fraction
0.22
0.5
2
Y 1 - Y0 (Log-wages)
0.24
Y 1 - Y0 (Log-wages)
0.6
1
Fraction
Y 1 - Y 0 (Log-wages)
Y 1 - Y0 (Log-wages)
IN
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1
-0.2
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
0.3
0.1
0.2
0
0.1
-0.1
0.02
-0.2
1
2
3
4
5
6
7
8
9
10
Decile of Cognitive
0
-0.2
1
2
3
4
5
6
7
8
9
10
9
10
Decile of Socio-Emotional
E. GED vs. GED with some College
IM
10
0.02
-0.2
Decile of Cognitive
PR
EL
9
Decile of Socio-Emotional
0.1
0.1
0
0.04
-0.1
1
0.4
0.06
0.02
5
8
0.5
0.08
AR
Y
0.1
0.06
4
7
0.1
0.08
3
6
0.6
0.12
0.2
0
0.04
0.24
Y 1 - Y0 (Log-wages)
0.6
0.1
2
5
9 10
7 8
itive
of Cogn
Decile
0.1
1
4
0.2
-0.1
-0.2
D. Some College vs. 4-year college degree
Fraction
3
0.12
-0.2
3
Y 1 - Y 0 (Log-wages)
0.14
-0.1
2
0
FT
0.16
0.2
0.3
0
Decile of Cognitive
Y 1 - Y0 (Log-wages)
0.18
0.3
0.4
0.2
0.05
DR
A
0.2
Fraction
0.22
2
Y 1 - Y0 (Log-wages)
Y 1 - Y 0 (Log-wages)
Y 1 - Y0 (Log-wages)
1
0.24
0.4
0.5
0.3
0.1
Decile of Socio-Emotional
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1
-0.2
10 9
De
cil 8
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Y 1 - Y0 (Log-wages)
0.4
0.1
-0.2
0
10
0.6
0.5
0.15
0
C. HS Graduate vs. College Enrollment
0.5
0.25
0.7
Y 1 - Y0 (Log-wages)
0.6
0.2
0.02
-0.2
0
Decile of Cognitive
0.6
0.3
0.2
0.04
-0.1
0.02
9 10
7 8
itive
of Cogn
Decile
0
Decile of Socio-Emotional
Notes: Each panel in this figure studies the average effects of education on the outcome of interest. The effect is defined as
the differences in the outcome associated with two schooling levels (not necessarily final or terminal schooling levels). For
each panel, let Y0 and Y1 denotes the outcomes associated with schooling levels 0 and 1, respectively. Importantly, each
schooling level might provide the option to pursuing higher schooling levels. Final schooling levels do not allow for further
options. Final schooling levels are highlighted using bold letters. For each pair of schooling levels 0 and 1, the first figure
(top) presents E(Y1 − Y0 |dC , dSE ) where dC and dSE denote the cognitive and socio-emotional deciles computed from the
marginal distributions of cognitive and socio-emotional endowments. E(Y1 − Y0 |dC , dSE ) is computed for those who reach the
decision node involving a decision between levels 0 and 1. The second figure (bottom left) presents E(Y1 − Y0 |dC ) so that
the socio-emotional factor is integrated out. The bars in this87
figure displays, for a given decile of cognitive endowment, the
fraction of individuals visiting the node leading to the educational decision involving levels 0 and 1. The last figure (bottom
right) presents E(Y1 − Y0 |dSE ) as well as, for a given decile of socio-emotional endowment, the fraction of individuals visiting
the node leading to the educational decision involving levels 0 and 1.
0
Fraction
-0.1
0.35
0.06
0
0.04
-0.2
0.4
0.3
0.08
0.1
0.06
0
0.4
6
Fraction
0.14
0.3
0.45
0.5
5
4
no
0.4
Y 1 - Y0 (Log-wages)
0.6
Fraction
0.1
0.16
3
Y 1 - Y0 (Log-wages)
0.2
0.18
0.5
2
o
0.12
0.2
Y 1 - Y0 (Log-wages)
0.6
1
Fraction
0.14
0.3
9 10
7 8
itive
of Cogn
Decile
-d
0.4
6
5
4
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1
-0.2
10 9
De
cil 8
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Y 1 - Y0 (Log-wages)
Y 1 - Y 0 (Log-wages)
0.16
3
Y 1 - Y0 (Log-wages)
0.18
0.5
2
Fraction
1
0.2
Y 1 - Y0 (Log-wages)
0.6
B. HS Dropout vs. Getting a GED
Fraction
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1
-0.2
10 9
De
cil 8
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Y 1 - Y0 (Log-wages)
Y 1 - Y0 (Log-wages)
Y 1 - Y 0 (Log-wages)
A. Dropping from HS vs. Graduating from HS
Figure 20: Average Treatment Effect of Education on Probability of Being Obese (2006), by
Decision Node and Endowment Levels
-0.3
-0.4
0.14
0.12
-0.1
0.2
Y 1 - Y0 (Obesity)
0.18
0.1
0.16
0.14
0
-0.1
0.08
0.3
7
8
9
10
1
2
3
4
5
6
7
8
9
1
-0.2
3
4
5
6
7
8
9
10
0.1
1
2
3
4
5
6
7
8
10
0
0.2
0.1
0
-0.1
-0.2
0.16
0
0.12
0.18
0
0.16
0.08
-0.1
0.02
7
8
9
10
0.3
0.25
0
-0.2
-0.1
0
1
2
3
4
5
6
8
9
0.2
Y 1 - Y0 (Obesity)
0.1
0.25
0
0.2
-0.1
0.15
-0.2
0.1
-0.3
-0.4
0
10
9 10
7 8
itive
of Cogn
Decile
0.05
0.05
0.02
7
Decile of Cognitive
-0.3
0.04
-0.4
6
0.1
0.06
-0.3
5
4
0.15
-0.2
0.08
3
0.2
0.1
AR
Y
0.04
-0.4
Y 1 - Y0 (Obesity)
0.1
0.12
0.06
-0.3
0.2
0.14
0.1
-0.2
0.22
0.2
0.14
-0.1
0.24
Y 1 - Y0 (Obesity)
0.1
2
-0.4
1
2
3
4
5
6
7
8
9
10
0
Decile of Cognitive
Decile of Socio-Emotional
1
2
3
4
5
6
7
8
Y 1 - Y 0 (Obesity)
IN
0.1
0
-0.1
-0.2
-0.3
-0.4
0.22
0.2
0.18
0
0.16
3
5
4
6
9 10
7 8
itive
of Cogn
Decile
0.2
Y 1 - Y0 (Obesity)
0.7
0.1
0.6
0
0.5
-0.1
0.4
0.14
-0.1
0.12
0.1
-0.2
0.08
0.06
-0.3
0.04
0.02
-0.4
1
2
3
4
5
6
7
8
9
10
Decile of Cognitive
0
Fraction
0.24
Y 1 - Y0 (Obesity)
0.1
2
Y 1 - Y0 (Obesity)
0.2
Fraction
Y 1 - Y0 (Obesity)
IM
0.2
1
0.3
-0.2
0.2
-0.3
0.1
-0.4
1
2
3
4
5
6
7
8
9
10
10
Decile of Socio-Emotional
E. GED vs. GED with some College
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
9
0
Decile of Socio-Emotional
Notes: Each panel in this figure studies the average effects of education on the outcome of interest. The effect is defined as
the differences in the outcome associated with two schooling levels (not necessarily final or terminal schooling levels). For
each panel, let Y0 and Y1 denotes the outcomes associated with schooling levels 0 and 1, respectively. Importantly, each
schooling level might provide the option to pursuing higher schooling levels. Final schooling levels do not allow for further
options. Final schooling levels are highlighted using bold letters. For each pair of schooling levels 0 and 1, the first figure
(top) presents E(Y1 − Y0 |dC , dSE ) where dC and dSE denote the cognitive and socio-emotional deciles computed from the
marginal distributions of cognitive and socio-emotional endowments. E(Y1 − Y0 |dC , dSE ) is computed for those who reach the
decision node involving a decision between levels 0 and 1. The second figure (bottom left) presents E(Y1 − Y0 |dC ) so that
the socio-emotional factor is integrated out. The bars in this88
figure displays, for a given decile of cognitive endowment, the
fraction of individuals visiting the node leading to the educational decision involving levels 0 and 1. The last figure (bottom
right) presents E(Y1 − Y0 |dSE ) as well as, for a given decile of socio-emotional endowment, the fraction of individuals visiting
the node leading to the educational decision involving levels 0 and 1.
0
Fraction
0.2
1
Fraction
5
4
Y 1 - Y0 (Obesity)
0.18
3
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
9 10
7 8
itive
of Cogn
Decile
Fraction
0.22
2
6
-0.4
DR
A
0.24
Fraction
1
0.2
PR
EL
9
Decile of Socio-Emotional
-0.3
Y 1 - Y0 (Obesity)
Y 1 - Y0 (Obesity)
2
Y 1 - Y 0 (Obesity)
-0.1
6
0.2
-0.4
0
FT
Y 1 - Y 0 (Obesity)
0
Y 1 - Y0 (Obesity)
5
0.3
-0.3
D. Some College vs. 4-year college degree
0.1
0.1
4
0.5
Decile of Cognitive
0.2
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
3
0.6
0
0.05
-0.3
2
0.1
Decile of Socio-Emotional
-0.4
1
0.7
Y 1 - Y0 (Obesity)
0.1
-0.2
-0.3
C. HS Graduate vs. College Enrollment
0.2
0.2
0.15
0
10
9 10
7 8
itive
of Cogn
Decile
0.4
-0.2
-0.4
Decile of Cognitive
6
-0.1
Y 1 - Y0 (Obesity)
6
5
4
0.2
0.02
0
3
0.25
-0.1
-0.4
5
ite
0.35
0.04
-0.3
0.02
4
0.4
0.06
-0.4
3
0.45
2
0
0.08
-0.2
0.04
2
Y 1 - Y0 (Obesity)
0.1
0.1
0.06
-0.3
0.2
0.12
0.1
-0.2
0.2
1
o
0
De
-d
0.16
6
5
4
Y 1 - Y0 (Obesity)
0.18
3
Fraction
0.2
Y 1 - Y0 (Obesity)
2
Fraction
Y 1 - Y0 (Obesity)
1
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
9 10
ve
7 8
Cogniti
cile of
Fraction
-0.3
-0.4
0.1
1
0
-0.2
tc
-0.2
0.1
-0.1
no
-0.1
0.2
Fraction
0
Y 1 - Y0 (Obesity)
Y 1 - Y 0 (Obesity)
0.1
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
0.2
B. HS Dropout vs. Getting a GED
0.2
Y 1 - Y0 (Obesity)
Y 1 - Y 0 (Obesity)
A. Dropping from HS vs. Graduating from HS
Figure 21: Average Treatment Effect of Probability of Regular Exercise (2006), by Decision
Node and Endowment Levels
0.12
0.1
-0.1
0.18
0.16
0.1
0.14
0.12
0
0.1
-0.1
0.08
0.06
-0.2
0.2
Y 1 - Y 0 (Reg. Exercise)
0.2
-0.3
-0.3
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
-0.3
1
3
4
5
ite
0.5
0
0.4
-0.1
0.3
-0.2
0.2
6
7
8
9
10
0.1
-0.3
0
1
2
3
4
5
6
7
8
Y 1 - Y 0 (Reg. Exercise)
0
0.14
0.12
-0.1
3
0.1
5
4
0.24
Y 1 - Y 0 (Reg. Exercise)
0.2
0.22
0.2
0.1
0.18
0.16
0
0.14
0.12
-0.1
10
0
0.04
-0.3
0.02
7
8
9
10
0
1
2
3
4
5
6
0.02
8
9
0.3
0.25
0.2
2
3
5
4
6
9 10
7 8
itive
of Cogn
Decile
Y 1 - Y 0 (Reg. Exercise)
0.2
0.25
0.1
0.2
0
0.15
-0.1
0.15
-0.1
-0.2
0.1
-0.2
-0.3
0.05
-0.3
0
10
Y 1 - Y 0 (Reg. Exercise)
1
0.1
0.04
7
Decile of Cognitive
-0.3
0
0.06
-0.3
0
-0.1
-0.2
0.1
0.08
-0.2
AR
Y
0.06
0.1
0.2
0.1
0.08
-0.2
1
2
3
4
5
6
7
8
9
10
0
Decile of Cognitive
Decile of Socio-Emotional
0.05
1
2
3
4
5
6
7
8
0.1
0
-0.1
-0.2
-0.3
0.22
0.2
0.1
0.18
0.16
0
0.14
0.12
-0.1
0.1
0.08
-0.2
0.06
0.04
-0.3
0.02
1
2
3
4
5
6
7
8
9
10
Decile of Cognitive
0
3
5
4
6
9 10
7 8
itive
of Cogn
Decile
Y 1 - Y 0 (Reg. Exercise)
0.2
0.7
0.6
0.1
Fraction
0.24
Y 1 - Y 0 (Reg. Exercise)
0.2
2
Y 1 - Y0 (Reg. Exercise)
Y 1 - Y0 (Reg. Exercise)
1
Fraction
Y 1 - Y 0 (Reg. Exercise)
IN
IM
0.2
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
0.5
0
0.4
-0.1
0.3
-0.2
0.2
0.1
-0.3
1
2
3
4
5
6
7
8
9
10
9
10
Decile of Socio-Emotional
E. GED vs. GED with some College
PR
EL
9
Decile of Socio-Emotional
0.2
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
9 10
7 8
itive
of Cogn
Decile
Y 1 - Y0 (Reg. Exercise)
0.16
2
6
Fraction
0.22
Fraction
Y 1 - Y0 (Reg. Exercise)
1
0.18
6
2
DR
A
-0.3
0.2
5
0.6
0.1
FT
-0.1
-0.2
Y 1 - Y0 (Reg. Exercise)
Y 1 - Y 0 (Reg. Exercise)
0
0.24
4
0.7
Y 1 - Y 0 (Reg. Exercise)
0.2
D. Some College vs. 4-year college degree
0.1
Y 1 - Y 0 (Reg. Exercise)
3
9 10
7 8
itive
of Cogn
Decile
Decile of Cognitive
0.2
0.1
2
0.05
Decile of Socio-Emotional
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
1
0.15
0
10
6
0.1
C. HS Graduate vs. College Enrollment
0.2
0.2
-0.2
0.02
Decile of Cognitive
5
4
0
Decile of Socio-Emotional
Notes: Each panel in this figure studies the average effects of education on the outcome of interest. The effect is defined as
the differences in the outcome associated with two schooling levels (not necessarily final or terminal schooling levels). For
each panel, let Y0 and Y1 denotes the outcomes associated with schooling levels 0 and 1, respectively. Importantly, each
schooling level might provide the option to pursuing higher schooling levels. Final schooling levels do not allow for further
options. Final schooling levels are highlighted using bold letters. For each pair of schooling levels 0 and 1, the first figure
(top) presents E(Y1 − Y0 |dC , dSE ) where dC and dSE denote the cognitive and socio-emotional deciles computed from the
marginal distributions of cognitive and socio-emotional endowments. E(Y1 − Y0 |dC , dSE ) is computed for those who reach the
decision node involving a decision between levels 0 and 1. The second figure (bottom left) presents E(Y1 − Y0 |dC ) so that
the socio-emotional factor is integrated out. The bars in this89
figure displays, for a given decile of cognitive endowment, the
fraction of individuals visiting the node leading to the educational decision involving levels 0 and 1. The last figure (bottom
right) presents E(Y1 − Y0 |dSE ) as well as, for a given decile of socio-emotional endowment, the fraction of individuals visiting
the node leading to the educational decision involving levels 0 and 1.
0
Fraction
4
0.25
-0.1
Fraction
3
3
Y 1 - Y0 (Reg. Exercise)
2
0
2
0.3
0.04
0.02
1
0.35
0
0.06
0.04
0.45
0.4
0.1
0.08
-0.2
Y 1 - Y 0 (Reg. Exercise)
0.2
1
o
0.14
0
De
-d
0.16
6
5
4
Y 1 - Y0 (Reg. Exercise)
0.18
3
-0.3
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
9 10
ve
7 8
Cogniti
cile of
Fraction
0.2
Y 1 - Y 0 (Reg. Exercise)
0.1
2
Fraction
Y 1 - Y0 (Reg. Exercise)
1
-0.2
Fraction
-0.3
0
tc
-0.2
0.1
-0.1
no
-0.1
0.2
Fraction
0
Y 1 - Y0 (Reg. Exercise)
Y 1 - Y 0 (Reg. Exercise)
0.1
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
0.2
B. HS Dropout vs. Getting a GED
0.2
Y 1 - Y0 (Reg. Exercise)
Y 1 - Y 0 (Reg. Exercise)
A. Dropping from HS vs. Graduating from HS
Figure 22: Average Treatment Effect of Education on BMI, by Decision Node and Endowment
Levels
1
0
-1
-2
-3
0.14
0.18
0.16
0.14
0
0.12
5
6
7
8
9
10
1
2
3
4
5
6
7
Decile of Cognitive
8
9
10
0.4
-2
1
6
7
8
9
10
0.1
1
2
3
4
5
6
7
8
10
0
Y 1 - Y 0 (BMI)
1
0
-1
5
4
0.18
-1
-2
AR
Y
-3
9
10
0
1
2
3
4
5
6
5
4
6
9 10
7 8
itive
of Cogn
Decile
Y 1 - Y0 (BMI)
1
0.25
0
0.2
-1
0.15
-2
0.05
0.1
-3
0.05
0.02
7
Decile of Cognitive
-3
0.04
0.02
8
0.1
0.06
0.04
3
0.15
-2
0.08
0.06
2
0.2
0.1
0.08
7
0.25
-1
0.12
0.1
1
0.3
0.14
0.12
-3
Y 1 - Y0 (BMI)
1
0
0.16
0.14
-2
0.22
0.2
0
0.16
-1
0.24
Y 1 - Y0 (BMI)
1
Y 1 - Y0 (BMI)
3
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
9 10
7 8
itive
of Cogn
Decile
Fraction
2
6
DR
A
0.22
Fraction
1
0.24
0
8
9
0
10
1
2
3
4
5
6
7
8
9
10
0
Decile of Cognitive
Decile of Socio-Emotional
1
2
3
4
5
6
7
8
Y 1 - Y 0 (BMI)
IN
-1
-2
-3
0.2
0.18
0.16
3
5
4
6
9 10
7 8
itive
of Cogn
Decile
Y 1 - Y0 (BMI)
1
0.7
0.6
0
0.5
0.14
-1
0.12
0.4
-1
0.1
-2
0.08
0.3
-2
0.2
0.06
-3
0.04
Fraction
0.22
0
2
Y 1 - Y0 (BMI)
0.24
Y 1 - Y0 (BMI)
1
1
Fraction
Y 1 - Y0 (BMI)
IM
1
0
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
-3
0.1
0.02
1
2
3
4
5
6
7
8
9
10
Decile of Cognitive
0
1
2
3
4
5
6
7
8
9
10
9
10
Decile of Socio-Emotional
E. GED vs. GED with some College
PR
EL
9
Decile of Socio-Emotional
-3
Y 1 - Y0 (BMI)
Y 1 - Y0 (BMI)
5
-2
0.18
6
4
FT
Y 1 - Y 0 (BMI)
-1
0.2
5
3
0
D. Some College vs. 4-year college degree
1
Y 1 - Y0 (BMI)
4
0.2
-3
Decile of Cognitive
0
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
3
2
Decile of Socio-Emotional
-3
2
0.1
0.05
-2
1
0.3
-2
0.15
-3
C. HS Graduate vs. College Enrollment
1
0.5
0
Decile of Socio-Emotional
Notes: Each panel in this figure studies the average effects of education on the outcome of interest. The effect is defined as
the differences in the outcome associated with two schooling levels (not necessarily final or terminal schooling levels). For
each panel, let Y0 and Y1 denotes the outcomes associated with schooling levels 0 and 1, respectively. Importantly, each
schooling level might provide the option to pursuing higher schooling levels. Final schooling levels do not allow for further
options. Final schooling levels are highlighted using bold letters. For each pair of schooling levels 0 and 1, the first figure
(top) presents E(Y1 − Y0 |dC , dSE ) where dC and dSE denote the cognitive and socio-emotional deciles computed from the
marginal distributions of cognitive and socio-emotional endowments. E(Y1 − Y0 |dC , dSE ) is computed for those who reach the
decision node involving a decision between levels 0 and 1. The second figure (bottom left) presents E(Y1 − Y0 |dC ) so that
the socio-emotional factor is integrated out. The bars in this90
figure displays, for a given decile of cognitive endowment, the
fraction of individuals visiting the node leading to the educational decision involving levels 0 and 1. The last figure (bottom
right) presents E(Y1 − Y0 |dSE ) as well as, for a given decile of socio-emotional endowment, the fraction of individuals visiting
the node leading to the educational decision involving levels 0 and 1.
0
Fraction
4
0.6
-1
Fraction
3
0
0.7
Y 1 - Y0 (BMI)
0
Y 1 - Y0 (BMI)
2
0.02
0
9 10
7 8
itive
of Cogn
Decile
0.2
0.04
0.02
6
1
0.25
-1
0.06
-3
5
4
0.3
0.08
0.04
1
0.35
0
0.1
-2
0.06
-3
0.4
3
o
-1
0.08
-2
0.45
0.12
0.1
-1
Y 1 - Y0 (BMI)
1
2
no
0.2
Y 1 - Y0 (BMI)
1
1
Fraction
9 10
7 8
itive
of Cogn
Decile
-d
0
6
5
4
Y 1 - Y0 (BMI)
0.16
3
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Fraction
0.18
2
Fraction
0.2
Y 1 - Y0 (BMI)
1
1
Y 1 - Y0 (BMI)
Y 1 - Y0 (BMI)
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Y 1 - Y0 (BMI)
-3
tc
-2
Fraction
1
0
-1
ite
B. HS Dropout vs. Getting a GED
Y 1 - Y 0 (BMI)
Y 1 - Y 0 (BMI)
A. Dropping from HS vs. Graduating from HS
Figure 23: Average Treatment Effect of Education on Physical Health (PCS-12) at Age 40, by
Decision Node and Endowment Levels
0.14
0.12
0.1
0.18
0.16
0.2
0.14
0
0.12
0.1
-0.2
0.08
0.04
-0.6
1
2
3
4
5
6
7
8
9
10
-0.4
1
2
3
4
5
6
7
8
9
1
0.14
0.12
3
5
4
0.24
Y 1 - Y0 (SF-12 Physical)
0.4
0.08
0.22
0.2
0.2
0.18
0.16
0
0.14
0.12
-0.2
0.1
7
8
9
10
7
8
9
10
0.4
-0.2
0.3
0.2
-0.6
0
0.1
-0.8
1
2
3
4
5
6
7
8
-0.6
0
0
0
-0.4
-0.6
-0.8
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Y 1 - Y0 (SF-12 Physical)
1
2
3
4
5
6
1
0.3
0.25
0
0.2
-0.2
0.15
-0.4
0.1
-0.6
0.05
2
3
5
4
6
9 10
7 8
itive
of Cogn
Decile
Y 1 - Y0 (SF-12 Physical)
0.4
0.25
0.2
0.2
0
0.15
-0.2
0.1
-0.4
-0.6
0.05
0.02
7
8
9
-0.8
0
10
1
2
3
4
5
6
7
8
9
10
0
Decile of Cognitive
Decile of Socio-Emotional
-0.8
1
2
3
4
5
6
7
8
0.2
0
-0.2
-0.4
-0.6
0.22
0.2
0.2
0.18
0.16
0
0.14
0.12
-0.2
3
5
4
6
9 10
7 8
itive
of Cogn
Decile
Y 1 - Y0 (SF-12 Physical)
0.4
0.7
0.6
0.2
0.5
0
0.4
-0.2
0.1
0.08
-0.4
0.3
-0.4
0.2
0.06
-0.6
0.04
Fraction
0.24
Y 1 - Y0 (SF-12 Physical)
0.4
2
Y 1 - Y0 (SF-12 Physical)
1
Fraction
Y 1 - Y 0 (SF-12 Physical)
Y 1 - Y0 (SF-12 Physical)
IN
0.4
-0.8
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
-0.6
0.1
0.02
-0.8
1
2
3
4
5
6
7
8
9
10
Decile of Cognitive
0
-0.8
1
2
3
4
5
6
7
8
9
10
9
10
Decile of Socio-Emotional
E. GED vs. GED with some College
IM
10
0.2
0.2
0.04
-0.8
Decile of Cognitive
PR
EL
9
Decile of Socio-Emotional
-0.2
0.06
0.02
6
0.5
0
0.4
0.4
0.08
-0.4
AR
Y
0.04
5
6
0.1
0.06
4
5
Y 1 - Y 0 (SF-12 Physical)
0.16
3
0.6
0.2
FT
0.18
2
Y 1 - Y0 (SF-12 Physical)
0.2
2
4
10
8 9
itive
6 7
of Cogn
Decile
Fraction
0.22
Fraction
1
0.24
1
3
DR
A
-0.8
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Y 1 - Y0 (SF-12 Physical)
Y 1 - Y 0 (SF-12 Physical)
Y 1 - Y0 (SF-12 Physical)
-0.6
-0.8
0.7
Y 1 - Y0 (SF-12 Physical)
0.4
D. Some College vs. 4-year college degree
-0.4
-0.6
9 10
7 8
itive
of Cogn
Decile
Decile of Cognitive
-0.2
-0.4
2
Decile of Socio-Emotional
0
-0.2
6
0.05
0.2
0
0.1
-0.8
0
10
5
4
-0.4
0.15
-0.6
0.4
Y 1 - Y0 (SF-12 Physical)
0.25
0.02
-0.8
0
3
0.2
C. HS Graduate vs. College Enrollment
0.2
0.3
-0.2
0.04
-0.6
Decile of Cognitive
0.4
ite
0.35
0
0.06
0.02
-0.8
0.4
0.08
-0.4
0.06
0.45
0.2
2
0
Decile of Socio-Emotional
Notes: Each panel in this figure studies the average effects of education on the outcome of interest. The effect is defined as
the differences in the outcome associated with two schooling levels (not necessarily final or terminal schooling levels). For
each panel, let Y0 and Y1 denotes the outcomes associated with schooling levels 0 and 1, respectively. Importantly, each
schooling level might provide the option to pursuing higher schooling levels. Final schooling levels do not allow for further
options. Final schooling levels are highlighted using bold letters. For each pair of schooling levels 0 and 1, the first figure
(top) presents E(Y1 − Y0 |dC , dSE ) where dC and dSE denote the cognitive and socio-emotional deciles computed from the
marginal distributions of cognitive and socio-emotional endowments. E(Y1 − Y0 |dC , dSE ) is computed for those who reach the
decision node involving a decision between levels 0 and 1. The second figure (bottom left) presents E(Y1 − Y0 |dC ) so that
the socio-emotional factor is integrated out. The bars in this91
figure displays, for a given decile of cognitive endowment, the
fraction of individuals visiting the node leading to the educational decision involving levels 0 and 1. The last figure (bottom
right) presents E(Y1 − Y0 |dSE ) as well as, for a given decile of socio-emotional endowment, the fraction of individuals visiting
the node leading to the educational decision involving levels 0 and 1.
0
Fraction
-0.4
Y 1 - Y0 (SF-12 Physical)
0.4
Fraction
-0.2
0.2
Y 1 - Y0 (SF-12 Physical)
0.4
1
Y 1 - Y0 (SF-12 Physical)
0
9 10
7 8
itive
of Cogn
Decile
-0.8
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
o
0.16
6
5
4
-0.6
-d
0.18
3
Y 1 - Y0 (SF-12 Physical)
0.2
0.2
2
Fraction
1
Fraction
-0.8
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
-0.4
Fraction
-0.6
0
tc
-0.4
0.2
-0.2
no
-0.2
0.4
Fraction
0
Y 1 - Y0 (SF-12 Physical)
Y 1 - Y 0 (SF-12 Physical)
0.2
Y 1 - Y0 (SF-12 Physical)
0.4
B. HS Dropout vs. Getting a GED
0.4
Y 1 - Y0 (SF-12 Physical)
Y 1 - Y0 (SF-12 Physical)
Y 1 - Y 0 (SF-12 Physical)
A. Dropping from HS vs. Graduating from HS
Figure 24: Average Treatment Effect of Education on Pearlin’s “Personal Mastery Scale”
(1992), by Decision Node and Endowment Levels
0.4
0.2
0.8
0.6
0.4
0.2
0
0.14
0.18
0.16
0.6
0.14
0.12
0.08
6
7
8
9
10
0.02
0
0
1
2
3
4
5
6
7
Decile of Cognitive
8
9
10
1
0.2
4
5
6
7
8
9
10
0.1
1
2
3
4
5
6
7
8
0.14
0.4
0.12
6
5
4
10
0
0.8
0.6
0.4
0.2
0.08
0.24
Y 1 - Y0 (Pearlin)
0.8
0.22
0.2
0.18
0.6
0.16
0.2
10
0.2
2
3
5
4
6
9 10
7 8
itive
of Cogn
Decile
Y 1 - Y0 (Pearlin)
0.8
0.25
0.6
0.2
0.4
0.15
0.15
0.2
0.2
0
0
1
2
3
4
5
6
0
0.04
0.05
0
0.05
0.02
7
Decile of Cognitive
0.1
0.1
0.06
AR
Y
9
0.25
0.4
0.08
0.02
8
0.3
0.6
0.1
0.04
7
1
0.12
0.06
0
Y 1 - Y0 (Pearlin)
0.8
0.14
0.4
0.1
0.2
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
9 10
7 8
itive
of Cogn
Decile
Y 1 - Y0 (Pearlin)
0.16
3
Fraction
0.22
2
DR
A
0.24
Fraction
1
0.18
8
9
0
10
1
2
3
4
5
6
7
8
9
10
0
Decile of Cognitive
Decile of Socio-Emotional
1
2
3
4
5
6
7
8
Y 1 - Y 0 (Pearlin)
IN
0.6
0.4
0.2
0
0.2
0.18
0.16
0.14
0.4
3
5
4
6
9 10
7 8
itive
of Cogn
Decile
Y 1 - Y0 (Pearlin)
0.8
0.7
0.6
0.6
0.5
0.4
0.4
0.12
0.1
0.2
0.08
0.3
0.2
0.2
0.06
0
0.04
Fraction
0.22
0.6
2
Y 1 - Y0 (Pearlin)
0.24
Y 1 - Y0 (Pearlin)
0.8
1
Fraction
Y 1 - Y0 (Pearlin)
IM
0.8
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
0
0.1
0.02
1
2
3
4
5
6
7
8
9
10
Decile of Cognitive
0
1
2
3
4
5
6
7
8
9
10
9
10
Decile of Socio-Emotional
E. GED vs. GED with some College
PR
EL
9
Decile of Socio-Emotional
0
Y 1 - Y0 (Pearlin)
Y 1 - Y0 (Pearlin)
3
Y 1 - Y 0 (Pearlin)
0.2
6
2
0
FT
Y 1 - Y 0 (Pearlin)
0.4
0.2
5
0.3
0
D. Some College vs. 4-year college degree
0.6
Y 1 - Y0 (Pearlin)
4
0.4
Decile of Cognitive
0.8
0.6
3
0.5
0.4
0.1
Decile of Socio-Emotional
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
2
0.6
0.6
0.05
0
1
0.7
Y 1 - Y0 (Pearlin)
0.8
0.2
0
C. HS Graduate vs. College Enrollment
0.8
9 10
7 8
itive
of Cogn
Decile
0.15
0.04
0.02
6
0
Decile of Socio-Emotional
Notes: Each panel in this figure studies the average effects of education on the outcome of interest. The effect is defined as
the differences in the outcome associated with two schooling levels (not necessarily final or terminal schooling levels). For
each panel, let Y0 and Y1 denotes the outcomes associated with schooling levels 0 and 1, respectively. Importantly, each
schooling level might provide the option to pursuing higher schooling levels. Final schooling levels do not allow for further
options. Final schooling levels are highlighted using bold letters. For each pair of schooling levels 0 and 1, the first figure
(top) presents E(Y1 − Y0 |dC , dSE ) where dC and dSE denote the cognitive and socio-emotional deciles computed from the
marginal distributions of cognitive and socio-emotional endowments. E(Y1 − Y0 |dC , dSE ) is computed for those who reach the
decision node involving a decision between levels 0 and 1. The second figure (bottom left) presents E(Y1 − Y0 |dC ) so that
the socio-emotional factor is integrated out. The bars in this92
figure displays, for a given decile of cognitive endowment, the
fraction of individuals visiting the node leading to the educational decision involving levels 0 and 1. The last figure (bottom
right) presents E(Y1 − Y0 |dSE ) as well as, for a given decile of socio-emotional endowment, the fraction of individuals visiting
the node leading to the educational decision involving levels 0 and 1.
0
Fraction
5
0.2
0.06
0
5
4
0.2
Fraction
4
0.25
Y 1 - Y0 (Pearlin)
3
0.3
0.4
0.2
0.04
2
0.35
0.1
0.06
1
0.6
0.4
0.08
0
0.4
0.12
0.1
0.2
0.45
3
o
0.4
Y 1 - Y0 (Pearlin)
0.8
2
no
0.2
Y 1 - Y0 (Pearlin)
0.8
1
Fraction
9 10
7 8
itive
of Cogn
Decile
-d
0.6
6
5
4
Y 1 - Y0 (Pearlin)
0.16
3
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Fraction
0.18
2
Fraction
0.2
Y 1 - Y0 (Pearlin)
0.8
1
Y 1 - Y0 (Pearlin)
Y 1 - Y0 (Pearlin)
10
De 9 8
cil
eo 7
fS 6
oc 5
io- 4
Em 3
otio 2
na 1
l
Y 1 - Y0 (Pearlin)
0
Fraction
0.6
tc
0.8
ite
B. HS Dropout vs. Getting a GED
Y 1 - Y 0 (Pearlin)
Y 1 - Y 0 (Pearlin)
A. Dropping from HS vs. Graduating from HS