[for online publication] web appendix to “socioemotional
TRANSCRIPT
[FOR ONLINE PUBLICATION]
Web Appendix to “Socioemotional Skills, Education, and Health-Related
Outcomes of High-Ability Individuals”1
Peter Savelyev2 Kegon T. K. Tan3
The College of William & Mary The University of Rochester
November 30, 2017
1A version of this paper was presented to the 6th Annual Health Econometrics Workshop in Toronto; tothe 5th ASHEcon Conference in Los-Angeles; to the IZA/OECD/World Bank Workshop on Cognitive andNoncognitive Skills in Bertinoro, Italy; to the Singapore Economic Review Conference; to the EmpiricalMicro Lunch at the University of Wisconsin at Madison; and to the Vanderbilt Empirical Applied MicroWork-In-Progress Lunch. We thank participants of these meetings for their productive feedback. We arealso grateful to Laura Argys, Gabriella Conti, Thomas Deleire, Evan Elmore, Erik Meijer, Frank Sloan, ChrisTaber, Benjamin Ward, and journal referees for their comments and suggestions, which greatly contributedto progress with this paper. Atticus Bolyard provided excellent proofreading of the manuscript. PeterSavelyev gratefully acknowledges research support from the College of William and Mary, the Grey Fund,and the ERC at the University of Chicago. Kegon Tan gratefully acknowledges the support of the HumanCapital and Economic Opportunity Global Working Group sponsored by the Institute for New EconomicThinking. The authors have no potential conflicts of interest. Sponsors of this research did not participate instudy design, collection, analysis, interpretation of data, or in writing the manuscript. The Terman data areprovided by the ICPSR, Ann Arbor, MI.
2Peter Savelyev, the corresponding author, is an Assistant Professor of Economics at The College ofWilliam & Mary, Research Assistant Professor at Vanderbilt University, and Research Affiliate at IZA.Address: Economics Department, College of William & Mary, 300 James Blair Drive, Tyler Hall Room 317,Williamsburg, VA 23185, USA. Email: [email protected]. Phone: 1(757)221-2371; Fax: 1(757)221-1175.
3Kegon Tan is an Assistant Professor of Economics at the University of Rochester. Address:280 Hutchison Road, Box 270156, University of Rochester, Rochester, NY 14627, USA. Email:[email protected].
Contents
A DETAILED REGRESSION RESULTS 1
B FACTOR ANALYSIS 10
B.1 Exploratory Factor Analysis (EFA) . . . . . . . . . . . . . . . . . . . . . . . . . . 10
B.2 Confirmatory Factor Analysis (CFA) . . . . . . . . . . . . . . . . . . . . . . . . . 11
C SUPPLEMENTARY RESULTS 17
References 28
A DETAILED REGRESSION RESULTS
In order to present a large number of results effectively, the main paper contains only summary
regression tables, which show only statistically significant regression coefficients and levels of
significance denoted by asterisks. This Appendix provides the reader with a full set of p-values,
both before and after the stepdown adjustment.1 The adjusted p-values are more conservative:
they are never below the unadjusted ones, which is a property of the stepdown procedure. We also
show joint tests (effects of skills and education tested jointly for each outcome) and do stepdown
adjustment on the joint tests since joint tests are multiple. The adjusted joint tests are rejected in
most cases. All substantive results of these tables are already discussed in the main paper using
summary tables.
1The stepdown procedure involves ordering hypotheses by the absolute value of the t-statistic. We do so in ourcodes for every type of regressor, but for readers’ convenience we show hypotheses in their usual order in the outcometables.
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es:S
tatis
tical
lysi
gnifi
cant
p-v
alue
sat
the
10%
leve
lare
bold
ed.S
tepd
own
adju
stm
enti
sno
tper
form
edfo
rthi
sta
ble
sinc
eou
tcom
esof
each
type
are
mea
sure
don
lyon
ceov
ertim
e,an
dso
ther
eis
nom
ultip
lehy
poth
esis
test
ing
for
thes
eou
tcom
esw
ithre
spec
tto
year
sof
obse
rvat
ion.
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step
dow
nad
just
men
tfor
“eve
rsm
oked
”in
Tabl
eA
-7,a
spa
rtof
the
adju
stm
entf
ora
grou
pof
vari
able
ssu
mm
ariz
ing
beha
vior
over
the
lifet
ime.
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imat
ion
isco
nditi
onal
onba
ckgr
ound
vari
able
spr
esen
ted
inTa
ble
4of
the
mai
npa
per.
Cal
cula
tions
are
base
don
the
Term
anda
ta.
5
Tabl
eA
-5:G
roup
Mem
bers
hip
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es:
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istic
ally
sign
ifica
ntp
-val
ues
atth
e10
%le
vela
rebo
lded
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-val
ues
are
calc
ulat
edus
ing
boot
stra
pte
chni
ques
,and
furt
her
adju
sted
usin
gth
est
epdo
wn
proc
edur
e(R
oman
oan
dW
olf,
2005
).E
stim
atio
nis
cond
ition
alon
back
grou
ndva
riab
les
pres
ente
din
Tabl
e4
ofth
em
ain
pape
r.C
alcu
latio
nsar
eba
sed
onth
eTe
rman
data
.
6
Tabl
eA
-6:M
arri
age
Stat
us
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Not
es:
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istic
ally
sign
ifica
ntp
-val
ues
atth
e10
%le
vela
rebo
lded
.p
-val
ues
are
calc
ulat
edus
ing
boot
stra
pte
chni
ques
,and
furt
her
adju
sted
usin
gth
est
epdo
wn
proc
edur
e(R
oman
oan
dW
olf,
2005
).E
stim
atio
nis
cond
ition
alon
back
grou
ndva
riab
les
pres
ente
din
Tabl
e4
ofth
em
ain
pape
r.C
alcu
latio
nsar
eba
sed
onth
eTe
rman
data
.
7
Table A-7: Lifetime Outcomes
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Notes: Statistically significant p-values at the 10% level are bolded. p-values are calculated using bootstrap techniques,and further adjusted using the stepdown procedure (Romano and Wolf, 2005). Estimation is conditional on backgroundvariables presented in Table 4 of the main paper. Calculations are based on the Terman data.
8
Table A-8: Midlife Outcomes
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Notes: Midlife outcomes represent year 1960, which correspond approximately to age 50. Statistically significantp-values at the 10% level are bolded. p-values are calculated using bootstrap techniques, and further adjusted usingthe stepdown procedure (Romano and Wolf, 2005). Estimation is conditional on background variables presented inTable 4 of the main paper. Calculations are based on the Terman data.
9
B FACTOR ANALYSIS
In this section we present a justification for the five-factor measurement system used in the esti-
mated model. Factors that are close to the Big Five were first identified in the Terman data by
psychologists (Martin and Friedman, 2000; Martin et al., 2007), but these authors did not docu-
ment their factor analysis. To be sure of our factor model specification, we reanalyse the measures
and find factors that are similar to those obtained by psychologists.
This analysis builds on results from Savelyev (2017), who analyzes the 1922 measures in search
of childhood factors related to the Big Five personality taxonomy and finds three such factors,
namely childhood Conscientiousness, Openness, and Extraversion. This paper complements the
1922 measures with measures of Agreeableness and Neuroticism from 1940 to complete the Big
Five. It would be ideal to base these two additional factors on 1922 measures, but such data are not
available. Previous research by psychologists based on the same data also did not find satisfying
measures of Agreeableness and Neuroticism among the 1922 measures.
Factor analysis is broken into two standard parts: (1) the exploratory factor analysis (EFA)
and (2) the confirmatory factor analysis (CFA).2 EFA establishes the number of factors and the
clustering of measures by factors. CFA tests the specification and confirms a factor model with
exclusion restrictions that are implied by EFA. The final confirmed factor model is embedded as a
measurement system in the main statistical model in the paper.
B.1 Exploratory Factor Analysis (EFA)
EFA establishes the number of latent factors behind a set of available measures. Our set of mea-
sures includes measures of childhood Conscientiousness, Openness, and Extraversion, as estab-
lished in Savelyev (2017), plus available 1940 measures of Agreeableness and Neuroticism, as
suggested by psychologists (Martin et al., 2007).
While we are guided by the prior papers to expect five latent factors, we still conduct an analysis
2See, for instance, the Web appendix to Savelyev (2017) available online for a detailed discussion aimed ateconomists of technicalities related to exploratory and confirmatory factor analyses.
10
of the number of factors as a precaution against possible model misspecification. Our choice of
the number of factors is informed by a variety of tests typically used for this purpose, including the
scree test3 (see Figure B-1), the Horn’s test4, the minimum average partial correlation criterion,5
and the Onatski test6 (see Table B-1 for a summary of results). As usual, these tests give somewhat
different numbers, so we have to choose between 4- and 5-factor models.7
The next standard step in EFA is to perform a specific oblique rotation such as quartimin
conditional on the established number of factors. We apply the quartimin rotation separately for
4 and 5 factors. We keep measures in the analysis that have a factor loading of at least 0.4 for
the factor extracted from it. This is a conservative threshold that allows us to keep some less-
informative proxies but exclude especially weak ones.8
Conditional on five factors, we obtain a diagonal structure presented in Table B-2, which sup-
ports of the five-factor representation (see factor loadings that are greater than 0.4 in bold, all of
which happen to be diagonal elements), We can therefore dismiss the four-factor specification for
missing important heterogeneity that supports an additional latent factor dimension.
Hence, we have established a set of measures, each of which is strongly loaded on one of five
factors and only weakly loaded on other factors under the quartimin rotation shown in Table B-2.
The next step is CFA, which tests the specification of the factor model implied by EFA.
B.2 Confirmatory Factor Analysis (CFA)
In our CFA, we compare two alternatives for the main models, both in line with the EFA (see Table
B-3). In model B, we impose stringent exclusion restrictions such that each measure loads on one
3The scree test is a visual test using a plot of the eigenvalues from the covariance matrix of the measures.4This test, suggested by Horn (1965), is based on comparing the eigenvalues from the observed covariance matrix
against eigenvalues from a randomly drawn covariance matrix.5The criterion is based on Velicer (1976).6The test is proposed in Onatski (2009).7 Another test for the number of factors is the Guttman-Kaiser rule. The rule is based on the absolute cutoff for
the eigenvalues, where the number of eigenvalues greater than one indicates the number of factors. The rule providesan upper–bound on the number of factors usually leading to an overestimation of the optimal number. In our case, therule suggests 7–8 factors, considerably more than all other methods.
8Excluding weak proxies by using a threshold set by the statistician is a standard procedure in EFA.
11
and only one factor corresponding to the diagonal elements in Table B-2 (such a measure is called
“dedicated”). In model A, we allow factors to load on multiple measures when the correlations
between certain factors and measures are present even though weak.
We present standard model fit statistics which show a better fit for model A. As they should, CFI
and TLI are above 90% for models of type A, while RMSEA are below 5%.9 We choose model A
as our primary model since exclusion restrictions are better empirically justified, leading to better
fit statistics. Since cross-loadings introduced in model A are comparatively weak, latent factors are
still interpreted based on the diagonal loadings. Non-diagonal loadings represent the fact that real-
life measures may not be purely dedicated: a measure of one particular factor may also correlate
with another. For instance, while “leadership” is mainly a measure of Extraversion, we find that it
also somewhat correlates with Openness. Thus, we find a well-justified model specification while
relaxing the overly-restrictive assumption that each measure is dedicated to only one factor.
The five factors as defined here are similar to corresponding factors defined in other papers
looking at the Terman data (Martin and Friedman, 2000; Martin et al., 2007), but the literature
uses indexes based on dedicated measures (like in model B), not latent factors with cross-correlated
measures (like in model A).
9The Chi-squared test is rejected for both models, but we expect this test to be most likely rejected even for a goodfit for studies with modest or high sample sizes, as discussed in detail in the Web Appendix to Savelyev (2017).
12
Figu
reB
-1:S
cree
Plot
s
(a)M
ales
(b)F
emal
es
01234567
Eigenvalues
12
34
56
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Num
ber
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ctor
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12
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Num
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otof
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ned
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13
Table B-1: Procedures Determining the Number of Factors
����� �������
��������
� �
�������
� �
��������
� �
����������
� �
Notes: (a)Scree test is obtained based on Figure B-1. (b)Horn’s test is executed using a code from Dinno (2009).(c)Minimum average pairwise correlation criterion is based on Velicer (1976). (d)The Onatski test is based on Onatski(2009). The upper–bound on the number of factors is estimated to be 7 for males and 8 for females for our case basedon the Guttman–Kaiser rule.
14
Table B-2: Factor Loadings Estimated as Part of the EFA (Oblique Quartimin Rotation)
Openess Desire to know .630 .100 .003 .061 .013 .690 .148 .035 .060 .004
Originality .564 .099 .154 .024 .013 .668 .076 .071 .035 .041
Intelligence .687 .111 .026 .021 .029 .676 .090 .063 .010 .006
Conscientiousness Prudence .088 .563 .014 .090 .070 .150 .625 .020 .006 .027
Conscientiousness .007 .935 .009 .036 .007 .033 .896 .008 .044 .000
Truthfulness .121 .708 .018 .002 .013 .051 .749 .062 .048 .045
Extraversion Fondness for large groups .041 .088 .713 .045 .017 .024 .114 .647 .015 .001
Leadership .285 .046 .570 .097 .047 .167 .035 .605 .074 .018
Popular with other children .017 .168 .659 .048 .000 .054 .080 .817 .057 .011
Agreeableness Easy to get along with .015 .001 .076 .389 .380 .061 .031 .053 .527 .251
Avoids arguments .073 .010 .005 .603 .121 .093 .013 .061 .534 .025
Critical .033 .013 .021 .685 .089 .074 .036 .058 .631 .083
Tactful .052 .033 .059 .805 .036 .107 .056 .013 .776 .153
Unfeeling .092 .023 .014 .683 .010 .007 .084 .007 .582 .096
Domineering .080 .100 .101 .584 .050 .122 .098 .077 .457 .042
Inflated opinion of self .001 .134 .015 .634 .142 .025 .012 .058 .619 .094
Neuroticism Miserable .015 .101 .108 .044 .791 .069 .065 .021 .097 .780
Touchy .143 .057 .032 .305 .491 .005 .051 .011 .121 .577
Periods of loneliness .005 .061 .061 .028 .657 .241 .097 .056 .042 .619
Lonely when with others .049 .058 .024 .093 .510 .178 .021 .012 .036 .609
Remorseful and regretful .032 .062 .013 .018 .611 .090 .125 .017 .004 .713
Lack self confidence .051 .035 .229 .346 .510 .126 .003 .045 .248 .494
Worry about humiliating experiences .063 .106 .082 .055 .631 .058 .136 .003 .056 .808
Emotionally unstable .041 .042 .055 .036 .748 .021 .033 .069 .135 .733
Easily hurt .079 .121 .007 .039 .676 .127 .080 .087 .156 .812
Hard to be serene .208 .112 .109 .051 .768 .010 .036 .068 .233 .659
Moody .083 .055 .020 .039 .691 .071 .031 .035 .110 .635
Sensitive .037 .062 .016 .020 .581 .140 .079 .008 .119 .657
Factor Loadings
Females Males
Notes: Factor loadings greater than 0.4 are bolded and italicized.
15
Table B-3: CFA Estimates: Factor Loadings and Model Fit Statistics
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16
C SUPPLEMENTARY RESULTS
Below we present miscellaneous supplementary results that support the paper.
Survival by Health Behaviors and Health Figures C-1–C-3 document survival functions by
health behaviors and health status and demonstrate expected correlations between survival and
beneficial health behaviors, health-enhancing lifestyles, and self-reported health. In particular, we
see that heavy drinking and being overweight are negatively associated with survival for both men
and women, though a smaller portion of women are heavy drinkers or overweight, and so results
for women are less precise (see Figure C-1). In Figure C-2 we see that marriage is associated with
longevity for men and women. We also see a positive correlation between group memberships
and men’s longevity. For women, the survival difference is imprecise. Finally, we see associa-
tions between health measures and survival in Figure C-3. For men, differences are strong for
both general and mental health. For women, we see some difference for general health and no
distinguishable difference for mental health. Figures for health provide another representation of
a well-known paradox: women seem to be less healthy than men but live longer. Indeed, a higher
share of women report poor or fair health in the Terman sample, but women with poor or fair health
are twice as likely to survive to age 80, compared to men (50% for women vs 25% for men).
Correlations Among the Big Five Personality Traits Table C-1 presents correlations among
Big Five personality traits for the Terman population, grouped by gender. These correlations are in
line with those documented in the Big Five literature, though some peculiarities exist, which might
be specific to the Terman population.
For comparison of our estimates with the literature, we use a meta-analysis by van der Linden
et al. (2010), which provides correlations among each Big Five Personality trait. Because genders
are pooled in the meta-analysis, we compare the correlations from the meta-analysis to the average
of our Terman male/female correlations.
The correlation between Conscientiousness and Neuroticism is negative in the meta-analysis.
17
We estimate it to be both negative for women and negative on average (for men and women).
The Conscientiousness-Agreeableness correlation is positive in the literature. We obtained
positive estimates too, but only the estimate for males is statistically significant.
Extraversion and Agreeableness correlate positively in the meta-analysis. We find a positive
correlation on average, with only the estimate for males being statistically significant.
Neuroticism-Agreeableness are negatively correlated in the literature. We find a negative cor-
relation on average, but it is only statistically significant for men.
The literature reports a positive correlation between Extraversion and Openness. We find it
positive for Terman men, statistically insignificant for women, and positive on average.
Openness and Conscientiousness show a positive, borderline statistically significant correlation
in the general population. Likewise, they show a positive (and statistically significant) correlation
for the Terman sample.
Finally, Conscientiousness and Extraversion are positively correlated, according to the meta-
analysis. We find the correlation to be positive for men, but negative for women. The correlation
coefficients are similar by gender and give a small negative average.
Specification Robustness Checks In our main specification we control for both skills and ed-
ucation. Only Conscientiousness, Openness, Extraversion, and IQ are measured in early life (in
1922, at about age 12) before the college education choice is made. To control for the full set of
Big Five traits, in our main model we also control for Neuroticism and Agreeableness, which were
measured in 1940 (about age 32).
One concern with adding Neuroticism and Agreeableness is that they could be affected by
education. On the other hand, dropping them may lead to the omitted variable bias in the education
coefficient, and so neither model is preferable ex ante. We provide a robustness check in which
we control for 1922 skills only and compare the education coefficient with our main model that
controls for both 1922 and 1940 skills. We show that the alternative specification does not make a
difference.
18
Another robustness check is related to associations between skills and outcomes. Control-
ling for education in our main model could bias the conditional associations between skills and
outcomes. Our second robustness check shows that there is no practical difference between con-
trolling for education or not. This result has to do with the lack of any strong relationship between
the Big Five and education in the Terman sample, as documented in a companion paper (Savelyev,
2017).
Tables C-2 and C-3 show the robustness results described above for men and women. Panels
(A) and (C) of each table present statistically significant coefficients for our main results. Panels
(B) and (D) present the corresponding coefficients based on robustness checks. By comparing
panel (A) with panel (B), and panel (C) with panel (D), we conclude that the estimates are close
and specification makes no practical difference.
Placebo Test Table C-4 presents three placebo tests for each gender. We expect to find effects of
education on health-related life-outcomes, but we expect to find no effect of education on predeter-
mined health outcomes, which include “Normal Birth or no Birth Problems,” “Childhood health,”
and “Childhood energy.” We regress each of these predetermined outcomes on the education vari-
able conditional on other controls, skills, and IQ. We test and do not reject any individual test
(p-values for the tests range from 0.232 to 0.813). The result supports our conditional indepen-
dence assumption.
The Role of Selection The literature reports substantial selection on skills and observables (Bi-
jwaard et al., 2015; Conti et al., 2010). We perform a similar exercise for Terman data and find
no such selection. We view this not as a contradiction to previous work, but as a supplementary
result obtained for a unique high-IQ population. Figure C-4 presents education coefficients with
a full set of controls and without any controls and shows that results are almost identical, with an
exception of mental health.
19
Figure C-1: Survival by Health Behaviors and Their Proxies
(a) Heavy Drinking, Males (b) Heavy Drinking, Females
20
30
40
50
60
70
80
90
10
0
20
30
40
50
60
70
80
90
10
0
Su
rviv
al P
rob
ab
ility
, %
35 40 45 50 55 60 65 70 75 80
Participant’s Age
20
30
40
50
60
70
80
90
10
0
20
30
40
50
60
70
80
90
10
0
Su
rviv
al P
rob
ab
ility
, %
35 40 45 50 55 60 65 70 75 80
Participant’s Age
No (399) Yes (259) No (403) Yes (104)
(c) Overweight, Males (d) Overweight, Females
20
30
40
50
60
70
80
90
10
0
20
30
40
50
60
70
80
90
10
0
Su
rviv
al P
rob
ab
ility
, %
35 40 45 50 55 60 65 70 75 80
Participant’s Age2
03
04
05
06
07
08
09
01
00
20
30
40
50
60
70
80
90
10
0
Su
rviv
al P
rob
ab
ility
, %
35 40 45 50 55 60 65 70 75 80
Participant’s Age
Normal (470) Abnormal (106) Normal (433) Abnormal (29)
Notes: Heavy drinking is an indicator of whether the subject has ever reported drinking heavily over the period of1940–1960. Overweight refers to subjects who had a BMI above 25 in 1940. Survival graphs are based on life-tablecalculations; standard errors above and below the estimate are represented by the thinner lines. Calculations are basedon the Terman data.
20
Figure C-2: Survival by Lifestyles
(a) Group Membership, Males (b) Group Membership, Females
20
30
40
50
60
70
80
90
10
0
20
30
40
50
60
70
80
90
10
0
Su
rviv
al P
rob
ab
ility
, %
45 50 55 60 65 70 75 80
Participant’s Age
20
30
40
50
60
70
80
90
100
20
30
40
50
60
70
80
90
100
Surv
ival P
robabili
ty, %
45 50 55 60 65 70 75 80
Participant’s Age
Low (297) High (264) Low (252) High (180)
(c) Ever Divorced, Males (d) Ever Divorced, Females
20
30
40
50
60
70
80
90
100
20
30
40
50
60
70
80
90
100
Surv
ival P
robabili
ty, %
30 35 40 45 50 55 60 65 70 75 80
Participant’s Age2
03
04
05
06
07
08
09
01
00
20
30
40
50
60
70
80
90
10
0
Su
rviv
al P
rob
ab
ility
, %
30 35 40 45 50 55 60 65 70 75 80
Participant’s Age
No (461) Yes (168) No (360) Yes (122)
Notes: For group membership in 1950, “high” refers to subjects having a greater number of organization membershipsthan the median, “low” for at or below the median. “Ever divorced” indicates whether the subject was divorced at leastonce. Survival graphs are based on life-table calculations; standard errors above and below are represented by thethinner lines. Calculations are based on the Terman data.
21
Figure C-3: Survival by Self-Reported Health
(a) General Health, Males (b) General Health, Females
20
30
40
50
60
70
80
90
10
0
20
30
40
50
60
70
80
90
10
0
Su
rviv
al P
rob
ab
ility
, %
35 40 45 50 55 60 65 70 75 80
Participant’s Age
20
30
40
50
60
70
80
90
100
20
30
40
50
60
70
80
90
100
Surv
ival P
robabili
ty, %
35 40 45 50 55 60 65 70 75 80
Participant’s Age
Good or Very Good (600) Poor or Fair (48) Good or Very Good (435) Poor or Fair (63)
(c) Mental Difficulty, Males (d) Mental Difficulty, Females
20
30
40
50
60
70
80
90
10
0
20
30
40
50
60
70
80
90
10
0
Su
rviv
al P
rob
ab
ility
, %
35 40 45 50 55 60 65 70 75 80
Participant’s Age2
03
04
05
06
07
08
09
01
00
20
30
40
50
60
70
80
90
10
0
Su
rviv
al P
rob
ab
ility
, %
35 40 45 50 55 60 65 70 75 80
Participant’s Age
No (374) Yes (265) No (266) Yes (229)
Notes: General health indicates whether the subject has ever experienced poor or fair health over the years 1940–1960.Mental difficulty indicates whether or not the subject experienced any mental difficulty over the years 1950–1960.Survival graphs are based on life-table calculations; standard errors above and below the estimate are represented bythe thinner lines. Calculations are based on the Terman data.
22
Table C-1: Correlations Among the Big Five Personality Traits
Males
Openness 0.387 *** 1
(0.048)
Extraversion 0.147 * 0.238 ** 1
(0.089) (0.097)
Agreeableness 0.183 *** -0.03 0.161 ** 1
(0.065) (0.064) (0.063)
Neuroticism 0.028 0.052 -0.044 -0.179 **
(0.053) (0.055) (0.057) (0.080)
Females
Openness 0.425 *** 1
(0.058)
Extraversion -0.232 * 0.038 1
(0.124) (0.109)
Agreeableness 0.027 -0.036 -0.06 1
(0.074) (0.079) (0.079)
Neuroticism -0.109 * 0.131 * -0.071 -0.14
(0.058) (0.067) (0.071) (0.087)
Conscientio-
usnessOpenness Extraversion Agreeableness
Notes: We estimate 5-factor models separately for men and women and identify correlations among latent factors.Standard errors are reported in parentheses. Statistical significance is represented by asterisks, where ***,** and *
indicate p < 0.01, 0.05, and 0.10 respectively. Calculations are based on the Terman data.
23
Table C-2: Specification Robustness Check Males
Lifetime Outcomes, Males
A. Lifetime Outcomes, Main Model
1940–1960 Ever Drank Heavily -.055 .061 -.109
1991 Ever Smoked -.107
1940–1960 Any Organization .084
Ever Divorced -.055 -.137
Ever Poor/Fair MH -.071 .085 .134
Never Poor/Fair GH
B. Lifetime Outcomes, Robustness checks*
1940–1960 Ever Drank Heavily -.061 .064 -.106
1991 Ever Smoked -.105
1940–1960 Any Organization .084
Ever Divorced -.062 -.136
Ever Poor/Fair MH -.072 .084 .134
Never Poor/Fair GH
C. Midlife Outcomes, Main Model
Drank Heavily -.072
# of Organizations .327 1.501
Mental Difficulty -.080 .091 -.101 .120
General Health -.211
D. Midlife Outcomes, Robustness checks*
Drank Heavily -.075
# of Organizations .368 1.477
Mental Difficulty -.080 .091 -.101 .120
General Health -.210
EducationConscientio-
usnessOpenness Extraversion
Agreeable-
nessNeuroticism IQ
Notes: In panels (A) and (C) only coefficients of the main model with p < 0.15 (after stepdown adjustment) arereported, while a blank cell refers to a coefficient with p-value above 0.15. These are the same coefficients that arereported in the main paper. The results are typeface coded so that bolded coefficients refer to associations that areconsidered in the literature to be beneficial for longevity (such as a decrease in heavy drinking or an increase in socialparticipation), and italicized coefficients refer to adverse associations. Estimation is conditional on background vari-ables presented in Table 4 of the main paper. In panels (B) and (D), coefficients are reported that are correspondingto those in panels (A) and (C), but for alternative models: for skills coefficients, we use an alternative model thatdoes not control for education. For education coefficients, we use an alternative model that controls for skills mea-sured in 1922 (Conscientiousness, Openness, and Extraversion), but omits skills measured in 1940 (Neuroticism andAgreeableness). Calculations are based on the Terman data.
24
Table C-3: Specification Robustness Check Females
Lifetime Outcomes, Females
A. Main Model
1940–1960 Ever Drank Heavily -.073
1991 Ever Smoked
1940–1960 Any Organization
Ever Divorced -.111
Ever Poor/Fair MH .152
Never Poor/Fair GH -.044 .116
B. Robustness checks(a)
1940–1960 Ever Drank Heavily -.074
1991 Ever Smoked
1940–1960 Any Organization
Ever Divorced -.120
Ever Poor/Fair MH .153
Never Poor/Fair GH -.052 .128
C. Midlife Outcomes, Main Model
Drank Heavily
# of Organizations -.352 1.213
Mental Difficulty .123
General Health -.241
D. Midlife Outcomes, Robustness checks*
Drank Heavily
# of Organizations -.299 1.175
Mental Difficulty .126
General Health -.245
EducationConscientio-
usnessOpenness Extraversion
Agreeable-
nessNeuroticism IQ
Notes: In panels (A) and (C) only coefficients of the main model with p < 0.15 (after stepdown adjustment) arereported, while a blank cell refers to a coefficient with p-value above 0.15. These are the same coefficients that arereported in the main paper. The results are typeface coded so that bolded coefficients refer to associations that areconsidered in the literature to be beneficial for longevity (such as a decrease in heavy drinking or an increase in socialparticipation), and italicized coefficients refer to adverse associations. Estimation is conditional on background vari-ables presented in Table 4 of the main paper. In panels (B) and (D), coefficients are reported that are correspondingto those in panels (A) and (C), but for alternative models: for skills coefficients, we use an alternative model thatdoes not control for education. For education coefficients, we use an alternative model that controls for skills mea-sured in 1922 (Conscientiousness, Openness, and Extraversion), but omits skills measured in 1940 (Neuroticism andAgreeableness). Calculations are based on the Terman data.
25
Table C-4: Placebo Tests
Predetermined Health Outcomes Males Females
Normal Birth or no Birth Problems -0.054 -0.016
(0.045) (0.045)
[0.232] [0.731]
Childhood Health -0.043 -0.099
(0.078) (0.087)
[0.579] [0.259]
Childhood Energy 0.038 -0.019
(0.071) (0.082)
[0.594] [0.813]
Notes: Each of three predetermined health outcomes is regressed on education, latent skills, and a full set of back-ground variables excluding that one predetermined outcome. Coefficients for education are reported. Standard errorsare in round parentheses. p-values are in square parentheses. Calculations are based on the Terman data.
26
Figure C-4: Selection Bias in the Terman Data
(a) Males-.
2-.
10
.1.2
Education C
oef.
Hea
vy D
rinking
Ove
rweigh
t
Phy
sica
l Exe
rcise
Eve
r Sm
oked
Any
Mem
bers
hips
Eve
r Divor
ced
Men
tal H
ealth
Gen
eral H
ealth
(b) Females
-.2
-.1
0.1
.2
Education C
oef.
Hea
vy D
rinking
Ove
rweigh
t
Phy
sica
l Exe
rcise
Eve
r Sm
oked
Any
Mem
bers
hips
Eve
r Divor
ced
Men
tal H
ealth
Gen
eral H
ealth
Full Model No Controls
Notes: Coefficients for education are shown for two cases: (1) “full model” (conditional on latent skills and back-ground variables) and (2) “no controls model” (a univariate model with education as the only regressor). Calculationsare based on the Terman data.
27
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