The relationship between suicidal behavior and productive activities of young adults.
Tekin, Erdal ; Markowitz, Sara
1. Introduction
Suicides among youths have reached an alarming rate in recent years
and are now the third leading cause of death for those aged 15-24 years
(Anderson and Smith 2003). Since 1950, the suicide rate has tripled
among youths (Cutler, Glaeser, and Norberg 2001). Even more striking is
the number of suicide attempts by young individuals. For every teen who
commits suicide, as many as 150 teens attempt suicide (Chatterji et al.
2004). Concern over the health and well-being of youths has prompted the
U.S. Department of Health and Human Services to develop a national
strategy for suicide prevention. This comprehensive campaign includes
developing public education campaigns; increasing the number of suicide
prevention programs in schools, work sites, and community services; and
incorporating screening at primary health care facilities.
Suicide attempts, regardless of whether or not they are completed,
impose real health care and other costs on individuals and society. For
example, the direct medical costs associated with both completed and
medically treated suicides by youths under the age of 21 years amounted
to $945 million in 1996, and lost future earnings are estimated at $2.85
billion (Miller, Covington, and Jensen 1999). A suicide attempt can have
adverse effects on one's current and future labor market
productivity as a result of a bodily injury or permanent disability,
lost credibility in the workplace, interruptions at work and school,
lost interest in future employment efforts, and continuing psychological
problems. Despite this strong link between suicidal behavior and labor
market outcomes, our knowledge of the potential effects of suicidal
behavior on labor market and school outcomes is very limited. This
relationship is also confounded by the potential effects that poor
school or labor market outcomes have in contributing to suicidal
behaviors.
This paper explores in depth the link between suicidal behaviors
and engaging in productive activities. Specifically, we focus on labor
market and educational outcomes of young adults who are at a stage in
life characterized by intense investment in human capital. These adults
are in school, participating in job training, or are just starting their
careers. Disruptions to these investments can have profound, long-term
implications for future earnings and occupational choices. If there is a
positive link between the quality of the initial job and future labor
market success, the answer to this question will provide important
insights into the long-term effects of suicidal behavior. In addition,
it will help structure a better-informed policy debate over the
effectiveness of cognitive behavioral therapies and anti-suicide
programs such as those implemented at high schools in the United States.
A study by the Centers for Disease Control and Prevention (1992)
documents that most anti-suicide programs focus on teenagers, with
little emphasis given to suicide among young adults. This is partly due
to the fact that teenagers in high school are easier to reach than young
adults and partly due to a failure to appreciate that the suicide rate
is generally twice as high among persons 20-24 years of age as it is
among adolescents 15-19 years of age. The study recommends an expansion
of the suicide prevention efforts for young adults 20-24 years of age.
2. Background
Researchers believe that almost all individuals who commit suicide
have a diagnosable mental disorder, and mental illnesses are also
primary risk factors for suicide thoughts and attempts (Johnson,
Weissman, and Klerman 1990; Maris et al. 1992; Alexopoulos et al. 1999).
It has been estimated that two-thirds of people who commit suicide have
a depressive illness; 5% suffer from schizophrenia; and 10% meet the
criteria for other mental illnesses, including borderline personality
disorder. The relationship between mental illness and suicidal behaviors
also holds for youth (Fergusson and Woodward 2002). One estimate shows
that over 90% of children and adolescents who commit suicide have a
mental disorder (Shaffer and Craft 1999).
While depressive illnesses are most commonly associated with
suicidal behaviors, other disorders are also frequently observed,
including substance abuse disorders, attention deficit disorder, anxiety
disorders, panic disorder, schizophrenic disorders, post-traumatic
stress disorders, and borderline personality disorders (Johnson,
Weissman, and Klerman 1990; Alexopoulos et al. 1999; Goldsmith et al.
2002). For example, in a study of youth in a psychiatric hospital, Borst
and Noam (1989) find that conduct disorders are the most prevalent type
of disorders diagnosed among suicide attempters. The authors conclude
that "factors such as impulsivity and anger may contribute
significantly to suicidal behavior in children and adolescents" (p.
174). Personality disorders are also highly prevalent, with a diagnosis
rate of 40-53% among youth who have committed suicide (Goldsmith et al.
2002).
It is important to note that the mental illnesses that manifest
themselves through suicidal behaviors likely represent the most severe
cases of illness. Simon and Von Korff (1998) find that among insured
patients receiving treatment for depression, the highest risk of suicide
was among those receiving inpatient treatment and medication, and the
lowest risk was found among individuals receiving outpatient treatment
without medication.
Cutler, Glaeser, and Norberg (2001) argue that there is a
fundamental difference between suicide attempts and completions among
youth, where the latter is a result of the desire to die and the former
is not. The authors discuss four reasons for suicide attempts among
youth: The first involves strategic motives to "... signal others
that they are unhappy or to punish others for their unhappiness"
(p. 233). The second is the depression theory, in which youths cross
some unhappiness threshold and desire to take their own lives. The third
is the contagion theory, in which a '"social multiplier'
may amplify the effects of stressors leading to depression or may
amplify the effects of factors leading to suicidal signaling as a method
of conflict resolution among youths" (pp. 233-4). The fourth theory
involves the combination of unhappiness and the means to kill
themselves. Even in the absence of the intent to die, it is clear that
underlying mental states are extremely important in the theories
predicting suicidal behaviors.
In contrast to the conclusions drawn by Cutler, Glaeser, and
Norberg (2001), Boergers, Spirito, and Donaldson (1998) find that most
adolescents who attempt suicide cite the desire to die or the desire to
obtain relief from a terrible state of mind as the primary reasons for
the attempt. Few identify the attempt as a cry for help or as a way of
"getting back" at someone. Although it is difficult to
pinpoint the exact underlying motives for a suicide thought or attempt,
the link between suicide and mental illness cannot be denied.
In this paper, we focus on suicidal behaviors such as suicidal
thoughts and attempts rather than depression or any other specific
mental illness. Based on the literature described above, we argue that
suicide ideation and attempts are most likely manifestations of severe
mental illnesses. However, we also allow for the possibility that, in
youth, these behaviors may be methods of signaling or punishing others
or of conflict resolution. We believe that given the vast number of
different mental illnesses and related stressors that are believed to
contribute to suicide behaviors, estimating models of one or two
specific illnesses is unlikely to draw a complete and informative
picture of the effects of mental well-being on productive activities.
Regardless of the underlying causes, suicide thoughts and attempts
may have negative consequences for school and labor market outcomes
through multiple channels and are therefore important to study. For
example, injuries from failed suicide attempts may contribute to
absenteeism and reduced productivity at work and school. About 116,000
individuals who survive a suicide attempt are hospitalized, with an
average hospital stay of 10 days and an average cost of $15,000 (Miller
1995). Seventeen percent of these people are permanently disabled and
are therefore restricted in their ability to work (Miller 1995).
Suicidal behavior may also result in lower engagement in work and
schooling activities as a result of reduced concentration and cognitive
abilities (Greenberg et al. 1990; Conti and Burton 1994). These
functional limitations may also contribute to worsened labor market and
school outcomes. Furthermore, the underlying mental illnesses and life
stressors that we observe through suicidal behaviors may also contribute
to outcomes such as teenage pregnancy and marital instability, which may
then result in low educational attainment, poor labor market
productivity, and lower wages (Kessler et al. 1997; Overbeek et al.
2003).
There is evidence that individuals with mental illnesses and those
who exhibit suicidal behaviors are less likely to reach their potential
academically. According to the Department of Education, 50% of children
with serious emotional and behavioral problems drop out of high school,
compared to 30% of students with other disabilities (U.S. Department of
Education 2001). Stoep et al. (2003) find that over half of the
adolescents in the United States who fail to complete their secondary
education have a diagnosable psychiatric disorder. Using a twin sample
from Minnesota, Marmorstein and Iacono (2001) conclude that depression
is related to significant difficulties in functioning and school
adjustment, which result in an increased number of suspensions and
failure of classes. Slap, Goodman, and Huang (2001) document that those
who attempt suicide perform more poorly at school and have a lower level
of school connectedness than do non-attempters.
In sum, suicidal behaviors can affect an individual's
productivity and academic achievement, which may have implications for
future outcomes. The mechanisms may be direct, for example, when
injuries and reduced concentration result from suicide thoughts or
attempts, or indirect, when the outcomes associated with underlying
factors negatively influence productivity and academic success.
The relationship between labor market outcomes and poor mental
health also cannot be ignored. Mental health may certainly be affected
by labor market involvement, as higher wages may improve mental health.
In the simplest case, more income can allow a person to purchase
treatment for mental illness. Higher incomes might also remove stress
related to financial insecurity and contribute to good health. Hamermesh
and Soss (1974) propose that suicide occurs when an individual's
taste for living plus the total discounted lifetime utility, which is a
function of permanent income, equals zero. Aggregate suicide rates
should, therefore, fluctuate with expectations about future income and
the unemployment rate. Indeed, a number of studies on the economic
determinants of suicide show that suicide rates fall with rising incomes
and rise with the unemployment rate (see Marcotte [2003] for a review).
In short, mental health status and labor market outcomes may be
intertwined. In this case, it is necessary to model the link between
suicidal behavior and labor market outcomes as simultaneous equations in
order to obtain unbiased estimates.
3. Previous Research
To the best of our knowledge, only one previous study, that of
Marcotte (2003), has directly estimated the effects of suicide thoughts
and attempts on labor market outcomes. The lack of economic studies on
suicidal behavior is largely due to a lack of individual-level data. The
majority of studies on the topic use aggregate data from sources such as
vital statistics to look at the correlations between economic outcomes,
such as income and suicidal behavior. However, to the extent that the
underlying behavioral mechanism that leads to suicide decisions and
thoughts is based on micro-level utility maximization decisions,
aggregate data analysis is unsatisfying (Marcotte 2003). Using data from
the National Comorbidity Survey conducted in 1991 1992, Marcotte (2003)
finds that suicidal behaviors are associated with lower current income;
although, suicide attempters are associated with a higher current income
than those who only thought about suicide. The higher income may result
from income transfers from family members or the government following
the attempt. Subsequent mental health treatment may also improve mental
health and labor market outcomes. The sample size in this paper is 5877,
and the target population is all adults between the ages of 18 and 54
years. In contrast, we use a much larger sample in this study from a
more recent survey, and we explicitly look at the responses of a young
adult population between the ages of 18 and 26 years. Our outcome
measures differ in that we examine school and work activities. Finally,
the cross-sectional nature of the data set used in the Marcotte study
and the lack of any potential instruments do not allow the author to
rule out the possibility that his findings are due to heterogeneity
(Marcotte 2003, p. 640).
Despite the lack of evidence in the literature of the labor market
effects of suicidal behaviors, a number of studies have examined the
relationship between mental illness and labor market outcomes. Given the
close link between suicide and mental illness, this literature can
provide insight into the true nature of the relationship. Most of this
research shows that poor mental health is associated with reduced
success in the labor market among adults. The first generation of papers
focusing on the effects of mental health status on labor market outcomes
acknowledges but ignores the potential endogeneity of mental illness.
Studies such as those of Bartel and Taubman (1979, 1986), Mullahy and
Sindelar (1990), and Frank and Gertler (1991) all show that individuals
with reported or diagnosed mental disorders have worse labor market
outcomes than other individuals. Bartel and Taubman (1979, 1986) find
that earnings are lower among individuals with a recent or past mental
illness diagnosis. Mullahy and Sindelar (1990) find that people with
both self-reported and diagnosed mental illnesses are associated with a
lower probability of working. Frank and Gertler (1991) show that having
a mental illness reduces one's earnings. This paper is also
important because it shows the bias introduced by using a
utilization-based measure that disregards mental health status rather
than a population-based measure of mental illness. The bias arises
because only a subset of the mentally ill seeks treatment.
The second generation of papers explicitly tests for and, if
necessary, accounts for the potential endogeneity of mental illness in
the equations for labor market outcomes. The results of these studies
are generally consistent with the first-generation studies and find
worse labor market outcomes among mentally ill individuals. For example,
Ettner, Frank, and Kessler (1997) use the National Comorbidity Survey to
study the effects of the presence of specific mental illnesses (such as
schizophrenia and major depression) on the probability of being
employed, usual hours of work, and annual income. The number of
psychiatric disorders experienced during childhood and parental history
of mental illness serve as instrumental variables. Results show that
psychiatric disorders have detrimental effects on all three labor market
outcomes. French and Zarkin (1998) examine the relationship between
symptoms of emotional and psychological problems and earnings at a large
work site in the United States. Results of tests for the endogeneity of
mental health in the earnings equation lead the authors to treat mental
health as exogenous. They find that earnings are lower and absenteeism
higher among those reporting mental health problems. Hamilton, Merrigan,
and Dufresne (1997) examine the simultaneous relationship between
unemployment and mental health. Using maximum likelihood estimation, the
authors find evidence that being employed is associated with improved
mental health, and that being in poor mental health is associated with a
lower probability of employment.
Our paper expands the second-generation literature by looking at
the effects of suicide thoughts and attempts on the probability of
engaging in a productive activity, that is, work or school. Whereas the
average age in many of the above-mentioned studies ranges from 35 to 40
years, this is the first paper to examine schooling and labor market
outcomes for a sample of young adults. This paper also uses a variety of
methods to control for the potential endogeneity of suicidal behaviors,
which allows us to asses the validity of our conclusions. Suicide
thoughts and attempts are advantageous in that we are able to identify
people in severe mental distress and are not limited to drawing
conclusions for one particular mental illness, such as depression or
schizophrenia. Another advantage is that these measures of mental health
are population based rather than treatment based, the latter of which,
as Frank and Gertler (1991) point out, can produce biased results.
4. Methods
The goal of this paper is to model the effect of suicidal behavior
on schooling and labor market outcomes. Therefore, the basic econometric
model can be expressed as follows:
[L.sub.i] = [beta][S.sub.i] + [X.sub.i][alpha] + [[gamma].sub.s] +
[[delta].sub.h] + [[epsilon].sub.i], (1)
where [L.sub.i] is a dichotomous indicator for whether the
individual, i, is either at work or school or both, and is 0 otherwise.
[S.sub.i] is a measure of suicidal behavior, and [X.sub.i] is a vector
of personal and family characteristics. The [[gamma].sub.s] and the
[[delta].sub.h] are vectors of state and school fixed effects,
respectively.
Estimating unbiased effects of suicidal behaviors on labor market
outcomes is a difficult task. Biased estimates can come from two sources
of endogeneity. The first, statistical endogeneity, results from
unobserved factors in the error term of Equation 1 that are correlated
with both the schooling/labor market outcome and the suicidal behaviors.
For example, lack of a caring home environment might lead to
insufficient investment in activities of child development and
nutrition. This in turn could result simultaneously in poor labor market
and schooling outcomes, as well as poor mental health status. Estimates
of the impact of suicidal behavior that do not take account of this type
of effect would be biased. The second source of endogeneity, structural
endogeneity, comes from the potential reverse causality from labor
market and schooling outcomes to mental illness and suicidal tendencies.
For example, unemployment and poor school performance may contribute to
stress and poor mental health outcomes. Not accounting for this
relationship would bias the estimates of the suicidal behaviors in
Equation 1. In addition to the potential endogeneity of suicidal
behavior measures, these measures may contain measurement error, since
they are self-reported, despite the fact that additional steps have been
taken to ensure confidentiality. Therefore, measurement error is another
potential source for bias in the estimated effect of suicidal behavior
on our outcome measures.
We address the potential endogeneity problems in a number of ways.
First, we will control for the statistical endogeneity by utilizing a
full set of variables designed to minimize or eliminate the unobserved
factors left in the error term. We also include vectors of fixed effects
for the individual's state of residence in Wave 3 and school
attended in Wave 1. The school fixed effects will control for any
school-level experiences and environmental/neighborhood characteristics
that may be correlated with both the future productivity of the
individuals and their mental health, such as their suicidal tendencies.
For example, there may be a higher incidence of suicide attempts in one
school for one reason or another. The higher exposure of students to
suicide events in these schools would likely affect their suicidal
tendencies as well as their school and, possibly, their labor market
performance. Also, school fixed effects will capture any neighborhood
and school characteristics, such as poverty and school violence, that
could be correlated with both suicidal behavior and productive
activities. The state fixed effects are included in the model to control
for the unobservables that might be correlated with both
depression/suicide and labor market outcomes, such as unattractive
economic conditions in the state.
In addition to the fixed effects, we include in our models a rich
set of variables designed to account for the home and family
environment. By comparing models with and without these background
variables, we will be able to see the extent to which correlation
between suicidal behavior and the outcome variable is affected by these
control variables. We are also able to control for suicidal behaviors of
the respondents and their family members from Wave 1, when these
individuals were at high school. These will further help us eliminate
the unobserved heterogeneity. (1) Lastly, we include measures of current
and previously observed depressive symptoms to account for one of the
many mental illnesses that may confound the relationship between human
capital and suicidal behaviors. These measures are all discussed in
further detail below.
In order to guard against any bias from potential measurement error
and endogeneity, we will next turn to the instrumental variables (IV)
method. The IV method can be used to address both forms of endogeneity
discussed above. The IV method will yield unbiased estimates of the
effects of suicidal behaviors if instruments can be found that (i) are
correlated with suicidal behaviors and (ii) are not correlated with the
outcomes, except through their effects on the probability that an
individual is suicidal. Variables describing the suicidal behaviors of
friends from Wave 1 and Wave 3 will be used as instruments for
identification under the assumption that these variables will predict an
individual's own decision on suicide while having no direct impact
on his/her work and school decisions.
One potential concern with the IV method is that if individuals
with suicidal tendencies associate with other people who are suicidal
themselves, then the IV strategy would not work. As we will show below,
this does not appear to be a problem, and suicidal behavior of friends
appears to be a valid instrument. Using lagged suicidal behavior of
friends might be an alternative way to address some of these concerns,
but the lagged value does not have much predictive power in the
first-stage suicide models. We rely on friends' suicidal behaviors
as instruments in the absence of better alternatives.
The third way we address the endogeneity issue and guard against
unobserved omitted family and background characteristics is to exploit
the genetic oversample of the data and to estimate sibling and twin
fixed effects models. Any observed or unobserved background measures
common to both siblings and especially to twins will be controlled by
estimating a model with family fixed effects. To the extent that sibling
or twin pairs are exposed to the same unobservables, a family fixed
effects model will further eliminate unobserved heterogeneity. In order
to implement this design, we restrict our sample to siblings, and
estimate models of the form
[L.sub.i] = [delta][S.sub.i] + [X'.sub.i][lambda] +
[[gamma].sub.s] + [[delta].sub.h] + [gamma][FAMID.sub.f] +
[[eta].sub.i], (2)
where X' is a vector of fewer control variables specified in
Equation 1 and [FAMID.sub.f] is a vector of family identifiers. Since
any observed or unobserved background measures common to both siblings
will be controlled for by the fixed effects, the only things that differ
between siblings, such as gender, marital status, test scores, and drug
use, will be included in the vector X'. These fixed effects models
comprise a powerful method with which to control for family background
characteristics and experiences common to both individuals that might be
correlated with the suicidal behaviors and the outcome measures.
Unfortunately, one potential problem with this fixed effects
approach is that the results may be biased if there are individual
experiences that are correlated with suicidal behavior and that differ
between siblings/twins. Given that our sample is of young adults, it is
likely that events and environments related to the family will be picked
up by the fixed effects. Also, it is important to acknowledge the
possibility that an individual may be depressed or traumatized by the
suicidal behavior of his or her sibling or twin, which may in turn cause
him or her to engage in suicidal thoughts or attempts. In this case, the
difference in labor market outcomes between the two will be reduced,
which may cause bias in the estimated coefficients. However, we guard
against this problem by controlling for depression in Waves 1 and 3 in
some of our specifications.
5. Data
The data for this project come from the National Longitudinal Study
of Adolescent Health (Add Health). (2) The Add Health is the largest and
most comprehensive nationally representative survey of adolescents ever
undertaken. The first wave of the survey was administered between
September 1994 and April 1995 to 20,745 youths in grades 7 through 12.
Approximately 200 adolescents were randomly selected from each of 132
schools that are representative of U.S. schools with respect to county,
urbanicity, school size, school type, and ethnicity. The adolescents
were interviewed for the second time between April and August 1996 for
Wave 2. The interviews were administered using a Computer Assisted
Personal Interviewing/Computer Assisted Self Interviewing survey
instrument.
Of the original Wave 1 respondents, 15,170 were re-interviewed
between August 2001 and April 2002 for Wave 3. There are about 5500
cases excluded from Wave 3 for various reasons, including respondents
moving out of the country, involvement in active military duty,
incarceration and institutionalization, death, and failure to locate
respondents despite repeated attempts. In order to assess whether
individuals who are suicidal in Wave 1 are more likely to be excluded
from Wave 3, we compared the means of suicide thoughts and suicide
attempts, as reported in wave, between those who exit the sample from
Waves 1 to 3 and those who stayed in the sample. For both suicide
thoughts and suicide attempts, we could not reject the hypothesis that
these means are equal to each other. Therefore, we believe that the
sample attrition is unlikely to be correlated with the suicidal
behaviors in our data. The Wave 3 respondents constitute our main
analysis sample. As described below, we also utilize a number of
questions from Wave 1. (3)
One interesting feature of Add Health is the genetic oversample,
which consists of a large number of siblings and twins. As one of our
identification strategies, we limit our sample and estimate fixed
effects models. There are a total of 2134 siblings and 760 twins in the
sample. It is noteworthy that the fractions of siblings and twins who
report suicide thoughts and suicide attempts are similar to those
reported in the full sample.
Dependent Variables
Our dependent variable is a dichotomous indicator for whether or
not the individual is currently engaged in a productive (or
work-related) activity. This indicates whether the individual currently
works, attends school, or both. That is, the variable Work-school equals
1 if the respondent is either working, in school, or both, and it equals
0 otherwise. We focus on both outcomes because the age range of our
sample, 18-26 years, represents the ages at which individuals are
commonly engaged in both activities. In fact, about 38% reported going
to school, and 63% of those who are working are also in school, which
makes it difficult to separate these two outcomes. (4)
Suicide Variables
The Add Health contains a series of questions about suicidal
behaviors of the respondents, their friends, and family members in each
wave. The self-suicidal behavior questions include whether the
respondent seriously thought about committing suicide in the past 12
months (termed suicide thoughts) and whether she/he attempted suicide in
the past 12 months (termed suicide attempt). To maintain
confidentiality, no paper questionnaires were used in Add Health.
Rather, data were recorded on laptop computers. For more sensitive
material such as suicidal behavior, the respondent entered his or her
own answers in privacy. (5) The two questions on suicide from Wave 3
constitute our primary measures of suicidal behavior. To the extent that
suicide attempts are reflective of a more serious mental health problem
than having suicide thoughts, these two measures provide an opportunity
to assess the differential effect of the degree of suicidal behavior on
our outcome measures.
Table 1 presents the descriptive statistics of the variables used
in the analysis, as well as their definitions. The first column displays
the means for the full sample. The next two columns display the means
for the subsample of individuals who report that they had suicide
thoughts in the past 12 months and those who did not report having such
thoughts. As shown in Table 1, 6% of the sample seriously thought about
committing suicide during the past 12 months, and 1.6% reported
attempting suicide during the past 12 months. The same figures from Wave
1 are 13.4% and 3.7%, respectively. Note that among individuals with
suicide thoughts, about 27% actually attempted suicide. These statistics
correspond well with figures from other surveys. For example, the rates
of suicide thoughts and attempts from the 1991-1992 National Comorbidity
Survey are 5.2% and 1.4%, respectively, for youths ages 15-24 years.
In Wave 3, 6.7% of our sample reported having friends who tried to
kill themselves during the past 12 months, and about 3% reported having
family members who tried to kill themselves during the same period.
These numbers are down from 17.5% for friends and 4.4% for family
members in Wave 1. The decline in the suicidal behavior of family
members between Wave 1 and Wave 3 is consistent with the general decline
in suicides that started in 1992 (Lubell et al. 2004).
As illustrated in Table 1, 83.4% of our sample is engaged in a work
or schooling activity. The engagement in productive activity is less
common (79.2% vs. 83.7%) among those with suicide thoughts than those
with none.
Other Control Variables
The richness of the Add Health allows us to control for a large set
of background variables in our analyses. Their definitions and
descriptive statistics are reported in Table 1. The set includes
indicators for age, gender, race, ethnicity, U.S. resident status,
marital status, mother's educational attainment, non-wage income,
religion, physical health status, and standard Picture Peabody
Vocabulary Test scores from Wave 1. (6) We also explore expanded models
with additional covariates. Three groups of variables are
included--background family characteristics, background individual
characteristics, and history of substance use. The variables in each are
described below.
Background family characteristics include indicators of whether the
person experienced any type of abuse during childhood, whether she/he
spent time in foster care, and whether the father had ever been in jail.
The background individual characteristics include indicators for whether
the respondent exhibits the following behaviors or attitudes: volunteer
work as a teenager; ran away from home when in high school; had
psychological or emotional counseling in Wave 1; repeated a grade as of
Wave 1; was suspended from school in Wave 1; had trouble with teachers
everyday in Wave 1; had trouble with students everyday in Wave 1; felt
close to people at school in Wave 1; felt part of school in Wave 1; and
felt that students were prejudiced in Wave 1. Measures of past substance
use include the following dichotomous indicators: cocaine use in Wave 1;
marijuana use in Wave 1; participation in a drug abuse program in Wave
1; being drunk at least once a week past 12 months in Wave 1; reports of
alcohol being easily available at home in Wave 1; and reports of drugs
being easily available at home in Wave 1.
It is important to include the extensive sets of variables because
they will help reduce the amount of unobserved factors in the error term
that are correlated with both suicidal behavior and productive activity.
For example, negative experiences early in life could predispose
individuals to risky, self-destructive, or aggressive behaviors by
impairing their self-esteem and damaging their ability to form
relationships with others (Felitti et al. 1998; Veltman and Browne 2001;
Dube et al. 2003). However, many of these variables may be endogenous
themselves; therefore, models are estimated with and without these
potentially endogenous variables, so that we can gauge the effects of
the inclusion or exclusion of these variables on the coefficients of
interest.
As part of the expanded set of variables, we also include in some
models measures of mental health problems and depressive symptoms.
Depressive symptoms are measured in Wave 1 of Add Health using 18 of the
20 standard questions from the Center for Epidemiological Scale for
Depression (CES-D). (7) In Wave 3, Add Health includes only nine of
these questions. Responses in the depressive symptoms scale include 0
(never or rarely), 1 (sometimes), 2 (a lot of the time), and 3 (most of
the time or all of the time). (8) After summing up scores from these
questions, we generate dichotomous indicators that equal 1 if the
individual's score places them at the 80th percentile or higher in
the sample distribution for that wave and that equal 0 otherwise. The
80th percentile corresponds to the frequently used CES-D score cut-off
of 16 or more to indicate depression (Civic and Holt 2000). (9) We
consider these indicators to reflect elevated levels of depressive
symptoms. The CES-D scale does not correspond to a DSM-IV diagnosis of
major depression. It is used primarily as a screening tool for
depression, not as a diagnostic tool (Eaton et al. 2004). We recognize
that depression is endogenous in our models for the same reasons that
suicidal behaviors are endogenous. For this reason, we present models
with and without the measures of depression. In addition, we present
some models that only include the depression measure from Wave 1 in
order to avoid some of the bias due to potential reverse causality, as
depression will have been measured before the labor market and schooling
choices are observed. Lastly, we show models that include depression
measured both at Wave 1 and Wave 3.
Note that the effects of depressive symptoms on labor market and
school outcomes are not the main focus of this paper, because the CES-D
is primarily a screening tool for depression and may not be able to
identify cases of depression precisely. Also, the scale of depressive
symptoms is a very narrow measure of mental illness, as depression is
only one of the many factors that affect human capital formation.
Suicide behaviors, by contrast, are associated with many different
mental illnesses and therefore represent a broad scope of illnesses.
Suicidal behaviors are much more likely than the CES-D score to
represent the most severe cases of illness. It is the severe cases that
are the most likely to have negative schooling and labor market
outcomes.
Despite the problems with the depression measures, we believe it is
important to show models that include and exclude these measures in
order to see the influence of this mental illness on the estimates of
suicidal behaviors. This exercise will help us to gauge, albeit rather
imperfectly, whether or not suicidal behaviors are merely representing
the underlying depressive symptoms. Large decreases in the magnitude of
the coefficients on suicide thoughts and attempts that result when our
measures of depression are included will indicate that the suicidal
behaviors are likely representing the effects of this one particular
mental illness, which is commonly associated with suicide. Small or no
changes in the coefficients will imply that suicidal behaviors affect
human capital formation independent of depressive symptoms; however, we
still will not be able to isolate indirect effects from other mental
illness. That is, even with depression held constant, the coefficients
on suicidal behaviors will still represent a combination of the direct
and indirect effects, since depression is only one of the many
conditions associated with suicides. Unfortunately, data limitations do
not allow us to control for all of the possible comorbid conditions.
In order to conserve a sample as large and representative as
possible, we constructed a dummy variable for "missing
category" for the variables for which at least one observation was
missing for any reason. This method allows us to utilize a sample size
of 14,401, which is larger than those usually employed in most other
studies. Age in our sample only ranges from 18 to 26 years. We use dummy
variables for age in order to capture any non-linear association between
age and the outcomes variables. Certain variables from Wave 1 are used
to avoid the potential bias from any reverse causality. For example, we
use the standard test scores and alcohol and illicit drug use from Wave
1 because the current values may be endogenous to the current productive
activity. Furthermore, we do not include the individual's own years
of schooling into the models because (i) this variable may be
endogenous, and (ii) 38% of our sample is still in school. Instead, we
adopt a quasi-reduced form approach by substituting in the determinants
of human capital accumulation, such as mother's education, physical
health, and non-wage income. (l0) However, we experimented with models
that include the number of years of schooling, models that include the
standard test scores from Wave 3, and models that are only estimated for
the non-school sample (the outcome is "work" in that case).
Results are all similar to those presented in this paper and are
available upon request.
The large sample size, the longitudinal nature of the data, along
with the genetic oversample, and the richness of the available
individual and family characteristics make the Add Health an excellent
data set for examining the link between suicidal behaviors, underlying
mental health issues, and productive activities. We believe that until
other longitudinal labor market surveys include comprehensive measures
of mental illness, the Add Health is probably the best available data
set to address the question studied in this paper.
6. Results
We begin by presenting OLS results for the effects of suicide
thoughts and attempts on the probability of engaging in a productive
activity, as measured by working or attending school. Linear probability
models are shown with robust standard errors to adjust for
heteroskedasticity in the error term. (11) Tables 2A and B display the
results for the effects of suicide thoughts and attempts, respectively.
The columns in these tables present increasingly rich specifications.
All of these specifications contain both the state and school fixed
effects. Column 1 shows the baseline estimates from a specification that
includes variables representing only the individual's own
characteristics. Column 2 adds to this list six variables on past own
and family suicide attempts along with three variables representing
family environment. Column 3 expands the variables in column 2 by adding
depression and counseling variables from Wave I, and column 4 adds to
this set the depression indicator from Wave 3. Lastly, column 5 includes
additional family background and individual attitude characteristics
from Wave 1. These variables may be endogenous, but as we will discuss
below, their inclusion has very little influence on the estimated
effects of the suicidal behaviors on the productive activities.
A comparison of the results across the columns in Tables 2A and B
shows that including the larger set of variables does not affect the
sign or the statistical significance of the suicide coefficients in the
work-school models. It is interesting to note that both the coefficients
on suicide thoughts and attempts fall about 1 percentage point between
columns 1 and 2 and then another half a percentage point between columns
2 and 3 and columns 3 and 4. But there is little change in the estimated
effects between columns 4 and 5. The results from Tables 2A and B
indicate that having suicide thoughts is associated with a decrease in
the probability of being in a productive activity by a range of about
3-5 percentage points, and attempting suicide is associated with a
decrease in the probability of being in a productive activity by a range
of 9-12 percentage points. These figures represent fairly narrow ranges
that point to the robustness of our estimates to additional covariates.
In fact, these coefficients indicate that suicide thoughts and attempts
are associated with a 4-6% and an 11 14% decrease in the probability of
productive activities on average, respectively.
Another interesting finding is that the inclusion of the measures
of depression has no appreciable effect on the magnitude of these
coefficients. Also, using a specification with a rich set of control
variables does not have much effect on the overall fit of the model, as
indicated by the [R.sup.2] values. The fact that controlling for a large
set of background characteristics and past suicidal behavior only
slightly reduces the effect of current suicide thoughts and attempts can
be interpreted to mean that unobserved heterogeneity accounts for only a
small percentage of the effect of suicidal behavior on human capital
activities. However, an alternative interpretation is that the
additional variables, though statistically significant in the
work-school equation, are not able to capture the omitted variables that
are correlated with suicide thoughts and attempts. This indicates that
the results from the two-state least squares (TSLS) estimation and the
fixed effects models are necessary for drawing more solid conclusions.
Given that the coefficient estimates exhibit very little change
between columns 4 and 5, and to minimize the risk of including
potentially endogenous variables into our model, we pick the more
parsimonious column 4 specification, as opposed to column 5, as our
preferred specification. It is interesting that current suicidal
thoughts and attempts are associated with the decision to go to work or
school even after holding constant past suicide attempts and while
holding constant current and past suicide attempts of a family member.
In fact, none of these other suicide measures are statistically
significant predictors of the work-school decision in any OLS model. It
appears that these factors primarily affect the outcomes of individuals
through their influence on the current suicidal behaviors of
individuals.
The other control variables in Tables 2A and B are usually
consistent with our expectations and the results from the relevant
literature. The indicators for depressive symptoms in Waves 1 and 3 are
negatively associated with the probability of work-school. High standard
test scores at high school and having a mother with more than a high
school education are both associated with increases in working and
schooling. Being in good physical health is a strong predictor of
engaging in productive activities, while having spent time in foster
care and having a father who was jailed in the past are negatively
associated with these outcomes. Finally, having had trouble with
teachers and other students in high school, having repeated a grade and
having been suspended from school, and having run away from home in the
past are all negatively associated with working or schooling, while
having spent time on volunteering activities in high school is
positively associated with these outcomes.
Next, we present the results from the models that estimate the
determinants of suicide thoughts and attempts in Table 3. These also
constitute our first-stage models, which we use for the TSLS estimation.
The first column displays the results for the determinants of suicide
thoughts, and the second column displays the results for the
determinants of suicide attempts. In addition to the variables in our
preferred specification, as explained above, these models also include
the instrumental variables--suicidal behavior of friends in the past 12
months, from Waves 1 and 3. State and school fixed effects are also
included.
The most striking result from Table 3 is that the suicidal
behaviors of peers and family members have a strong, positive
relationship with the suicidal behaviors of respondents. For example,
having a friend who attempted suicide in Wave 3 increases the
probability of suicide thoughts by about 12 percentage points and the
probability of suicide attempts by more than 3 percentage points.
Similarly, having a family member who had attempted suicide in the past
12 months increases suicide thoughts by 9 percentage points and suicide
attempts by 3 percentage points. A past suicide attempt is highly
associated with current suicidal behavior, increasing suicide thoughts
and suicide attempts by about 8 and 4 percentage points, respectively.
This result indicates that mental health problems during adolescence may
have persistent effects on the mental health of individuals even after
they enter adulthood. The past suicidal experiences of the friends and
family members have insignificant and negligible effects on an
individual's own suicidal behavior. The effects of these variables
are likely to be captured by the individual's own suicidal behavior
in the past. Nevertheless, the coefficients on the two instrumental
variables--suicide attempts by friends in Waves 1 and 3--are jointly
significant at less than the 1% level.
A few other variables are worth mentioning for their efficacy in
predicting suicide thoughts and attempts. Individuals in the top 20th
percent of the CES-D distribution (the depression indicator) are more
likely to have suicide thoughts and attempts. Having a standardized test
score in higher quartiles actually increases the probability of having
suicide thoughts compared with those individuals having scores in the
bottom quartile. It is interesting to note that the effect monotonically
increases as one moves in the direction of higher test scores. However,
the differences disappear when suicidal attempts are considered. Having
suffered any type of abuse as a child is positively related to current
suicide thoughts and attempts. A similar pattern is observed for the
effect of having a father who was ever jailed; although, the effect is
only statistically significant for suicide thoughts. Being married and
being in good physical health are negatively related to both suicide
thoughts and attempts.
Table 4 shows the results from TSLS estimation of the effect of
suicidal behaviors on the outcome variable for our preferred
specification. 12 Suicide attempts by friends in Waves 1 and 3 serve as
the instruments. Intuitively, these instruments are plausible because
there is existing strong evidence that peer suicide affects one's
own state of mental health and the resulting behaviors. For example, in
a sample of high school students, Ho et al. (2000) found that there is a
high risk of suicidal behaviors and psychiatric disturbances among peers
of individuals who completed suicide. Cerel, Roberts, and Nilsen (2005)
found that adolescents who are exposed to peer suicide are more likely
to have suicidal thoughts and attempts and to engage in other
destructive behavior, such as substance use.
While it is not hard to imagine that a friend's suicidal
behavior strongly predicts an individual's own behavior, we believe
it is unlikely that the suicidal behavior of a friend will have direct
effects on one's own labor market and schooling decisions above and
beyond the effects on the individual's own suicidal behavior,
particularly when a measure of depression is held constant. However, one
possibility is that youths react to peer suicide by engaging in risky
behavior, such as substance abuse, and if that behavior has a direct
effect on labor market and school outcomes, the exogeneity of the
instruments may be called into question. One can guard against this
criticism by including controls for current risky behavior, such as drug
and alcohol use. The problem is that these variables may themselves be
endogenous to our outcome measures. This is why we control for measures
of past drug and alcohol use in the models. Despite the potential
endogeneity problem, we re-estimated our models including current drug
and alcohol use. These models did not alter the estimates of suicidal
behavior in any significant way. (13)
The validity of our instrumental variables analyses hinges on the
assumption that friends' suicidal behaviors are strongly associated
with the individuals' own suicidal behaviors, while having no
direct association with the outcome measures that we examine. The
strength of the instruments can be gauged in two way--theoretically and
empirically. As reported at the bottom of Table 4, the two instruments,
a friend's attempt in the first wave and a friend's attempt in
the third wave, are strong predictors of current suicidal behaviors, as
indicated by the F-statistics. The overidentification test (Hansen J
statistic) indicates that the instruments are appropriately excluded
from the second stage. However, the Durbin-Wu-Hausman tests are not
rejected for either of the models, indicating that the OLS and IV
coefficients are not statistically different from each other. Indeed,
the TSLS results are qualitatively similar to those in Tables 2A and B,
but the magnitudes are much larger. Having suicide thoughts decreases
the probability of being in a productive activity by a range of about 21
percentage points, while a suicide attempt decreases the probability of
being in a productive activity by about 74 percentage points. Taken
together, these tests indicate that the endogeneity of suicidal behavior
does not improve upon OLS estimates after controlling for all of these
variables and the fixed effects. We also prefer OLS over the IV results
because OLS estimates have lower standard errors (Cawley 2004; Chou,
Rashad, and Grossman 2005).
The results from the twin and sibling fixed effects analyses are
presented in Table 5. Fixed effects models are a powerful way to control
for omitted variable bias, and they are used increasingly in economics
(e.g., Currie and Tekin 2006; Currie and Stabile 2007). Only the
coefficients on variables of interest are shown for brevity. We believe
the results of these specifications are reliable, since the fixed
effects can control for a host of unmeasured, time-invariant
characteristics that might be correlated with the suicidal behaviors and
the outcome measures. Obviously, the number of control variables in
these models is much lower than the OLS and IV models, as many of the
background variables exhibit no variation between twin and sibling
pairs.
As documented in Table 5, these results are usually consistent with
those from the OLS models. Looking at the sibling fixed effects,
suicidal thoughts and attempts are associated with about a 9% decrease
in the probability of engaging in productive activities; although, the
latter is not estimated with much precision. The estimates from the twin
fixed effects are larger than the OLS and sibling fixed effects
estimates; although, they are qualitatively similar. Suicide thoughts
and attempts are associated with 19% and 31% decreases in the
probability of engaging in productive activities, respectively. Note
that the identification in fixed effects models comes from discordant
reports of suicide thoughts and attempts between pairs, and the number
of discordant pairs for the twin fixed effects model is expectedly low.
In particular, there are only 10 twin pairs with discordant reports of
suicide attempts out of a total of 380 twin pairs, while the number of
twin pairs with discordant reports of suicide thoughts is 45. This may
explain in part why the estimates in the twin fixed effects model are
larger than both the OLS and the sibling fixed effects estimates.
Nevertheless, the qualitative similarity between the OLS and the fixed
effects estimates indicates that measurement error is unlikely to be the
source driving the estimates.
Specification Checks
One can argue that family members' suicide attempts may be
endogenous to the individuals' own suicidal behavior. For example,
a respondent may attempt suicide and a parent may follow in response. If
this is the case, our results from the OLS and twin fixed effects could
be biased. However, the coefficients on the family members' suicide
attempts are not statistically significant in any of our models. In
fact, our results basically remain the same when we exclude these
variables from our models.
Another useful exercise is to estimate models that only include the
past suicidal behavior, since these models are not subject to any
reverse causality problem to begin with. Also, having both the current
and past suicidal behavior in the same models might be problematic as a
result of multicollinearity. Therefore, we estimated our models
excluding the current suicidal behavior variables. In these models, the
past suicidal attempt has a negative coefficient in the work-school
model.
In order to see if suicidal behaviors have a differential impact on
the decisions to go to work, school, or both, we estimate a multinomial
logit model in Table 6. In doing this, we have separated the dichotomous
indicator of being in a productive activity into its possible
components. The decisions modeled in this table are (i) school, (ii)
work, (iii) school and work together, or (iv) no work and no school,
which is the omitted reference category. Note that estimating a fixed
effects multinomial logit model in our context may be problematic
because it has been shown by Monte Carlo simulations that the fixed
effects estimator produces a large finite sample bias in discrete choice
models when the number of observations in each group is very small
(Greene 2002). In our case, there are two individuals in each twin pair,
by definition. Therefore, the fixed effects model in this context will
be unreliable, so we rely on the rich set of variables in our preferred
specification from Tables 2A and B to help control for omitted
variables. Since we do not explicitly account for the potential
endogeneity of the suicide behaviors in the multinomial logit, we treat
these results as merely demonstrative and do not place much emphasis on
the magnitude of the coefficients.
The estimates from the multinomial logit model are presented in
Table 6. The coefficients in the first three columns show the results
for suicidal thoughts, and the coefficients in the last three columns
show the results for suicide attempts. The omitted outcome in the
multinomial logit models is no work and no school. The results indicate
that suicidal behaviors, as measured by suicide thoughts and suicide
attempts, decrease the probability that an individual is engaged in
work, school, or both of these activities, in comparison to the omitted
category of not working and not going to school.
7. Conclusion
This paper expands our understanding of the link between mental
health and human capital formation by providing insights into the
effects of suicidal behavior on the outcome of productive activities of
young adults. The suicidal behaviors are measured as suicide thoughts
and suicide attempts, and productive activities are measured as engaging
in work and/or schooling activities. Obtaining a reliable effect of
suicidal behavior on productivity outcomes can be problematic because of
the presence of unobserved heterogeneity and a potential reverse
causality. In this paper, we employ three strategies to eliminate these
problems. First, we control for a very large set of background variables
that are likely to be correlated with both suicidal behavior and our
outcome measure. Second, we use instrumental variables to control for
both unobserved heterogeneity and reverse causality. Finally, we
estimate models with family fixed effects to sweep out any unobservables
that are common to both twins and siblings.
The results from all three approaches indicate that suicide
thoughts and attempts have negative effects on the work and schooling
decisions of young adults. All of the effects are found to be robust to
different sets of control variables and various specification tests. It
is also interesting to note that the size of the effect of suicide
attempt is larger than that of the suicidal thoughts. This is a sensible
result, given that suicide attempt is likely to be an indicator of a
more serious mental health problem than is having suicidal thoughts
only.
The results shown in this paper highlight the costs to individuals
and to society resulting from suicidal behaviors. The fact that all of
the three strategies that we employ to tease out both the unobserved
heterogeneity and reverse causality point to a negative link between the
suicidal behaviors and the outcome measure makes us believe that the
detrimental effects are consistent with a causal explanation.
Furthermore, the small and statistically insignificant coefficients on
past suicide attempts arising from models that both include and exclude
current suicidal behaviors indicate that there is no long-term effect of
past attempts (i.e., attempts during high school) on future human
capital formation. This result should be interpreted with caution,
however, as teenagers who attempt suicide may receive mental health
treatment that prevents future deleterious effects.
We would like to express our sincere thanks to Carol Tremblay for
her very helpful suggestions and comments. Jason Delaney and Solomon
Tesfu provided excellent research assistance.
Received October 2006; accepted December 2007.
References
Alexopoulos, G. S., M. L. Bruce, J. Hull, J. A. Sirey, and T.
Kakuma. 1999. Clinical determinants of suicidal ideation and behavior in
geriatric depression. Archives of General Psychiatry 56:1048 53.
Anderson, R. N., and B. L. Smith. 2003. Deaths: Leading causes for
2001. National Vital Statistics' Report 52(9):1 86.
Angrist, J. D., and A. B. Krueger. 1999. Empirical strategies in
labor economics. In Handbook of labor economics', Volume 3A, edited
by Orley Ashenfelter and David Card. Amsterdam: Elsevier, pp. 1277 366.
Bartel, A., and P. Taubman. 1979. Health and labor market success:
The role of various diseases. Review of Economics and Statistics 61:1 8.
Bartel, A., and P. Taubman. 1986. Some economic and demographic
consequences of mental illness. Journal of Labor Economics 4:243-56.
Boergers, J., A. Spirito, and D. Donaldson. 1998. Reasons for
adolescent suicide attempts: Associations with psychological
functioning. Journal of the American Academy of Child and Adolescent
Psychiatry 37:1287 93.
Borst, S. R., and G. G. Noam. 1989. Suicidality and psychopathology
in hospitalized children and adolescents. Acta Paedopsychiatrica
52:165-75.
Cawley, John. 2004. The impact of obesity on wages. Journal of
Human Resources 39:451-74. Centers for Disease Control and Prevention.
1992. Youth suicide prevention programs: A resource guide. Atlanta, GA:
U.S. Department of Health and Human Services, Public Health Service,
National Center for Injury Prevention and Control.
Cerel, J., T. A. Roberts, and W. J. Nilsen. 2005. Peer suicidal
behavior and adolescent risk behavior. Journal of Nervous and Mental
Disease 193:237-43.
Chatterji, P., D. Dave, R. Kaestner, and S. Markowitz. 2004.
Alcohol abuse and suicide attempts among youth. Economics and Human
Biology 2:159-80.
Chou, S.-Y., I. Rashad, and M. Grossman. 2005. Fast-food restaurant
advertising on television and its influence on childhood obesity. NBER
Working Paper No. 11879.
Civic, D., and V. L. Holt. 2000. Maternal depressive symptoms and
child behavior problems in a nationally representative birthweight
sample. Maternal and Child Health Journal 4:215-21.
Conti, D. J., and W. N. Burton. 1994. Economic impact of depression
in a workplace. Journal of Occupational Environmental Medicine 36:983-8.
Currie, J., and M. Stabile. 2007. Mental health in childhood and
human capital. NBER Working Paper No. 13217.
Currie, J., and E. Tekin. 2006. Does child abuse cause crime? NBER
Working Paper No. 12171.
Cutler, D. M., E. L. Gtaeser, and K. E. Norberg. 2001. Explaining
the rise in youth suicide. In Risky behavior among youth, edited by J.
Gruber. Chicago: University of Chicago Press, pp. 219-69.
Dube, S., V. Felitti, M. Dong, W. H. Giles, and R. Anda. 2003. The
impact of adverse childhood experiences on health problems: Evidence
from four birth cohorts dating back to 1900. Preventive Medicine
37:268-77.
Eaton, W. W., C. Muntaner, C. Smith, A. Tien, and M. Ybarra. 2004.
Center for epidemiologic studies depression scale: Review and revision
(CESD and CESDR). In The use of psychological testing for treatment
planning and outcomes assessment, 3rd edition, volume 3, edited by M. E.
Maruish. Mahwah, NJ: Lawrence Erlbaum Associates, pp. 363 78.
Ettner, S., R. Frank, and R. Kessler. 1997. The impact of
psychiatric disorders on labor market outcomes. Industrial and Labor
Relations Review 51:64-81.
Felitti, V. J., R. Anda, D. Nordenberg, D. Williamson, A. Spitz, V.
Edwards, M. Koss, and J. Marks. 1998. Relationship of childhood abuse
and household dysfunction to many of the leading causes of death in
adults. American Journal of Preventive Medicine 14:245-58.
Fergusson, D. M., and L. J. Woodward. 2002. Mental health,
educational, and social role outcomes of adolescents with depression.
Archives of General Psychiatry 59:225-31.
Frank, R., and P. Gertler. 1991. An assessment of measurement error
bias for estimating the effect of mental distress on income. Journal of
Human Resources 26:154-64.
French, M., and G. Zarkin. 1998. Mental health, absenteeism and
earnings at a large manufacturing worksite. Journal of Mental Health
Policy and Economics 1:161-72.
Goldsmith, S. K., T. C. Pellmar, A. M. Kleinman, and W. E. Bunney,
editors. 2002. Reducing suicide, A national imperative. Washington, DC:
The National Academies Press.
Goodman, E. 1999. The role of socioeconomic status gradients in
explaining differences in US adolescents' health. American Journal
of Public Health 89:1522-8.
Greenberg, P. E., L. E. Stiglin, S. N. Finkelstein, and E. R.
Berndt. 1990. The economic burden of depression in 1990. Journal of
Clinical Psychiatry 2:32-5.
Greene, William. 2002. The bias of the fixed effects estimator in
nonlinear models. Working Paper, Department of Economics, New York
University.
Hamermesh, D. S., and N. M. Soss. 1974. An economic theory of
suicide. Journal of Political Economy 82:83-98.
Hamilton, V. H., P. Merrigan, and E. Dufresne. 1997. Down and out:
Estimating the relationship between mental health and unemployment.
Health Economics 6:397-406.
Ho, T. P., P. W. Leung, S. F. Hung, C. C. Lee, and C. P. Tang.
2000. The mental health of the peers of suicide completers and
attempters. Journal of Child Psychology and Psychiatry 41:301 8.
Johnson, J., M. M. Weissman, and G. L. Klerman. 1990. Panic
disorder, comorbidity, and suicide attempts. Archives of General
Psychiatry 47:805-8.
Kessler, R. C., P. A. Berglund, C. L. Foster, W. B. Saunders, P. E.
Stang, and E. E. Waiters. 1997. Social consequences of psychiatric
disorders, II: Teenage parenthood. American Journal of Psychiatry
154:1405-11.
Lubell, K. M., M. H. Swahn, A. E. Crosby, and S. R. Kegler. 2004.
Methods of suicide among persons aged 10-19 years United States,
1992-2001. Morbidity and Mortality Weekly Report 53:471-3, Available
http://www.cdc. gov/mmwr/PDF/wk/mm5322.pdf.
Marcotte, D. E. 2003. The economics of suicide, revisited. Southern
Economic Journal 69:628-43.
Maris, R. W., A. L. Berman, J. T. Maltsberger, and R. 1. Yufit,
editors. 1992. Assessment and prediction of suicide. New York: The
Guilford Press.
Marmorstein, N. R., and W. G. Iacono. 2001. An investigation of
female adolescent twins with both major depression and conduct disorder.
Journal of the American Academy of Child and Adolescent Psychiatry
40:299 306.
Miller, T. R., K. L. Covington, and A. F. Jensen. 1999. Costs of
injury by major cause, United States, 1995: Cobbling together estimates.
In Measuring the burden of injuries: Proceedings of a conference in
Noordwijkerhout, Netherlands, May 13-15, 1998, edited by S. Mulder,
Available http://www.edarc.org/pubs/tables/youthsui.htm.
Miller, T. R. 1995. Databook on nonfatal injury: Incidence, costs,
and consequences. Washington, DC: Urban Institute Press, pp. 166-7.
Mullahy, J., and J. Sindelar. 1990. Gender differences in the
effects of mental health on labor force participation. In Research in
Human Capital and Development 6:125-46, JAI Press Inc., pp. 125-46.
Overbeek, G., W. Vollebergh, R. C. M. E. Engels, and W. Meeus.
2003. Young adults' relationship transitions and the incidence of
mental disorders: A three-wave longitudinal study. Social Psychiatry
& Psychiatric Epidemiology 38:669-76.
Radloff, L. 1977. The CES-D Scale: A self-reported depression scale
for research in the general population. Applied Psychological
Measurement 1:385 401.
Shaffer, D., and L. Craft. 1999. Methods of adolescent suicide
prevention. Journal of Clinical Psychiatry 69(suppl. 2):70-4.
Simon, G. E., and M. Von Korff. 1998. Suicide mortality among
patients treated for depression in an insured population. American
Journal of Epidemiology 147:155-60.
Slap, G., E. Goodman, and B. Huang. 2001. Adoption as a risk factor
for attempted suicide during adolescence. Pediatrics 108:E30.
Stoep, A. V., N. S. Weiss, E. S. Kuo, D. Cheney, and P. Cohen.
2003. What proportion of failure to complete secondary school in the
U.S. population is attributable to adolescent psychiatric disorder?
Journal of Behavioral Health Services & Research 30:119-24.
U.S. Department of Education. 2001. 23rd Annual report to Congress
on the implementation of the Individuals with Disabilities Education
Act. Washington, DC: U.S. Department of Education.
Veltman, M., and K. D. Browne. 2001. Three decades of child
maltreatment research: Implications for the school years. Trauma,
Violence, and Abuse 2:215-39.
(1) Selection bias may be present in our data, as it is possible
that individuals with severe mental illness in Wave 1 may have dropped
out of the sample because of hospitalization or because they committed
suicide (although only 96 individuals are dropped from the data between
Waves 1 and 3 as a result of death, the causes of which are unknown). If
this is the case, then our sample would represent people with less
severe illnesses. The extent of the problem should be very small, as
only 41 of the original 20,745 adolescents were not re-interviewed
because they were physically or mentally incapable of participating.
(2) The Add Health is a program project designed by J. Richard
Udry, Peter S. Bearman, and Kathleen Mullan Harris, and it was funded by
a grant (P01-HD31921) from the National Institute of Child Health and
Human Development, with cooperative funding from 17 other agencies.
Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle
for assistance in the original design. Persons interested in obtaining
data files from Add Health should contact Add Health, Carolina
Population Center, 123 W. Franklin Street, Chapel Hill, NC 27516-2524
(www.cpc.unc.edu/ addhealth/contract.html).
(3) We exclude responses from Wave 2 because a large number of
individuals were not interviewed in Wave 2 but were interviewed in Waves
1 and 3.
(4) Another possible outcome to analyze is the wage rate. However,
many of these students are employed at or near minimum wage (with nearly
40% making less than $7 per hour), and the job choices in school and the
associated wages may not reflect accurately their future wage
trajectory.
(5) For less sensitive material, the interviewer read the questions
and entered the respondent's answers.
(6) The Add Health Picture Vocabulary Test (AHPVT) is a
computerized, abridged version of the Peabody Picture Vocabulary
Test-Revised. The AHPVT is a test of hearing vocabulary, designed for
persons aged 2 1/2 to 40 years who can see and hear reasonably well and
who understand standard English to some degree.
(7) See Radloff (1977) for more on the CES-D scale.
(8) Several items assess positive symptoms. These were reversed
before the scale scores were calculated. The Add Health CES-D is shown
to have a high internal consistency (Goodman 1999).
(9) Using a continuous CES-D scale yields very similar results to
those presented below.
(10) Our health variable is a measure of physical health.
Therefore, it should not be co-linear with the suicidal behavior. In any
case, we estimated models excluding physical health as a control, and
the results remained the same.
(11) We specify linear equations for ease of estimation and
interpretation. Least-squares estimates of coefficients in linear
probability models are consistent estimates of average probability
derivatives, but standard error estimates are biased as a result of
heteroskedasticity (Angrist and Krueger 1999). We report standard error
estimates that are robust to any form of heteroskedasticity.
(12) To economize on space, we present only the key coefficients.
The coefficients from other variables largely remain the same as those
in Tables 2A and B. The TSLS results for other specifications are very
similar to those presented in the paper. The full results are available
from the authors upon request.
(13) Reporting bias may also present a problem for the instruments:
That is, individuals who are suicidal themselves may be more likely to
report that they had suicidal friends. Unfortunately, we cannot know for
sure whether reporting bias plays an important role or not given the
data set that we have.
Erdal Tekin * and Sara Markowitz ([dagger])
* Department of Economics, Andrew Young School of Policy Studies,
Georgia State University, P.O. Box 3992, Atlanta, GA 30302-3992, USA;
E-mail
[email protected]; corresponding author.
([dagger]) Department of Economics, Emory University, Atlanta, GA
30322-2240, USA; E-mail sara.markowitz@emory. edu.
Table 1. Definitions and Descriptive Statistics
Full
Variable Definition Sample
Work-school Dummy variable = 1 if working 0.834
and/or attending school, (0.372)
= 0 otherwise
Suicide thoughts Dummy variable = 1 if ever 0.059
thought seriously about (0.235)
committing suicide during
the past 12 months,
= 0 otherwise
Suicide attempt Dummy variable = 1 if 0.016
actually attempted suicide (0.124)
during the past 12 months,
= 0 otherwise
Suicidal friend Dummy variable = 1 if any 0.067
friends tried to kill (0.250)
themselves, = 0 otherwise
Suicidal family Dummy variable = 1 if any 0.029
family members tried to (0.167)
kill themselves,
= 0 otherwise
Suicide attempt_w1 Dummy variable = 1 if 0.037
actually attempted suicide (0.189)
during the past 12 months
(reported at Wave 1),
= 0 otherwise
Suicidal friend_w1 Dummy variable = 1 if any 0.175
friends tried to kill (0.380)
themselves (reported at
Wave 1), = 0 otherwise
Suicidal family_w1 Dummy variable = 1 if any 0.044
family members tried to (0.204)
kill themselves (reported
at Wave 1), = 0 otherwise
Catholic Dummy variable = 1 if 0.255
Catholic, = 0 otherwise (0.436)
Protestant Dummy variable = 1 if 0.406
Protestant, = 0 otherwise (0.491)
No religion Dummy variable = 1 if no 0.205
religion or agnostic, (0.403)
= 0 otherwise
Other religion (a) Dummy variable = 1 if other 0.135
religion, = 0 otherwise (0.341)
Healthy Dummy variable = 1 if in good 0.954
physical health, = 0 otherwise (0.209)
Any abuse Dummy variable = 1 if 0.245
experienced sexual abuse, (0.430)
physical abuse, or neglect
from parents or other adult
caregivers by the start of
sixth grade, = 0 otherwise
Foster Dummy variable = 1 if ever 0.023
spent time in foster care, (0.151)
= 0 otherwise
Jailed father Dummy variable = 1 if father 0.147
ever spent time in prison, (0.354)
= 0 otherwise
Cocaine_w1 Dummy variable = 1 if ever 0.032
used cocaine (reported at (0.175)
Wave 1), = 0 otherwise
Marijuana_w1 Dummy variable = 1 if ever 0.277
used marijuana (reported at (0.448)
Wave 1), = 0 otherwise
Age Age in years 21.956
(1.772)
Age18 (a) Dummy variable = 1 if 18 0.01
years of age, = 0 otherwise (0.098)
Age19 Dummy variable = 1 if 19 0.095
years of age, = 0 otherwise (0.293)
Age20 Dummy variable = 1 if 20 0.132
years of age, = 0 otherwise (0.339)
Age21 Dummy variable = 1 if 21 0.161
years of age, = 0 otherwise (0.368)
Age22 Dummy variable = 1 if 22 0.189
years of age, = 0 otherwise (0.391)
Age23 Dummy variable = 1 if 23 0.191
years of age, = 0 otherwise (0.393)
Age24 Dummy variable = 1 if 24 0.163
years of age, = 0 otherwise (0.369)
Age25 Dummy variable = 1 if 25 0.051
years of age, = 0 otherwise (0.220)
Age26+ Dummy variable = 1 if 26 0.008
years of age, = 0 otherwise (0.090)
Male Dummy variable = 1 if male, 0.468
= 0 otherwise (0.499)
White Dummy variable = 1 if white, 0.662
= 0 otherwise (0.473)
Black Dummy variable = 1 if black, 0.227
= 0 otherwise (0.419)
Other race (a) Dummy variable = 1 if other 0.111
race, = 0 otherwise (0.315)
Hispanic Dummy variable = 1 if Hispanic 0.161
ethnicity, = 0 otherwise (0.368)
U.S. born Dummy variable = 1 if born in 0.92
the United States, (0.271)
= 0 otherwise
PVT_w1A (a) Standard Peabody test score 0.25
ranking from Wave 1 in the (0.434)
lowest 25th percentile
PVT_w1B Standard Peabody test score 0.257
ranking from Wave 1 in the (0.438)
25-50th percentile
PVT_w1C Standard Peabody test score 0.233
ranking from Wave 1 in the (0.423)
50-75th percentile
PVT_w1D Standard Peabody test score 0.26
ranking from Wave 1 in the (0.439)
highest 25th percentile
Married Dummy variable = 1 if 0.171
married, = 0 otherwise (0.377)
Mother high Dummy variable = 1 if mother 0.159
school--(a) has less than a high school (0.366)
degree reported at Wave 1,
= 0 otherwise
Mother high Dummy variable = 1 if mother 0.352
school has a high school degree (0.477)
reported at Wave 1,
= 0 otherwise
Mother high Dummy variable = 1 if mother 0.489
school+ has more than a high school (0.500)
degree at Wave 1,
= 0 otherwise
Non-wage Non-wage income in the past 1909.3
year in dollars (14,238.2)
Non-wage1 (a) Dummy variable = 1 if 0.031
non-wage income is <0, (0.173)
= 0 otherwise
Non-wage2 Dummy variable = 1 if 0.536
non-wage income is =0, (0.499)
= 0 otherwise
Non-wage3 Dummy variable = 1 if 0 < 0.158
non-wage income [less than (0.365)
or equal to] 1000,
= 0 otherwise
Non-wage4 Dummy variable = 1 if 1000 0.067
< non-wage income [less (0.250)
than or equal to] 2000,
= 0 otherwise
Non-wage5 Dummy variable = 1 if 2000 0.098
< non-wage income [less (0.297)
than or equal to] 5000,
= 0 otherwise
Non-wage6 Dummy variable = 1 if 5000 0.054
< non-wage income [less (0.227)
than or equal to] 10,000,
= 0 otherwise
Non-wage7 Dummy variable = 1 if 10,000 0.056
< non- wage income, (0.230)
= 0 otherwise
Depression_w1 Dummy variable = 1 if the 0.206
individual's CES-D score (0.404)
places him at the 80th
percentile or higher in the
sample distribution for
Wave 1
Depression_w3 Dummy variable = 1 if the 0.216
individual's CES-D score (0.412)
places him at the 80th
percentile or higher in the
sample distribution for
Wave 3
Volunteer work Dummy variable = 1 if did 0.365
volunteer work as a teenager (0.481)
Ran away Dummy variable = 1 if ran 0.079
away from home when in high (0.270)
school
Counseling_w1 Dummy variable = 1 if had 0.117
psychological or emotional (0.321)
counseling in the past 12
months in Wave 1
Drug abuse Dummy variable = 1 if 0.026
program_w1 participated in drug abuse (0.159)
program in Wave 1
Drunk_w1 Dummy variable = 1 if was 0.052
drunk at least once a week (0.221)
past 12 months in Wave 1
Alcohol at Dummy variable = 1 if alcohol 0.296
home_w1 was easily available at (0.457)
home-Wave 1
Drug at home_w1 Dummy variable = 1 if drugs 0.031
were easily available at (0.175)
home-Wave 1
Repeat grade_w1 Dummy variable = 1 if 0.207
repeated a grade as of (0.405)
Wave 1
School Dummy variable = 1 if 0.271
suspension_w1 suspended from school in (0.445)
Wave 1
Trouble with Dummy variable = 1 if had 0.076
teachers_w1 trouble with teachers (0.265)
everyday in Wave 1
Trouble with Dummy variable = 1 if had 0.077
students_w1 trouble with students (0.266)
everyday in Wave 1
Felt close to Dummy variable = 1 if felt 0.125
people_w1 close to people at school (0.331)
in Wave 1
Felt part of Dummy variable = 1 if felt 0.118
school_w1 part of school in Wave 1 (0.323)
Felt students Dummy variable = 1 if felt 0.329
prejudice_w1 that students were (0.470)
prejudiced in Wave 1
Number of
observations 14,401
Suicide Thoughts Suicide
Variable = 1 Thoughts = 0
Work-school 0.792 *** 0.837
(0.406) (0.370)
Suicide thoughts 1.000 0.000
(0.000) (0.000)
Suicide attempt 0.265 *** 0.000
(0.442) (0.000)
Suicidal friend 0.223 *** 0.057
(0.416) (0.233)
Suicidal family 0.095 *** 0.025
(0.294) (0.155)
Suicide attempt_w1 0.107 *** 0.032
(0.310) (0.178)
Suicidal friend_w1 0.245 *** 0.171
(0.431) (0.376)
Suicidal family_w1 0.064 *** 0.042
(0.245) (0.201)
Catholic 0.230 * 0.256
(0.421) (0.437)
Protestant 0.348 *** 0.410
(0.477) (0.492)
No religion 0.281 *** 0.200
(0.450) (0.400)
Other religion (a) 0.141 0.134
(0.349) (0.341)
Healthy 0.888 *** 0.958
(0.316) (0.200)
Any abuse 0.419 *** 0.234
(0.494) (0.423)
Foster 0.041 *** 0.022
(0.199) (0.147)
Jailed father 0.233 *** 0.142
(0.423) (0.349)
Cocaine_w1 0.052 *** 0.031
(0.222) (0.172)
Marijuana_w1 0.331 *** 0.274
(0.471) (0.446)
Age 21.649 *** 21.975
(1.837) (1.776)
Age18 (a) 0.014 0.009
(0.118) (0.097)
Age19 0.138 *** 0.092
(0.345) (0.289)
Age20 0.156 ** 0.131
(0.363) (0.337)
Age21 0.165 0.161
(0.371) (0.368)
Age22 0.191 0.187
(0.393) (0.391)
Age23 0.136 *** 0.195
(0.343) (0.396)
Age24 0.145 0.164
(0.352) (0.370)
Age25 0.052 0.051
(0.222) (0.220)
Age26+ 0.004 0.008
(0.059) (0.091)
Male 0.442 0.470
(0.497) (0.499)
White 0.713 *** 0.658
(0.453) (0.474)
Black 0.178 *** 0.230
(0.383) (0.421)
Other race (a) 0.109 0.112
(0.312) (0.315)
Hispanic 0.149 0.162
(0.357) (0.369)
U.S. born 0.926 0.920
(0.262) (0.271)
PVT_w1A (a) 0.177 *** 0.255
(0.382) (0.436)
PVT_w1B 0.257 0.256
(0.437) (0.437)
PVT_w1C 0.257 0.231
(0.437) (0.422)
PVT_w1D 0.310 *** 0.257
(0.463) (0.437)
Married 0.099 *** 0.176
(0.299) (0.381)
Mother high 0.139 0.160
school--(a) (0.346) (0.367)
Mother high 0.353 0.351
school (0.478) (0.477)
Mother high 0.508 0.488
school+ (0.500) (0.500)
Non-wage 1662.2 1924.8
(7768.9) (14,551.6)
Non-wage1 (a) 0.039 0.030
(0.196) (0.171)
Non-wage2 0.507 * 0.538
(0.500) (0.499)
Non-wage3 0.161 0.158
(0.367) (0.365)
Non-wage4 0.072 0.067
(0.259) (0.249)
Non-wage5 0.103 0.97
(0.305) (0.296)
Non-wage6 0.070 ** 0.053
(0.255) (0.225)
Non-wage7 0.047 0.056
(0.212) (0.231)
Depression_w1 0.315 *** 0.199
(0.465) (0.399)
Depression_w3 0.446 *** 0.202
(0.497) (0.402)
Volunteer work 0.362 0.365
(0.481) (0.481)
Ran away 0.138 *** 0.075
(0.345) (0.264)
Counseling_w1 0.198 *** 0.112
(0.399) (0.315)
Drug abuse 0.040 *** 0.025
program_w1 (0.196) (0.156)
Drunk_wl 0.064 *** 0.051
(0.245) (0.219)
Alcohol at 0.347 *** 0.293
home_w1 (0.476) (0.455)
Drug at home_w1 0.048 *** 0.030
(0.215) (0.172)
Repeat grade_w1 0.2 0.207
(0.401) (0.405)
School 0.291 0.270
suspension_w1 (0.455) (0.445)
Trouble with 0.092 * 0.075
teachers_w1 (0.289) (0.263)
Trouble with 0.118 *** 0.074
students_w1 (0.323) (0.262)
Felt close to 0.171 *** 0.123
people_w1 (0.377) (0.328)
Felt part of 0.186 *** 0.114
school_w1 (0.390) (0.318)
Felt students 0.308 0.330
prejudice_w1 (0.462) (0.470)
Number of
observations 848 13,553
Standard deviations are in parentheses. *, **, and *** indicate that
the mean is statistically different between the sample with suicide
thoughts and those without at the 10%, 5%, and 1% levels,
respectively. For each explanatory variable, we also created a dummy
variable representing the missing observations.
(a) Omitted category.
Table 2A. OLS Estimates for Work-School Model: Suicide Thoughts
Work-School Work-School
Variable (1) (2)
Suicide thoughts -0.048 *** -0.038 ***
(0.014) (0.014)
Male 0.020 *** 0.019 ***
(0.006) (0.006)
White 0.022 0.021
(0.013) (0.013)
Black -0.027 * -0.022
(0.015) (0.015)
Hispanic 0.005 0.007
(0.011) (0.011)
U.S. born -0.061 *** -0.059 ***
(0.012) (0.012)
Age l9 0.005 0.006
(0.033) (0.033)
Age20 -0.005 -0.004
(0.032) (0.032)
Age21 -0.030 -0.027
(0.033) (0.033)
Age22 -0.043 -0.039
(0.033) (0.033)
Age23 -0.030 -0.027
(0.033) (0.033)
Age24 -0.020 -0.019
(0.033) (0.033)
Age25 -0.027 -0.026
(0.036) (0.036)
Age26+ -0.027 -0.028
(0.049) (0.049)
PVT_w1B 0.067 *** 0.067 ***
(0.010) (0.010)
PVT_w1C 0.087 *** 0.087 ***
(0.010) (0.010)
PVT_w1D 0.113 *** 0.110 ***
(0.010) (0.010)
Married -0.046 *** -0.045 ***
(0.009) (0.009)
Mother high school 0.028 ** 0.026 **
(0.011) (0.011)
Mother high school+ 0.065 *** 0.061 ***
(0.011) (0.011)
Non-wage2 0.018 0.016
(0.019) (0.019)
Non-wage3 -0.011 -0.013
(0.020) (0.020)
Non-wage4 0.012 0.009
(0.022) (0.022)
Non-wage5 -0.034 -0.035 *
(0.021) (0.021)
Non-wage6 -0.026 -0.028
(0.023) (0.023)
Non-wage7 -0.002 -0.006
(0.023) (0.022)
Catholic -0.001 -0.005
(0.011) (0.011)
Protestant -0.002 -0.003
(0.010) (0.010)
No religion -0.042 *** -0.042 ***
(0.011) (0.011)
Healthy 0.104 *** 0.097 ***
(0.018) (0.018)
Suicide attempt_w1 -0.025
(0.018)
Suicide family -0.014
(0.020)
Suicide family_w1 -0.023
(0.017)
Any abuse -0.012
(0.008)
Foster -0.086 ***
(0.025)
Jailed father -0.035 ***
(0.010)
Depressed_w1
Counseling-w1
Depressed_3
Cocaine_w1
Marijuana_w1
Drug abuse program_w1
Drunk_w1
Alcohol at home_w1
Drug at home_w1
Volunteer work
Ran away
Repeat grade_w1
School suspension_w1
Trouble with teachers_w1
Trouble with students_w1
Felt close to people_w1
Felt part of school_w1
Felt students prejudice_w1
Constant 0.694 *** 0.708 ***
(0.070) (0.069)
State fixed effects Yes Yes
School fixed effects Yes Yes
[R.sup.2] 0.09 0.10
Observations 14,401 14,401
Work-School Work-School
Variable (3) (4)
Suicide thoughts -0.033 ** -0.029 **
(0.014) (0.014)
Male 0.014 ** 0.013 **
(0.006) (0.006)
White 0.021 0.021
(0.013) (0.013)
Black -0.023 -0.021
(0.015) (0.015)
Hispanic 0.006 0.006
(0.011) (0.011)
U.S. born -0.057 *** -0.056 ***
(0.012) (0.012)
Age l9 0.007 0.007
(0.033) (0.033)
Age20 0.000 0.001
(0.032) (0.032)
Age21 -0.023 -0.022
(0.033) (0.033)
Age22 -0.034 -0.033
(0.033) (0.033)
Age23 -0.020 -0.020
(0.033) (0.033)
Age24 -0.013 -0.012
(0.033) (0.034)
Age25 -0.019 -0.019
(0.036) (0.036)
Age26+ -0.018 -0.019
(0.049) (0.049)
PVT_w1B 0.064 *** 0.063 ***
(0.010) (0.010)
PVT_w1C 0.081 *** 0.081 ***
(0.010) (0.010)
PVT_w1D 0.104 *** 0.104 ***
(0.010) (0.010)
Married -0.044 *** -0.045 ***
(0.009) (0.009)
Mother high school 0.024 ** 0.023 **
(0.011) (0.011)
Mother high school+ 0.059 *** 0.058 ***
(0.011) (0.011)
Non-wage2 0.017 0.016
(0.019) (0.019)
Non-wage3 -0.011 -0.012
(0.020) (0.020)
Non-wage4 0.010 0.010
(0.022) (0.022)
Non-wage5 -0.034 -0.035 *
(0.021) (0.021)
Non-wage6 -0.027 -0.028
(0.023) (0.023)
Non-wage7 -0.005 -0.005
(0.022) (0.022)
Catholic -0.004 -0.004
(0.011) (0.011)
Protestant -0.003 -0.003
(0.010) (0.010)
No religion -0.040 *** -0.040 ***
(0.011) (0.011)
Healthy 0.090 *** 0.087 ***
(0.018) (0.018)
Suicide attempt_w1 0.001 0.001
(0.018) (0.018)
Suicide family -0.011 -0.011
(0.020) (0.020)
Suicide family_w1 -0.015 -0.015
(0.017) (0.017)
Any abuse -0.007 -0.006
(0.008) (0.008)
Foster -0.075 *** -0.075 ***
(0.025) (0.025)
Jailed father -0.034 *** -0.033 ***
(0.010) (0.010)
Depressed_w1 -0.045 *** -0.042 ***
(0.009) (0.009)
Counseling-w1 -0.041 *** -0.040 ***
(0.011) (0.011)
Depressed_3 -0.023 ***
(0.008)
Cocaine_w1
Marijuana_w1
Drug abuse program_w1
Drunk_w1
Alcohol at home_w1
Drug at home_w1
Volunteer work
Ran away
Repeat grade_w1
School suspension_w1
Trouble with teachers_w1
Trouble with students_w1
Felt close to people_w1
Felt part of school_w1
Felt students prejudice_w1
Constant 0.721 *** 0.749 ***
(0.069) (0.069)
State fixed effects Yes Yes
School fixed effects Yes Yes
[R.sup.2] 0.10 0.10
Observations 14,401 14,401
Work-School
Variable (5)
Suicide thoughts -0.029 **
(0.014)
Male 0.031 ***
(0.006)
White 0.025 *
(0.013)
Black -0.009
(0.015)
Hispanic 0.009
(0.011)
U.S. born -0.045 ***
(0.012)
Age l9 0.008
(0.033)
Age20 0.010
(0.032)
Age21 -0.004
(0.033)
Age22 -0.009
(0.033)
Age23 0.004
(0.033)
Age24 0.013
(0.033)
Age25 0.015
(0.036)
Age26+ 0.027
(0.049)
PVT_w1B 0.053 ***
(0.010)
PVT_w1C 0.062 ***
(0.010)
PVT_w1D 0.076 ***
(0.010)
Married -0.044 ***
(0.009)
Mother high school 0.015
(0.011)
Mother high school+ 0.042 ***
(0.011)
Non-wage2 0.019
(0.019)
Non-wage3 -0.011
(0.020)
Non-wage4 0.009
(0.022)
Non-wage5 -0.034
(0.021)
Non-wage6 -0.027
(0.023)
Non-wage7 -0.003
(0.022)
Catholic -0.006
(0.011)
Protestant -0.006
(0.010)
No religion -0.033 ***
(0.011)
Healthy 0.079 ***
(0.017)
Suicide attempt_w1 0.014
(0.018)
Suicide family -0.012
(0.020)
Suicide family_w1 -0.010
(0.017)
Any abuse -0.001
(0.008)
Foster -0.063 **
(0.025)
Jailed father -0.025 **
(0.010)
Depressed_w1 -0.029 ***
(0.009)
Counseling-w1 -0.024 **
(0.011)
Depressed_3 -0.019 **
(0.008)
Cocaine_w1 -0.034
(0.022)
Marijuana_w1 -0.001
(0.008)
Drug abuse program_w1 -0.003
(0.021)
Drunk_w1 0.011
(0.015)
Alcohol at home_w1 0.014 **
(0.006)
Drug at home_w1 -0.007
(0.019)
Volunteer work 0.049 ***
(0.006)
Ran away -0.024 *
(0.013)
Repeat grade_w1 -0.047 ***
(0.009)
School suspension_w1 -0.044 ***
(0.008)
Trouble with teachers_w1 -0.029 **
(0.014)
Trouble with students_w1 -0.032 **
(0.014)
Felt close to people_w1 -0.005
(0.011)
Felt part of school_w1 0.002
(0.012)
Felt students prejudice_w1 -0.008
(0.007)
Constant 0.709 ***
(0.069)
State fixed effects Yes
School fixed effects Yes
[R.sup.2] 0.11
Observations 14,401
Robust standard errors are in parentheses.
* Indicates statistical significance at the 10% level.
** Indicates statistical significance at the 5% level.
*** Indicates statistical significance at the 1% level.
Table 2B. OLS Estimates for Work-School Models: Suicide Attempts
Work-School Work-School Work-School
Variable (1) (2) (3)
Suicide attempt -0.118 *** -0.106 *** -0.101 ***
(0.030) (0.030) (0.030)
Male 0.020 *** 0.019 *** 0.014 **
(0.006) (0.006) (0.006)
White 0.021 0.020 0.020
(0.013) (0.013) (0.013)
Black -0.026 * -0.022 -0.022
(0.015) (0.015) (0.015)
Hispanic 0.004 0.007 0.006
(0.011) (0.011) (0.011)
U.S. born -0.061 *** -0.059 *** -0.057 ***
(0.012) (0.012) (0.012)
Age19 0.007 0.008 0.009
(0.033) (0.033) (0.033)
Age20 -0.004 -0.003 0.001
(0.032) (0.032) (0.032)
Age21 -0.028 -0.026 -0.021
(0.033) (0.033) (0.033)
Age22 -0.041 -0.038 -0.033
(0.033) (0.033) (0.033)
Age23 -0.028 -0.025 -0.018
(0.033) (0.033) (0.033)
Age24 -0.018 -0.017 -0.012
(0.033) (0.033) (0.034)
Age25 -0.026 -0.025 -0.019
(0.036) (0.036) (0.036)
Age26+ -0.025 -0.027 -0.017
(0.049) (0.049) (0.049)
PVT_w1B 0.067 *** 0.067 *** 0.064 ***
(0.010) (0.010) (0.010)
PVT_w1C 0.086 *** 0.086 *** 0.081 ***
(0.010) (0.010) (0.010)
PVT_w1D 0.112 *** 0.110 *** 0.103 ***
(0.010) (0.010) (0.010)
Married -0.046 *** -0.044 *** -0.044 ***
(0.009) (0.009) (0.009)
Mother high school 0.028 ** 0.025 ** 0.023 **
(0.011) (0.011) (0.011)
Mother high school+ 0.065 *** 0.061 *** 0.058 ***
(0.011) (0.011) (0.011)
Non-wage2 0.018 0.016 0.016
(0.019) (0.019) (0.019)
Non-wage3 -0.011 -0.013 -0.012
(0.020) (0.020) (0.020)
Non-wage4 0.011 0.008 0.010
(0.022) (0.022) (0.022)
Non-wage5 -0.034 -0.035 * -0.035
(0.021) (0.021) (0.021)
Non-wage6 -0.027 -0.029 -0.027
(0.023) (0.023) (0.023)
Non-wage7 -0.003 -0.006 -0.005
(0.023) (0.022) (0.022)
Catholic -0.001 -0.005 -0.004
(0.011) (0.011) (0.011)
Protestant -0.002 -0.004 -0.003
(0.010) (0.010) (0.010)
No religion -0.043 *** -0.042 *** -0.040 ***
(0.011) (0.011) (0.011)
Healthy 0.104 *** 0.096 *** 0.089 ***
(0.017) (0.017) (0.018)
Suicide attempt_w1 -0.025 0.001
(0.018) (0.018)
Suicide family -0.015 -0.011
(0.020) (0.020)
Suicide family_w1 -0.022 -0.014
(0.017) (0.017)
Any abuse -0.012 -0.007
(0.008) (0.008)
Foster -0.085 *** -0.074 ***
(0.025) (0.025)
Jailed father -0.035 *** -0.034 ***
(0.010) (0.010)
Depressed_w1 -0.045 ***
(0.009)
Counseling_w1 -0.041 ***
(0.011)
Depressed_w3
Cocaine_w1
Marijuana_w1
Drug abuse
program_w1
Drunk_w1
Alcohol at home_w1
Drug at home_w1
Volunteer work
Ran away
Repeat grade_wl
School
suspension_wl
Trouble with
teachers_w1
Trouble with
students_w1
Felt close to
people_w1
Felt part of
school_wl
Felt students
prejudice_w1
Constant 0.694 *** 0.708 *** 0.722 ***
(0.069) (0.069) (0.069)
State fixed effects Yes Yes Yes
School fixed effects Yes Yes Yes
[R.sup.2] 0.09 0.10 0.10
Observations 14,401 14,401 14,401
Work-School Work-School
Variable (4) (5)
Suicide attempt -0.095 *** -0.092 ***
(0.030) (0.030)
Male 0.013 ** 0.031 ***
(0.006) (0.006)
White 0.021 0.025 *
(0.013) (0.013)
Black -0.021 -0.009
(0.015) (0.015)
Hispanic 0.006 0.009
(0.011) (0.011)
U.S. born -0.056 *** -0.046 ***
(0.012) (0.012)
Age19 0.009 0.010
(0.033) (0.033)
Age20 0.002 0.012
(0.032) (0.032)
Age21 -0.021 -0.003
(0.033) (0.033)
Age22 -0.032 -0.008
(0.033) (0.033)
Age23 -0.018 0.005
(0.033) (0.033)
Age24 -0.012 0.014
(0.034) (0.033)
Age25 -0.018 0.016
(0.036) (0.036)
Age26+ -0.017 0.028
(0.049) (0.049)
PVT_w1B 0.063 *** 0.053 ***
(0.010) (0.010)
PVT_w1C 0.081 *** 0.062 ***
(0.010) (0.010)
PVT_w1D 0.104 *** 0.076 ***
(0.010) (0.010)
Married -0.044 *** -0.044 ***
(0.009) (0.009)
Mother high school 0.023 ** 0.015
(0.011) (0.011)
Mother high school+ 0.058 *** 0.042 ***
(0.011) (0.011)
Non-wage2 0.015 0.018
(0.019) (0.019)
Non-wage3 -0.012 -0.011
(0.020) (0.020)
Non-wage4 0.010 0.009
(0.022) (0.022)
Non-wage5 -0.035 * -0.034
(0.021) (0.021)
Non-wage6 -0.028 -0.028
(0.023) (0.023)
Non-wage7 -0.006 -0.004
(0.022) (0.022)
Catholic -0.004 -0.006
(0.011) (0.011)
Protestant -0.003 -0.006
(0.010) (0.010)
No religion -0.040 *** -0.033 ***
(0.011) (0.011)
Healthy 0.087 *** 0.078 ***
(0.018) (0.017)
Suicide attempt_w1 0.002 0.014
(0.018) (0.018)
Suicide family -0.011 -0.012
(0.020) (0.019)
Suicide family_w1 -0.014 -0.009
(0.017) (0.017)
Any abuse -0.006 -0.001
(0.008) (0.008)
Foster -0.074 *** -0.062 **
(0.025) (0.025)
Jailed father -0.034 *** -0.025 **
(0.010) (0.010)
Depressed_w1 -0.042 *** -0.029 ***
(0.009) (0.009)
Counseling_w1 -0.040 *** -0.024 **
(0.011) (0.011)
Depressed_w3 -0.022 *** -0.019 **
(0.008) (0.008)
Cocaine_w1 -0.034
(0.022)
Marijuana_w1 -0.001
(0.008)
Drug abuse -0.002
program_w1 (0.021)
Drunk_w1 0.011
(0.015)
Alcohol at home_w1 0.014 **
(0.006)
Drug at home_w1 -0.007
(0.019)
Volunteer work 0.049 ***
(0.006)
Ran away -0.024 *
(0.013)
Repeat grade_w1 -0.047 ***
(0.009)
School -0.044 ***
suspension_w1 (0.008)
Trouble with -0.029 **
teachers_w1 (0.014)
Trouble with -0.031 **
students_w1 (0.014)
Felt close to -0.005
people_w1 (0.011)
Felt part of 0.001
school_w1 (0.012)
Felt students -0.008
prejudice_w1 (0.007)
Constant 0.750 *** 0.709 ***
(0.069) (0.069)
State fixed effects Yes Yes
School fixed effects Yes Yes
[R.sup.2] 0.10 0.12
Observations 14,401 14,401
Robust standard errors are in parentheses.
* Indicates statistical significance at the 10% level.
** Indicates statistical significance at the 5% level.
*** Indicates statistical significance at the 1% level.
Table 3. Determinants of Suicide Thoughts and Attempts
Variable Suicide Thoughts Suicide Attempts
Suicidal friend_W3 0.115 *** 0.033 ***
(0.013) (0.007)
Suicidal friend_w1 0.005 0.002
(0.006) (0.003)
Male 0.002 -0.002
(0.004) (0.002)
White 0.007 -0.001
(0.008) (0.004)
Black -0.007 -0.001
(0.009) (0.005)
Hispanic -0.003 -0.002
(0.007) (0.004)
U.S. born -0.016 ** -0.007
(0.008) (0.004)
Agel9 -0.004 0.017
(0.024) (0.011)
Age20 -0.022 0.005
(0.024) (0.011)
Age21 -0.028 0.004
(0.024) (0.011)
Age22 -0.028 0.003
(0.024) (0.011)
Age23 -0.043 * 0.001
(0.024) (0.011)
Age24 -0.027 0.001
(0.024) (0.011)
Age25 -0.018 0.000
(0.025) (0.011)
Age26+ -0.051 * -0.003
(0.028) (0.014)
PVT_w1B 0.017 *** 0.006 *
(0.005) (0.003)
PVT_w1C 0.020 *** 0.004
(0.006) (0.003)
PVT_w1D 0.030 *** 0.003
(0.006) (0.003)
Married -0.027 *** -0.007 ***
(0.005) (0.002)
Mother high school 0.006 -0.005
(0.006) (0.004)
Mother high school+ 0.005 -0.005
(0.006) (0.004)
Non-wage2 -0.009 -0.008
(0.013) (0.008)
Non-wage3 -0.013 -0.006
(0.013) (0.008)
Non-wage4 -0.012 -0.006
(0.015) (0.009)
Non-wage5 -0.012 -0.008
(0.014) (0.009)
Non-wage6 -0.002 -0.006
(0.015) (0.009)
Non-wage7 -0.015 -0.012
(0.015) (0.009)
Catholic -0.005 -0.003
(0.007) (0.004)
Protestant -0.005 -0.005
(0.006) (0.004)
No religion 0.012 0.001
(0.007) (0.004)
Healthy -0.061 *** -0.026 *
(0.014) (0.009)
Suicide attempt_w1 0.080 *** 0.035 ***
(0.016) (0.010)
Suicide family_w3 0.089 *** 0.029 **
(0.018) (0.011)
Suicide family_w1 -0.002 0.011
(0.011) (0.007)
Any abuse 0.037 *** 0.010 ***
(0.006) (0.003)
Foster 0.010 0.015
(0.016) (0.011)
Jailed father 0.024 *** 0.004
(0.007) (0.004)
Depressed_w1 0.017 *** 0.003
(0.006) (0.003)
Counseling_w1 0.017 ** 0.005
(0.008) (0.004)
Depressed_w3 0.065 *** 0.022 ***
(0.006) (0.003)
Constant 0.025 0.016
(0.048) (0.028)
State fixed effects Yes Yes
School fixed effects Yes Yes
[R.sup.2] 0.09 0.05
Observations 14,401 14,401
Robust standard errors are in parentheses.
* Indicates statistical significance at the 10% level.
** Indicates statistical significance at the 5% level.
*** Indicates statistical significance at the 1% level.
Table 4. TSLS Estimates for Work-School Model
Variable Work-School Work-School
Suicide thoughts -0.214 *
(0.113)
Suicide attempt -0.736 *
(0.407)
State fixed effects Yes Yes
School fixed effects Yes Yes
[R.sup.2] 0.09 0.06
Observations 14,401 14,401
F-test on instruments 42.46 *** 10.87 ***
Durbin-Wu-Hausman test statistic 0.64 0.39
Hansen J 0.15 0.20
statistic--overidentification test
Robust standard errors are in parentheses. *, **, and *** indicate
statistical significance at the 10%, 5%, and 1% levels, respectively.
Instruments are indicators of whether the respondent reports having
friends who attempted suicide in Wave 1 and in Wave 3.
Table 5. Fixed Effects Estimates for Work-School Model from the
Sibling and Twin Samples
Sibling Fixed Effects Twin Fixed Effects
Variable Work-School Work-School Work-School Work-School
Suicide -0.085 * -0.190 **
thoughts (0.052) (0.076)
Suicide -0.088 -0.310 *
attempt (0.095) (0.168)
[R.sup.2] 0.16 0.16 0.09 0.09
Observations 2134 2134 760 760
Robust standard errors are in parentheses. *, **, and *** indicate
statistical significance at the 10%, 5%, and 1% levels, respectively.
The models with sibling fixed effects also include school fixed
effects. There was little variation to warrant including state fixed
effects for both sibling and twin fixed effects models. The twin fixed
effects model included neither the state nor the school fixed effects
as a result of little variation.
Table 6. Multinomial Logit Coefficients for the Work-School Model
School, No No School, School,
Variable Work Work Work
Suicide thoughts -0.285 ** -0.188 * -0.228 *
(0.139) (0.103) (0.119)
Suicide attempt -- -- --
Male -0.151 ** 0.269 *** -0.237 ***
(0.066) (0.051) (0.058)
White -0.183 * 0.376 *** 0.291 ***
(0.109) (0.086) (0.099)
Black -0.194 -0.179 * 0.031
(0.122) (0.096) (0.112)
Hispanic -0.128 -0.048 0.283 ***
(0.111) (0.076) (0.087)
U.S. born -0.720 *** -0.271 ** -1.001 ***
(0.141) (0.108) (0.120)
Agel9 -0.209 0.504 -0.012
(0.288) (0.314) (0.279)
Age20 -0.466 0.745 ** -0.142
(0.284) (0.308) (0.275)
Age21 -0.687 ** 0.795 *** -0.250
(0.283) (0.306) (0.273)
Age22 -1.213 *** 1.024 *** -0.493 *
(0.285) (0.305) (0.274)
Age23 -1.441 *** 1.232 *** -0.563 **
(0.287) (0.305) (0.275)
Age24 -1.584 *** 1.372 *** -0.759 ***
(0.292) (0.307) (0.278)
Age25 -1.633 *** 1.277 *** -0.892 ***
(0.332) (0.317) (0.301)
Age26+ -1.551 *** 1.163 *** -0.971 **
(0.520) (0.370) (0.426)
PVT_w1B 0.402 *** 0.423 *** 0.538 ***
(0.097) (0.067) (0.082)
PVT_w1C 0.742 *** 0.508 *** 1.001 ***
(0.103) (0.076) (0.089)
PVT_w1D 1.385 *** 0.724 *** 1.470 ***
(0.107) (0.084) (0.094)
Married -0.836 *** -0.156 ** -0.910 ***
(0.104) (0.062) (0.084)
Mother high school 0.428 *** 0.132 * 0.311 ***
(0.119) (0.075) (0.091)
Mother high school+ 1.216 *** 0.248 *** 0.847 ***
(0.116) (0.077) (0.091)
Non-wage2 -0.056 0.146 -0.067
(0.200) (0.145) (0.166)
Non-wage3 0.086 -0.196 -0.013
(0.210) (0.155) (0.176)
Non-wage4 0.448 * -0.112 0.176
(0.231) (0.177) (0.197)
Non-wage5 0.133 -0.376 ** -0.309*
(0.215) (0.161) (0.183)
Non-wage6 0.259 -0.398** -0.206
(0.232) (0.176) (0.199)
Non-wage7 0.431 * -0.150 -0.151
(0.235) (0.180) (0.205)
Catholic -0.024 0.055 0.149
(0.114) (0.090) (0.101)
Protestant -0.005 0.003 -0.026
(0.100) (0.080) (0.090)
No religion -0.505 *** -0.179 ** -0.449 ***
(0.114) (0.087) (0.100)
Healthy 0.564 *** 0.462 *** 0.802 ***
(0.155) (0.101) (0.135)
Suicide attempt_w1 -0.013 0.078 -0.144
(0.182) (0.124) (0.152)
Suicide family_w3 -0.075 -0.053 -0.143
(0.195) (0.138) (0.166)
Suicide family_w1 -0.190 -0.077 -0.139
(0.165) (0.112) (0.136)
Any abuse -0.189 ** 0.002 -0.088
(0.083) (0.060) (0.070)
Foster -0.664 *** -0.392 *** -0.625 ***
(0.242) (0.140) (0.192)
Jailed father -0.527 *** -0.124 * -0.357 ***
(0.102) (0.069) (0.083)
Depressed_w1 -0.356 *** -0.184 *** -0.417 ***
(0.087) (0.060) (0.073)
Counseling_w1 -0.519 *** -0.228 *** -0.303 ***
(0.108) (0.075) (0.087)
Depressed_w3 -0.167 ** -0.189 *** -0.131 *
(0.079) (0.059) (0.068)
Constant 0.373 -0.712 * 0.255
(0.410) (0.372) (0.371)
Observations 14,392 14,392 14,392
School, No No School, School,
Variable Work Work Work
Suicide thoughts -- -- --
Suicide attempt -0.492 ** -0.568 *** -0.707 ***
(0.239) (0.176) (0.214)
Male -0.152 ** 0.267 *** -0.239 ***
(0.066) (0.051) (0.058)
White -0.188 * 0.372 *** 0.285 ***
(0.109) (0.086) (0.099)
Black -0.194 -0.181 * 0.028
(0.122) (0.096) (0.112)
Hispanic -0.128 -0.049 0.282 ***
(0.111) (0.076) (0.087)
U.S. born -0.723 *** -0.275 ** -1.005 ***
(0.141) (0.108) (0.120)
Agel9 -0.198 0.514 0.002
(0.289) (0.315) (0.281)
Age20 -0.460 0.749 ** -0.135
(0.285) (0.310) (0.277)
Age21 -0.678 ** 0.801 *** -0.242
(0.284) (0.308) (0.275)
Age22 -1.206 *** 1.027 *** -0.487 *
(0.285) (0.307) (0.275)
Age23 -1.431 *** 1.237 *** -0.555 **
(0.288) (0.307) (0.276)
Age24 -1.578 *** 1.374 *** -0.755 ***
(0.293) (0.308) (0.280)
Age25 -1.630 *** 1.276 *** -0.891 ***
(0.332) (0.318) (0.303)
Age26+ -1.539 *** 1.168 *** -0.962 **
(0.521) (0.372) (0.427)
PVT_w1B 0.401 *** 0.424 *** 0.539 ***
(0.097) (0.067) (0.082)
PVT_w1C 0.740 *** 0.509 *** 1.002 ***
(0.103) (0.076) (0.089)
PVT_w1D 1.378 *** 0.720 *** 1.467 ***
(0.107) (0.084) (0.094)
Married -0.833 *** -0.156 ** -0.910 ***
(0.104) (0.062) (0.084)
Mother high school 0.425 *** 0.127 * 0.305 ***
(0.119) (0.075) (0.091)
Mother high school+ 1.212 *** 0.245 *** 0.842 ***
(0.116) (0.077) (0.091)
Non-wage2 -0.056 0.143 -0.070
(0.200) (0.145) (0.166)
Non-wage3 0.087 -0.197 -0.013
(0.210) (0.156) (0.176)
Non-wage4 0.450 * -0.112 0.175
(0.231) (0.178) (0.197)
Non-wage5 0.133 -0.378 ** -0.311*
(0.215) (0.161) (0.184)
Non-wage6 0.255 -0.403 ** -0.211
(0.232) (0.177) (0.199)
Non-wage7 0.428 * -0.157 -0.158
(0.235) (0.180) (0.205)
Catholic -0.025 0.054 0.148
(0.114) (0.090) (0.101)
Protestant -0.007 0.001 -0.030
(0.100) (0.080) (0.090)
No religion -0.510 *** -0.182 ** -0.452***
(0.114) (0.088) (0.100)
Healthy 0.568 *** 0.458 *** 0.796 ***
(0.155) (0.101) (0.134)
Suicide attempt_w1 -0.014 0.084 -0.135
(0.182) (0.124) (0.152)
Suicide family_w3 -0.090 -0.054 -0.146
(0.193) (0.136) (0.165)
Suicide family_w1 -0.183 -0.068 -0.129
(0.165) (0.112) (0.136)
Any abuse -0.194 ** 0.002 -0.088
(0.083) (0.060) (0.070)
Foster -0.659 *** -0.386 *** -0.616***
(0.242) (0.140) (0.192)
Jailed father -0.531 *** -0.127 * -0.361***
(0.102) (0.069) (0.083)
Depressed_w1 -0.359 *** -0.185 *** -0.418***
(0.087) (0.060) (0.073)
Counseling_w1 -0.521 *** -0.229 *** -0.303***
(0.108) (0.075) (0.087)
Depressed_w3 -0.173** -0.187 *** -0.128*
(0.078) (0.059) (0.067)
Constant 0.372 -0.698 * 0.269
(0.410) (0.373) (0.372)
Observations 14,392 14,392 14,392
Omitted outcome in the multinomial logit model is No-school/No-work.
Robust standard errors are in parentheses.
* Indicates statistical significance at the 10% level.
** Indicates statistical significance at the 5% level.
*** Indicates statistical significance at the 1% level.