Kali McFarland
10/27/11
Capstone Proposal
Wealth Effects on Education Attainment in Persons with Mental Illness
For my capstone project, I would look at how wealth impacts people with
mental illnesses’ ability to complete high school. Research suggests that people who
suffer from mental disorders such as anxiety, post-traumatic stress disorder (PTSD),
and depression are less likely to complete high school. This lack of education can
create serious economic repercussions in society at large, affecting the labor force,
welfare participation, and national health concerns. However, those children that
grow up in homes with higher income may have the resources to mitigate some of
the negative effects of these disorders, through counseling and therapy, or
alternative schooling options. Therefore, mental illness may affect people from
different income levels differently, and those with more money may have a better
chance of completing high school.
I. Background
The field is currently inconclusive on how wealth, mental status, and education
interact with each other. As Robst explains, “While the economics literature
recognizes that physical health problems can impede children’s human capital
accumulation, the link between mental health problems and human capital
accumulation has received less attention.”1 However, there are several clinical
studies that have examined why people do not complete high school, and mental
John Robst (2010): Childhood sexual victimization, educational attainment,
and the returns to schooling, Education Economics, 18:4, 407-421
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10/27/11
distress has been shown to be a cause. For example, a 2000 study by Stein and Kein
revealed that being diagnosed with a social phobia was associated with a
significantly higher chance of not attaining the level of education they wanted.
Nearly half of all patients reported leaving school early, mainly due to discomfort
interacting with students and teachers, speaking in front of the class, or feeling too
depressed or uncomfortable to continue.2 However, it is important to note that
these results are based on patients who were already diagnosed with a mental
illness, which may represent a bias against people of lower incomes who are less
likely to be diagnosed. Also, the fact that they chose to get help and attend a clinic
means they are not representative of the population of people with mental illness as
a whole.
Similarly, several studies have confirmed a high prevalence of mental
disorder among high school dropouts. Stoep used population associated risk
percentages (PAR) to calculate what portion of dropouts can be attributed to mental
illnesses such as depression, anxiety, substance abuse, or depressive behavior.3
Stoep also examined differences in income by stratifying students into
socioeconomic groups. Twenty-three percent of children of low socioeconomic
status were diagnosed with a psychotic disorder, and of these, fifty percent failed to
complete high school. In the wealthier stratification, only twenty-six percent of
children with mental illness fail to complete high school. However, only forty-four
Van Amerigan, Michael and Catherine Mancinia, b, Peter Farvoldenb. Department of Psychiatry and
Behavioural Neurosciences, McMaster University, Hamilton, Ont., Canada L8N 3Z5
3 Stoep, Ann Vander, et al. “What Proportion of Failure to Complete Secondary School in the US
Population Is Attributable to Adolescent Psychiatric Disorder?” The Journal of Behavioral Health
Services & Research 30:1 January~February 2003
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10/27/11
percent of high school failure is attributable to a mental illness, whereas sixty-one
percent of failure in high-income families is attributable to mental illness. This
suggests that poorer children are more likely to fail regardless of their mental state,
while wealthier children have fewer other reasons to drop out, which leads to
higher PAR scores. Unfortunately, this study is not generalizable to the population
as a whole because the sample was drawn from two small counties in upstate New
York. Not only is this geographically biased, but the sample had very low minority
representation.
A 1995 study by Kessler confirmed that the proportion of high school dropouts with a mental disorder is growing. Using the National Comorbidity Study,
which will be included in my data set, Kessler computed that there are
approximately three and a half million people who did not complete high school that
have a psychotic disorder, based on the numbers in the survey.4 A study by the
National Center for Education Statistics done the same year reports that fifty-six
percent of all adolescents that fail to finish high school have a mental illness.5
However, little is known about the income distribution of these dropouts.
II. How this study will contribute
My study will allow me to improve methodologically upon past studies.
Because I will be using a large, national sample drawn from a random survey, my
Kessler, RC, et al. “Social consequences of psychiatric disorders, I: Educational attainment.” Am J
Psychiatry 1995; 152:1026-1032
5 National Center for Education Statistics. “Dropping Out and Disabilities”. Dropout Rates in the
United States: 1995. Institute for Education Sciences.
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results will not suffer from the same selection bias as those that use clinical surveys.
In addition, my incidences of mental distress are the result of respondents’ answers
to surveys designed by clinical psychologists. Therefore, there is not a bias based on
under-diagnosis in poorer populations that would result if I only used clinical
diagnoses. In addition, my data contains the date the respondent first experienced
mental distress. This will allow me to align a proper timeline with the relationship
between their distress and drop out or completion. For example, if a person has not
completed high school, but did not experience his or her first instance of mental
distress until the age of thirty, it is less likely that the mental distress contributed to
his or her failure to graduate. By only including people who experienced their first
episode of distress in adolescence, I can draw stronger conclusions about the
relationship between psychotic disorders and educational attainment.
I will also contribute to the literature by including an interaction term
between income and mental illness. This will allow me not only to conclude how
being distressed affects the likelihood a person will complete high school, but also
how this effect varies across different income levels. By examining whether or not
mental illness hinders people in a consistent way, we can better understand how to
help those affected get a good education.
III. The Data
I plan to use a compilation data set programmed by Prof. Timothy Diette and
Prof. Art Goldsmith. The dataset contains observations from The National Survey of
American Life (NSAL), the Nation Latino and Asian-American Study (NLAAS), and
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10/27/11
the National Comorbidity Survey Replication (NCS-R). It contains an over sampling
of minority respondents, but due to the fact that these groups are more likely to
have lower incomes, I think this is acceptable for my study.
The survey asks respondents a series of questions about their mental health
that are coded by trained researchers to reach a clinical diagnosis. Though this
method does suffer from recall bias, it explores mental health in a wider population
that would normally go to a psychologist for evaluation. Respondents also answer a
series of questions regarding their exposure to traumatic events that may be related
to mental distress, such as rape, sexual abuse, or unemployment. I will be able to
use these responses as controls in my study.
The responses are converted into diagnoses of either anxiety, depression, or
substance abuse issues. They have also been collapsed into a general “distress”
variable that indicates the presence of significant symptoms of any of these
disorders. This is useful because previous studies using have suggested a high rate
of comorbidity between these disorders, and so respondents may exhibit symptoms
of multiple mental illnesses (Kessler). While I plan to use the aggregated distress
variable, I can run the model on the individual illnesses as a robustness check.
IV. The Method
As a preliminary model, I would like to regress mental health and income on
a measure of educational achievement, controlling for demographic characteristics
typically known to influence education such as ethnicity, gender, siblings, etc. I will
Kali McFarland
10/27/11
also control for whether or not the respondent experienced a trauma that could
cause them to develop a mental illness, and if so, the type of trauma. I would like to
use a ordered probit model, with dependent variable categories of “didn’t complete
high school”, “completed high school”, “some college”, and “completed college or
above”. The interaction term between wealth and mental health status will be my
independent variable of interest. The proposed model will resemble:
Education = µ mental health + π income + Ω distress*income + X trauma + ø
demographic controls