Psychiatric Disorders and Labor Market Outcomes: Evidence from the National Latino and Asian American Study

Harvard University, Cambridge, Massachusetts, United States
Health Economics (Impact Factor: 2.23). 10/2007; 16(10):1069-90. DOI: 10.1002/hec.1210
Source: PubMed
ABSTRACT
This paper investigates to what extent psychiatric disorders and mental distress affect labor market outcomes in two rapidly growing populations that have not been studied to date-ethnic minorities of Latino and Asian descent, most of whom are immigrants. Using data from the National Latino and Asian American Study (NLAAS), we examine the labor market effects of meeting diagnostic criteria for any psychiatric disorder in the past 12 months as well as the effects of psychiatric distress in the past year. The labor market outcomes analyzed are current employment status, the number of weeks worked in the past year among those who are employed, and having at least one work absence in the past month among those who are employed. Among Latinos, psychiatric disorders and mental distress are associated with detrimental effects on employment and absenteeism, similar to effects found in previous analyses of mostly white, American born populations. Among Asians, we find more mixed evidence that psychiatric disorders and mental distress detract from labor market outcomes. Our findings suggest that reducing disparities and expanding access to effective treatment may have significant labor market benefits-not just for majority populations, as has been demonstrated, but also for Asians and Latinos.

Full-text

Available from: David Takeuchi
NBER WORKING PAPER SERIES
PSYCHIATRIC DISORDERS AND LABOR MARKET OUTCOMES:
EVIDENCE FROM THE NATIONAL LATINO
AND ASIAN AMERICAN STUDY
Pinka Chatterji
Margarita Alegria
Mingshan Lu
David Takeuchi
Working Paper 11893
http://www.nber.org/papers/w11893
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
December 2005
Corresponding author: Pinka Chatterji, Ph.D., Center for Multicultural Mental Health Research, Cambridge
Health Alliance/Harvard Medical School, 120 Beacon Street 4th Floor, Somerville MA 02143 617-503-8449
(phone), 617-503-8430 (fax), pchatterji@charesearch.org (e-mail). The NLAAS data used in this analysis
was provided by the Center for Multicultural Mental Health Research at the Cambridge Health Alliance. The
NLAAS received approval from IRBs at Cambridge Health Alliance, the University of Washington, and the
Institute for Social Research at the University of Michigan. The project was supported by NIH Research
Grant # U01 MH62209 funded by the National Institute of Mental Health as well as SAMHSA/CMHS.
Chatterji also acknowledges support from Grant K01 AA000328-03 from the National Institute of Alcohol
Abuse and Alcoholism. Lu thanks the Alberta Heritage Foundation for Medical Research and Institute of
Health Economics for financial support. We thank Zhun Cao, Naihua Duan, Thomas McGuire, Richard
Scheffler, Ken Wells, Dhaval Dave, seminar participants at the Academy Health 2004 Annual Research
meeting, the Third International Conference on Urban Health, and the Centre for Applied Economic
Research at University of New South Wales, Sydney for their valuable comments and suggestions. The
authors alone are responsible for the analysis and conclusions. The views expressed herein are those of the
author(s) and do not necessarily reflect the views of the National Bureau of Economic Research.
©2005 by Pinka Chatterji, Margarita Alegria, Mingshan Lu, David Takeuchi. All rights reserved. Short
sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full
credit, including © notice, is given to the source.
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Psychiatric Disorders and Labor Market Outcomes: Evidence from the National Latino and Asian
American Study
Pinka Chatterji, Margarita Alegria, Mingshan Lu, David Takeuchi
NBER Working Paper No. 11893
December 2005
JEL No. I1
ABSTRACT
This paper investigates to what extent psychiatric disorders and mental distress affect labor market
outcomes among ethnic minorities of Latino and Asian descent, most of whom are immigrants.
Using data from the National Latino and Asian American Study, we examine the labor market effects
of meeting diagnostic criteria for any psychiatric disorder in the past 12 months as well as the effects
of psychiatric distress in the past year. Among Latinos, psychiatric disorders and mental distress are
associated with detrimental effects on employment and absenteeism, similar to effects found in
previous analyses of mostly white, American born populations. Among Asians, we find mixed
evidence that psychiatric disorders and mental distress detract from labor market outcomes.
Pinka Chatterji
Center for Multicultural Mental Health Research
Cambridge Health Alliance/Harvard Medical School
120 Beacon Street 4th Floor
Somerville MA 02143
and NBER
pchatterji@charesearch.org
Margarita Alegria
Center for Multicultural Mental Health Research
Cambridge Health Alliance/Harvard Medical School
120 Beacon Street 4th Floor
Somerville MA 02143
malegria@charesearch.org
Mingshan Lu
Department of Economics
University of Calgary
2500 University Drive NW, SS 440
Calgary, Alberta, Canada T2N 1N4
lu@ucalgary.edu
David Takeuchi
Department of Sociology
and School of Social Work
University of Washington
4101 15th Avenue NE
Seattle WA 98105-6299
dt5@u.washington.edu
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3
1 Introduction
Untreated mental illness in the workplace is a critically important and costly problem
worldwide [1]. Psychiatric disorders are highly prevalent, affecting about 26 percent of the US
population in a given year [2], frequently are recurrent and debilitating [3], and can impair
workplace productivity by affecting factors such as memory, concentration, decisiveness,
motivation, and social relations. In the US, a very large portion of the total cost of mental illness
– about 77 million dollars, or 48 percent, in 1992 –has been attributed to reduced workplace
productivity [4-5]. There also is substantial empirical evidence that psychiatric disorders are
associated with a range of specific, adverse labor market outcomes, including unemployment,
reduced labor supply, absenteeism, disability-related work leaves, lower perceived workplace
productivity, and reduced earnings [6-12].
Much of the recent research on psychiatric disorders and labor market outcomes in the
US context is based on two data sources, the epidemiologic catchment area (ECA) surveys,
which were conducted in five communities during the early 1980’s, and the National
Comorbidity Survey (NCS), which took place on a national scale during the early 1990’s. The
ECA surveys and the NCS are unique in that they are large, population-based surveys that
include diagnostic interviews for a range of psychiatric illnesses. However, these surveys were
administered to English speaking respondents only, and, as a result, they excluded large numbers
of first generation immigrants with limited English proficiency. Although the ECA and NCS do
include English-proficient non-white respondents, sample sizes for ethnic minorities, particularly
Asians, and immigrants are small [13].
The US labor force, on the other hand, reflects changes in the population as a whole and
is increasingly comprised of Latinos, Asian-Americans, and immigrants. In 2000, Latino and
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Asian Americans made up about 16 percent of the US labor force [14]. The number of Latinos
in the labor force is expected to increase by more than 5.5 million by 2010, increasing to 20.9
million workers, compared to 15.4 million workers in 2000. Although the Asian population
remains relatively small, about 3.2 million Asian workers will enter the labor force by 2010 – a
net increase of 44 percent from the year 2000 [14]. Foreign born persons represent 14 percent of
the labor US force [15]. Given the growing size of the Latino, Asian American, and immigrant
populations in the US and their increasing presence in the labor force, it is essential that we
understand how mental health impacts labor market success for these groups.
This study is the first to investigate the effects of psychiatric disorders and symptoms of
mental distress on labor market outcomes using a national sample of Latino and Asian
Americans. Data come from the National Latino and Asian American Study (NLAAS), a
national psychiatric epidemiologic study conducted to measure psychiatric disorders and mental
health service usage in a US representative sample of Asians and Latinos [16]. Unlike most
population-based surveys, the NLAAS provides detailed and high quality diagnostic data on
mental health status, as well as information on demographics, chronic physical health conditions,
and labor market outcomes. We consider the effects of meeting diagnostic criteria for any
psychiatric disorder in the past 12 months, as well as the effects of three broad classes of
disorders (affective disorders, anxiety disorders and substance use disorders). We also estimate
the effect of the K10 measure of psychiatric distress on labor market outcomes. The labor
market outcomes analyzed are current employment status, the number of weeks worked in the
past year among those who are employed, and having at least one work absence in the past
month among those who are employed.
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The findings suggest that among Latinos, having any recent psychiatric disorder is
associated with a decrease in the probability of being employed by about 10 percentage points
for males and by about 23 percentage points for females. The magnitudes of these effects are in
the range of those found for mostly White, native-born populations. Moreover, recent
psychiatric disorders are associated with an increase the probability of at least one work absence
in the past month by 19 percentage points for employed Latino males and by 14 percentage
points for employed Latino females. Higher levels of mental distress are associated with both
unemployment and work absence among Latinos. Psychiatric disorders and distress, however,
do not appear to reduce the number of weeks worked by employed Latinos. Moreover, in the
Asian samples, we find suggestive, but much less consistent, evidence that psychiatric disorders
and mental distress are associated with any of the labor market outcomes studied.
2 Background
There is considerable evidence from the economics literature that psychiatric disorders
detract from earnings, employment, and work hours. Frank and Gertler [8], for example, use
data on men from the Baltimore ECA and find that mental distress is associated with a 21
percent reduction in earnings. Mental distress in this study is captured by whether or not the
individual has at least two of the following three indications of psychiatric disorder – last year
DSMIII diagnosis, at least four symptoms of psychiatric distress as measured on the General
Health Questionnaire, and at least one self-reported disability day [8].
Ettner et al. [9] build on this study by using the NCS, which includes a nationally
representative sample, and by addressing the possibility that unobserved factors may confound
an observed relationship between psychiatric disorders and labor market outcomes. They report
that among both men and women, a diagnosis of any psychiatric disorder during the past 12
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months is associated with a reduction of about 11 percentage points in the probability of being
employed. Ettner et al. also find some effects of psychiatric disorders on earnings and hours
worked, but these associations were more sensitive to the model specification, sample and
estimation methods used.
Alexandre and French [6] also find that depression is associated with poor labor market
outcomes, reducing the probability of being employed by about 19 percentage points and
decreasing the number of weeks worked in the past year by 7-8 weeks. The measure of
depression in this study is a self-rated depression scale rather than a DSM diagnosis. Although
this measure has some disadvantages compared to a diagnostic interview, the sample is
ethnically diverse -- it includes low-income adults from Miami, most of whom are African-
American or Latino, and 19 percent of whom are foreign born.
In addition to having negative effects on employment, labor supply, and earnings, there
also is evidence that psychiatric disorders detract from on-the-job performance by impairing
productivity and causing work absences. Kouzis and Eaton [11], using 1981 data from the
Baltimore ECA, find that psychiatric disorders are strongly associated with work absences
among employed persons. Kessler et al [12] and Kessler and Frank [7] confirm these findings
using data from the NCS and the Midlife Development in the US Survey. Kessler et al. [12] find
that depressed employees report between 1.5 and 3.2 more short-term disability days in the past
month than other employees, defined as full or partial days when the respondent could not work.
Kessler and Frank [7] also show that workers with more than one psychiatric disorder have more
disability days than workers with one or no psychiatric disorders. Berndt et al. [10] offer further
evidence that mental illness interferes with the ability to carry out work functions. Using data
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from a clinical trial involving chronically depressed patients, they report that reduction in the
severity of depression improves patient perceptions about work performance.
The ECA and NCS datasets, which are used in much of the previous research on
psychiatric disorders and labor market outcomes, are community-based and national samples.
The samples do include some English-speaking, racially and ethnically diverse respondents, but
individuals who were not English proficient at the time of the surveys were not sampled. This
restriction may have affected the composition of these samples considerably.
In the NLAAS, about 50 percent of Latinos and 35 percent of Asians rated their English
proficiency as fair or poor, which may have excluded them from English diagnostic assessments
[16]. Moreover, ECA and NCS sample sizes generally do not permit separate estimation of the
effects of psychiatric disorders on labor market outcomes by ethnicity and gender. These data
constraints limit our understanding of how psychiatric disorders affect labor market outcomes
among Latinos and Asian-Americans, ethnic groups in the US that are projected to triple in size
by 2050 [17].
Estimating models by ethnicity and gender groups, and including non-English proficient
individuals, is important for at least two reasons. First, ethnic minorities and immigrants
generally work in different types of occupations than whites because of historical and
institutional factors, and, in the case of immigrants, because of the context of exit from their
home countries and the time it takes to assimilate into the labor market [18]. In 2003, 35.5
percent of the white employed population had occupations classified as “management,
professional and related,” the occupational class associated with the highest levels of autonomy
and social standing. In 2003, only 17.0 percent of employed Latinos had occupations in this
category, while the number is 43.3 percent among employed Asians. Employed Latinos are much
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more likely to work in service sector jobs than employed whites and Asians [19]. Asians are
disproportionately represented in computer and mathematical occupations, as well as in
engineering and life, physical and social science occupations, which reflects the fact that Asians
are more likely than any other racial/ethnic group to have a college degree [20].
Symptoms of a psychiatric disorder may be less likely to visibly affect job performance in
occupations with high levels of autonomy and privacy. On the other hand, high-status jobs may
require more concentration and cognitive ability than lower-status jobs, making it harder to
function when dealing with a mental illness [7].
Ethnic and racial minorities also may face different labor market consequences of mental
illness than whites because of labor market discrimination. Discrimination may result from place
of birth, language proficiency or accent, skin color or culture. If labor market discrimination
exists, the negative consequences of both having a psychiatric disorder and being an ethnic
minority may be compounded in the labor market. While there is considerable evidence that
inter-racial and ethnic earnings differentials exist, the source of these differentials – and whether
labor market discrimination plays a role – is controversial [21]. While this analysis cannot test
for discrimination, estimating the effects of psychiatric disorders on labor market outcomes in a
large sample of Latino and Asian Americans allows one to gauge whether these groups face
different labor market impacts than majority populations.
3 Methods
In the human capital framework, individuals embody a stock of productive capital that
determines their productivity in the market. This stock is accumulated throughout the lifetime
through schooling, training, work and other experiences [22-24]. The onset of a psychiatric
illness, particularly during youth, may interfere with the process of human capital accumulation,
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such as by disrupting educational plans. In this way, psychiatric disorders may affect
employment and performance on the job indirectly, via reduced education [25]. Psychiatric
illness, however, also may affect job prospects and productivity directly through its effects on
motivation, judgment, cognition and social interactions (see Chapter 2, Mental Health: A Report
of the Surgeon General for signs and symptoms of common disorders [26]). We seek to estimate
this latter effect – the direct effect of current psychiatric disorders on labor market outcomes
among adults – while controlling for other human capital pathways through which mental illness
may affect labor market outcomes indirectly.
Guided by the above theoretical framework, we adopt models of the following type:
iiii
MHXE
εδβα
+++= (1)
where E
i
is a measure of individual i’s labor market outcomes, is an intercept, X
i
is a
vector of observable exogenous individual characteristics that may affect labor market success,
MH is a measures of psychiatric illness, and
i
is an error term. and are the unknown
parameters of interest to be estimated.
Empirically, estimating the effect of psychiatric illness on labor market outcomes () is
complicated by two factors. First, the causation between psychiatric disorders and labor market
outcomes may run in the opposite direction, with employment factors influencing mental
distress. Job loss, for example, may lead to subsequent mental health problems [27]. Second,
the relationship between psychiatric disorders and labor market outcomes may be confounded by
an unmeasured, causal factor, such as a stressful life event. It may be difficult to measure some
important correlates of mental illness using secondary data, and these unmeasured correlates of
mental illness also may affect labor market outcomes directly. In both of these cases, estimating
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the coefficients using standard methods such as OLS would violate a central assumption
underlying the classical linear regression model framework, which is that the right-hand
side variables should be exogenous with respect to the error term [28].
We use three approaches to deal with this problem. Our first approach is to use data on
observed characteristics to proxy unobserved characteristics to the fullest extent possible.
Specific details about the covariates included in the models are described in the next section of
the paper. Notably, the NLAAS includes information on chronic physical health conditions,
which frequently co-exist with psychiatric disorders and could confound an observed association
between psychiatric disorders and adverse labor market outcomes [29]. We use a standard OLS
model for continuous dependent variables, and a standard probit model for binary outcomes to
estimate (1).
Our second empirical approach is to estimate standard OLS and probit models that
include a measure of any prior psychiatric illness in addition to a measure of last year psychiatric
illness or distress, which is the main covariate of interest. Prior psychiatric condition is defined
as the existence of any lifetime diagnosis with most recent symptoms exhibited prior to the
previous year. This measure is a proxy for unobserved, indirect channels through which
previous mental illness may affect current labor market outcomes. Regardless of an individual’s
current mental status, prior symptoms may affect current work performance indirectly. For
example, previous mental health problems may have interrupted the development of social skills
that are important in the workplace. Even our detailed set of covariates would not account for
this factor, and it could confound an observed association between recent mental illness and
labor market outcomes. When we include both recent and prior measures of psychiatric illness
in the same model, we essentially are isolating the impact of incident mental illness or distress,
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above and beyond one’s previous experiences with mental illness, on current labor market
outcomes.
These two approaches use observed data to proxy unobserved factors that might
confound an association between recent psychiatric disorders and labor market outcomes. The
methods do not directly address the problem of reverse causality. Also, important correlates of
psychiatric disorders and labor market outcomes still may be left out of the model. For this
reason, we implement a third approach, which is to use empirical methods that directly address
the possibility that unobserved factors may exist that are associated with both psychiatric
disorders/distress and labor market outcomes.
Where the labor market outcome measure is continuous, we use the two-stage least
squares (TSLS) method:
Stage 1:
iiii
ZXMH
222
εξβα
+++=
Stage 2:
iiii
MHXE
111
εδβα
+++=
(2)
in which
i
MH
is the predicted value from estimating psychiatric disorder on observable
individual characteristics (X
i
) and instrumental variables (Z
i
) in stage 1.
In the case of binary labor market outcome and psychiatric disorder variables, we
use a bivariate probit model:
iiii
iiii
ZXMH
MHXE
222
*
111
*
εξβα
εδβα
+++=
+++=
1=
i
E if 0
*
i
E
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1=
i
MH if 0
*
i
MH (3)
Under the bivariate probit model, employment (E
i
) and psychiatric illness (MH
i
) are
simultaneously specified by the following likelihood function [28]:
),,()1,1Pr(
21
ρξβδβ
iiiiii
ZXMHXMHE ++Φ=== (4)
where is the bivairate cumulative distribution function and ],cov[
η
ε
ρ
=
is the
correlation coefficient. The model is estimated using maximum likelihood. (For recent studies
using the bivariate probit model, see MacDonald and Shields [30], Evans and Schwab [31], and
Goldman [32]).
The practical challenge of implementing the TSLS method and the bivariate probit model
is that in order to identify the labor market outcome equation, there must be at least one variable
that affects mental illness, but that is also exogenous and not directly related to labor market
outcomes (Z
i
). The identifying variable or variables should be reasonably good predictors of
mental illness. In the case of TSLS, a low first stage F-statistic for the identifying instrumental
variables may suggest that TSLS estimates are no better than biased OLS estimates [33-36].
Similarly, if the identifying variables are poor predictors of mental illness, the bivariate probit
model does not work well, yielding imprecise estimates (see Rashad and Kaestner [37]).
Previous work on mental health (including substance abuse) and labor market outcomes
have employed a large range of identifying variables, mostly used in the context of instrumental
variables estimation. Some examples are parental alcohol dependency [38] or parental history of
mental health problems [9]; number of childhood psychiatric disorders [9]; long-term non-acute
illnesses such as asthma or diabetes [30, 39]; religiosity [6, 30, 39-41]; social support [6, 42]; and
state-level alcohol and illicit drug policies and prices [43-44]. Following the above literature, we
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test and use three identifying instruments in our study (Z
i
): (1) the number of psychiatric
disorders with early onset (before age 18); (2) whether the respondent attends religious services
at least weekly; and (3) whether the respondent often uses spiritual or religious means (such as
praying, meditation, or speaking to a religious provider) to handle problems.
The number of psychiatric disorders with onset before age 18 should be a good predictor
of current psychiatric disorders because of the chronic nature of psychiatric illness. Early
psychiatric problems may interfere with schooling, and this factor in turn is likely to impact labor
market outcomes as an adult. However, after controlling for education and a range of other
covariates, early onset of mental illness would not be expected to have a direct effect on labor
market outcomes. We use religiosity as a measure of individual social capital. Studies have
shown that higher social capital is correlated with better mental health [39, 45-48].
One limitation of our study is that all three identifying variables are personal
characteristics, and it is difficult to make a strong theoretical argument that they are exogenous.
For example, as Alexandre & French [6] note, it is possible that religious beliefs directly impact
work habits, such as the number of hours worked. It is also possible that employment and
business information is conveyed through networks developed through church activities, which
may help one’s career. We deal with this problem by carefully assessing whether the identifying
variables as a group are reasonably strong predictors of mental illness, and if they can be validly
excluded from the labor market outcomes equation. Moreover, because we lack a strong
theoretical justification for the exogeneity of the identifying variables, we interpret the TSLS and
bivariate probit results with caution, and we do not emphasize these findings. Instead, we focus
on the standard estimation results and use the TSLS and bivariate probit findings as a check on
the main results of the paper.
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4 The National Latino and Asian American Study
Data used in the study come from The National Latino and Asian American Study
(NLAAS), a 2002-2003 survey of non-institutionalized Asian and Latin American adults residing
in the coterminous United States. The goal of the survey was to measure psychiatric diagnoses
and mental health service usage among Asian and Latino Americans. The NLAAS is a
nationally representative household sample of 4,864 individuals ages 18 and over, including
2,554 Latinos, 2,095 Asians, and 215 Whites (who were not included in this paper). NLAAS
interviews were conducted in English, Spanish, Chinese (Mandarin), Tagalog and Vietnamese,
based on the respondents’ language preferences. Originally, all interviews had been planned to
be conducted in-person, but due to budgetary constraints, approximately 1,000 interviews were
conducted by telephone. The weighted response rates for the NLAAS samples were: 73.2
percent for the total sample; 75.5 percent for the Latino Sample; and 65.6 percent for the Asian
sample [49]. We limit the analysis samples to 2,228 Latino respondents (1,016 males and 1,212
females) and 1,818 Asians (864 males and 954 females) between 18 and 65 years old who are
not in school at the time of the survey. All models are estimated separately by gender and broad
ethnic group (Latino or Asian) with techniques that acknowledge the complex survey design. A
fully interacted model of all covariates interacted with Latino ethnicity empirically supports our
estimation based on ethnicity specific samples. There are statistically significant interactions
between Latino and many key predictors of mental disorders, such as age, marital status, chronic
health conditions and English language proficiency (results not shown).
We consider the following three, self-reported labor market outcomes as dependent
variables in this analysis: (1) a dummy variable indicating whether or not the respondent is
currently employed; (2) among employed individuals, the number of weeks during which the
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respondent was employed in the past year; and (3) among employed individuals, whether or not
the respondent missed at least one day of work in the past month. The employment indicator
was created from a survey question regarding the respondent’s work situation “as of today.”
Employed persons include respondents who work full or part time and respondents who report
that they are self-employed. In the employment models, the omitted category combines
individuals who are unemployed with individuals who are out of the labor force, such as
homemakers, early retirees and discouraged workers. (Students and persons over 65 are
excluded from the analysis samples.) In order to distinguish the effects of mental illness on
unemployment and out of labor force status, we experiment with some multinomial logit models
that allow for three, qualitative categories as the dependent variable (out of labor force,
unemployed, with employed as the omitted category). Results from this analysis are discussed
later in the paper.
The NLAAS contains detailed information on psychiatric disorders that were collected by
trained, lay interviewers using the World Mental Health Survey Initiative version of the World
Health Organization Composite International Diagnostic Interview (WMH-CIDI). This fully
structured diagnostic instrument is based on the criteria of the Diagnostic and Statistics Manual
of Mental Disorders, Version 4 (DSM-IV). The CIDI has been tested extensively for test-retest
reliability and validity [50-51]. The NLAAS includes prior, 12 month and 30 days diagnoses for
a range of psychiatric disorders. The survey also includes scales of mental distress [51] and
psychiatric impairment [52].
Our main measure of recent psychiatric disorder is a dummy variable indicating whether
or not the respondent met diagnostic criteria for any psychiatric disorder in the past 12 months.
Any psychiatric disorder includes the following diagnoses: (1) major depression; (2) dysthymia;
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(3) agoraphobia; (4) generalized anxiety disorder (GAD); (5) panic attack; (6) panic disorder; (7)
social phobia; (8) alcohol abuse; (9) alcohol dependence; (10) illicit drug abuse; (11) illicit drug
dependence; (12) post-traumatic stress disorder; (13) anorexia; and (3) bulimia. We also
consider the effects of three broad classes of psychiatric disorders separately (any affective
disorder, any anxiety disorder, any substance use disorder – see Table 1 for definitions of these
disorder classes), and the effects of mental distress, on labor market outcomes. To capture
mental distress, we use the respondent’s continuous score on the K10, a 10 question scale of non-
specific psychological distress (see Kessler et al. [53] for a description of this scale). The K10
has demonstrated, strong psychometric properties in demographic sub-samples [53]. Because of
multicollinearity between the psychiatric measures, we include each of the diagnosis and mental
distress measures separately in the models.
Our models include a rich set of covariates that are intended to control for personal
characteristics that may be correlated with both labor market outcomes and mental illness. These
variables are: (1) sub-ethnicity (within the broader Latino and Asian categories); (2) age in years;
(3) education (high school, at least some college, with high school dropout as the baseline); (4)
marital status (married/cohabiting, widowed/divorced/separated with single as the baseline); (5)
the number of children in the household; (6) whether the respondent is not English proficient; (7)
immigrant; (8) US citizen; (9) a set of dummy indicators for current physical health conditions –
asthma, diabetes, chronic obstructive pulmonary disease, cancer, cardiovascular conditions; and
(10) the state unemployment rate in the interview year. The sub-ethnicity variables for the
Latino samples are: Puerto Rican; Cuban; and Mexican, with Other Latino as the baseline. For
the Asian samples, the sub-ethnicity variables are: Chinese; Vietnamese; Filipino, with Other
Asian as the baseline. The identifying variables used in the bivariate probit and TSLS models are
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two dummy indicators of frequent religious attendance (at least weekly) and often using religious
means to handle life’s problems, as well as a continuous measure of the number of psychiatric
disorders with onset during childhood.
As described earlier, in some models we include measures of both prior and current
disorder. Prior disorder is measured by a dummy variable indicating whether the respondent met
lifetime diagnostic criteria for any psychiatric disorder, but whose most recent symptoms
occurred prior to the past year. Thus, this variable excludes disorders and symptoms that
occurred in the past 12 months. It is important to note that in the models where both prior and
recent disorder measures are included as covariates, the number of incident cases is relatively
small in some cases (e.g. number of cases where a respondent without any prior disorder
develops a disorder in the past 12 months). The number of incident cases was 107 for Latino
females, 48 for Latino males, 38 for Asian females, and 33 for Asian males.
Observations were dropped if they had missing information on employment status (n=2),
marital status (n = 2), English proficiency (n=4), immigrant (n=2), US citizen (n=7), state
unemployment rate (n = 2), religious service attendance (n = 14), reliance on religious means to
deal with problems (n = 4), and K10 distress score (n = 1). In models of absences and labor
supply, we also dropped respondents with missing information on work absences and the number
of weeks worked.
Across the samples, 81 to 84 percent of males and 56 to 64 percent of females are
currently employed (Table 1). In the female analysis samples, 27 to 36 percent of respondents
are out of the labor force, which includes individuals who are homemakers, retired, disabled or
not looking for work. Approximately 11 to 12 percent of males report being out of the labor
force. Among employed respondents, the number of weeks worked in the past year ranged from
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about 47 to 50 weeks. In the male samples, 20 to 25 percent of respondents reported missing at
least one day of work in the past month; among females, 26 to 27 percent responded missing at
least one day of work in the past month.
The unemployment rate is about 8-9 percent in the female samples, and ranges from 5 to
8 percent in the male samples. The sample unemployment rates are consistent with national
unemployment rates for Latinos during the time period when the NLAAS data were collected. In
January 2003, the unemployment rate for Latino women age 20 and over was 8.4 percent, and
the unemployment rate for Latino males age 20 and over was 7.5 percent [19]. Nationally, the
unemployment rate for Asian males was 6 percent and the unemployment rate for females was 5
percent in 2002.
Recent psychiatric conditions are relatively common in all four samples, with the highest
rates among Latino females. Among Latino females, 17 percent meet diagnostic criteria for at
least one psychiatric disorder in the past 12 months – a large proportion of these women are
experiencing affective disorders (9 percent) and/or anxiety disorders (12 percent), but very few
have a diagnosis of substance abuse or dependence (1 percent). Among Latino males, 14 percent
have a 12 month DSM-IV diagnosis for at least one disorder, with 6 percent experiencing
affective disorders, 7 percent having an anxiety disorder, and 5 percent having a diagnosis of
substance abuse or dependence. In the Asian samples, rates of psychiatric disorders are lower (9
percent of males and 10 percent of females report any past year psychiatric disorder), but the
degree of mental distress, as measured by the K10, is similar to what is experienced in the Latino
samples.
All four samples consist of individuals who mainly are of working age (25 to 64 years
old) because persons over 65 years old and students were excluded from the analysis. The
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largest ethnic group among the Latinos is the Mexican Americans; in the Asian samples, the
largest ethnic group is Chinese Americans. In the Latino samples, about 42 percent of male and
43 percent of female respondents have less than a high school education (not shown in Table 1),
and 59-78 percent of the Latino and Asian samples are immigrants. These characteristics are
very different from those of other samples used to study psychiatric disorders and labor market
outcomes. For example, in the NCS sample used by Ettner et al. (1997), only 13 percent of
females and 16 percent of males had less than a high school education, and 6 to 7 percent were
immigrants. Individuals who are foreign born and have less than a high school education are
likely to face quite different job circumstances, and possibly different labor market consequences
of mental illness, compared to American born, more educated workers.
5 Results
Tables 2-5 show estimation results from employment, weeks worked and work absence
models for the four samples (Latino males, Latino females, Asian males, Asian females). In
each table, Panel A shows results from three models where any current psychiatric disorder is the
main covariate of interest. The first model is a standard probit model, the second model is a
standard probit model with prior disorder included as a covariate, and the third model is either a
bivariate probit model (in the case of a binary dependent variable), or a TSLS model (in the case
of a continuous dependent variable). These models directly account for the possibility that the
association between psychiatric disorders and labor market outcomes may be confounded by
unobserved factors. Panel B in each table (Tables 2-5) shows the same set of model results from
the analysis of the K10 mental distress score.
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5.1 Main Findings for Latino Males and Females
Among Latino males, the standard probit model suggests that having a psychiatric
disorder in the past year is associated with a statistically significant, 10 percentage point
reduction in the probability of being currently employed (about a 12 percent reduction at the
sample mean employment rate of 81 percent) (Table 2, Panel A, Column 1). Including prior
disorder as a covariate (Table 2, Panel A, Column 2) does not appreciably change the magnitude
or statistical significance of this effect, and estimating mental health and employment equations
simultaneously (Table 2, Panel A, Column 3) decreases the magnitude of the result to an 8
percentage point (about a 10 percent at the sample mean) reduction. The magnitude of this effect
of psychiatric disorders on the probability of employment for Latino males is very similar to
findings of Ettner et al. [9], who report an 11 percentage point reduction for men (about a 12
percent reduction at the sample mean employment rate of 91 percent) using the NCS sample.
Among employed Latino men, psychiatric disorders do not have a statistically significant
association with the number of weeks worked in the past year (Table 2, Panel A, Columns 4-6).
This also is the case when the TSLS method is used to account for the potential endogeneity of
psychiatric disorders (Table 2, Panel A, Column 6), and when broad disorder classes are
considered separately (results not shown). Having a recent psychiatric disorder, however, does
appear to increase the probability of having at least one work absence in the past month among
employed Latino men (Table 2, Panel A, Columns 7-9). The magnitude of this effect ranges
from 15 to 19 percentage points (an increase of 60 to 76 percent at the sample mean of 25
percent), depending on the model specification. Although the effect loses statistical significance
in the bivariate probit model, the estimated rho in this model is close to zero and statistically
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insignificant at the 0.05 level, suggesting that estimating the mental health and labor market
equations jointly is not advantageous.
Thus far, this analysis focuses on estimating the association between DSM IV psychiatric
diagnoses, as measured through diagnostic interviews, and labor market outcomes among Latino
men. Diagnostic interviews have become standard tools in psychiatric epidemiology [53], and
allow us to compare results with other analyses that have used the same measures. The measures
used up to this point, however, are dummy variables, indicating whether or not the respondent
meets the threshold for diagnosis, and are not informative in providing information about the
severity of the condition. For this reason, we also consider the effects of the severity of
symptoms on labor market outcomes using the K10 measure, which is a continuous scale of non-
specific psychological distress. We use the same approach as before and estimate models with
and without prior psychiatric disorder. Because we have a binary outcome (such as employment)
and a continuous outcome (the K10 measure), we use TSLS methods for all models and estimate
linear probability models in cases where the dependent variable is binary.
Panel B of Table 2 shows that for Latino males, higher K10 score is associated with
statistically significant, lower probability of being employed and statistically significant, higher
probability of having a work absence in the past month. The TSLS results are consistent in sign
with the OLS findings, but they lose statistical significance at conventional levels. The
identifying instruments performed well -- they were good predictors of the K10 score, and
passed the overidentification test.
Findings for Latino females were similar to those of Latino males, but the effects for
females are larger in magnitude and more robust across the models relative to the male findings
(Table 3). Having a recent psychiatric disorder is associated with a 23 percentage point
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reduction in the probability of being employed (this reduction translates into a 41 percent
reduction at the sample mean employment rate of 56 percent). The effect remains large and
statistically significant in the bivariate probit model (Table 3, Panel A, Column 3). The size of
this effect is larger than what Ettner et al. [9] report for women in the NCS – they find that
psychiatric disorder is associated with an 11 percentage point, or about a 14 percent (at the
sample mean), reduction in employment probability. Among employed Latino females, current
disorder also is associated with an increased probability of reporting at least one work absence in
the past month. While this effect is marginally statistically significant in some cases, having a
disorder appears to appreciably increase the probability of work absence – the size of this effect
is 13-18 percentage points, about a 48-67 percent increase at the sample mean absence rate of 27
percent.
As seen for Latino males, higher levels of mental distress among Latino females, as
measured by the K10, are associated with statistically significant, adverse effects on the
probability of employment and work absences in the past month (Table 3, Panel B). These
effects persist across models, although they lose some statistical significance in the TSLS models
(Table 2, Panel B, Columns 3 and 9). Like Latino males, Latino females with current psychiatric
disorders do not appear to work fewer weeks than similar individuals who do not meet diagnostic
criteria for current disorders.
5.2 Main Findings for Asian Males and Females
Among Asian males, having a current psychiatric disorder is associated with a 13
percentage point reduction in the probability of employment, which is a 15 percent reduction at
the sample mean of 84 percent (Table 3, Panel A, Column 1). The magnitude of this effect is
similar to the 12 percent reductions we find for Latino males and the sample of mostly white,
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native born, NCS males used by Ettner et al. This effect, however, is less robust for Asian males
that it was for Latino males. Including the prior psychiatric disorder covariate (Table 3, Panel A,
Column 2) , and explicitly modeling correlation between unobserved factors using the bivariate
probit model (Table 3, Panel A, Column 3), reduce the size and statistical significance of the
effect.
Similarly, in the work absence models (Table 3, Panel A, Columns 7-9), having a current
psychiatric disorder is associated with a small, statistically insignificant increase in the
probability of having an absence. This effect does not persist when prior psychiatric disorder is
included as a covariate, or when the bivariate probit model is used to model correlation between
unobserved factors. In Panel B of Table 3, we see that higher levels of mental distress are
associated with some detrimental effects on employment and labor supply. These effects,
however, are small and not statistically significant in many cases.
In Table 5, we find an even more inconsistent pattern for Asian females. The measure of
any psychiatric disorder has a negative, but statistically insignificant association with the
probability of being employed and the number of weeks worked (Table 5, Panel A, Columns 1-
6). There is a positive association but not statistically significant association between psychiatric
disorders and absences (Table 5, Panel A, Columns 7-9). Panel B of Table 5 shows suggestive
but small and statistically insignificant effects of mental distress on labor market outcomes for
Asian females. In sum, these results for Asian females are consistent with the results found for
Asian males, but not consistent with those for Latino females, which showed that psychiatric
disorders detract significantly from labor market outcomes.
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5.3 Further Analyses and Sensitivity Checks
For brevity, we discuss in the text but do not show results from some sensitivity analyses
including: analyses of specific disorder classes; analyses in which the out of labor force
respondents are considered as a separate category; and findings from statistical tests related to
TSLS and bivariate probit estimation.
5.3.1 Considering out of labor force as a separate outcome
In our analysis of employment, we initially ignored the distinction between respondents
who reported they were out of the labor force, and respondents who stated they were
unemployed. Both of these categories were combined as the baseline category. To gauge
whether separating these groups affects our interpretation of the results, we experimented with
multinomial logit models, which allow for a polychotomous outcome and are estimated using
maximum likelihood. This model distinguishes respondents who are unemployed and looking
for work from respondents who are out of the labor force. The out of labor force category
includes some early retirees and disabled individuals, but it mostly includes individuals who
were not looking for a job and did not work at all in the past 52 weeks for unspecified reasons.
We focus on Latino males, Latino females, and Asian females -- the three samples in which we
found statistically significant effects of psychiatric disorders and mental distress on labor market
outcomes in the main analysis.
For Latino males and females, we find that having a psychiatric disorder has larger and
more robust effects on being out of the labor force than on being unemployed, but mental
distress has similar effects on both the unemployment and out of labor force outcomes. Among
Asian males, the opposite is true – we see that having a psychiatric disorder increases the
probability of unemployment, and not being out of the labor force. For Asian males, however,
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mental distress is associated with being out of the labor force rather than unemployment. Our
data are not detailed enough for a comprehensive analysis of this issue, since we only know the
exact reason for being out of the labor force for a sub-set of respondents. However, it does
suggest that among Latinos, psychiatric disorders are associated with leaving the labor force
entirely rather than a temporary bout of unemployment, while for Asian males, the opposite is
true.
5.3.3 Effects of specific disorder classes
As another sensitivity check, we estimated models of employment, labor supply and
absenteeism where dummy indicators for current affective disorders, current anxiety disorders,
and current substance use disorders were considered separately, instead of using a dummy
indicator of any current mental disorder. We also estimated the multinomial logit models with
these specific disorder classes as covariates. In general, the effects of these broad diagnostic
groups were consistent with the main findings, although in some cases we lose precision. Again,
we focus on Latino males and females and Asian males, since we did not find evidence in the
main analysis of detrimental effects of mental disorders for Asian females.
Affective disorders appear to be more debilitating to labor market outcomes than anxiety
disorders, particularly for males. The findings indicate: (1) affective disorders are associated
with lower probability of being employed in all three samples; and (2) affective disorders are
associated with being out of the labor force, and not with temporary unemployment, for Latino
males and females. Anxiety disorders have less consistent effects across the samples. Among
Latino males and females, anxiety disorders are associated with being out of the labor force;
however, this effect is not evident for Asian males, and anxiety disorders are associated with
work absences only in the Latino female sample. In the case of substance use disorders, this
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disorder class was associated with being out of the labor force for Latino males and females, and
was associated with being unemployed for Asian males.
5.3.4 Empirical performance of identifying variables
In all but 1 of the 8 bivariate probit models estimated, the estimated correlation between
the error terms in the labor market outcome and psychiatric disorder equations was not
statistically significant at conventional levels. This finding suggests that in general there is no
advantage of the bivariate probit model over the standard probit model in this analysis. We note
that this conclusion hinges on the validity of the identifying assumptions made. As an informal
test, we re-estimated bivariate probit models of employment that included the three identifying
variables (number of early disorders, religious support, religious frequency) in both the
psychiatric disorder and labor market equations. The religious frequency and support variables
were statistically significant (at the 0.05 level) predictors of labor market outcomes in the Latino
female model for absenteeism, which reduces confidence in our identification strategy in this
case. However, using only the number of early disorders variable to identify the model (and
including the two religion variables in the employment equation) yielded very similar results.
In the TSLS models, the identifying variables perform moderately well in terms of
predictive power. In all but two models, in which the F statistic was 6 and 8 respectively,
the F statistic on the identifying variables ranged from 9 to 88. The identifying variables
passed the over-identification test at the 0.05 level in all but two cases. In these models,
however, the identifying restrictions passed the over-identification test when the religion
variables were not used as identifying instruments, and the findings were insensitive to this
modification.
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6 Conclusions
This paper examines the labor market consequences of psychiatric disorders in a
population that has not been studied to date – ethnic minorities of Latino and Asian descent,
most of whom are immigrants. As these demographic groups become a larger portion of the
workforce, much more research is needed on their labor market experiences. The availability of
the NLAAS, which measures psychiatric disorders and labor market outcomes in a national
sample of Latino and Asian Americans, allows us to study the labor market consequences of
mental illness in these demographic groups for the first time.
In this study, we find somewhat different results for Latinos and Asians. Psychiatric
disorders and mental distress have appreciable, negative associations with the probability of
being employed, and large, positive associations with work absences for Latino males and
females. While mental distress impacts both unemployment and being out of the labor force
among Latinos, meeting diagnostic criteria for a disorder is associated with being out of the labor
force, rather than being unemployed. As a whole, the findings indicate that mental illness
imposes labor market costs on Latinos that are at least as large and as important as those found in
studies based on mostly white samples. The similarity between NLAAS Latino and NCS males
[9] in the effects of psychiatric disorder on employment (12 percent reductions for both NCS
males and NLAAS Latino males) is striking, given the large differences in education, immigrant
status, English language proficiency and occupation.
The effect of having a recent psychiatric disorder is associated with a 15 percent
reduction in the probability of employment for Asian males, which again is similar to what we
find for Latino males and what previous researchers have reported for NCS males. However,
particularly among female respondents, having a recent psychiatric disorder and symptoms of
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mental distress appear to have smaller and less consistent impacts on labor market outcomes for
Asians compared to Latinos. This differential effect of psychiatric disorders on employment
outcomes between Latinos and Asians might be linked to differential coping resources afforded
by greater education. Compared to Latinos, Asian Americans in NLAAS are 1.8 times more
likely to have a college degree, which may facilitate maintaining productivity and decreased
likelihood of disability, even in the presence of psychiatric disorders. Another potential
explanation for the limited effect of psychiatric illness on employment outcomes in Asians could
be their higher proportion working in professional and managerial occupations (45.2 percent)
than Latinos (16.8 percent) [54], with more discretion to be less productive or unproductive
rather than take a sick day. A third possibility is cultural differences between Latinos and Asians
in their reactions to mental illness. Asians have been found to show lower levels of absenteeism
that has been explained as due to internalization of Confucianism upon work values [55]. There
is some evidence that Asians are less likely than individuals from majority groups to endorse
illness, stress, and depression as legitimate reasons for absence [56]. These work values may
possibly restrain Asians with mental disorders from missing work, for stigma of being labeled as
mentally ill by their employers or coworkers.
A primary limitation of this study is we cannot definitively address the potential
endogeneity of psychiatric disorders. Although we attempt to do so using bivariate probit and
TSLS models, our identification strategy is difficult to defend theoretically and does not perform
well empirically for Latino females. Thus, we emphasize our standard results, stressing that our
rich data source substantially reduces the possibility of unobserved confounding variables. The
NLAAS was designed specifically to capture the most important determinants of mental
disorders for Latino and Asian populations. In particular, we are able to control both prior
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mental illness and for comorbid physical health conditions, both of which were likely to be
important confounding variables in this analysis.
Our results suggest that there may be important labor market benefits associated with
public health policies targeted at the prevention and effective treatment of mental illnesses in
ethnic minority groups. In the US, there is ample evidence of health care disparities, or unequal
treatment of patients based on race and ethnicity that is not related to need for services or patient
preferences [57]. Wells et al. [58], for example, find that that despite similar levels of
psychiatric need between Latinos and non-Latinos, Latinos were much more likely than non-
Latinos to report not receiving mental health services (26 percent versus 12 percent). Our
findings suggest that eliminating these kinds of disparities and expanding access to services may
have significant labor market benefits – not just for majority populations, as has been
demonstrated, but also for Asian and Latino Americans. We recommend that future research
continue to focus on these demographic groups and as well as other under-studied and under-
served populations in order to fully understand the labor market benefits of preventing and
treating mental illness.
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Table 1: Weighted Means and Standard Errors
Variable Definition Latino
Males
(n = 1,016)
Asian
Males
(n = 864)
Latino
Females
(n = 1,212)
Asian
Females
(n = 954)
Labor Market Outcomes
Employed
Dummy variable = 1 if
respondent is employed
part-time or full-time, 0
otherwise
0.806
(0.019)
0.837
(0.014)
0.564
(0.022)
0.639
(0.023)
Out of labor
force
Dummy variable = 1 if
respondent is retired,
disabled, a homemaker,
or otherwise not
employed and not
looking for work, 0
otherwise
0.116
(0.014)
0.111
(0.012)
0.355
(0.021)
0.274
(0.027)
Unemployed
Dummy variable = 1 if
respondent is
unemployed or
temporarily laid off, 0
otherwise
0.078
(0.013)
0.052
(0.007)
0.081
(0.008)
0.086
(0.013)
Weeks worked
Number of weeks that
respondent worked in
past year (among
employed respondents)
49.84
(0.282)
49.89
(0.381)
47.35
(0.617)
49.01
(0.324)
Absent in past
month
Dummy variable = 1 if
respondent missed at
least 1 full day of work in
the past 30 days (among
employed respondents), 0
otherwise
0.252
(0.018)
0.196
(0.018)
0.264
(0.019)
0.274
(0.022)
Psychiatric disorders
Any current
psychiatric
disorder
Dummy variable = 1 if
respondent has diagnosis
of any 12 month
psychiatric disorder, 0
0.137
(0.015)
0.090
(0.015)
0.172
(0.012)
0.102
(0.010)
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36
otherwise
Any current
affective disorder
Dummy variable = 1 if
respondent has diagnosis
of any affective disorder
(major depression or
dysthymia) in past 12
months, 0 otherwise
0.058
(0.010)
0.044
(0.010)
0.093
(0.007)
0.056
(0.009)
Any current
anxiety disorder
Dummy variable = 1 if
respondent has diagnosis
of any anxiety disorder
(agoraphobia, social
phobia, generalized
anxiety disorder, panic
disorder) in past 12
months, 0 otherwise
0.071
(0.009)
0.051
(0.014)
0.119
(0.009)
0.067
(0.009)
Any current
substance use
disorder
Dummy variable = 1 if
respondent has diagnosis
of any substance use
disorder (alcohol abuse
or dependence, drug
abuse or dependence) in
past 12 months, 0
otherwise
0.046
(0.007)
0.022
(0.007)
0.013
(0.005)
0.010
(0.002)
K10 score
Score on the K10, a 10
question screening scale
of psychiatric distress
12.96
(0.190)
12.97
(0.257)
14.67
(0.288)
13.61
(0.168)
Any prior
disorder
Dummy variable = 1 if
respondent had any
psychiatric disorder
before the past 12 months
(does not include
disorders in past 12
months), 0 otherwise
0.207
(0.019)
0.120
(0.017)
0.175
(0.012)
0.105
(0.017)
Chronic Physical Health Conditions
Asthma Dummy variable = 1 if
respondent had asthma in
prior, 0 otherwise
0.068
(0.014)
0.069
(0.009)
0.110
(0.013)
0.085
(0.012)
Diabetes
Dummy variable = 1 if
respondent had diabetes
in prior, 0 otherwise
0.056
(0.007)
0.041
(0.005)
0.073
(0.007)
0.056
(0.011)
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37
COPD
Dummy variable =1 if
respondent had chronic
lung disease, such as
chronic obstructive
pulmonary disease or
emphysema, in prior, 0
otherwise
0.004
(0.002)
0.007
(0.003)
0.003
(0.001)
0.005
(0.003)
Cancer
Dummy variable =1 if
respondent had cancer in
prior, 0 otherwise
0.006
(0.003)
0.003
(0.002)
0.019
(0.005)
0.017
(0.004)
Cardiovascular
Dummy variable =1 if
respondent had
cardiovascular disease
(stroke, heart attack, or
high blood pressure) in
prior, 0 otherwise
0.118
(0.014)
0.158
(0.017)
0.159
(0.014)
0.115
(0.014)
Demographics
Cuban
Dummy variable =1 if
self-reported ethnicity is
Cuban, 0 otherwise
0.038
(0.004)
0.042
(0.005)
Mexican
Dummy variable =1 if
self-reported ethnicity is
Mexican, 0 otherwise
0.632
(0.028)
0.566
(0.039)
Other Hispanic
Dummy variable =1 if
self-reported ethnicity is
Other Hispanic, 0
otherwise
0.220
(0.027)
0.253
(0.027)
Vietnamese
Dummy variable =1 if
self-reported ethnicity is
Vietnamese, 0 otherwise
0.117
(0.021)
0.123
(0.021)
Filipino
Dummy variable =1 if
self-reported ethnicity is
Filipino, 0 otherwise
0.183
(0.021)
0.201
(0.023)
Other Asian
Dummy variable =1 if
self-reported ethnicity is
Other Asian, 0 otherwise
0.359
(0.033)
0.323
(0.025)
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38
Number of
family members
under age 18
Number of family
members under age 18
0.880
(0.072)
0.572
(0.065)
1.18
(0.058)
0.726
(0.061)
Citizen
Dummy variable = 1 if
respondent is a US
citizen, 0 otherwise
0.590
(0.023)
0.676
(0.032)
0.594
(0.029)
0.684
(0.029)
Immigrant Dummy variable = 1 if
respondent is an
immigrant to the US, 0
otherwise
0.591
(0.025)
0.755
(0.027)
0.587
(0.029)
0.782
(0.035)
Poor English
proficiency
Dummy variable =1 if
respondent reports poor
language proficiency in
English, 0 otherwise
0.478
(0.028)
0.275
(0.025)
0.483
(0.031)
0.356
(0.024)
Age 55 to 64
Dummy variable =1 if
age 55 to 64, 0 otherwise
0.063
(0.009)
0.110
(0.014)
0.082
(0.008)
0.103
(0.013)
Age 45 to 54
Dummy variable =1 if
age 45 to 54, 0 otherwise
0.147
(0.017)
0.207
(0.013)
0.174
(0.012)
0.233
(0.015)
Age 35 to 44
Dummy variable =1 if
age 35 to 44, 0 otherwise
0.244
(0.015)
0.265
(0.023)
0.245
(0.016)
0.262
(0.023)
Age 25 to 34
Dummy variable =1 if
age 25 to 34, 0 otherwise
0.319
(0.014)
0.286
(0.021)
0.304
(0.014)
0.272
(0.020)
High school
graduate
Dummy variable =1 if
high school graduate, 0
otherwise
0.263
(0.014)
0.184
(0.022)
0.245
(0.010)
0.161
(0.015)
At least some
college
Dummy variable =1 if
college graduate, 0
otherwise
0.262
(0.020)
0.438
(0.028)
0.288
(0.019)
0.514
(0.023)
Married/Cohabiti
ng
Dummy variable =1 if
married or cohabiting, 0
otherwise
0.699
(0.015)
0.703
(0.027)
0.628
(0.019)
0.729
(0.021)
Separated/
Divorced/
Widowed
Dummy variable =1 if
separated/divorced/wido
wed, 0 otherwise
0.115
(0.012)
0.059
(0.014)
0.211
(0.018)
0.098
(0.014)
6.27 6.24 6.25 6.10
Page 38
39
Unemployment
rate
State unemployment rate
in year of interview
(0.082) (0.075) (0.076) (0.083)
Identifying Variables
Number of
psychiatric
disorders with
age of onset
before age 18
Number of the following
illnesses with onset
before age 18: major
depression, dysthymia,
agoraphobia, alcohol or
drug abuse/dependence,
social phobia,
generalized anxiety
disorder, panic attack,
panic disorder, or post
traumatic stress disorder
anorexia/bulimia
0.326
(0.037)
0.220
(0.050)
0.340
(0.027)
0.206
(0.031)
Attends religious
services at least
weekly
Dummy variable =1 if
respondent attends
religious services at least
once a week, 0 otherwise
0.232
(0.021)
0.277
(0.026)
0.366
(0.022)
0.313
(0.021)
Often uses
religious means
to deal with life’s
problems
Dummy variable =1 if
respondent often uses
religious or spiritual
means to deal with life’s
problems (such as
praying, mediating etc.),
0 otherwise
0.214
(0.017)
0.207
(0.022)
0.392
(0.019)
0.330
(0.023)
Page 39
Table 2 Effects of psychiatric disorders on labor market outcomes: Weighted regression results for Latino males
Employed
Log of weeks worked among employed
individuals
At least one absence in the past month among
employed individuals
PANEL A: EFFECTS OF PSYCHIATRIC DISORDER IN PAST 12 MONTHS
(1)
Probit
(2)
Probit
(3)
Bivariate
probit
(4)
OLS
(5)
OLS
(6)
TSLS
(7)
Probit
(8)
Probit
(9)
Bivariate
probit
Any current
psychiatric
disorder
-0.361***
(-3.01)
[-0.103]
-0.393***
(-3.12)
[-0.114]
-0.279
(-0.410)
[-0.078]
-0.045
(-0.630)
-0.031
(-0.460)
0.059
(1.44)
0.541***
(3.41)
[0.188]
0.497***
(3.44)
[0.171]
0.456
(0.550)
[0.152]
Prior disorder
covariate
No Yes No No Yes No No Yes No
PANEL B: EFFECTS OF K10 MENTAL DISTRESS SCORE
Probit
Probit
TSLS
OLS
OLS
TSLS
Probit
Probit
TSLS
K10
Mental Distress
Score
-0.042***
(-4.49)
[-0.011]
-0.045***
(-4.88)
[-0.011]
-0.006
(-0.660)
-0.013
(-1.49)
-0.013
(-1.49)
0.006
(1.35)
0.033***
(2.64)
[0.010]
0.030**
(2.41)
[0.009]
0.022
(1.21)
Prior disorder
covariate
No Yes No No Yes No No Yes No
N
1,016 796 783
Notes:
1. Table only shows estimated coefficients on “any current psychiatric disorder.”
2. All models adjust for survey design characteristics.
3. T-statistics are in parentheses and marginal effects (for probit models) are in brackets.
4. Models include the following covariates: Age categories, sub-ethnicity categroies, number of household members under age 18, US citizen,
immigrant, poor English proficiency, education categories, state unemployment rate, chronic physical health condition categories, and marital
status.
5. Prior disorder defined as any psychiatric diagnosis with recent symptoms prior to previous year
.
Page 40
41
Table 3 Effects of psychiatric disorders on labor market outcomes: Weighted regression results for Latino females
Employed
Log of weeks worked among employed
individuals
At least one absence in the past month among
employed individuals
PANEL A: EFFECTS OF PSYCHIATRIC DISORDER IN PAST 12 MONTHS
(1)
Probit
(2)
Probit
(3)
Bivariate
probit
(4)
OLS
(5)
OLS
(6)
TSLS
(7)
Probit
(8)
Probit
(9)
Bivariate probit
Any current
psychiatric
disorder
-0.582***
(-5.85)
[-0.229]
-0.564***
(-5.49)
[-0.222]
-0.724***
(-2.77)
[-0.282]
0.094*
(1.80)
0.087
(1.59)
0.156
(1.02)
0.415*
(1.86)
[0.140]
0.396*
(1.72)
[0.134]
0.504*
(1.78)
[0.177]
Prior disorder
covariate
No Yes No No Yes No No Yes No
PANEL B: EFFECTS OF K10 MENTAL DISTRESS SCORE
Probit
Probit
TSLS
OLS
OLS
TSLS
Probit
Probit
TSLS
K10
Mental Distress
Score
-0.028***
(-3.03)
[-0.011]
-0.028***
(-3.05)
[-0.011]
-0.016*
(-1.79)
0.004
(0.980)
0.004
(0.900)
0.017
(1.26)
0.055***
(4.88)
[0.016]
0.054***
(4.81)
[0.016]
0.031***
(3.13)
Prior disorder
covariate
No Yes No No Yes No No Yes No
N 1,239 726 708
Notes:
1. Table only shows estimated coefficients on “any current psychiatric disorder.”
2. All models adjust for survey design characteristics.
3. T-statistics are in parentheses and marginal effects (for probit models) are in brackets.
4. Models include the following covariates: Age categories, sub-ethnicity categories, number of household members under age 18, US citizen,
immigrant, poor English proficiency, education categories, state unemployment rate, chronic physical health condition categories, and marital
status.
5. Prior disorder defined as any psychiatric diagnosis with recent symptoms prior to previous year
.
Page 41
42
Table 4 Effects of psychiatric disorders on labor market outcomes: Weighted regression results for Asian males
Employed
Log of weeks worked among employed
individuals
At least one absence in the past month among
employed individuals
PANEL A: EFFECTS OF PSYCHIATRIC DISORDER IN PAST 12 MONTHS
(1)
Probit
(2)
Probit
(3)
Bivariate
probit
(4)
OLS
(5)
OLS
(6)
TSLS
(7)
Probit
(8)
Probit
(9)
Bivariate probit
Any current
psychiatric
disorder
-0.491
***
(-2.27)
[-0.129]
-0.364*
(-1.74)
[-0.091]
-0.079
(-0.110)
[-0.018]
-0.113
(-1.00)
-0.082
(-0.980)
-0.527
(-1.16)
0.074
(0.290)
[0.020]
-0.067
(-0.300)
[-0.017]
-0.406*
(-2.03)
[-0.087]
Prior disorder
covariate
No Yes No No Yes No No Yes No
PANEL B: EFFECTS OF K10 MENTAL DISTRESS SCORE
Probit
Probit
TSLS
OLS
OLS
TSLS
Probit
Probit
TSLS
K10
Mental Distress
Score
-0.033*
(-1.96)
[-0.007]
-0.025
(-1.59)
[-0.005]
-0.007
(-0.350)
-0.009*
(-1.82)
-0.007*
(-1.76)
-0.046
(-0.95)
0.026
(1.44)
[0.007]
0.023
(1.29)
[0.006]
-0.006
(-0.470)
Prior disorder
covariate
No Yes No No Yes No No Yes No
N 864 717 709
Notes:
1. Table only shows estimated coefficients on “any current psychiatric disorder.”
2. All models adjust for survey design characteristics.
3. T-statistics are in parentheses and marginal effects (for probit models) are in brackets.
4. Models include the following covariates: Age categories, sub-ethnicity categroies, number of household members under age 18, US citizen,
immigrant, poor English proficiency, education categories, state unemployment rate, chronic physical health condition caegories, and marital
status.
5. Prior disorder defined as any psychiatric diagnosis with recent symptoms prior to previous year
.
Page 42
43
Table 5 Effects of psychiatric disorders on labor market outcomes: Weighted regression results for Asian females
Employed
Log of weeks worked among employed
individuals
At least one absence in the past month among
employed individuals
PANEL A: EFFECTS OF PSYCHIATRIC DISORDER IN PAST 12 MONTHS
(1)
Probit
(2)
Probit
(3)
Bivariate
probit
(4)
OLS
(5)
OLS
(6)
TSLS
(7)
Probit
(8)
Probit
(9)
Bivariate probit
Any current
psychiatric
disorder
-0.110
(-0.470)
[-0.042]
-0.096
(-0.420)
[-0.036]
-0.021
(-0.180)
[-0.008]
-0.103
(-1.27)
-0.108
(-1.40)
-0.090
(-0.830)
0.031
(0.190)
[0.010]
0.061
(0.360)
[0.020]
0.132
(0.270)
[0.044]
Prior disorder
covariate
No Yes No No Yes No No Yes No
PANEL B: EFFECTS OF K10 MENTAL DISTRESS SCORE
Probit
Probit
TSLS
OLS
OLS
TSLS
Probit
Probit
TSLS
K10
Mental Distress
Score
-0.007
(-0.530)
[-0.003]
-0.006
(-0.500)
[-0.002]
0.007
(0.680)
-0.008
(-1.31)
-0.008
(-1.35)
-0.008
(-0.600)
0.018
(1.34)
[0.006]
0.019
(1.46)
[0.006]
0.022
(1.48)
Prior disorder
covariate
No Yes No No Yes No No Yes No
N 954 626 618
Notes:
1. Table only shows estimated coefficients on “any current psychiatric disorder.”
2. All models adjust for survey design characteristics.
3. T-statistics are in parentheses and marginal effects (for probit models) are in brackets.
4. Models include the following covariates: Age categories, sub-ethnicity categories, number of household members under age 18, US citizen,
immigrant, poor English proficiency, education categories, state unemployment rate, chronic physical health condition categories, and marital
status.
5. Prior disorder defined as any psychiatric diagnosis with recent symptoms prior to previous year
.
Page 43
Page 44
    • "Psychological Distress, the main dependent variable, was measured using the 10-item Kessler Psychological Distress Scale (K-10) that provides a global assessment of distress over the past 30 days (Kessler et al., 2002). The K-10 is widely used in population-based studies in several countries and has been previously used with Asian American populations (Chatterji, Alegria, Lu, & Takeuchi, 2007; Yip, Gee, & Takeuchi, 2008). Respondents were asked, how often in the past 30 days they felt: depressed, so sad nothing could cheer them up, hopeless, restless or fidgety, so restless they could not sit still, tired out for no good reason, that everything was an effort, worthless, nervous , and so nervous that nothing could calm them down. "
    [Show abstract] [Hide abstract] ABSTRACT: Citizenship is both a system of privilege and a source of social identity. This study examines whether there are disparities in psychological distress between citizens and noncitizens, and whether these disparities may be explained by markers of social disadvantage (e.g., poverty, discrimination) or perceptions of success in the United States (i.e., subjective social status). We analyze data from the Asian subsample (n = 2,095) of the National Latino and Asian American Study. The data show that noncitizens report greater psychological distress compared with naturalized citizens and native-born citizens after accounting for sociodemographics (e.g., age, gender, Asian subgroup), socioeconomic characteristics (education, employment, income-to-poverty ratio), immigration (e.g., interview language, years in the United States, acculturative stress), health care visits, and everyday discrimination. Preliminary evidence suggests that subjective social status may explain some of the disparities between naturalized citizen and noncitizen Asian Americans.
    No preview · Article · Mar 2016 · American Behavioral Scientist
  • Source
    • "We use different specifications of the model, each using a different set of instruments: (1) an external instrument (w); (2) covariance instruments; and (3) both external and covariance instruments . First, following past research (Ettner et al., 1997; Chatterji et al., (2007 Chatterji et al., ( , 2011)), we use the number of psychiatric disorders with onset prior to age 18 as an external instrument for the possibly endogenous mental illness latent variable. Note that there are conceptual as well as empirical issues about the validity of this instrument (Chatterji et al., 2011 ). "
    [Show abstract] [Hide abstract] ABSTRACT: In this paper, we estimate the effect of psychiatric disorders on labor market outcomes using a structural equation model with a latent index for mental illness, an approach that acknowledges the continuous nature of psychiatric disability. We also address the potential endogeneity of mental illness using an approach proposed by Lewbel (2012) that relies on heteroscedastic covariance restrictions rather than questionable exclusion restrictions for identification. Data come from the US National Comorbidity Survey – Replication and the National Latino and Asian American Study. We find that mental illness adversely affects employment and labor force participation and also reduces the number of weeks worked and increases work absenteeism. To assist in the interpretation of findings, we simulate the labor market outcomes of individuals meeting diagnostic criteria for mental disorder if they had the same mental health symptom profile as individuals not meeting diagnostic criteria. We estimate potential gains in employment for 3.5 million individuals, and reduction in workplace costs of absenteeism of $21.6 billion due to the resultant improvement in mental health.
    Full-text · Article · Nov 2015 · Health Economics
  • Source
    • "African Americans and Latinos are less likely to suffer from depression and anxiety than non-Hispanic Whites (NHWs) [12, 13]. Asian Americans have lower rates of mental illness, when compared to Latinos [14]. The lower mental health burden among racial minorities is surprising given, minorities have less access to health care [15], lower socioeconomic status [16] and tend face higher levels of race-based discrimination [17], all of which are linked to poorer health. "
    [Show abstract] [Hide abstract] ABSTRACT: Background Little research has examined the interactive effect of cancer status and race/ethnicity on mental health. As such, the present study examined the mental health of adults, 18 and over, diagnosed with cancer. This study examined the extent to which a cancer diagnosis is related to poorer mental health because it erodes finances and the extent to which the mental health impact of cancer differs across racial/ethnic groups. Furthermore, this study aimed to test the stress process model, which posits that the proliferation of stress can lead to mental illness and this process can differ across racial/ethnic groups. Methods Data from the 2005 Adult California Health Interview Survey was used (N = 42,879). The Kessler 6, a validated measure of psychological distress, was used to measure mental health, with higher scores suggesting poorer mental health. Scores on the Kessler 6 ranged from 0 to 24. Linear regression models estimating psychological distress tested each aim. The mediating effect of income and the race by cancer interaction were tested. Results After controlling for gender, age, insurance status, education and race/ethnicity, cancer was associated with higher Kessler 6 scores. About 6% of this effect was mediated by household income (t = 4.547; SE = 0.011; p < 0.001). The mental health impact of cancer was significantly worse for Latinos and Blacks than for non-Hispanic Whites. Conclusions The mental health impact of cancer is not uniform across groups. Future work should explore reasons for these disparities. Efforts to increase access to mental health services among minorities with cancer are needed.
    Full-text · Article · Sep 2014 · BMC Public Health
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