THE IMPACT OF MENTAL HEALTH TREATMENT ON
LOW-INCOME MOTHERS’ WORK
PAMELA LOPREST
AUSTIN NICHOLS
URBAN INSTITUTE
JUNE 2008
THE IMPACT OF MENTAL HEALTH TREATMENT ON LOW-INCOME MOTHERS’ WORK
CONTENTS
I. Introduction ................................................................................................................................. 1
II. Literature Review ....................................................................................................................... 2
III. Data ........................................................................................................................................... 5
IV. Methods .................................................................................................................................... 6
V. Results ........................................................................................................................................ 9
Low-Income Mothers’ Mental Health and Employment .............................................................. 10
Mental Health Treatment and Employment .................................................................................. 12
VI. Conclusions............................................................................................................................. 13
Endnotes ........................................................................................................................................ 16
References ..................................................................................................................................... 18
Tables ............................................................................................................................................ 21
ABSTRACT
This study’s objective is to analyze the impact of mental health problems and mental
health treatment services on low-income mothers’ employment. To do this we use the 2002
National Survey of America’s Families. We use instrumental variables (IV) estimation to
estimate the impact of receipt of mental health treatment on the probability of work. We find that
all mothers, low-income mothers, and low-income single mothers in very poor mental health are
significantly less likely to work. Mental health treatment is associated with lower probability of
work in a naïve specification, but in our IV estimation, we find that mothers receiving mental
health treatment are significantly more likely to work. The effect of treatment on hours of work
is largely insignificant. These findings suggest that mental health problems are an important
barrier to work among low-income women and that access to treatment for these problems can
substantially improve the probability of work for this group.
MENTAL HEALTH, WORK, AND MENTAL HEALTH SERVICE USE
iii
The Impact of Mental Health Treatment on Low-Income Mothers’ Work
I. Introduction
There is a high incidence of poor mental health among low-income mothers, including
those in the welfare system or at risk of entering this system. Many of these women are single
parents and solely responsible for the well-being of their children. Mental health problems have
the potential to limit these women’s ability to work, affecting a major source of income for many
of these families. Experts suggest that effective treatments exist for the mental health issues
facing the largest number of women—depression and anxiety disorders. However, there is little
evidence for the impact of mental health treatment on low-income mothers’ work. This study
analyzes how mental health problems impede low-income mothers’ ability to work and the
impact of mental health treatment services on improving their work outcomes.
The study uses data from the 2002 National Survey of America’s Families (NSAF).
These cross-sectional data include measures of mental health and mental health service use as
well as income and employment. We examine to what extent poor mental health negatively
affects low-income mothers’ work outcomes and whether the use of mental health treatment
services increases work among these women.
We find that low-income mothers in very poor mental health are significantly less likely
to work than those in better mental health. However, conditional on working, these mothers do
not work fewer hours than those without mental health problems. We also find using
instrumental variables (IV) estimation that women receiving mental health treatment services are
significantly more likely to work, controlling for mental health status and other factors. These
1
findings suggest that mental health problems are an important barrier to work among low-income
women and that access to treatment for these problems can improve work outcomes.
II. Literature Review
Estimates of the prevalence of mental health problems in the United States vary
significantly across different measures and definitions of mental health issues. For example, the
Epidemiological Catchment Area Study (ECA) estimates that 2.9 percent of women and 1.6
percent of men in the United States are afflicted with major depression, as defined by the
Diagnostic and Statistical Manual of Mental Disorders (DSM-III) criteria.1 The more recent
National Comorbidity Study (NCS) estimates prevalence as 5.9 percent of women and 3.8
percent of men.2
Studies have consistently found that women have a higher prevalence than men of mood
disorders (including major depression) and anxiety disorders (including posttraumatic stress
disorder and panic disorder), which are the most prevalent mental health disorders generally
(Blazer et al. 1994; Ettner, Frank, Kessler 1997; Kessler 2004).3 The NCS (carried out in the
early 1990s) found that the 12-month prevalence for mood disorders was 12.9 percent for women
and 7.7 percent for men. The same data show a 3.2 percent prevalence for anxiety disorders in
women and 1.3 percent for men (Kessler 2004).
In addition, the NCS and the ECA reveal that individuals with lower socioeconomic
status are more likely to have mental health problems. In the ECA, those in the lowest
socioeconomic quartile were 2.86 times more likely to have a mental disorder than those in the
highest quartile. In the NCS, those with less than $20,000 in annual income were 1.92 times
more likely to have a disorder than those with income at or above $70,000 (Yu and Williams
1999). In an analysis of data from the National Household Survey of Drug Abuse, Jayakody and
2
Stauffer (2001) find that among single mothers, those with incomes below $20,000 are
significantly more likely to have a mental health disorder, as are those on welfare. In addition,
single mothers are significantly more likely to have a disorder than their married counterparts.
Several studies show that low-income women, especially those on welfare, have
disproportionately high rates of mental health problems compared to all women. Hauan and
Douglas (2004), reporting results on single-parent welfare recipients in six different geographic
areas that all use similar measures of mental health (with a focus on depression), find the
incidence of mental health problems ranging from 21 to 41 percent. Using national data, Loprest
and Zedlewski (2006) find that 24 percent of women receiving welfare and 13 percent of lowincome mothers who have never received welfare have mental health problems.4
A number of studies estimating the impact of mental health on employment for the
general population find a significant effect (Alexandre, Fede, and Mullings 2004; Dooley,
Catalano, and Wilson 1994; Ettner, Frank, and Kessler 1997; Jayakody and Stauffer 2001). The
results for women versus men are somewhat mixed. For example, Ettner, Frank, and Kessler
(1997) find a negative impact of psychiatric disorders on women’s employment but not on
conditional work hours. Alexandre, Fede, and Mullings (2004) find no impact of serious mental
illness on unemployment for women but a significant impact on workdays skipped conditioned
on employment.
The literature for low-income women primarily focuses on welfare recipients and
provides inconclusive results. Hauan and Douglas (2004) find no significant relationship
between mental health problems (with a focus on depression) and employment for women on
welfare. Other studies on small welfare-related samples of low-income women also find
insignificant or mixed results (Corcoran, Danziger, and Tolman 2004; Dasinger, Speigelman,
3
and Norris 2002; White 2004). Since all these studies involved small surveys, insignificant
effects may be the result of small sample sizes.
Although mental health problems are fairly prevalent, a large body of literature
documents the positive effect of mental health treatment on disorders (U.S. Department of
Health and Human Services 1999). This is particularly true for depression, due to the frequent
use of clinical trials to evaluate new medications and treatment protocols. For example, Wells
and colleagues (2000) conducted a randomized control trial, with the test group receiving
significantly more mental health visits (including an increased probability of longer-term visits
with a specialist at 6 and 12 months) and proper adherence to treatment regimens compared with
a standard managed care plan. The test group had significantly lower rates of depression at 6 and
12 months. This and other current literature indicates that better care results in better health
outcomes.
Very few studies have evaluated the effect of mental health treatment on work, even
fewer have been performed in recent years, and few have focused on women or the low-income
or low-skill population. Mintz and colleagues (1992) reviewed major research articles on the
treatment of depression and found 10 published studies with data addressing employment
outcomes. Due to the limitations of the data, the authors were not able to draw many
conclusions. However, they found that work outcomes were significantly and consistently better
for individuals who experienced symptom remission. In addition, work outcomes were
significantly better for individuals who were in longer-term treatment (10–16 weeks versus 6–9
months), indicating that duration and continuity of treatment are important in determining
employment. The treatment study described above (Wells et al. 2000) also found that the
probability of stopping work in 12 months was significantly lower for the test group. However,
there was no difference between test and control groups for the probability of entering the
workforce.
Our study seeks to fill our gap in understanding of the impact of mental health and mental
health treatment on work for low-income women by using a large national sample and directly
estimating the impact of treatment on work. In addition, given the endogeneity of mental health
treatment and work, this study adds to the literature in its use of instrumental variables to obtain
unbiased estimates of the impact mental health treatment on work.
4
III. Data
We use data from the third round of the NSAF conducted in 2002. The NSAF collects
data on the economic, health, and social characteristics of American adults and children, with an
emphasis on low-income families. When properly weighted, the NSAF is representative of the
noninstitutionalized U.S. population under the age of 65 (Abi-Habib, Safir, and Triplett 2003).
Our measure of mental health status is a scale created to encompass issues of anxiety,
depression, loss of behavioral or emotional control, and psychological well-being. All the
mothers in our sample were asked five questions about the frequency of certain feelings over the
past month prior to the interview. These questions are adapted from the Mental Health Inventory
(MHI)-5, used in the Medical Outcomes Study (Ehrle and Moore 1999). The MHI-5 consists of
the five questions that best predict the outcome of the 38-item Mental Health Inventory (MHI38) and cover anxiety, depression, loss of behavioral or emotional control, and psychological
well-being. While the MHI-5 cannot provide a clinical diagnosis, Berwick and colleagues (1991)
find that it performs as well as or better than several other short-form mental health measures in
detecting major psychological disorders, including longer surveys such as the GHQ-30 and SSI28. The answers to these questions are summed and multiplied by five to create a 25–100 scale,
with higher scores indicating better mental health.
For this study we use this scale to create two indicator variables for mothers in “very
poor” mental health and “poor” mental health. We follow previous studies using the MHI-5,
which have established the baseline for poor mental health as the bottom 20 percent of scores
nationally (Stewart, Hays, and Ware 1988). In the NSAF, 18.3 percent of all mothers in the
country scored 67 or lower. Women below this cutoff score are in “poor” mental health. (Due to
the noncontinuous nature of our score, we cannot precisely identify the bottom 20 percent.) To
further refine this measure and identify those with the most serious mental health problems, we
5
also create an indicator for “very poor” mental health—defined as those below the 5th percentile
of mental health for all women in the United States (a score of 55 or less in NSAF data). We use
these indicator variables rather than the mental health score itself because we expect a nonlinear
relationship between the score and labor supply. That is, increases in mental health scores at the
upper end of the distribution are expected to have less impact than at the lower end of the
distribution.
Our measure of mental health treatment is number of mental health treatment visits,
based on women’s responses to a question asking how many times they had received mental
health services in the past 12 months. A mental health visit is defined as a visit to a psychiatrist,
psychologist, psychiatric nurse, clinical social worker, therapist, or doctor for the purpose of
mental health care. Visits for drug abuse treatment or smoking cessation were not included in the
measure.
IV. Methods
Our analysis first examines the impact of mental health status on labor supply. We
measure labor supply in two ways: whether the woman is currently employed at the time of the
survey, and log of usual hours worked per week for those employed.5 We estimate the following
labor supply equations:
Pr(Employed=1) = (b1Very Poor MH + b2 Poor MH + X b3)
(1)
Ln(Weekly Hours| Employed=1) = b1Very Poor MH + b2 Poor MH + X b3 + e
(2)
where X is a vector of individual characteristics, including physical health, age, race, age of
youngest child, number of children, marital status, education, health insurance coverage in past
year, presence of a working spouse or partner (for the samples of all mothers and low-income
mothers), and income under two times the poverty level (for the sample of all mothers). Health
6
insurance coverage is measured using three different variables: received Medicaid at any time
last year; received employer-sponsored insurance any time last year; without health insurance
any time last year. Equation 1 is estimated using a probit and equation 2 is estimated using
ordinary least squares (OLS).6 We expect that mothers with mental health problems will be less
likely to be employed because of the increased difficulties of holding down a job while
experiencing poor mental health. Those with mental health problems who do work may be less
likely to work full time because of these same difficulties as well as the greater flexibility that
may be afforded by part-time work to accommodate the episodic nature of many mental health
problems. On the other hand, these low-income mothers may have greater need for employer
health insurance and, all else equal, may be more likely to work full time to secure eligibility for
this benefit when offered.
The second part of our analysis adds mental health treatment (measured by number of
mental health visits last year) to the above labor supply equations. Given the problems of
measuring mental health status, it is reasonable to expect that naïve estimates are plagued by bias
due to the endogeneity of mental health status caused by measurement error, omitted variables
correlated with both mental health and treatment, and simultaneity (treatment affects work, but
work also affects treatment). In particular, the estimated effect of treatment on labor market
outcomes will also capture the effect of unobserved variation in mental health status, a variety of
measurement error that is likely to change the estimated sign of the coefficient on treatment in a
regression that controls for measured mental health. Our IV strategy can exploit exogenous
variation to identify a Local Average Treatment Effect that is policy-relevant, if we use excluded
instruments that affect access to treatment and are manipulable by policy.
7
Since our primary interest is in the effect of mental health treatment on work, we exploit
exogenous variation in the availability of psychiatric treatment from hospitals in an individual’s
county of residence. Specifically, we use the number of hospitals with any psychiatric beds and
the number of hospitals with a psychiatric outpatient service (measured using survey data from
the American Hospital Association) in the mother’s county of residence as excluded instruments,
and treat mental health treatment as endogenous. These excluded instruments are strongly related
to treatment (both number of visits and any treatment measures), and identification and weak
identification statistics reflect the strength of identification (Nichols 2008). They are also
plausibly exogenous to measurement error components in the error of the estimating equation,
and unlikely to be related to joint decisions about work and treatment, except insofar as they
affect the likelihood of receiving any treatment (i.e., they satisfy the criteria for excluded
instruments).
In our IV estimation we also include several other features of the mother’s county of
residence, such as the number of hospitals with an emergency department, with alcohol/chemical
dependency services, with a social work service (which nearly all hospitals have), and the total
number of beds, number of alcohol/chemical dependency beds, and total admissions in hospitals
in the county. Thus, the availability of other closely allied services does not drive the
identification; only psychiatric services are taken as exogenous predictors of treatment.
In addition, prior literature has noted the potential endogeneity of mental health status in
the labor supply equation, given that the decision to seek mental health treatment is jointly
determined with labor force participation. Finding valid instruments for mental health status is
not easy. Ettner, Frank, and Kessler (1997) use parental psychiatric disorders and an individual’s
childhood psychiatric disorders as an instrument for current mental health status, and Alexandre,
8
Fede, and Mullings (2004) use individual’s reported religiosity as an instrument for serious
mental illness. Both sets of instruments have potential problems in that they may plausibly
impact unobservable human and social capital accumulation and labor market experience, with
direct consequences for employment and earnings. However, using IV estimation, both studies
reject the endogeneity of mental health status in the labor supply equation. Given these results
and because we want to focus on mental health treatment here, we do not instrument directly for
mental health status.
We carry out our analysis using three separate samples: all mothers (those living with
their own children under age 18), all low-income mothers, and low-income single mothers.7 We
define low-income as having family income in the prior year less than 200 percent of the poverty
level. The low-income samples allow us to focus on a group at risk for welfare when mental
health interferes with the ability to work. From all samples we exclude mothers who are
receiving disability benefits from the Supplemental Security Income (SSI) program. These
women are highly unlikely to work because of the severity of their disabilities and SSI program
rules that are a strong disincentive to work. This excludes 3.4 percent of all low-income mothers.
For all samples, we include women ages 18 to 64. Our final samples include 16,301 mothers,
6,206 low-income mothers, and 3,783 single low-income mothers.8 Table 1 shows the mean
characteristics of the women in our three samples using complex sample weights from the
NSAF.9
V. Results
A higher proportion of low-income mothers in the United States has mental health
problems than all mothers without regard to income. Table 1 shows that 8.6 percent of lowincome mothers and 12.8 percent of low-income single mothers in our sample are in very poor
9
mental health, compared with 5.2 percent of all mothers. Similarly, a greater percentage of these
low-income mothers are in poor mental health than all mothers.
Although almost a quarter of low-income mothers are in either very poor or poor mental
health, fewer than one-tenth reported receiving any mental health treatment (any visits for mental
health services) in the past year. The average number of mental health treatment visits for lowincome women in very poor mental health was 3.7. Conditional on receiving treatment, this
number of visits is 13.8. As expected, the average number of visits in the past year is
significantly higher for mothers in very poor mental health than for those in poor mental health.
Low-Income Mothers’ Mental Health and Employment
Low-income mothers with very poor mental health are significantly less likely to work
than those in better mental health. Our estimation of the relationship between employment
outcomes and mental health status are shown in table 2. The first three columns (1–3) show
results for our three samples from probits regressing whether a mother is currently employed on
explanatory variables. The second three columns (4–6) show results for linear regression of log
weekly hours of work conditional on being employed on the same explanatory variables.10
Within each group of models, the first includes married/cohabiting and low-income indicators as
controls (columns 1 and 4), the second restricts the sample to low-income mothers and includes a
married/cohabiting indicator as a control (columns 2 and 5), and the third restricts the sample to
low-income single mothers (columns 3 and 6). Standard errors are adjusted for clustering on
county.11
Our results indicate that low-income mothers with severe mental health problems are
significantly less likely to work. The point estimate for mothers in very poor mental health is the
sum of the coefficients for very poor mental health and poor mental health, but the difference
10
between mothers in very poor mental health and those in merely poor mental health is simply the
coefficient for very poor mental health.12
We find that, holding other factors constant, about 71.5 percent of all mothers in very
poor mental health work, whereas 73.1 percent of those in merely poor mental health are
predicted to work. About 56.2 percent of low-income mothers in very poor mental health are
predicted to work, holding other factors constant, but about 60 percent of low-income mothers in
poor mental health work, implying an effect on work of worse mental health more than twice the
size of that observed for all mothers. The estimated coefficient for low-income single mothers is
even larger, but the marginal effect is smaller: About 66.3 percent of low-income mothers in
very poor mental health are predicted to work, holding other factors constant, but about 67.5
percent of low-income mothers in better mental health work, implying an effect on work half the
size of that observed for all mothers. For all three samples the likelihood that mothers in poor
mental health are working is higher than for those in very poor mental health but smaller than the
rest of the population.
It is important to note that these results for mental health control for fair or poor general
health, which is also associated with significantly lower probability of work. About 12 percent of
mothers, 22 percent of low-income mothers, and 27 percent of low-income single mothers report
being in fair or poor general health.13 If low-income mothers in poorer general health base their
self-assessment in part on their mental health, inclusion of this control could reduce the
estimated coefficient on poor mental health. Reestimation without including poor general health
slightly increases the estimated relationship between mental health and work. These results
indicate that very poor mental health has a strong negative connection to work for low-income
women over and above the negative impact of poor general health status on work.
11
The other variable coefficients in the regression model of work are generally as expected.
Women less likely to work include those who are older, black, Hispanic, married/cohabiting with
a working spouse/partner, with more or younger children, and those with lower levels of
education. These results are generally consistent with the literature on employment of lowincome women.
Our OLS estimates in table 2 indicate little significant connection between mental health
problems and hours of work for working mothers. For our three samples, the only significant
result is an 8 percent increase in weekly hours worked for employed low-income mothers with
very poor mental health.14 None of the coefficients on poor mental health are significant.
Lack of a significant relationship between mental health status and hours of work could
reflect that mothers with very poor or poor mental health who are working have some
unobserved characteristic or circumstance that differs from those who are not working. This
could be unmeasured aspects of their mental health problem, personal characteristics such as
motivation, or other family or community supports. It could also signal that the two possible
theories advanced above—the difficulty of full-time work with mental health and the increased
need for health insurance of workers in poor mental health—are having opposite effects.
Mental Health Treatment and Employment
Our focus is on the effect of mental health treatment on work, but first we examine the
effect of explanatory variables on mental health treatment (table 3). These are the first-stage
regressions for IV results in subsequent tables. Results are much as expected: Poor mental health
is associated with more mental health visits, and very poor mental health is associated with still
more mental health visits (as before, the point estimate for mothers in very poor mental health is
the sum of the coefficients for very poor mental health and poor mental health). The excluded
12
instruments (the number of hospitals with any psychiatric beds and the number of hospitals with
a psychiatric outpatient service) pass an identification test, but in the smaller samples the Fstatistic measuring their joint significance falls below the threshold where weak instruments are
more of a concern. Stock and Yogo (2005), however, do not provide rules of thumb for IV probit
models.
Table 4 reports our key results. The first three columns show the naïve estimates of the
effect of mental health treatment on employment using probit models for the three samples
(mothers, low-income mothers, low-income single mothers). In each sample, the apparent effect
of an additional mental health visit is to lower the likelihood that a mother works. In contrast, the
IV results indicate that an additional mental health visit has a negative, statistically insignificant
effect in the full sample, but increases the likelihood that a mother works among low-income and
low-income single mothers.15
Table 5 examines the intensive margin, the number of hours of work conditional on
working. A regression of log usual hours per week indicates no effect of treatment on work
intensity, though the IV estimates indicate that low-income mothers who receive treatment may
work less, conditional on working.
VI. Conclusions
This investigation of the relationships among mental health status, work, and mental
health treatment provides important findings. Almost 10 percent of low-income mothers are in
very poor mental health, defined as the bottom fifth percentile of all women in the United States.
Another 18 percent are in poor mental health. Fewer than a third of these mothers in very poor
mental health, and only 16 percent of those in poor mental health, received any treatment in the
13
past year. Low-income mothers in very poor mental health are at a disadvantage on a host of
other socioeconomic risk factors.
The previous literature has mixed results on the impact of mental health on women’s
work. In our study, we find that low-income mothers and low-income single mothers in very
poor mental health are significantly less likely to work than other low-income mothers, even
after controlling for many risk factors, including physical health. We find no impact for mothers
at all income levels. This reduced likelihood of work for low-income mothers could lead to
significant economic hardship for these women and their families, especially among single
mothers.
Although about one-quarter of low-income mothers are in very poor or poor mental
health, fewer than one-tenth received any mental health treatment services in the past year.
Contrary to our hypothesis that mental health treatment improves work outcomes, our initial
probit estimates find that the receipt of mental health treatment (measured by number of visits in
the past year) is associated with a lower likelihood of current employment. Given that our
measure of mental health imperfectly reflects true mental health status, it is likely that a higher
number of mental health treatment visits in the past year is correlated with this unmeasured
dimension of mental health status and our results simply reflect less work among those with
more severe mental health problems (within each category of measured mental health status).
Using a set of instruments reflecting regional availability of health care, our IV estimates find
that low-income mothers and low-income single mothers with a greater number of mental health
treatment visits are significantly more likely to be currently employed.
These results show that mental health treatment does have a positive impact on work for
low-income mothers. Since poor mental health is an impediment to work, these results suggest
14
that increased access to mental health treatment may be a way to improve work outcomes for
low-income mothers generally. Other research shows that health insurance coverage significantly
increases the likelihood of receiving mental health services, controlling for mental health status
and other factors (Loprest, Schaner, and Zedlewski 2007).
Yet in our sample, 45 percent of low-income mothers were without health insurance at
some point in the prior year. Even among low-income women with health insurance, absolute
receipt of mental health treatment is still relatively low. Only a little more than a third of lowincome mothers in very poor mental health with health insurance received any mental health
services in the past year. Putting these results together, it is possible that increasing access to
public or private health insurance for low-income mothers with mental health problems could
increase employment.
Further investigation into the reasons for lack of receipt of mental health treatment even
among the insured is warranted. In addition, this study focuses on receipt of treatment and says
nothing about the adequacy of treatment. Other literature suggests that low-income women, even
those receiving some mental health services, may lack access to mental health specialists and
appropriate treatments.
More public policy is focusing on work as the avenue for disadvantaged families to move
ahead. Welfare programs increasingly focus on work and work preparation, and the nation’s
largest income support program, the earned income tax credit, is available only to workers.
Given the negative impact of mental health problems on work and the evidence that mental
health treatment increases employment, it is important for policymakers to consider how to
improve access to and use of treatment for low-income mothers.
15
Endnotes
1
The ECA uses the Diagnostic Interview Schedule to identify those with mental health
disorders.
2
The NCS uses the Composite International Diagnostic Interview. Both the ECA and NCS
estimates can be found in Blazer and colleagues (1994).
3
Men have higher prevalence of addictive disorders (including substance abuse and dependence)
and antisocial personality disorder (Kessler 2004).
4
For an extensive literature review on poverty, welfare, and mental health, see Lennon, Blome,
and English (2001) and Belle (1990).
5
Additional analysis was carried out on two additional outcome variables: whether employed at
all in the prior year and total hours worked in the prior year. These results are available from the
authors upon request.
6
Estimation of weekly hours for the entire sample using Poisson estimation yielded similar
results to the OLS estimation for the impact of mental health as well as the impact of mental
health treatment.
7
When sampling families with children under the age of 18, the NSAF selects up to two focal
children for detailed survey, one under the age of 6 and one age 6 to 17. The adult who knows
the most about the child (the most knowledgeable adult or MKA) responds to these questions.
The MKA is the child’s primary caregiver and generally the biological mother. As such, when
we refer to mothers in our analysis, we are referring to female MKAs.
8
In NSAF data, certain key questions for this analysis (e.g., receipt of mental health treatment)
are only asked of one adult, randomly chosen, in two-parent households. Therefore, we do not
include mothers who were not chosen for these questions. We have used appropriate weights to
adjust for this exclusion.
9
See Brick, Strickler, and Ferraro (2002) for detail on the variance estimation methodology used
with the NSAF.
10
We include the county-level variables that we use in our analysis of mental health treatment on
work to allow comparison of results across tables 2, 4, and 5.
11
Results are virtually identical when replicate weights are used to account for the sample
design, but not the construction of county-level covariates from another data source, since
weights and high-level clustering are used.
12
Because mental health covaries with other explanatory variables, the marginal effects
evaluated over the sample need not be similar to the marginal effect evaluated at the mean of all
covariates, the coefficient b1 times a factor Φ(E(X)b), where E(X) is a vector containing the
sample means of covariates.
13
The current health status question includes individuals’ self-assessment of their current health,
both physical and mental.
14
A Generalized Linear Model (GLM) regression model (with log link) for hours of work
including the full sample was also estimated, along with Generalized Method of Moments
(GMM) log-link regressions using excluded instruments (see Nichols 2007,2008) corresponding
to IV models below. The GLM model directly takes into account the left censoring of hours and
the selection of individuals who are working. Results from this type of model are intermediate
between results for the logit on employment and the OLS model for hours of work. We present
16
only the logit and OLS specification, since these demonstrate that the effects differ along the
intensive and extensive margins, which the GLM regression does not reflect.
15
Estimates of receipt of any mental health treatment in the past year (as opposed to number of
visits) do not show conclusive results. Identification tests for our instruments are much weaker
for the “any treatment” outcome, suggesting that measures of concentration of psychiatric
services are better instruments on the intensive than the extensive margin. A possible reason is
that mothers with fewer visits are more likely to be receiving treatment from general
practitioners, leading to lower correlation with concentration of psychiatric services per se.
17
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20
Tables
Table 1. Weighted Sample Means (Standard Deviations for Selected Variables)
(1)
(2)
Mothers
Low-Income
Mothers
Very Poor Mental Health
0.0516
0.0859
Poor–Very Poor Mental Health
0.153
0.238
Fair–Poor Physical Health
0.121
0.223
Number Mentah Health (MH) Visits
0.712
0.773
(4.142)
(4.497)
Number MH Visits for those in
3.406
3.729
Very Poor Mental Health
(9.278)
(10.85)
Number MH Visits for those in
2.287
2.014
Poor to Very Poor Mental Health
(7.586)
(7.791)
Married/cohabiting
0.747
0.550
Low-Income
0.313
Age
37.10
34.66
(8.661)
(9.032)
African American
0.142
0.222
Latino
0.169
0.306
Other Minority
0.0476
0.0338
Age Youngest Child
6.823
5.651
(5.206)
(4.900)
Number of Children
1.842
2.081
(0.773)
(0.803)
Youngest Child Under 3
0.281
0.366
HS—No College Degree
0.334
0.413
College Degree
0.539
0.296
Medicaid Any Time Last Yr
0.126
0.310
ESI Any Time Last Yr
0.728
0.410
W/o Health Insurance (HI) Any Time
0.225
0.451
Last Yr
Working Spouse-Partner
0.674
0.441
Alc/Chem Dep Beds
27.73
28.88
(47.24)
(47.79)
No. Hosp. with Alc/Chem beds
1.166
1.210
(1.724)
(1.754)
Beds set up and staffed
2697.1
3040.3
(4372.2)
(4986.6)
Hosp. admissions in 1000s
114.4
128.2
(189.9)
(216.8)
No. Hosp. with Social Work Serv.
9.696
10.89
(15.39)
(17.63)
No. Hosp. with ER
8.295
9.279
(12.86)
(14.67)
Observations
16301
6206
(3)
Low-Income
Single Mothers
0.128
0.299
0.268
1.089
(5.526)
3.519
(10.60)
2.279
(8.517)
34.94
(9.561)
0.375
0.232
0.0247
6.625
(4.974)
1.997
(0.812)
0.284
0.440
0.290
0.413
0.372
0.419
31.99
(50.39)
1.339
(1.850)
3254.7
(5013.6)
136.1
(217.8)
11.23
(17.60)
9.517
(14.70)
3783
Source: Authors’ calculations from the National Survey of America’s Families, 2002.
Note: Low-income is defined as family income less than 200 percent of the federal poverty line. ESI refers to
employer-sponsored health insurance.
21
Table 2. Association of Mental Health with Employment and Hours of Work
(1)
(2)
(3)
(4)
Dep. Variable
Employed
Employed
Employed
ln Weekly
Last Year
Last Year
Last Year
Hours
Very Poor Mental Hlth
Poor Mental Hlth
Fair–Poor Phys Hlth
Age
African American
Latino
Other Minority
Age Youngest Child
Number of Children
Youngest Child Under3
HS—No College Deg
College Deg
Medicaid Any Time Last Yr
ESI Any Time Last Yr
W/o HI Any Time Last Yr
Working Spouse-Partner
Alc/Chem Dep Beds
# Hosp. w/ Alc/Chem beds
Beds set up and staffed
Hosp. admissions in 1000s
# Hosp. w/ social work serv.
No. Hosp. with ER
Married/cohabiting
Low-income
Constant
Observations
-0.281***
(-3.02)
0.231***
(3.85)
-0.353***
(-6.55)
-0.017***
(-5.29)
0.262***
(4.16)
0.102**
(1.97)
0.081
(0.71)
0.031***
(4.63)
-0.087***
(-3.09)
-0.144**
(-2.37)
0.193***
(3.22)
0.168***
(2.73)
-0.080
(-1.24)
0.487***
(9.61)
0.161***
(3.21)
-0.853***
(-8.93)
0.001
(1.20)
0.005
(0.25)
-0.00003
(-0.40)
-0.001
(-0.28)
0.010
(0.70)
-0.00004
(-0.00)
0.149
(1.46)
-0.624***
(-12.42)
1.710***
(10.72)
16301
-0.400***
(-3.57)
0.294***
(4.08)
-0.170***
(-2.68)
-0.021***
(-5.41)
0.208***
(2.61)
0.032
(0.42)
0.118
(0.73)
0.031***
(3.30)
0.031
(0.78)
-0.125
(-1.36)
0.187**
(2.45)
0.213***
(2.71)
-0.031
(-0.44)
0.515***
(7.38)
0.140**
(2.12)
-0.999***
(-9.21)
0.002
(1.59)
-0.0479*
(-1.86)
0.00004
(0.60)
-0.003*
(-1.79)
-0.026
(-1.59)
0.062**
(2.52)
0.235**
(2.11)
-0.464***
(-3.47)
0.0728
(0.68)
-0.190**
(-2.26)
-0.025***
(-5.36)
0.137
(1.52)
0.023
(0.24)
-0.209
(-0.90)
0.002
(0.11)
-0.012
(-0.23)
-0.311**
(-2.09)
0.225**
(2.19)
0.291***
(2.74)
-0.181*
(-1.77)
0.795***
(7.02)
0.214**
(2.45)
0.927***
(4.64)
6206
1.471***
(5.53)
3783
0.002
(1.35)
-0.061*
(-1.82)
-0.00008
(-0.85)
0.0002
(0.06)
-0.042*
(-1.88)
0.074**
(2.52)
0.001
(0.02)
0.0183
(0.85)
-0.045
(-1.58)
-0.002**
(-2.12)
0.097***
(6.39)
0.038*
(1.76)
0.081***
(2.99)
0.007***
(3.80)
-0.019*
(-1.96)
-0.023
(-0.91)
-0.043*
(-1.70)
-0.040*
(-1.65)
-0.050**
(-1.98)
0.130***
(5.40)
0.013
(0.61)
-0.229***
(-9.11)
0.0001
(0.31)
-0.013
(-1.36)
-0.00001
(-0.64)
-0.0004
(-1.03)
0.012***
(2.97)
-0.005
(-0.94)
0.130***
(5.65)
-0.061***
(-3.29)
3.661***
(58.63)
11811
(5)
ln Weekly
Hours
(6)
ln Weekly
Hours
0.081*
(1.86)
0.00412
(0.14)
-0.078***
(-3.27)
-0.002
(-1.21)
0.030
(1.04)
0.047*
(1.66)
0.030
(0.74)
0.008**
(2.11)
0.038**
(2.18)
0.034
(1.03)
-0.019
(-0.67)
-0.0004
(-0.01)
-0.084***
(-2.81)
0.073**
(2.51)
-0.033
(-1.28)
-0.200***
(-5.20)
-0.001***
(-2.60)
0.017*
(1.67)
-0.000001
(-0.04)
0.0001
(0.18)
-0.006
(-1.13)
0.005
(0.55)
0.158***
(5.22)
0.076
(1.64)
-0.024
(-0.66)
-0.051
(-1.55)
-0.001
(-0.51)
0.042
(1.40)
0.046
(1.28)
0.075
(1.18)
0.0004
(0.09)
0.023
(1.36)
0.018
(0.47)
0.030
(0.83)
0.045
(1.29)
-0.120***
(-3.36)
0.118***
(3.52)
-0.036
(-1.11)
3.486***
(54.04)
3763
3.496***
(45.24)
2538
-0.001***
(-3.54)
0.029**
(2.50)
-0.00006
(-1.44)
0.002
(1.53)
0.005
(0.66)
-0.009
(-0.74)
Notes: t statistics in parentheses adjusted for sample clustering; * p < 0.10, ** p < 0.05, *** p < 0.01.
Low-income is defined as family income less than 200 percent of the federal poverty line. ESI refers to employersponsored health insurance.
22
Table 3: First Stage Regression of Number of Mental Health Treatment Visits
(1)
(2)
(3)
Number MH Visits
Number MH Visits
Number MH Visits
Very Poor Mental Hlth
Poor Mental Hlth
Fair–Poor Phys Hlth
Age
African American
Latino
Other Minority
Age Youngest Child
Number of Children
Youngest Child Under3
HS—No College Deg
College Deg
Medicaid Any Time Last Yr
ESI Any Time Last Yr
W/o HI Any Time Last Yr
Working Spouse-Partner
Alc/Chem Dep Beds
No. Hosp. with Alc/Chem beds
Beds set up and staffed
Hosp. admissions in 1000s
No. Hosp. with Social Work Serv.
No. Hosp. with ER
No. Hosp. with Any Psych Beds
No. Hosp. with Psych OP Services
Married/cohabiting
Under 2x FPL
Constant
Observations
F test of joint signif. of Psych vars
1.337***
(6.88)
1.461***
(11.51)
0.709***
(5.91)
0.008
(1.45)
-1.200***
(-9.95)
-0.732***
(-5.88)
-0.622***
(-2.91)
-0.014
(-1.16)
-0.027
(-0.49)
-0.274**
(-2.20)
0.437***
(3.24)
0.845***
(6.14)
0.822***
(6.22)
-0.153
(-1.32)
-0.610***
(-5.59)
0.087
(0.50)
0.007***
(4.56)
-0.173***
(-2.86)
-0.0003**
(-2.11)
0.002
(0.70)
0.036
(1.21)
-0.043
(-1.12)
0.085**
(2.51)
0.053*
(1.93)
-0.668***
(-3.74)
0.144
(1.43)
0.469
(1.60)
16301
10.31
2.064***
(7.28)
0.794***
(3.99)
0.691***
(3.90)
0.006
(0.68)
-1.388***
(-7.36)
-0.798***
(-4.09)
-0.849**
(-2.13)
-0.009
(-0.39)
-0.111
(-1.19)
-0.331
(-1.52)
0.472**
(2.53)
0.746***
(3.69)
1.029***
(5.63)
-0.245
(-1.38)
-0.697***
(-4.24)
-0.069
(-0.24)
0.012***
(4.51)
-0.258**
(-2.33)
-0.001***
(-3.00)
0.010*
(1.90)
0.048
(0.93)
-0.036
(-0.54)
0.014
(0.22)
0.128***
(2.65)
-0.426
(-1.54)
2.191***
(5.68)
0.921***
(3.22)
0.711***
(2.82)
0.011
(0.92)
-1.674***
(-6.56)
-1.085***
(-3.57)
-1.316**
(-2.13)
0.003
(0.09)
-0.078
(-0.56)
-0.304
(-0.92)
0.713***
(2.60)
1.078***
(3.63)
1.103***
(4.13)
-0.409
(-1.54)
-0.880***
(-3.63)
0
.
0.016***
(4.12)
-0.287*
(-1.79)
-0.001**
(-2.03)
0.008
(1.02)
0.047
(0.60)
-0.025
(-0.25)
0.022
(0.25)
0.151**
(2.21)
0.977**
(2.08)
6206
5.116
0.581
(0.86)
3783
3.690
Notes: t statistics in parentheses adjusted for sample clustering; * p < 0.10, ** p < 0.05, *** p < 0.01.
Low-income is defined as family income less than 200 percent of the federal poverty line. ESI refers to
employer-sponsored health insurance.
23
Table 4: Probit and IV Probit regression of Current Employment on Number of Mental Health Treatment
Visits
Probit
IV Probit
(1)
(2)
(3)
(4)
(5)
(6)
Dep. Variable
Employed
Employed
Employed
Employed
Employed
Employed
Number MH Visits
Very Poor Mental Hlth
Poor Mental Hlth
Fair-Poor Phys Hlth
Age
African American
Latino
Other Minority
Age Youngest Child
Number of Children
Youngest Child Under3
HS—No College Deg
College Deg
Medicaid Any Time Last
Yr
ESI Any Time Last Yr
W/o HI Any Time Last Yr
Working Spouse-Partner
Alc/Chem Dep Beds
No. Hosp. with Alc/Chem
beds
Beds set up and staffed
Hosp. admissions in 1000s
No. Hosp. with Social
Work Serv.
No. Hosp. with ER
Married/cohabiting
Low-income
Constant
Observations
-0.014***
(-4.17)
-0.111
(-1.20)
0.075
(1.21)
-0.354***
(-6.46)
-0.010***
(-3.80)
0.184***
(3.15)
0.200***
(3.71)
0.091
(0.88)
0.031***
(5.02)
-0.083***
(-3.20)
-0.205***
(-3.59)
0.176***
(2.87)
0.221***
(3.68)
-0.525***
-0.013**
(-2.45)
-0.224**
(-2.16)
0.117
(1.54)
-0.211***
(-3.21)
-0.013***
(-3.82)
0.062
(0.82)
0.143**
(2.04)
0.018
(0.09)
0.026***
(2.88)
-0.039
(-0.96)
-0.140
(-1.64)
0.141*
(1.80)
0.208***
(2.65)
-0.337***
-0.016***
(-2.91)
-0.330**
(-2.55)
0.049
(0.50)
-0.212***
(-2.83)
-0.019***
(-4.45)
-0.079
(-0.94)
0.151
(1.57)
0.037
(0.20)
0.009
(0.71)
-0.037
(-0.78)
-0.264**
(-2.12)
0.130
(1.41)
0.323***
(3.39)
-0.481***
-0.075
(-1.34)
-0.106
(-0.98)
0.135
(1.55)
-0.262***
(-3.48)
-0.017***
(-6.71)
-0.023
(-0.28)
0.116*
(1.72)
-0.012
(-0.16)
0.030***
(5.91)
-0.028*
(-1.83)
-0.227***
(-6.60)
0.199***
(5.39)
0.328***
(7.50)
-0.315***
0.098**
(2.05)
-0.459***
(-6.26)
0.012
(0.16)
-0.292***
(-6.63)
-0.015***
(-4.08)
0.134*
(1.71)
0.207***
(4.70)
0.134
(1.45)
0.020***
(3.07)
0.023
(1.11)
-0.104
(-1.54)
0.091
(1.35)
0.158
(1.59)
-0.361***
0.117***
(4.33)
-0.447***
(-6.47)
-0.076
(-1.20)
-0.269***
(-4.09)
-0.013***
(-2.88)
0.125
(1.51)
0.192***
(3.51)
0.177
(1.56)
0.010
(1.38)
0.015
(0.62)
-0.076
(-1.01)
0.031
(0.50)
0.075
(0.66)
-0.372***
(-9.32)
0.334***
(7.13)
-0.244***
(-4.64)
-0.811***
(-8.69)
0.001**
(2.11)
-0.012
(-4.98)
0.522***
(8.64)
0.018
(0.29)
-1.075***
(-9.33)
0.001*
(1.88)
-0.058***
(-5.48)
0.700***
(7.92)
0.003
(0.04)
(-8.73)
0.426***
(4.65)
0.085*
(1.77)
-0.689***
(-3.87)
-0.0001
(-0.12)
-0.020
(-5.20)
0.425***
(3.20)
0.085
(1.56)
0.0001
(0.07)
0.014
(-3.54)
0.389***
(7.17)
-0.193***
(-5.26)
-0.663***
(-7.82)
0.001
(1.57)
-0.002
(-0.61)
-0.000002
(-0.04)
-0.0005
(-0.33)
0.018
(-2.58)
0.00001
(0.14)
-0.002
(-1.00)
-0.024**
(0.34)
-0.00001
(-0.15)
-0.001
(-0.47)
-0.036**
(-0.09)
-0.00003
(-0.77)
-0.001
(-0.64)
0.016*
(-0.73)
0.00001
(0.21)
-0.001
(-1.05)
-0.006
(0.48)
0.00003
(0.53)
-0.001
(-0.80)
-0.004
(1.37)
-0.019
(-1.02)
0.235**
(2.44)
-0.381***
(-7.69)
1.219***
(8.66)
16301
(-2.23)
0.048***
(3.04)
0.333***
(3.02)
(-2.20)
0.060***
(2.79)
(-0.53)
0.022
(1.48)
0.154**
(2.43)
(-0.30)
0.013
(0.73)
0.684***
(3.52)
6206
1.089***
(4.49)
3783
(1.91)
-0.007
(-0.70)
0.0431
(0.61)
-0.351***
(-7.15)
1.307***
(10.90)
16301
0.523**
(2.35)
6206
0.497**
(1.96)
3783
-0.002*
(-1.81)
0.013
Notes: t statistics in parentheses adjusted for sample clustering; * p < 0.10, ** p < 0.05, *** p < 0.01.
Low-income is defined as family income less than 200 percent of the federal poverty line. ESI refers to employersponsored health insurance
24
Table 5: Semielasticity of Weekly Hours of Work with Respect to Mental Health Visits (OLS and IV
regression of logarithm of hours worked on number of visits)
OLS
IV
(1)
(2)
(3)
(4)
(5)
(6)
ln Weekly
ln Weekly
ln Weekly
ln Weekly
ln Weekly
ln Weekly
Hours
Hours
Hours
Hours
Hours
Hours
Number MH Visits
Very Poor Mental Hlth
Poor Mental Hlth
Fair-Poor Phys Hlth
Age
African American
Latino
Other Minority
Age Youngest Child
Number of Children
Youngest Child Under3
HS—No College Deg
College Deg
Medicaid Any Time Last Yr
ESI Any Time Last Yr
W/o HI Any Time Last Yr
Working Spouse-Partner
Alc/Chem Dep Beds
No. Hosp. with Alc/Chem beds
Beds set up and staffed
Hosp. admissions in 1000s
No. Hosp. with Social Work Serv.
No. Hosp. with ER
Married/cohabiting
Low-income
Constant
Observations
-0.003
(-1.09)
0.001
(0.03)
0.022
(1.03)
-0.043
(-1.54)
-0.002**
(-2.10)
0.096***
(6.25)
0.037*
(1.72)
0.080**
(2.95)
0.007***
(3.78)
-0.019**
(-1.99)
-0.024
(-0.92)
-0.042*
(-1.67)
-0.039
(-1.60)
-0.050**
(-1.97)
0.130***
(5.40)
0.012
(0.58)
-0.228***
(-9.12)
0.0001
(0.32)
-0.013
(-1.35)
-0.00001
(-0.61)
-0.0004
(-1.05)
0.012***
(2.98)
-0.005
(-0.95)
0.129***
(5.65)
-0.061***
(-3.29)
3.661***
(58.52)
11811
-0.005
(-1.64)
0.086*
(1.94)
0.006
(0.21)
-0.074***
(-3.23)
-0.002
(-1.20)
0.027
(0.93)
0.045
(1.59)
0.027
(0.68)
0.008**
(2.09)
0.038**
(2.17)
0.034
(1.02)
-0.018
(-0.63)
0.003
(0.09)
-0.082***
(-2.74)
0.072**
(2.49)
-0.035
(-1.37)
-0.198***
(-5.19)
-0.001**
(-2.53)
0.017*
(1.67)
-0.000001
(-0.05)
0.0001
(0.17)
-0.007
(-1.14)
0.005
(0.57)
0.156***
(5.23)
-0.005
(-1.36)
0.079*
(1.69)
-0.021
(-0.60)
-0.050
(-1.53)
-0.001
(-0.50)
0.038
(1.27)
0.045
(1.24)
0.072
(1.11)
0.0003
(0.06)
0.022
(1.31)
0.016
(0.41)
0.031
(0.85)
0.048
(1.35)
-0.119***
(-3.33)
0.116***
(3.49)
-0.039
(-1.22)
3.489***
(54.12)
3763
3.502***
(44.40)
2538
-0.001***
(-3.50)
0.029**
(2.53)
-0.0001
(-1.49)
0.002
(1.56)
0.004
(0.63)
-0.008
(-0.71)
-0.063
(-0.97)
0.015
(0.35)
0.112
(1.09)
-0.005
(-0.10)
-0.002
(-1.25)
0.067*
(1.82)
0.007
(0.17)
0.054
(1.43)
0.006***
(2.66)
-0.023**
(-2.07)
-0.028
(-1.02)
-0.023
(-0.73)
-0.006
(-0.15)
-0.039
(-1.33)
0.132***
(5.21)
-0.010
(-0.30)
-0.222***
(-8.16)
0.0001
(0.42)
-0.013
(-1.01)
-0.0000001
(-0.01)
-0.0013
(-1.53)
0.019***
(2.69)
-0.00
(-1.07)
0.104***
(2.73)
-0.058***
(-2.93)
3.672***
(52.04)
11811
-0.165**
(-2.03)
0.231*
(1.80)
0.073
(1.39)
0.023
(0.33)
-0.002
(-0.80)
-0.071
(-1.04)
-0.028
(-0.50)
-0.057
(-0.93)
0.007
(1.04)
0.029
(1.24)
0.028
(0.48)
0.019
(0.45)
0.102
(1.44)
-0.021
(-0.40)
0.057
(1.31)
-0.098*
(-1.83)
-0.155***
(-2.96)
0.00002
(0.03)
0.016
(1.07)
-0.00001
(-0.13)
-0.0001
(-0.09)
-0.007
(-0.83)
0.009
(0.85)
0.093**
(1.98)
-0.138
(-1.56)
0.152
(1.63)
0.053
(0.74)
-0.021
(-0.41)
-0.000003
(-0.00)
-0.066
(-0.74)
-0.004
(-0.07)
-0.019
(-0.19)
-0.003
(-0.44)
-0.003
(-0.12)
-0.038
(-0.47)
0.064
(1.09)
0.118
(1.62)
-0.081
(-1.58)
0.079
(1.51)
-0.123*
(-1.69)
3.579***
(34.99)
3763
3.672***
(23.28)
2538
-0.0004
(-0.69)
0.029**
(2.04)
-0.00004
(-0.49)
0.0009
(0.47)
-0.005
(-0.43)
0.003
(0.24)
Notes: t statistics in parentheses adjusted for sample clustering; * p < 0.10, ** p < 0.05, *** p < 0.01.
Low-income is defined as family income less than 200 percent of the federal poverty line. ESI refers to employersponsored health insurance.
25