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Early-Onset Psychiatric Disorders and Male Socioeconomic Status
SOCIAL SCIENCE RESEARCH ARTICLE NO.
27, 371–387 (1998)
SO970616
Early-Onset Psychiatric Disorders and Male Socioeconomic Status Rukmalie Jayakody Pennsylvania State University
Sheldon Danziger University of Michigan
and Ronald C. Kessler Harvard University We seek to integrate economic, sociological and psychological models by examining whether early-onset psychiatric disorders predict adult male socioeconomic status. Unlike most status attainment studies, we include information on major psychiatric disorders. We use data from the National Comorbidity Survey, the first survey to administer a structured psychiatric interview to a national probability sample in the U.S. Our sample includes men between the ages of 25 and 54. We find that disorders that occur before age 16 reduce educational attainment and the probability of being currently married and increase the probability of having a recent disorder, each of which is a predictor of adult male unemployment. We also find that these early-onset disorders have a direct negative effect on male employment. The estimated magnitudes of these effects are often as large as those of family background variables, suggesting that research on adult male SES should pay greater attention to mental health issues. r 1998 Academic Press
Rukmalie Jayakody is Assistant Professor of Human Development and Family Studies and Research Associate at the Population Research Institute at Pennsylvania State University. Sheldon Danziger is Professor of Social Work and Public Policy and Director of the Social Work Research and Development Center on Poverty, Risk, and Mental Health at the University of Michigan. Ronald C. Kessler is Professor of Health Care Policy at the Harvard Medical School. This research was supported by grants from the National Institute of Mental Health (R24 MH51363, R01 MH46376, and R01 MH49098), with supplemental support from the National Institute of Drug Abuse (through a supplement to R01 MH46376) and the W.T. Grant Foundation (Grant 90135190). The authors thank Markus Jantti, Samuel Lind, Kenneth Lutterman, and Michael Spencer for comments on prior drafts and Jilenne Gunther for table preparation. Address correspondence and reprint requests to Rukmalie Jayakody, Population Research Institute, 601 Oswald Tower, Pennsylvania State University, University Park, PA 16802. 371 0049-089X/98 $25.00 Copyright r 1998 by Academic Press All rights of reproduction in any form reserved.
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We seek to integrate economic, sociological, and psychological models by examining whether early-onset psychiatric disorders predict adult male socioeconomic status. Our work is primarily addressed to economists and sociologists, who typically do not incorporate information about mental health problems as independent variables in models of socioeconomic attainment (for a review of recent attainment studies, see Corcoran, 1995). There is a long history of research in psychiatric epidemiology examining the reciprocal relationship between social class and mental illness. This relationship has been widely studied and long observed, dating as far back as the nineteenth century study by Jarvis on the relationship between poverty and insanity (Hunter and Macalpine, 1963). Many researchers have evaluated the relative contributions of ‘‘social selection’’ of the mentally ill into the lower socioeconomic strata and ‘‘social causation,’’ by which the effects of lower socioeconomic status (SES) produce mental disorders (Eisenberg and Lazarsfeld, 1938; Dohrenwend and Dohrenwend, 1969; Feather, 1990; Dohrenwend, Levar, Shrout, Schwartz, Neveh, Link, Skodol, and Stueve, 1992). Most studies by psychiatric epidemiologists that analyze social class and mental health recognize the reciprocal relationship, but focus primarily on the presumed effects of the former on the latter, rather than the reverse. For example, a number of studies have shown that unemployment predicts the subsequent onset of anxiety and depression (Warr and Jackson, 1988; Liem and Liem, 1988; Kessler, Turner, and House, 1988) by lowering self-esteem (Jahoda, 1982) and producing feelings that life is not in one’s control (Goldsmith and Darity, 1992). Others have documented that low SES increases daily stresses which also predict psychological distress (Kessler, 1982). Studies that focus on the other causal direction typically analyze the effects of adult psychological disorders on occupational attainment or unemployment (Mullahy and Sindelar, 1990; Williams, Takeuchi, and Adair, 1992). One Canadian study, for example, found that a lifetime history of psychiatric disorders increases the odds of unemployment 2.8 times (Bland, Stebelsky, Orn, and Newman, 1988). Our goal in this paper is not to try to resolve the relative importance of these reciprocal effects. Rather we examine whether early-onset psychiatric disorders1 negatively affect adult male SES and investigate some of the pathways through which this effect operates. Previous studies suggest that substantial effects are plausible. For example, Kessler, Foster, Saunders, and Strang (1995) report that early-onset disorders predict truncated educational attainment, an intermediate outcome known to be a primary determinant of adult SES. Among the most important predictors of adult SES are educational attainment and marital status and stability, outcomes which also have a reciprocal relationship with psychiatric disorders. Marriage is beneficial to individual mental health—particularly for men (Gove, 1972). Marital disruption, whether due to 1
Early-onset disorders are defined as those whose symptoms appear at age 16 or earlier.
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divorce (Aseltine and Kessler, 1993) or widowhood (Umberson, Wortman, and Kessler, 1992), is a stressful life event that lowers both psychological and economic well-being. Aseltine and Kessler (1993) demonstrate that divorce leads to anxiety and depression; Umberson et al. (1992) show the same for widowhood. Other studies have analyzed how psychiatric disorders predict subsequent educational attainment, marriage, and marital stability. Mastekaasa (1994) finds that subjective well-being and overall life satisfaction predict the probability of marriage. A series of studies by Kessler and his colleagues (Forthofer, Kessler, Story, and Gotlib, 1996; Kessler et al., 1996; Kessler & Forthofer, 1996) demonstrate that early-onset psychiatric disorders predict teen childbearing, the probability and timing of first marriage, and marital stability. This paper addresses the hypothesis that early-onset psychological disorders are an important, but understudied, determinant of adult male socioeconomic status. In that regard, our work is related to the extensive literature in sociology and economics that, for example, documents that family background, educational attainment and marital status affect employment and financial well-being. Our contribution is to demonstrate that early-onset psychiatric disorders directly predict adult SES, controlling for family background, educational attainment, and marital status. In addition, these disorders also negatively predict educational attainment and marital status and thereby indirectly predict SES. THEORETICAL FRAMEWORK A long history of status attainment research in sociology has examined the impacts of family background and educational attainment on occupational prestige (Blau and Duncan, 1967; Sewall and Hauser, 1975; Featherman and Hauser, 1978). These studies found that parents’ status had a moderate effect on son’s occupational status, and that schooling was a more powerful predictor of occupational prestige than were family background variables. We extend this work by hypothesizing that psychiatric disorders are also important in determining SES. We hypothesize that psychiatric disorders have a direct effect on adult socioeconomic outcomes. In addition, psychiatric disorders also indirectly affect adult outcomes via their impacts on educational attainment and marital status. We avoid problems related to the reciprocal relationship between psychiatric disorders and SES by emphasizing disorders that appear prior to the outcome of interest. Psychiatric disorders that first occur at age 40, for example, can not have affected high school graduation, whereas those that occurred at an early age can have done so. Likewise, disorders that occurred during a man’s early teens can not have been caused by a period of adult unemployment. We hypothesize that early-onset psychiatric disorders, defined here as those occurring at age 16 or earlier, reduce educational attainment, reduce the probability of being currently married (defined at the same time that adult SES is measured), and increase the likelihood of having a recent psychiatric disorder (defined as one which occurred within the 12 months prior to the interview). Each
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of these mediators is then hypothesized to reduce adult socioeconomic status. Additionally, early-onset psychiatric disorders are expected to have a direct negative effect on adult SES, holding constant family background, educational attainment, marital status and recent disorders. Similarly, family background is expected to influence educational attainment, marital status, recent psychiatric disorders, and adult SES directly, as well as to have an indirect effect on adult SES that is mediated by educational attainment, marital status, and recent disorders. DATA AND METHODS We analyze data from the National Comorbidity Survey (NCS) (Kessler, McGonagle, Zhao, Nelson, Hughes, Eshleman, Wittchen, and Kendler, 1994) which surveyed 8098 respondents ranging in age from 15 to 54, between September, 1990, and February, 1992. The NCS is the first survey to administer a structured psychiatric diagnostic interview to a national probability sample in the U.S. Psychiatric diagnoses were ascertained through the use of the Composite International Diagnostic Interview (CIDI) (World Health Organization, 1990), designed to be administered by trained interviewers who were not clinicians. The CIDI generates diagnoses according to both the DSM-III-R and ICD-10 diagnostic systems. Field tests of the CIDI conducted by the World Health Organization have documented good reliability and validity with all diagnoses examined here (Wittchen, 1994). The NCS also gathered retrospective information on the age of onset of each disorder and on which disorders were present within 12 months of the interview. For purposes of this analysis, we have focused on a select number of disorders that have been found to be most serious in terms of role impairment. These include affective disorders (major depressive episode and manic episode), anxiety disorders (panic disorder, agoraphobia, and generalized anxiety disorder), substance (drug or alcohol) dependence, and conduct disorder. We group all other disorders ascertained in the NCS (dysthymia, social phobia, simple phobia, posttraumatic stress disorders, and alcohol and drug use without dependence) into a residual category of ‘‘other’’ (not seriously impairing disorders). The NCS interview was carried out in two parts. Part I included the core diagnostic interview and was administered to all respondents. Part II asked additional family background and work status questions and contained a detailed risk factor battery and a series of secondary diagnoses. Part II was administered to all respondents between the ages of 15 to 24, all those who screened positively for any lifetime diagnosis in Part I, and a random subsample of other respondents. The analyses reported here are based on the Part II subsample and are weighted to be representative of the general population. We limit our analyses to African American and white men between the ages of 25 and 54. We focus on prime-aged men because they were expected to be working at the time of the interview. In comparison, women may be out of the labor force for a variety of socially acceptable reasons (such as homemaking and/or child care). Because women’s SES is so dependent on their family roles,
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estimating the impact of psychiatric disorders on their employment status is far more complicated. To further focus on men who are expected to be attached to the labor force, we excluded students, the retired, and the permanently disabled. If early-onset disorders induce labor force withdrawal and if these men define themselves in the more socially accepted categories of ‘‘retired’’ or ‘‘disabled,’’ our sample exclusion will understate the negative effects of disorders on employment. Because of their small numbers, widowers are also excluded. Our final sample includes 1769 men (88.4 percent of whom are non-Hispanic, white and 11.6 percent African American). We utilize logistic regression techniques to examine the impacts of early-onset psychiatric disorders, family background, and other independent variables on the log of the odds of: (1) low educational attainment, defined as not graduating from high school; (2) high educational attainment, defined as graduating from college; (3) marital status, defined as whether or not the respondent was married at the time of the survey; (4) having a recent psychiatric disorder; and (5) employment status, defined as not working at all versus working at the time of the interview.2 In keeping with the status attainment literature, we include several family background measures: whether the respondent grew up in a one- or two-parent household, the education of the chief breadwinner of the respondent’s family of origin, the Duncan SEI score of the chief breadwinner, and whether either of the respondents’ parents had a psychiatric disorder. The last of these variables is based on respondents’ reports using a standard inventory (Endicott, Andreasen, and Spitzer, 1978). Control variables are also included for race, age cohort, and region of current residence. The Appendix defines these variables and presents weighted sample means. Because of the complex survey design and the weighting of the NCS, standard errors for the regression coefficients were estimated by the method of balanced repeated replication using a macro in SAS-UNIX (Kish & Frankel, 1970). RESULTS The first column in Table 1 shows the proportion of men in our sample who had an early-onset or recent-onset psychiatric disorder. Almost three-quarters of the sample report not having had any early-onset disorder. Having an early major affective, anxiety, or substance dependence disorder is relatively uncommon (1.9, 2.6, and 1.5%, respectively). The most common early-onset disorder is conduct disorder (16.1 percent).3 Although three-quarters of the men also report not having a recent psychiatric disorder, the prevalence of each specific recent disorder is higher than it was in the early years. For example, 6.5% report having had a major affective disorder in the past 12 months, about three times the number who had this disorder before age 16. Recent substance dependence, 13.9%, is the most prevalent specific recent disorder. 2
We do not examine occupational status because it is unavailable in the NCS. In Table 1, the sum of the values for the specific disorders exceeds the value of 100 less the percentage not having any disorder because some men have had multiple disorders. 3
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JAYAKODY, DANZIGER, AND KESSLER TABLE 1 Early-Onset Psychiatric Disorders by Work Status a Work status Total
Total (unweighted sample size 5 1769) Early onset psychiatric disorders Did not have an early disorder Major early onset affective disorder Major early onset anxiety disorder Early onset substance dependence Early onset conduct disorder Other early onset disorder Recent psychiatric disorders Did not have a recent disorder Major affective disorder Major anxiety disorder Recent substance dependence Other recent disorder
Not working
Currently working
7.9
92.1
74.3 2.4 2.6 3.1 16.1 16.0
49.4 7.9 8.1 5.3 27.9 30.3
72.8 2.0 2.3 3.0 14.8 13.8
75.3 6.5 4.3 10.7 13.3
49.2 20.3 14.5 24.9 26.3
77.7 5.4 3.5 9.6 12.2
a
The analyses are weighted using the final sample weight. The sample, in all tables, includes white and African American men between the ages of 25 and 54 who are not students, disabled, or retired.
The last two columns in Table 1 show that having any psychiatric disorder, regardless of timing, is negatively correlated with current work status. Our sample of prime-age men is significantly attached to the labor force—only 7.9% were not working at the time of the NCS interview. The prevalence of disorders, regardless of the specific diagnosis or timing, is substantially higher among those who are not currently working than among currently working men. For example, whereas only 2% of men who are working had an early-onset affective disorder, the prevalence of this diagnosis among nonworking men is 7.8%. Conduct disorder, which was the most prevalent early-onset disorder, has almost double the prevalence among nonworking compared to working men. A similar pattern exists for recent psychiatric disorders. Whereas 9.6% of those currently working had recent substance dependence, 25% of nonworking men had recent dependence. Educational Attainment The logistic regression results in Table 2 show that early-onset psychiatric disorders predict educational attainment, measured in the first model as whether or not the man graduated from high school, and in the second, as whether or not he graduated from college.4 Race, several indicators of family background, age at 4 The regression includes dichotomous variables for five of the six diagnosis categories shown in Table 1. The omitted variable represents the 74.3% of the sample who did not report any early-onset disorders. The other variables reflect the various other diagnoses. A man who has had multiple early-onset disorders will have a value of one for each diagnosis he has experienced.
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EARLY-ONSET PSYCHIATRIC DISORDERS TABLE 2 The Effect of Early-Onset Psychiatric Disorders on Educational Attainment a Model 1 Probability of NOT Finishing H.S. b Constant Early onset psychiatric disorders (No early onset disorder 5 0) Major early onset affective disorder Major early onset anxiety disorder Early onset substance dependence Early onset conduct disorder Other early onset disorder a Race (White 5 0) African American Family background Grew up in one parent home (2 parent 5 0) Duncan SEI scale less than 20 (20 or more 5 0) Parents had psychiatric disorder (No 5 0) Educ. of provider, less than h.s. (H.S. grad. 5 0) Missing Age (45 to 54 5 0) 25 to 34 35 to 44 Region (Northeast 5 0) Midwest South West Model x2 (d.f.) Mean of dependent variable
S.E. Odds ratio
23.363
0.235 0.768*** 20.127 1.121*** 0.447**
Model 2 Probability of Finishing College b
S.E. Odds ratio
.432
0.672 0.207 1.515 0.141 0.129
1.26 2.16 0.88 3.07 1.56
0.080 21.302*** 20.533 20.791 20.571**
0.344 0.029 2.031 0.694 0.195
1.08 0.27 0.59 0.45 0.56
0.266
0.430
1.30
20.689
0.392
0.50
20.020
0.024
0.98
20.139*** 0.038
0.87
.646*** 0.003
1.91
21.252*
0.618
0.29
0.132
0.99
0.386
0.292
1.47
1.018*** 0.307 2.467*** 0.045
2.77 11.79
20.893** 0.220 21.748*** 0.208
0.41 0.17
20.696*** 0.091 20.670*** 0.058
0.50 0.51
0.283 0.176 1.032*** 0.157 20.230 0.212 319.57 (16) .129
1.33 2.81 0.79
2.011
0.021 0.197
0.299 0.271
1.02 1.22
20.722*** 0.049 20.649*** 0.001 20.302 0.216 370.62 (16) .277
0.49 0.52 0.74
Unweighted sample size 5 1769. * p , .05; ** p , .01; *** p , .001.
a
interview, and region are included as control variables. Having a major anxiety disorder, conduct disorder, or other early disorder all significantly increase the probability of not finishing high school. The effects of major affective disorders and substance dependence are not significant. In terms of magnitude, the largest impact on high school graduation is due to conduct disorder. Respondents who had a conduct disorder are three times more likely not to have graduated from high school as those who did not. Children with conduct disorder, particularly those who go undiagnosed, are likely to create
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FIG. 1. Predicted probabilities of not finishing high school for white men, age 35 to 44, living in the Northeast, who grew up in a two-parent home, whose parents had no psychiatric disorder, and whose financial provider was not a high school graduate.
problems in the classroom and may be difficult to control. The negative attention they receive from teachers as a result of these problems may increase their probability of dropping out or of being expelled. Men who experienced a major anxiety disorder are about twice as likely to have not finished high school as those who had no such disorder. Having any other early-onset disorder increases the relative odds of not finishing high school by 56%. Similar to the effect of conduct disorders, having any psychiatric disorder probably leads to a negative schooling environment, which increases the risk of quitting school. To provide some notion of the relative magnitude of these effects, Fig. 1 uses the logistic regression coefficients from the first model in Table 2 and displays the predicted probabilities of not finishing high school for a hypothetical case—a white man, between the ages of 35 and 44, who lives in the Northeast, who grew up in a two-parent home, whose parents had no psychiatric disorders, and whose major financial provider while growing up had not graduated from high school. Given these characteristics, the figure shows how the predicted probability varies for the man as we vary his parent’s SES and his history of early disorders. Lower SES is denoted by parents whose scores on the Duncan socioeconomic index (SEI) were below 20, representing lower-status blue-collar jobs (about a third of parents were in this category). Controlling for the presence or the type of disorder, lower SES men have a higher probability of not finishing high school than those from higher SES families (compare each set of white and black bars). In the figure, the SES effect for a man reporting no early-onset disorders is 4 percentage points (.05 vs .09). For a man from a low SES family, the effect of having a father who had graduated from high school is 6 percentage points (data not shown).5 5 That is, the probability of not finishing high school drops from 9 to 3% for a man with characteristics shown in Fig. 1, as parental education increases from high school dropout to graduate.
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The effect of having had an early-onset disorder is at least as large as are these family background effects. For example, the hypothetical man from a low SES family has a predicted probability of not finishing high school of 9% if he had no early disorder (see Fig. 1). But if he experienced a major anxiety disorder, the probability rises to 17%, and it rises to 22% if he had a conduct disorder. The size of these early-onset effects—8 and 13 percentage points, respectively, is larger than both of the family background effects. Thus, just as parents’ education and parents’ occupation are considered important variables for inclusion in attainment models, so should information on psychiatric disorders. The second model in Table 2 examines the probability of graduating from college. Men who have had a major early-onset anxiety disorder are only one-fourth as likely to have graduated from college as those with no early disorders; those who have had some other early-onset disorder are only half as likely to have graduated as those with no disorders. Sociological research has consistently found that growing up in a one-parent home, as opposed to a home with both biological parents, has negative consequences for educational attainment (Garfinkel and McLanahan, 1987; McLanahan and Sandefur, 1994). Table 2 indicates that growing up in a one-parent home is not significantly related to high school graduation for this sample of adult men, but does significantly reduce the probability of graduating from college. In contrast, early-onset disorders negatively affect both high school and college graduation. Additionally, the size of the early-onset disorder effects are larger than the family structure effect. The status attainment literature also recognizes the importance of SES of family of origin as an important predictor of educational attainment. The effect on the probability of finishing college of having had a major anxiety disorder, as compared to having had no disorders, is just about the same as that of growing up in a lower, as compared to a higher, SES family (a Duncan SEI score below 20 vs one equal to or above 20). In both cases, the odds ratio is under .30. Given that early-onset disorders have an equal or greater impact as family structure and SES background, greater attention should be paid to psychiatric disorders in educational attainment research. Marital Status The impact of early-onset disorders on the probability of being currently married (as opposed to being never married, divorced or separated, or cohabiting) is shown in Table 3. Both major anxiety disorders and other early disorders significantly reduce the likelihood of being currently married. Those who experienced an early-onset major anxiety disorder are only about one half as likely to be currently married as those with no early disorders; those with other early disorders have a 20% reduction in their relative odds of being married. Recent Psychiatric Disorders We have shown that disorders that occur prior to the age of 16 predict educational attainment and not being currently married among men between the
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TABLE 3 The Effect of Early-Onset Psychiatric Disorders on the Probability of Being Currently Married a Probability of currently being married b Constant Early onset disorders (No early onset disorders 5 0) Major early onset affective disorder Major early onset anxiety disorder Early onset substance dependence Early onset conduct disorder Other early onset disorder Race (White 5 0) African American Family background Grew up in one parent home (2 parent 5 0) Duncan SEI scale less than 20 (20 or more 5 0) Parent had psychiatric disorder (No 5 0) Educ. of provider, less than h.s. (H.S. grad. 5 0) Missing Age (45 to 54) 25 to 34 35 to 44 Region (Northeast 5 0) Midwest South West Model x2 (d.f.) Mean of dependent variable
S.E.
Odds ratio
20.527 20.621*** 20.328 0.157 20.222***
0.979 0.149 0.824 0.142 0.095
0.59 0.54 0.72 1.17 0.80
21.347***
0.214
0.26
20.001 0.219 0.081** 0.463* 20.387**
0.017 0.342 0.035 0.221 0.103
1.00 1.24 1.08 1.59 0.68
20.158 0.263***
0.172 0.039
0.85 1.30
0.120 20.027 20.089 142.00 (16) .680
0.114 0.108 0.705
1.13 0.97 0.91
0.741
Unweighted sample size 5 1769. * p , .05; ** p , .01; *** p , .001.
a
ages of 25 and 54. Now we examine the impact of these disorders on the probability of having had any recent disorder (defined as one occurring within the 12 months prior to the interview). Table 4 presents the logistic regression results which indicate, not surprisingly, that having had an early-onset disorder increases the likelihood of having a recent disorder (3 of the 5 disorder coefficients are significant and all are positive). The effects are particularly large for major anxiety disorders and other early disorders—the odds ratios are 3.18 and 6.25, respectively. Predicted probabilities similar to those shown in Fig. 1, but based on the coefficients of Table 4 were calculated (but are not shown). They illustrate that the effect of early-onset disorders on current SES may be mediated by their effect on recent disorders. Consider the benchmark case of a white man, between the ages of 35 and 44, who lives in the Northeast, grew up in a two-parent home, with a financial provider who was a high school graduate who had a Duncan score above 20, and whose parents did not have a psychiatric disorder. If he had no early-onset
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TABLE 4 The Effect of Early-Onset Psychiatric Disorders on the Probability of Having a Recent Disorder a Probability of having a recent disorder b Constant Early onset disorders (No early onset disorders 5 0) Major early onset affective disorder Major early onset anxiety disorder Early onset substance dependence Early onset conduct disorder Other early onset disorder Race (White 5 0) African American Family background Grew up in one parent home (2 parent 5 0) Duncan SEI scale less than 20 (20 or more 5 0) Parent had psychiatric disorder (No 5 0) Educ. of provider, less than h.s. (H.S. grad. 5 0) Missing Age (45 to 54) 25 to 34 35 to 44 Region (Northeast 5 0) Midwest South West Model x2 (d.f.) Mean of dependent variable
S.E.
Odds ratio
0.906 0.240 1.150 0.059 0.197
1.89 3.18 1.28 1.96 6.25
20.611*
0.301
0.54
20.008 0.056 0.849*** 20.359** 20.054
0.020 0.570 0.233 0.144 0.130
0.99 1.06 2.34 0.70 0.95
0.202 0.073
0.227 0.494
1.22 1.08
0.246*** 0.170*** 20.027 383.97 (22) .247
0.018 0.090 0.143
1.28 1.19 0.97
22.560 0.639 1.158*** 0.248 0.675*** 1.833***
Unweighted sample size 5 1769. * p , .05; ** p , .01; *** p , .001.
a
disorder, his predicted probability of having any recent disorder is 8%. But if he experienced an early major anxiety disorder, it is 21%; if, a conduct disorder, 14%; and if any other disorder, 34%. Having a parent who had a psychiatric disorder did not significantly affect educational attainment (Table 2) and had only a small effect on marital status (Table 3). However, it doubles the relative odds of having a recent disorder (the odds ratio in Table 4 is 2.34). Adult Employment Status We now examine the relationship between early-onset disorders and adult male employment by focusing on their effects on the probability that the respondent was working at the time of the interview (Table 5). Consider first the reduced form model in the first three columns of Table 5. It excludes the man’s educational attainment, current marital status, and recent experience of disorders as indepen-
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TABLE 5 The Effect of Early-Onset Psychiatric Disorders on the Probability of Currently NOT Working a Reduced form Probability of NOT working b Constant 23.162 Early onset psychiatric disorders (No early onset disorder 5 0) Major early onset affective disorder .766*** Major early onset anxiety disorder 1.151*** Early onset substance dependence 2.108 Early onset conduct disorder .472 Other early onset disorder1 .076* Race (White 5 0) African American .525 Family background Grew up in one parent home (2 parent 5 0) .046 Duncan SEI scale less than 20 (20 or more 5 0) .411* Parents had psychiatric disorder (No 5 0) .326 Educ. of provider, less than h.s. (H.S. grad. 5 0) .185 Missing .874* Age (45 to 54 5 0) 25 to 34 2.249 35 to 44 2.673* Region (Northeast 5 0) Midwest .306 South .190 West .203 Marital status (Married 5 0) Divorced/separated Never married Cohabiting Education (College 5 0) Less than high school High school graduate More than high school Model x2 (d.f.) 68.56 (16) Mean of dependent variable .079 a
S.E. Odds ratio
Full model Probability of NOT working b
S.E. Odds ratio
24.044
.195 .185 .618 .400 .033
2.15 3.16 .90 1.60 1.08
.707*** .013 1.045*** .005 2.259 1.027 .281 .375 2.123* .053
.333
1.69
.074
.364
1.08
.096
1.05
.051
.126
1.05
.124
1.51
.298***
.007
1.35
.364
1.39
.352
.470
1.42
.203 .369
1.20 2.40
.097 .289
.216 .346
1.10 1.34
.349 .203
.78 .51
2.305 2.575*
.438 .206
0.74 0.56
.319 .242 .475
1.36 1.21 1.23
.244 .088 .240
.242 .165 .555
1.28 1.09 1.27
.562 1.451*** 1.040*
.504 .376 .508
1.75 4.27 2.83
1.891*** .522 .630*** 151.18 (22) .079
.164 .568 .081
6.63 1.69 1.88
Unweighted sample size 5 1769; * p , .05; ** p , .01; *** p , .001.
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dent variables, because, as we have shown, each of these outcomes is negatively affected by early-onset disorders. The reduced-form model captures the total effect of early-onset disorders on adult male employment. We find that having had an early major affective disorder, anxiety disorder, and other disorder all significantly increase the probability of nonwork. For example, a man who experienced a major anxiety disorder is about three times more likely to be not working as one with no early disorder; a man with a major affective disorder, about twice as likely. The rightmost columns in Table 5 present the full model of the determinants of nonwork, including educational attainment and marital status as independent variables.6 The effects of the disorders are mediated by educational attainment and marital status, which as shown above, are both negatively affected by early disorders. Marital status and educational attainment both have large, significant effects on nonwork. However, as in the reduced form, both major affective and anxiety disorders significantly increase the probability of nonwork. The magnitudes of these effects, though somewhat smaller than in the reduced form, remain large. In addition, other early-onset disorders have a small, but significant, impact on current work status. The sign of the coefficient is, however, reversed unexpectedly from what it was in the reduced form. Figure 2 displays predicted probabilities of currently not working for some hypothetical cases in order to demonstrate the relative magnitudes of the effects of the mental health, educational, and marital status variables from the full model. For each case, the predicted probability refers to a white man, between the ages of 35 and 44, who lives in the Northeast region, grew up in a two-parent family where the parents did not have a disorder, and where the financial provider was a high school graduate with a Duncan SEI score equal to or above 20. Comparing man A with man B displays the impact of a high school degree on current work status. With benchmark factors held constant, 6% of married, high school dropouts with no disorders, but 2% of similar high school graduates, are predicted to be not working. Comparing man A with man C demonstrates that having had an early major anxiety disorder, all other factors constant, increases the probability of not working for a married high school graduate from 2 to 5%. The size of the early-onset disorder effect (3 percentage points) is about the same as the high school dropout effect (4 percentage points). In addition, the earlier tables showed that early disorders also increase the probability of dropping out. 6 We also estimated a model like that shown in Table 5 that added the set of recent disorders shown in Table 1. In this model, which includes 11 disorder variables, there was high collinearity between the specific early and recent disorders. The coefficients for the recent disorders tending to be significant and the early ones insignificant. We then estimated a model that substituted specific recent disorders for the early ones. In this model, the magnitude of the effects of the recent disorders was similar to what is shown in Table 5 for the early disorders. We prefer the model in Table 5 because early disorders are causally prior to current nonwork. However, we can not distinguish causality in the relationship between recent disorders and nonwork, as we do not know if the recent disorder occurred prior to the current period of nonwork, or if nonwork triggered the recent disorder.
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FIG. 2. Predicted probability of currently not working for white men, age 35 to 44, living in the Northeast, who grew up in a two-parent home, whose parents did not have a disorder, whose Duncan SEI scale while growing up was over 20, and whose financial provider was a high school graduate.
The predicted probabilities for men D and E indicate the combined effects on nonwork of education, marital status, and early disorders. While man A has no early disorder, is currently married, and has a high school degree, man D is never married, not a high school graduate, and had an early major anxiety disorder. This combination results in a predicted probability of nonwork of 44%. Man E, who is similar to person D, except that he had a major affective, instead of an anxiety, disorder has a predicted probability of nonwork of 36%. CONCLUSION Numerous studies in both sociology and economics have documented the importance of family background, educational attainment, and marital status in determining male SES. The potential impacts of psychological disorders on these outcomes, however, have not been typically considered. Our results, based on data from the National Comorbidity Survey, demonstrate that major psychiatric disorders that occur prior to age 16 increase the probability that an adult man is not working. We showed that early disorders have both a direct negative effect on work and a negative effect that is mediated by the fact that disorders also reduce educational attainment and the probability of being currently married. Our results suggest that future research on male adult SES should pay greater attention to mental health issues, as the estimated size of the effects were often as large as those of various family background measures.
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APPENDIX A: Description of Independent Variables Used Race is measured as a dichotomous variable, coded 1 if African American and coded 0 if white. Although there are Hispanics in the NCS, their sample size was too small to separate them out into Cuban American, Mexican American, Puerto Rican, etc. Because these groups have such diverse socioeconomic backgrounds, rather than including them all in the same category, we decided to focus only on African Americans and whites. Whites compose 88.4 percent of the sample; African Americans, the remaining 11.6 percent. Family Background —Family structure while growing up is measured as whether or not the respondent grew up in a single-parent home or a two-parent home. This variable is coded 1 if the respondent grew up with a single parent (25 percent) and coded 0 if he grew up with both parents (75 percent). —The Duncan SEI scale while growing up is used as a SES family background measure. This variable is continuous with a mean of 38.85. We decided to focus on a dichotomy because this is common in the poverty literature (see Corcoran, 1995). A Duncan SEI score below 20 represents a low status, blue-collar occupation (32.9 percent had a SEI score below 20). —Whether or not either of the respondent’s parents had a psychiatric disorder is coded 1 if they did have a disorder (43 percent), and coded 0 if the parent(s) did not have a disorder (57 percent). —Education of the financial provider while growing up measures the educational level of the individual who was the primary financial provider for the respondent while he was growing up. In most cases, this is the respondent’s father. This variable is coded 1 if the major financial provider did not finish high school (30.8 percent) and coded 0 if the provider was a high school graduate (69.2 percent). —Because there were a number of missing values on the variable indicating the education level of the financial provider, we included a dichotomous variable to indicate whether or not this information was missing. For cases where this information was missing, the mean value of education for the sample was coded as the valid value, and the dichotomous variable MISSING was coded as 1 (11.8 percent). If the information was not missing, a code of 0 was given for this variable. Age is categorized into three groups: 25 to 34 years of age (39.7 percent), 35 to 44 years of age (39.4 percent), and 45 to 54 years of age (20.9 percent).
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Region is categorized into four groups: Midwest (23.5 percent), South (34.6 percent), West (18.7 percent), and the Northeast (23.2 percent). This variable measures the region of residence at the time of the interview. Marital Status is coded into four mutually exclusive categories. Married respondents are the excluded group (68 percent). Respondents who are divorced/separated or never married but were cohabiting at the time of the interview are placed into the COHABITING group (6.3 percent) and are not in the NEVER MARRIED (13.2 percent) or DIVORCED/SEPARATED (12.5 percent) categories. Education is coded into four categories: less than high school (12.9 percent), high school graduate (37.6 percent), more than high school but no college degree (21.8 percent), and college graduates (27.7 percent). REFERENCES Aseltine, R. H. Jr., and Kessler, R. C. (1993). ‘‘Marital disruption and depression in a community sample,’’ Journal of Health and Social Behavior 34, 237–251. Bland, Co-R. C., Stebelsky, G. H., and Newman, S. C. (1988). ‘‘Psychiatric disorders and unemployment in Edmonton,’’ Acta Psychiatrica Scandinavica 77, 72–80. Blau, P. M., and Duncan, O. D. (1967). The American Occupational Structure, Free Press, New York. Corcoran, M. (1995). ‘‘Rags to rags: Poverty and mobility in the United States,’’ Annual Review of Sociology 21, 237–267. Dohrenwend, B. P., and Dohrenwend, B. S. (1969) Social Status and Psychological Disorder: A Casual Inquiry, Wiley, New York. Dohrenwend, B. P., Levav, I., Shrout, P. E., Schwartz, S., Neveh, G., Link, B. G., Skodol, A. E., and Stueve, A. (1992). ‘‘Socioeconomic status and psychiatric disorders: The causation-selection issue,’’ Science 255, 946–952. Eisenberg, P., and Lazarsfield, P. F. (1938). ‘‘The psychological effects of unemployment,’’ Psychological Bulletin 35, 358–390. Endicott, J., Andreasen, N., and Spitzer, R. L. (1978). Family History Research Diagnostic Criteria, Biometrics Research, New York State Psychiatric Institute, New York. Feather, N. T. (1990). The Psychological Impact of Unemployment, Springer, New York. Featherman, D. L., and Hauser, R. M. (1978). Opportunity and Change, Academic Press, New York. Finlay-Jones, R., and Eckhardt, B. (1981). ‘‘Psychiatric disorders among the unemployed,’’ Journal of Psychiatry 15, 265–270. Forthofer, M. S., Kessler, R. C., Story, A. L., and Gotlib, I. H. (1996). ‘‘The effects of psychiatric disorders on the probability and timing of first marriage,’’ Journal of Health and Social Behavior 37, 121–132. Garfinkel, I., and McLanahan, S. (1987). Single Mothers and Their Children, The Urban Institute, Washington, DC. Goldsmith, A. H., and Darity, W. (1992). ‘‘Social psychology, unemployment exposure and equilibrium unemployment,’’ Journal of Economic Psychology 13, 449–471. Gove, W. R. (1972). ‘‘The relationship between sex roles, marital status, and mental illness,’’ Social Forces 51, 34–44.
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Hunter, R., and Macalpine, I. (eds.) (1963). Three Hundred Years of Psychiatry, Oxford Univ. Press, Oxford, UK. Jahoda, M. (1982). Employment and Unemployment, Cambridge Univ. Press, Cambridge, MA. Kessler, R. C. (1982). ‘‘A disaggregation of the relationship between socioeconomic status and psychological distress,’’ American Sociological Review 47, 752–764. Kessler, R. C., Berglund, P. A., Foster, C. L., Saunders, W. B., Strang, P. E., and Walters, E. E. (In press). ‘‘The social consequences of psychiatric disorders: II. Teenage childbearing,’’ American Journal of Psychiatry. Kessler, R. C., and Forthofer, M. S. (1996). ‘‘The social consequences of psychiatric disorders: III. Marriage and marital stability.’’ Working Paper, University of Michigan. Kessler, R. C., Foster, C. L., Saunders, W. B., and Strang, P. E. (1995). ‘‘Social consequences of psychiatric disorders: Educational attainment,’’American Journal of Psychiatry 152, 1026–1032. Kessler, R. C., McGonagle, K. A., Zhao, S., Nelson, C. B., Hughes, M., Eshleman, S., Wittchen, H-U., and Kendler, K. S. (1994). ‘‘Lifetime and 12 month prevalence of DSM-III-R psychiatric disorders in the United States: Results from the National Comorbidity Survey,’’ Archives of General Psychiatry 51, 8–19. Kessler, R. C., Turner, J. B., and House, J. S. (1988). ‘‘Effects of unemployment on health in a community survey: Main, modifying and mediating effects,’’ Journal of Social Issues 44, 69–85. Kish, L., and Frankel, M. R. (1970). ‘‘Balanced repeated replication for standard errors,’’ Journal of the American Statistical Association 65, 1071–1094. Liem, R., and Liem, J. H. (1988). ‘‘Psychological effects of unemployment on workers and their families,’’ Journal of Social Issues 44, 87–105. Mastekaasa, A. (1994). ‘‘Psychological well-being and marital dissolution,’’ Journal of Family Issues 15, 208–228. McLanahan, S., and Garfinkel, I. (1986). Single Mothers and Their Children, The Urban Institute Press, Washington, DC. McLanahan, S., and Sandefur, G. (1994). Growing Up with a Single Parent: What Hurts, What Helps, Harvard Univ. Press, Cambridge, MA. Mullahy, J., and Sindelar, J. (1990). ‘‘Gender differences in the effects of mental health on labor force participation,’’ in Research in Human Capital and Development (I. Sinageldin, Ed.), pp. 125–145, JAI Press, Greenwich, CT. Seligman, M. E. P., and Maier, S. F. (1967). ‘‘Failure to escape traumatic shock,’’ Journal of Experimental Psychology 74, 1–9. Sewell, W. H., and Hauser, R. M. (1975). Education, Occupation, Earnings, Academic Press, New York. Umberson, D., Wortman, C. B., and Kessler, R. C. (1992). ‘‘Widowhood and depression: Explaining long-term gender differences in vulnerability,’’ Journal of Health and Social Behavior 33, 10–24. Warr, P., and Jackson, P. (1988). ‘‘Factors influencing the psychological impact of prolonged unemployment and reemployment,’’ Psychological Medicine 15, 795–807. Williams, D. R., Takeuchi, D. T., and Adair, R. K. (1992). ‘‘Socioeconomic status and psychiatric disorders among blacks and whites,’’ Social Forces 71, 179–194. Wittchen, H.-U. (1994). ‘‘Reliability and validity studies of the WHO-Composite International Diagnostic Interview (CIDI): A critical review,’’ Journal of Psychiatric Research 28, 57–84. World Health Organization. (1990). Composite International Diagnostic Interview (CIDI, version 1.0), World Health Organization, Geneva, Switzerland.
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SO970616
Early-Onset Psychiatric Disorders and Male Socioeconomic Status Rukmalie Jayakody Pennsylvania State University
Sheldon Danziger University of Michigan
and Ronald C. Kessler Harvard University We seek to integrate economic, sociological and psychological models by examining whether early-onset psychiatric disorders predict adult male socioeconomic status. Unlike most status attainment studies, we include information on major psychiatric disorders. We use data from the National Comorbidity Survey, the first survey to administer a structured psychiatric interview to a national probability sample in the U.S. Our sample includes men between the ages of 25 and 54. We find that disorders that occur before age 16 reduce educational attainment and the probability of being currently married and increase the probability of having a recent disorder, each of which is a predictor of adult male unemployment. We also find that these early-onset disorders have a direct negative effect on male employment. The estimated magnitudes of these effects are often as large as those of family background variables, suggesting that research on adult male SES should pay greater attention to mental health issues. r 1998 Academic Press
Rukmalie Jayakody is Assistant Professor of Human Development and Family Studies and Research Associate at the Population Research Institute at Pennsylvania State University. Sheldon Danziger is Professor of Social Work and Public Policy and Director of the Social Work Research and Development Center on Poverty, Risk, and Mental Health at the University of Michigan. Ronald C. Kessler is Professor of Health Care Policy at the Harvard Medical School. This research was supported by grants from the National Institute of Mental Health (R24 MH51363, R01 MH46376, and R01 MH49098), with supplemental support from the National Institute of Drug Abuse (through a supplement to R01 MH46376) and the W.T. Grant Foundation (Grant 90135190). The authors thank Markus Jantti, Samuel Lind, Kenneth Lutterman, and Michael Spencer for comments on prior drafts and Jilenne Gunther for table preparation. Address correspondence and reprint requests to Rukmalie Jayakody, Population Research Institute, 601 Oswald Tower, Pennsylvania State University, University Park, PA 16802. 371 0049-089X/98 $25.00 Copyright r 1998 by Academic Press All rights of reproduction in any form reserved.
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We seek to integrate economic, sociological, and psychological models by examining whether early-onset psychiatric disorders predict adult male socioeconomic status. Our work is primarily addressed to economists and sociologists, who typically do not incorporate information about mental health problems as independent variables in models of socioeconomic attainment (for a review of recent attainment studies, see Corcoran, 1995). There is a long history of research in psychiatric epidemiology examining the reciprocal relationship between social class and mental illness. This relationship has been widely studied and long observed, dating as far back as the nineteenth century study by Jarvis on the relationship between poverty and insanity (Hunter and Macalpine, 1963). Many researchers have evaluated the relative contributions of ‘‘social selection’’ of the mentally ill into the lower socioeconomic strata and ‘‘social causation,’’ by which the effects of lower socioeconomic status (SES) produce mental disorders (Eisenberg and Lazarsfeld, 1938; Dohrenwend and Dohrenwend, 1969; Feather, 1990; Dohrenwend, Levar, Shrout, Schwartz, Neveh, Link, Skodol, and Stueve, 1992). Most studies by psychiatric epidemiologists that analyze social class and mental health recognize the reciprocal relationship, but focus primarily on the presumed effects of the former on the latter, rather than the reverse. For example, a number of studies have shown that unemployment predicts the subsequent onset of anxiety and depression (Warr and Jackson, 1988; Liem and Liem, 1988; Kessler, Turner, and House, 1988) by lowering self-esteem (Jahoda, 1982) and producing feelings that life is not in one’s control (Goldsmith and Darity, 1992). Others have documented that low SES increases daily stresses which also predict psychological distress (Kessler, 1982). Studies that focus on the other causal direction typically analyze the effects of adult psychological disorders on occupational attainment or unemployment (Mullahy and Sindelar, 1990; Williams, Takeuchi, and Adair, 1992). One Canadian study, for example, found that a lifetime history of psychiatric disorders increases the odds of unemployment 2.8 times (Bland, Stebelsky, Orn, and Newman, 1988). Our goal in this paper is not to try to resolve the relative importance of these reciprocal effects. Rather we examine whether early-onset psychiatric disorders1 negatively affect adult male SES and investigate some of the pathways through which this effect operates. Previous studies suggest that substantial effects are plausible. For example, Kessler, Foster, Saunders, and Strang (1995) report that early-onset disorders predict truncated educational attainment, an intermediate outcome known to be a primary determinant of adult SES. Among the most important predictors of adult SES are educational attainment and marital status and stability, outcomes which also have a reciprocal relationship with psychiatric disorders. Marriage is beneficial to individual mental health—particularly for men (Gove, 1972). Marital disruption, whether due to 1
Early-onset disorders are defined as those whose symptoms appear at age 16 or earlier.
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divorce (Aseltine and Kessler, 1993) or widowhood (Umberson, Wortman, and Kessler, 1992), is a stressful life event that lowers both psychological and economic well-being. Aseltine and Kessler (1993) demonstrate that divorce leads to anxiety and depression; Umberson et al. (1992) show the same for widowhood. Other studies have analyzed how psychiatric disorders predict subsequent educational attainment, marriage, and marital stability. Mastekaasa (1994) finds that subjective well-being and overall life satisfaction predict the probability of marriage. A series of studies by Kessler and his colleagues (Forthofer, Kessler, Story, and Gotlib, 1996; Kessler et al., 1996; Kessler & Forthofer, 1996) demonstrate that early-onset psychiatric disorders predict teen childbearing, the probability and timing of first marriage, and marital stability. This paper addresses the hypothesis that early-onset psychological disorders are an important, but understudied, determinant of adult male socioeconomic status. In that regard, our work is related to the extensive literature in sociology and economics that, for example, documents that family background, educational attainment and marital status affect employment and financial well-being. Our contribution is to demonstrate that early-onset psychiatric disorders directly predict adult SES, controlling for family background, educational attainment, and marital status. In addition, these disorders also negatively predict educational attainment and marital status and thereby indirectly predict SES. THEORETICAL FRAMEWORK A long history of status attainment research in sociology has examined the impacts of family background and educational attainment on occupational prestige (Blau and Duncan, 1967; Sewall and Hauser, 1975; Featherman and Hauser, 1978). These studies found that parents’ status had a moderate effect on son’s occupational status, and that schooling was a more powerful predictor of occupational prestige than were family background variables. We extend this work by hypothesizing that psychiatric disorders are also important in determining SES. We hypothesize that psychiatric disorders have a direct effect on adult socioeconomic outcomes. In addition, psychiatric disorders also indirectly affect adult outcomes via their impacts on educational attainment and marital status. We avoid problems related to the reciprocal relationship between psychiatric disorders and SES by emphasizing disorders that appear prior to the outcome of interest. Psychiatric disorders that first occur at age 40, for example, can not have affected high school graduation, whereas those that occurred at an early age can have done so. Likewise, disorders that occurred during a man’s early teens can not have been caused by a period of adult unemployment. We hypothesize that early-onset psychiatric disorders, defined here as those occurring at age 16 or earlier, reduce educational attainment, reduce the probability of being currently married (defined at the same time that adult SES is measured), and increase the likelihood of having a recent psychiatric disorder (defined as one which occurred within the 12 months prior to the interview). Each
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of these mediators is then hypothesized to reduce adult socioeconomic status. Additionally, early-onset psychiatric disorders are expected to have a direct negative effect on adult SES, holding constant family background, educational attainment, marital status and recent disorders. Similarly, family background is expected to influence educational attainment, marital status, recent psychiatric disorders, and adult SES directly, as well as to have an indirect effect on adult SES that is mediated by educational attainment, marital status, and recent disorders. DATA AND METHODS We analyze data from the National Comorbidity Survey (NCS) (Kessler, McGonagle, Zhao, Nelson, Hughes, Eshleman, Wittchen, and Kendler, 1994) which surveyed 8098 respondents ranging in age from 15 to 54, between September, 1990, and February, 1992. The NCS is the first survey to administer a structured psychiatric diagnostic interview to a national probability sample in the U.S. Psychiatric diagnoses were ascertained through the use of the Composite International Diagnostic Interview (CIDI) (World Health Organization, 1990), designed to be administered by trained interviewers who were not clinicians. The CIDI generates diagnoses according to both the DSM-III-R and ICD-10 diagnostic systems. Field tests of the CIDI conducted by the World Health Organization have documented good reliability and validity with all diagnoses examined here (Wittchen, 1994). The NCS also gathered retrospective information on the age of onset of each disorder and on which disorders were present within 12 months of the interview. For purposes of this analysis, we have focused on a select number of disorders that have been found to be most serious in terms of role impairment. These include affective disorders (major depressive episode and manic episode), anxiety disorders (panic disorder, agoraphobia, and generalized anxiety disorder), substance (drug or alcohol) dependence, and conduct disorder. We group all other disorders ascertained in the NCS (dysthymia, social phobia, simple phobia, posttraumatic stress disorders, and alcohol and drug use without dependence) into a residual category of ‘‘other’’ (not seriously impairing disorders). The NCS interview was carried out in two parts. Part I included the core diagnostic interview and was administered to all respondents. Part II asked additional family background and work status questions and contained a detailed risk factor battery and a series of secondary diagnoses. Part II was administered to all respondents between the ages of 15 to 24, all those who screened positively for any lifetime diagnosis in Part I, and a random subsample of other respondents. The analyses reported here are based on the Part II subsample and are weighted to be representative of the general population. We limit our analyses to African American and white men between the ages of 25 and 54. We focus on prime-aged men because they were expected to be working at the time of the interview. In comparison, women may be out of the labor force for a variety of socially acceptable reasons (such as homemaking and/or child care). Because women’s SES is so dependent on their family roles,
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estimating the impact of psychiatric disorders on their employment status is far more complicated. To further focus on men who are expected to be attached to the labor force, we excluded students, the retired, and the permanently disabled. If early-onset disorders induce labor force withdrawal and if these men define themselves in the more socially accepted categories of ‘‘retired’’ or ‘‘disabled,’’ our sample exclusion will understate the negative effects of disorders on employment. Because of their small numbers, widowers are also excluded. Our final sample includes 1769 men (88.4 percent of whom are non-Hispanic, white and 11.6 percent African American). We utilize logistic regression techniques to examine the impacts of early-onset psychiatric disorders, family background, and other independent variables on the log of the odds of: (1) low educational attainment, defined as not graduating from high school; (2) high educational attainment, defined as graduating from college; (3) marital status, defined as whether or not the respondent was married at the time of the survey; (4) having a recent psychiatric disorder; and (5) employment status, defined as not working at all versus working at the time of the interview.2 In keeping with the status attainment literature, we include several family background measures: whether the respondent grew up in a one- or two-parent household, the education of the chief breadwinner of the respondent’s family of origin, the Duncan SEI score of the chief breadwinner, and whether either of the respondents’ parents had a psychiatric disorder. The last of these variables is based on respondents’ reports using a standard inventory (Endicott, Andreasen, and Spitzer, 1978). Control variables are also included for race, age cohort, and region of current residence. The Appendix defines these variables and presents weighted sample means. Because of the complex survey design and the weighting of the NCS, standard errors for the regression coefficients were estimated by the method of balanced repeated replication using a macro in SAS-UNIX (Kish & Frankel, 1970). RESULTS The first column in Table 1 shows the proportion of men in our sample who had an early-onset or recent-onset psychiatric disorder. Almost three-quarters of the sample report not having had any early-onset disorder. Having an early major affective, anxiety, or substance dependence disorder is relatively uncommon (1.9, 2.6, and 1.5%, respectively). The most common early-onset disorder is conduct disorder (16.1 percent).3 Although three-quarters of the men also report not having a recent psychiatric disorder, the prevalence of each specific recent disorder is higher than it was in the early years. For example, 6.5% report having had a major affective disorder in the past 12 months, about three times the number who had this disorder before age 16. Recent substance dependence, 13.9%, is the most prevalent specific recent disorder. 2
We do not examine occupational status because it is unavailable in the NCS. In Table 1, the sum of the values for the specific disorders exceeds the value of 100 less the percentage not having any disorder because some men have had multiple disorders. 3
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JAYAKODY, DANZIGER, AND KESSLER TABLE 1 Early-Onset Psychiatric Disorders by Work Status a Work status Total
Total (unweighted sample size 5 1769) Early onset psychiatric disorders Did not have an early disorder Major early onset affective disorder Major early onset anxiety disorder Early onset substance dependence Early onset conduct disorder Other early onset disorder Recent psychiatric disorders Did not have a recent disorder Major affective disorder Major anxiety disorder Recent substance dependence Other recent disorder
Not working
Currently working
7.9
92.1
74.3 2.4 2.6 3.1 16.1 16.0
49.4 7.9 8.1 5.3 27.9 30.3
72.8 2.0 2.3 3.0 14.8 13.8
75.3 6.5 4.3 10.7 13.3
49.2 20.3 14.5 24.9 26.3
77.7 5.4 3.5 9.6 12.2
a
The analyses are weighted using the final sample weight. The sample, in all tables, includes white and African American men between the ages of 25 and 54 who are not students, disabled, or retired.
The last two columns in Table 1 show that having any psychiatric disorder, regardless of timing, is negatively correlated with current work status. Our sample of prime-age men is significantly attached to the labor force—only 7.9% were not working at the time of the NCS interview. The prevalence of disorders, regardless of the specific diagnosis or timing, is substantially higher among those who are not currently working than among currently working men. For example, whereas only 2% of men who are working had an early-onset affective disorder, the prevalence of this diagnosis among nonworking men is 7.8%. Conduct disorder, which was the most prevalent early-onset disorder, has almost double the prevalence among nonworking compared to working men. A similar pattern exists for recent psychiatric disorders. Whereas 9.6% of those currently working had recent substance dependence, 25% of nonworking men had recent dependence. Educational Attainment The logistic regression results in Table 2 show that early-onset psychiatric disorders predict educational attainment, measured in the first model as whether or not the man graduated from high school, and in the second, as whether or not he graduated from college.4 Race, several indicators of family background, age at 4 The regression includes dichotomous variables for five of the six diagnosis categories shown in Table 1. The omitted variable represents the 74.3% of the sample who did not report any early-onset disorders. The other variables reflect the various other diagnoses. A man who has had multiple early-onset disorders will have a value of one for each diagnosis he has experienced.
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EARLY-ONSET PSYCHIATRIC DISORDERS TABLE 2 The Effect of Early-Onset Psychiatric Disorders on Educational Attainment a Model 1 Probability of NOT Finishing H.S. b Constant Early onset psychiatric disorders (No early onset disorder 5 0) Major early onset affective disorder Major early onset anxiety disorder Early onset substance dependence Early onset conduct disorder Other early onset disorder a Race (White 5 0) African American Family background Grew up in one parent home (2 parent 5 0) Duncan SEI scale less than 20 (20 or more 5 0) Parents had psychiatric disorder (No 5 0) Educ. of provider, less than h.s. (H.S. grad. 5 0) Missing Age (45 to 54 5 0) 25 to 34 35 to 44 Region (Northeast 5 0) Midwest South West Model x2 (d.f.) Mean of dependent variable
S.E. Odds ratio
23.363
0.235 0.768*** 20.127 1.121*** 0.447**
Model 2 Probability of Finishing College b
S.E. Odds ratio
.432
0.672 0.207 1.515 0.141 0.129
1.26 2.16 0.88 3.07 1.56
0.080 21.302*** 20.533 20.791 20.571**
0.344 0.029 2.031 0.694 0.195
1.08 0.27 0.59 0.45 0.56
0.266
0.430
1.30
20.689
0.392
0.50
20.020
0.024
0.98
20.139*** 0.038
0.87
.646*** 0.003
1.91
21.252*
0.618
0.29
0.132
0.99
0.386
0.292
1.47
1.018*** 0.307 2.467*** 0.045
2.77 11.79
20.893** 0.220 21.748*** 0.208
0.41 0.17
20.696*** 0.091 20.670*** 0.058
0.50 0.51
0.283 0.176 1.032*** 0.157 20.230 0.212 319.57 (16) .129
1.33 2.81 0.79
2.011
0.021 0.197
0.299 0.271
1.02 1.22
20.722*** 0.049 20.649*** 0.001 20.302 0.216 370.62 (16) .277
0.49 0.52 0.74
Unweighted sample size 5 1769. * p , .05; ** p , .01; *** p , .001.
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interview, and region are included as control variables. Having a major anxiety disorder, conduct disorder, or other early disorder all significantly increase the probability of not finishing high school. The effects of major affective disorders and substance dependence are not significant. In terms of magnitude, the largest impact on high school graduation is due to conduct disorder. Respondents who had a conduct disorder are three times more likely not to have graduated from high school as those who did not. Children with conduct disorder, particularly those who go undiagnosed, are likely to create
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FIG. 1. Predicted probabilities of not finishing high school for white men, age 35 to 44, living in the Northeast, who grew up in a two-parent home, whose parents had no psychiatric disorder, and whose financial provider was not a high school graduate.
problems in the classroom and may be difficult to control. The negative attention they receive from teachers as a result of these problems may increase their probability of dropping out or of being expelled. Men who experienced a major anxiety disorder are about twice as likely to have not finished high school as those who had no such disorder. Having any other early-onset disorder increases the relative odds of not finishing high school by 56%. Similar to the effect of conduct disorders, having any psychiatric disorder probably leads to a negative schooling environment, which increases the risk of quitting school. To provide some notion of the relative magnitude of these effects, Fig. 1 uses the logistic regression coefficients from the first model in Table 2 and displays the predicted probabilities of not finishing high school for a hypothetical case—a white man, between the ages of 35 and 44, who lives in the Northeast, who grew up in a two-parent home, whose parents had no psychiatric disorders, and whose major financial provider while growing up had not graduated from high school. Given these characteristics, the figure shows how the predicted probability varies for the man as we vary his parent’s SES and his history of early disorders. Lower SES is denoted by parents whose scores on the Duncan socioeconomic index (SEI) were below 20, representing lower-status blue-collar jobs (about a third of parents were in this category). Controlling for the presence or the type of disorder, lower SES men have a higher probability of not finishing high school than those from higher SES families (compare each set of white and black bars). In the figure, the SES effect for a man reporting no early-onset disorders is 4 percentage points (.05 vs .09). For a man from a low SES family, the effect of having a father who had graduated from high school is 6 percentage points (data not shown).5 5 That is, the probability of not finishing high school drops from 9 to 3% for a man with characteristics shown in Fig. 1, as parental education increases from high school dropout to graduate.
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The effect of having had an early-onset disorder is at least as large as are these family background effects. For example, the hypothetical man from a low SES family has a predicted probability of not finishing high school of 9% if he had no early disorder (see Fig. 1). But if he experienced a major anxiety disorder, the probability rises to 17%, and it rises to 22% if he had a conduct disorder. The size of these early-onset effects—8 and 13 percentage points, respectively, is larger than both of the family background effects. Thus, just as parents’ education and parents’ occupation are considered important variables for inclusion in attainment models, so should information on psychiatric disorders. The second model in Table 2 examines the probability of graduating from college. Men who have had a major early-onset anxiety disorder are only one-fourth as likely to have graduated from college as those with no early disorders; those who have had some other early-onset disorder are only half as likely to have graduated as those with no disorders. Sociological research has consistently found that growing up in a one-parent home, as opposed to a home with both biological parents, has negative consequences for educational attainment (Garfinkel and McLanahan, 1987; McLanahan and Sandefur, 1994). Table 2 indicates that growing up in a one-parent home is not significantly related to high school graduation for this sample of adult men, but does significantly reduce the probability of graduating from college. In contrast, early-onset disorders negatively affect both high school and college graduation. Additionally, the size of the early-onset disorder effects are larger than the family structure effect. The status attainment literature also recognizes the importance of SES of family of origin as an important predictor of educational attainment. The effect on the probability of finishing college of having had a major anxiety disorder, as compared to having had no disorders, is just about the same as that of growing up in a lower, as compared to a higher, SES family (a Duncan SEI score below 20 vs one equal to or above 20). In both cases, the odds ratio is under .30. Given that early-onset disorders have an equal or greater impact as family structure and SES background, greater attention should be paid to psychiatric disorders in educational attainment research. Marital Status The impact of early-onset disorders on the probability of being currently married (as opposed to being never married, divorced or separated, or cohabiting) is shown in Table 3. Both major anxiety disorders and other early disorders significantly reduce the likelihood of being currently married. Those who experienced an early-onset major anxiety disorder are only about one half as likely to be currently married as those with no early disorders; those with other early disorders have a 20% reduction in their relative odds of being married. Recent Psychiatric Disorders We have shown that disorders that occur prior to the age of 16 predict educational attainment and not being currently married among men between the
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TABLE 3 The Effect of Early-Onset Psychiatric Disorders on the Probability of Being Currently Married a Probability of currently being married b Constant Early onset disorders (No early onset disorders 5 0) Major early onset affective disorder Major early onset anxiety disorder Early onset substance dependence Early onset conduct disorder Other early onset disorder Race (White 5 0) African American Family background Grew up in one parent home (2 parent 5 0) Duncan SEI scale less than 20 (20 or more 5 0) Parent had psychiatric disorder (No 5 0) Educ. of provider, less than h.s. (H.S. grad. 5 0) Missing Age (45 to 54) 25 to 34 35 to 44 Region (Northeast 5 0) Midwest South West Model x2 (d.f.) Mean of dependent variable
S.E.
Odds ratio
20.527 20.621*** 20.328 0.157 20.222***
0.979 0.149 0.824 0.142 0.095
0.59 0.54 0.72 1.17 0.80
21.347***
0.214
0.26
20.001 0.219 0.081** 0.463* 20.387**
0.017 0.342 0.035 0.221 0.103
1.00 1.24 1.08 1.59 0.68
20.158 0.263***
0.172 0.039
0.85 1.30
0.120 20.027 20.089 142.00 (16) .680
0.114 0.108 0.705
1.13 0.97 0.91
0.741
Unweighted sample size 5 1769. * p , .05; ** p , .01; *** p , .001.
a
ages of 25 and 54. Now we examine the impact of these disorders on the probability of having had any recent disorder (defined as one occurring within the 12 months prior to the interview). Table 4 presents the logistic regression results which indicate, not surprisingly, that having had an early-onset disorder increases the likelihood of having a recent disorder (3 of the 5 disorder coefficients are significant and all are positive). The effects are particularly large for major anxiety disorders and other early disorders—the odds ratios are 3.18 and 6.25, respectively. Predicted probabilities similar to those shown in Fig. 1, but based on the coefficients of Table 4 were calculated (but are not shown). They illustrate that the effect of early-onset disorders on current SES may be mediated by their effect on recent disorders. Consider the benchmark case of a white man, between the ages of 35 and 44, who lives in the Northeast, grew up in a two-parent home, with a financial provider who was a high school graduate who had a Duncan score above 20, and whose parents did not have a psychiatric disorder. If he had no early-onset
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TABLE 4 The Effect of Early-Onset Psychiatric Disorders on the Probability of Having a Recent Disorder a Probability of having a recent disorder b Constant Early onset disorders (No early onset disorders 5 0) Major early onset affective disorder Major early onset anxiety disorder Early onset substance dependence Early onset conduct disorder Other early onset disorder Race (White 5 0) African American Family background Grew up in one parent home (2 parent 5 0) Duncan SEI scale less than 20 (20 or more 5 0) Parent had psychiatric disorder (No 5 0) Educ. of provider, less than h.s. (H.S. grad. 5 0) Missing Age (45 to 54) 25 to 34 35 to 44 Region (Northeast 5 0) Midwest South West Model x2 (d.f.) Mean of dependent variable
S.E.
Odds ratio
0.906 0.240 1.150 0.059 0.197
1.89 3.18 1.28 1.96 6.25
20.611*
0.301
0.54
20.008 0.056 0.849*** 20.359** 20.054
0.020 0.570 0.233 0.144 0.130
0.99 1.06 2.34 0.70 0.95
0.202 0.073
0.227 0.494
1.22 1.08
0.246*** 0.170*** 20.027 383.97 (22) .247
0.018 0.090 0.143
1.28 1.19 0.97
22.560 0.639 1.158*** 0.248 0.675*** 1.833***
Unweighted sample size 5 1769. * p , .05; ** p , .01; *** p , .001.
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disorder, his predicted probability of having any recent disorder is 8%. But if he experienced an early major anxiety disorder, it is 21%; if, a conduct disorder, 14%; and if any other disorder, 34%. Having a parent who had a psychiatric disorder did not significantly affect educational attainment (Table 2) and had only a small effect on marital status (Table 3). However, it doubles the relative odds of having a recent disorder (the odds ratio in Table 4 is 2.34). Adult Employment Status We now examine the relationship between early-onset disorders and adult male employment by focusing on their effects on the probability that the respondent was working at the time of the interview (Table 5). Consider first the reduced form model in the first three columns of Table 5. It excludes the man’s educational attainment, current marital status, and recent experience of disorders as indepen-
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TABLE 5 The Effect of Early-Onset Psychiatric Disorders on the Probability of Currently NOT Working a Reduced form Probability of NOT working b Constant 23.162 Early onset psychiatric disorders (No early onset disorder 5 0) Major early onset affective disorder .766*** Major early onset anxiety disorder 1.151*** Early onset substance dependence 2.108 Early onset conduct disorder .472 Other early onset disorder1 .076* Race (White 5 0) African American .525 Family background Grew up in one parent home (2 parent 5 0) .046 Duncan SEI scale less than 20 (20 or more 5 0) .411* Parents had psychiatric disorder (No 5 0) .326 Educ. of provider, less than h.s. (H.S. grad. 5 0) .185 Missing .874* Age (45 to 54 5 0) 25 to 34 2.249 35 to 44 2.673* Region (Northeast 5 0) Midwest .306 South .190 West .203 Marital status (Married 5 0) Divorced/separated Never married Cohabiting Education (College 5 0) Less than high school High school graduate More than high school Model x2 (d.f.) 68.56 (16) Mean of dependent variable .079 a
S.E. Odds ratio
Full model Probability of NOT working b
S.E. Odds ratio
24.044
.195 .185 .618 .400 .033
2.15 3.16 .90 1.60 1.08
.707*** .013 1.045*** .005 2.259 1.027 .281 .375 2.123* .053
.333
1.69
.074
.364
1.08
.096
1.05
.051
.126
1.05
.124
1.51
.298***
.007
1.35
.364
1.39
.352
.470
1.42
.203 .369
1.20 2.40
.097 .289
.216 .346
1.10 1.34
.349 .203
.78 .51
2.305 2.575*
.438 .206
0.74 0.56
.319 .242 .475
1.36 1.21 1.23
.244 .088 .240
.242 .165 .555
1.28 1.09 1.27
.562 1.451*** 1.040*
.504 .376 .508
1.75 4.27 2.83
1.891*** .522 .630*** 151.18 (22) .079
.164 .568 .081
6.63 1.69 1.88
Unweighted sample size 5 1769; * p , .05; ** p , .01; *** p , .001.
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2.03 2.84 .77 1.32 .88
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dent variables, because, as we have shown, each of these outcomes is negatively affected by early-onset disorders. The reduced-form model captures the total effect of early-onset disorders on adult male employment. We find that having had an early major affective disorder, anxiety disorder, and other disorder all significantly increase the probability of nonwork. For example, a man who experienced a major anxiety disorder is about three times more likely to be not working as one with no early disorder; a man with a major affective disorder, about twice as likely. The rightmost columns in Table 5 present the full model of the determinants of nonwork, including educational attainment and marital status as independent variables.6 The effects of the disorders are mediated by educational attainment and marital status, which as shown above, are both negatively affected by early disorders. Marital status and educational attainment both have large, significant effects on nonwork. However, as in the reduced form, both major affective and anxiety disorders significantly increase the probability of nonwork. The magnitudes of these effects, though somewhat smaller than in the reduced form, remain large. In addition, other early-onset disorders have a small, but significant, impact on current work status. The sign of the coefficient is, however, reversed unexpectedly from what it was in the reduced form. Figure 2 displays predicted probabilities of currently not working for some hypothetical cases in order to demonstrate the relative magnitudes of the effects of the mental health, educational, and marital status variables from the full model. For each case, the predicted probability refers to a white man, between the ages of 35 and 44, who lives in the Northeast region, grew up in a two-parent family where the parents did not have a disorder, and where the financial provider was a high school graduate with a Duncan SEI score equal to or above 20. Comparing man A with man B displays the impact of a high school degree on current work status. With benchmark factors held constant, 6% of married, high school dropouts with no disorders, but 2% of similar high school graduates, are predicted to be not working. Comparing man A with man C demonstrates that having had an early major anxiety disorder, all other factors constant, increases the probability of not working for a married high school graduate from 2 to 5%. The size of the early-onset disorder effect (3 percentage points) is about the same as the high school dropout effect (4 percentage points). In addition, the earlier tables showed that early disorders also increase the probability of dropping out. 6 We also estimated a model like that shown in Table 5 that added the set of recent disorders shown in Table 1. In this model, which includes 11 disorder variables, there was high collinearity between the specific early and recent disorders. The coefficients for the recent disorders tending to be significant and the early ones insignificant. We then estimated a model that substituted specific recent disorders for the early ones. In this model, the magnitude of the effects of the recent disorders was similar to what is shown in Table 5 for the early disorders. We prefer the model in Table 5 because early disorders are causally prior to current nonwork. However, we can not distinguish causality in the relationship between recent disorders and nonwork, as we do not know if the recent disorder occurred prior to the current period of nonwork, or if nonwork triggered the recent disorder.
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FIG. 2. Predicted probability of currently not working for white men, age 35 to 44, living in the Northeast, who grew up in a two-parent home, whose parents did not have a disorder, whose Duncan SEI scale while growing up was over 20, and whose financial provider was a high school graduate.
The predicted probabilities for men D and E indicate the combined effects on nonwork of education, marital status, and early disorders. While man A has no early disorder, is currently married, and has a high school degree, man D is never married, not a high school graduate, and had an early major anxiety disorder. This combination results in a predicted probability of nonwork of 44%. Man E, who is similar to person D, except that he had a major affective, instead of an anxiety, disorder has a predicted probability of nonwork of 36%. CONCLUSION Numerous studies in both sociology and economics have documented the importance of family background, educational attainment, and marital status in determining male SES. The potential impacts of psychological disorders on these outcomes, however, have not been typically considered. Our results, based on data from the National Comorbidity Survey, demonstrate that major psychiatric disorders that occur prior to age 16 increase the probability that an adult man is not working. We showed that early disorders have both a direct negative effect on work and a negative effect that is mediated by the fact that disorders also reduce educational attainment and the probability of being currently married. Our results suggest that future research on male adult SES should pay greater attention to mental health issues, as the estimated size of the effects were often as large as those of various family background measures.
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APPENDIX A: Description of Independent Variables Used Race is measured as a dichotomous variable, coded 1 if African American and coded 0 if white. Although there are Hispanics in the NCS, their sample size was too small to separate them out into Cuban American, Mexican American, Puerto Rican, etc. Because these groups have such diverse socioeconomic backgrounds, rather than including them all in the same category, we decided to focus only on African Americans and whites. Whites compose 88.4 percent of the sample; African Americans, the remaining 11.6 percent. Family Background —Family structure while growing up is measured as whether or not the respondent grew up in a single-parent home or a two-parent home. This variable is coded 1 if the respondent grew up with a single parent (25 percent) and coded 0 if he grew up with both parents (75 percent). —The Duncan SEI scale while growing up is used as a SES family background measure. This variable is continuous with a mean of 38.85. We decided to focus on a dichotomy because this is common in the poverty literature (see Corcoran, 1995). A Duncan SEI score below 20 represents a low status, blue-collar occupation (32.9 percent had a SEI score below 20). —Whether or not either of the respondent’s parents had a psychiatric disorder is coded 1 if they did have a disorder (43 percent), and coded 0 if the parent(s) did not have a disorder (57 percent). —Education of the financial provider while growing up measures the educational level of the individual who was the primary financial provider for the respondent while he was growing up. In most cases, this is the respondent’s father. This variable is coded 1 if the major financial provider did not finish high school (30.8 percent) and coded 0 if the provider was a high school graduate (69.2 percent). —Because there were a number of missing values on the variable indicating the education level of the financial provider, we included a dichotomous variable to indicate whether or not this information was missing. For cases where this information was missing, the mean value of education for the sample was coded as the valid value, and the dichotomous variable MISSING was coded as 1 (11.8 percent). If the information was not missing, a code of 0 was given for this variable. Age is categorized into three groups: 25 to 34 years of age (39.7 percent), 35 to 44 years of age (39.4 percent), and 45 to 54 years of age (20.9 percent).
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Region is categorized into four groups: Midwest (23.5 percent), South (34.6 percent), West (18.7 percent), and the Northeast (23.2 percent). This variable measures the region of residence at the time of the interview. Marital Status is coded into four mutually exclusive categories. Married respondents are the excluded group (68 percent). Respondents who are divorced/separated or never married but were cohabiting at the time of the interview are placed into the COHABITING group (6.3 percent) and are not in the NEVER MARRIED (13.2 percent) or DIVORCED/SEPARATED (12.5 percent) categories. Education is coded into four categories: less than high school (12.9 percent), high school graduate (37.6 percent), more than high school but no college degree (21.8 percent), and college graduates (27.7 percent). REFERENCES Aseltine, R. H. Jr., and Kessler, R. C. (1993). ‘‘Marital disruption and depression in a community sample,’’ Journal of Health and Social Behavior 34, 237–251. Bland, Co-R. C., Stebelsky, G. H., and Newman, S. C. (1988). ‘‘Psychiatric disorders and unemployment in Edmonton,’’ Acta Psychiatrica Scandinavica 77, 72–80. Blau, P. M., and Duncan, O. D. (1967). The American Occupational Structure, Free Press, New York. Corcoran, M. (1995). ‘‘Rags to rags: Poverty and mobility in the United States,’’ Annual Review of Sociology 21, 237–267. Dohrenwend, B. P., and Dohrenwend, B. S. (1969) Social Status and Psychological Disorder: A Casual Inquiry, Wiley, New York. Dohrenwend, B. P., Levav, I., Shrout, P. E., Schwartz, S., Neveh, G., Link, B. G., Skodol, A. E., and Stueve, A. (1992). ‘‘Socioeconomic status and psychiatric disorders: The causation-selection issue,’’ Science 255, 946–952. Eisenberg, P., and Lazarsfield, P. F. (1938). ‘‘The psychological effects of unemployment,’’ Psychological Bulletin 35, 358–390. Endicott, J., Andreasen, N., and Spitzer, R. L. (1978). Family History Research Diagnostic Criteria, Biometrics Research, New York State Psychiatric Institute, New York. Feather, N. T. (1990). The Psychological Impact of Unemployment, Springer, New York. Featherman, D. L., and Hauser, R. M. (1978). Opportunity and Change, Academic Press, New York. Finlay-Jones, R., and Eckhardt, B. (1981). ‘‘Psychiatric disorders among the unemployed,’’ Journal of Psychiatry 15, 265–270. Forthofer, M. S., Kessler, R. C., Story, A. L., and Gotlib, I. H. (1996). ‘‘The effects of psychiatric disorders on the probability and timing of first marriage,’’ Journal of Health and Social Behavior 37, 121–132. Garfinkel, I., and McLanahan, S. (1987). Single Mothers and Their Children, The Urban Institute, Washington, DC. Goldsmith, A. H., and Darity, W. (1992). ‘‘Social psychology, unemployment exposure and equilibrium unemployment,’’ Journal of Economic Psychology 13, 449–471. Gove, W. R. (1972). ‘‘The relationship between sex roles, marital status, and mental illness,’’ Social Forces 51, 34–44.
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