International Journal of Law and Psychiatry, Vol. 14. pp. 377-366. Printed in the U.S.A. All rights reserved.

1991

Mental Health and Marital

0160.2527191 $3.00 + .OO Copyright 0 1991 Pergamon Press plc

Stability

Richard G. Frank* and Paul Gerfler**

Introduction

Mental illness has a variety of medical, social, and economic sequelae. For example, economists have estimated the direct and indirect social costs stemming from mental disorders to be on the order of $43 billion in 1980. Others have linked mental illness to homelessness (Fisher & Breakey, 1986) and reduced earnings (Frank & Gertler, 1991). In this research we are concerned with the impact of diagnosable mental illness on marriage and divorce patterns. We focus on marriage because marital instability has been linked to the “feminization of poverty” (Garfinkel & McLanahan, 1985), to mental health problems in children, and to the economic welfare of families (Frank & Gertler, 1989). Moreover, divorce itself is a major economic burden on the legal system directly and indirectly through litigation for continuation of support payments. Our analysis is presented in the spirit of an exploratory examination of a complex and methodologically difficult issue to understand. The point of departure for the analysis is the economic theory of marriage which has been most closely associated with Becker (1974, 1981). The model weighs the expected benefits and costs of marriage, and if the benefits outweigh the costs a marriage occurs. Similarly, if the marriage partners view the costs of continuing a marriage greater than the benefits a divorce occurs. Many of the costs and benefits are determined by the legal structure that governs personal relationships. An obvious example is the tax disadvantage in marriage in that married couples are in higher tax brackets than are two single individuals. States that have common property laws raise the cost of divorce to the higher income earner and raise the benefits to the lower income earner. States that strictly enforce child and spousal support after divorce raise the cost of divorce. A particularly large form of support after divorce can be the cost of caring for a mentally ill former spouse. Our empirical focus is on effect of mental distress on divorce. The analysis uses a unique set of data drawn from the National Institute of Mental Health (NIMH) sponsored Epidemiological Catchment Area (ECA) study. The data provide detailed information on the social, economic, and mental health characteristics of a probability sample of the population in the eastern part of Baltimore City (Eaton et al., 1986). The analysis presented below offers an *Professor, School of Hygiene and Public Health, The Johns Hopkins University, Baltimore, MD 21205. **Senior Economist, The RAND Corporation, Santa Monica, CA 90406-2138. This research was supported by grant MH42338 from the National Institute of Mental Health. We are indebted to Jeff Rubin and a referee for valuable comments. 377

378

empirical Baltimore

R. G. FRANK and P. GERTLER

model of mental health and divorce. This model ECA data using multivariate statistical models.

An Economic

is applied

to the

View of Marriage and Divorce

The theory of marriage developed by Becker (1974) is built on a rather simple notion. The theory states that two individuals marry when the benefits of marriage (both monetary and nonmonetary) exceed the benefits of remaining single. Thus, each individual facing a potential marriage opportunity assesses the opportunities available to them if they remain single relative to the marriage option. Relevant considerations include income under both states, personal and family compatibility, opportunities for child bearing, and preferences regarding how time is allocated between labor supply, family, and leisure. A decision to marry occurs if, on balance, the perceived gains from marriage dominate the benefits of being single for both people. The legal structure influences the expected costs and benefits of marriage. For example, marriage is a method in which partners insure that their assets are bequeathed to their partner in the event of death in states that do not have common law marriages. Also, in some states it legitimizes the rights of dependents to the credit history and insurance benefits of the head of household. The access to medical insurance benefits becomes very important in cases in which one of the potential partners has a mental illness that requires medical treatment. An example of a cost that the legal structure imposes is the additional income tax that dual married households must pay. During the course of a marriage investments are made that enhance the value of the contract. Children are perhaps the most obvious and tangible example of a specific investment in a marriage. Other examples include mutual friends, companionship, and relationships with families. These investments increase the value of the marriage contract thereby making maintenance of the contract more desirable. Each participant in the marriage decision evaluates their partner in terms of his/her contribution to the welfare of the family. Knowledge of the range of relevant characteristics can be quite uncertain. Thus, a considerable amount of learning may occur during the period following marriage. Also, individuals may change in a variety of ways over time, thereby changing the set of attributes associated with each individual. For example, a partner may develop some sort of mental illness that would certainly affect the expected costs and benefits of marriage. Another factor that changes the costs and benefits of marriages over time is that opportunities outside of the marriage may change over time. For example, career opportunities may make various constraints imposed by marriage unattractive (e.g., geographic constraints). Each of these developments may lead to a reassessment of the benefits of remaining married and therefore the value of the marriage contract. Based on the theory of marriage summarized here, divorce will be considered when the total benefits (accruing to both parties) arising from opportunities associated with not being in a given marriage are greater than those generated from remaining in the marriage. This is possible even if only one party in the marriage will collect the full gain from divorce because the individual who

MENTAL HEALTH AND MARITAL

STABILITY

379

gains can, in principle, compensate the “losing” party so as to make them better off by accepting the divorce. This assumes that information about gains and losses related to divorce to both parties are known by both parties. Peters (1986) provides some evidence to support this assumption. The legal structure determines much of the costs of divorce. Rules governing the division of property, child custody and visitation, and support payment are crucial to the evaluation of the potential costs of divorce. Within this economic model of marriage an individual’s mental health status can affect both the marriage and divorce decisions. The manner in which mental health status can influence the two decisions is multifaceted and differs between marriage and divorce. First and foremost, it affects the value of the interpersonal relationship and the expected economic returns. Mental disorders may also affect investments in marriage. For example, the ability to develop friendships, serve as a companion and confidante, and function within an extended family may be disrupted by mental disorders. Mental disorders in parents have been linked to mental problems in their children. This lowers the value of the investment in children by a married couple (OTA, 1986). This makes the marriage contract less valuable than it would be absent a partner with a mental problem. The impact of mental illness on marital stability may also differ for males and females. For instance, mental illness in males has been shown to lead to substantial reductions in earnings (Frank & Gertler, 1991). In a society where the male is often the primary earner, a male with a major mental disorder may be relatively unattractive in terms of his potential contribution to household income. The impact of mental illness on female earnings is considerably smaller partly due to their lower labor force participation rates. The economic theory of marriage and divorce described above provides several hypotheses that will be examined in the empirical analysis presented below. First, we expect that the presence of a diagnosed mental disorder in men will significantly lower the probability of marriage and significantly raise the likelihood of divorce if marriage does occur. This is largely because of the substantial impacts mental illness has on earnings and on the traditional male provider role. Mental illness will make males substantially less desirable as partners. A second hypothesis is that for females mental illness will have a substantially smaller impact on both the probability of marriage and the chances of divorce for those who marry. A third hypothesis is that the presence of young children will lower the probability of divorce. Finally, we would expect that the presence of children will tend to dampen the impact of mental illness on divorce rates. We will test this by including an interaction variable between mental illness and the number of children in our empirical model. Empirical Analysis In this section we present an empirical analysis designed to test the hypotheses derived from the economic model developed above. We begin with a description of the data set used for model estimation. This is followed by a discussion of the estimation method used. We conclude the section with a description of the detailed specifications of the marriage and divorce models.

380

R. G. FRANK and P. GERTLER

Data The Baltimore ECA survey is one of five in the Epidemiological Catchment Area program (Eaton et al., 1981). The study, which was principally aimed at establishing prevalence and incidence estimates for mental disorders in the adult population of East Baltimore, collected detailed information on health and mental health care utilization. The survey was based on a longitudinal multistage probability design which included two face-to-face interviews one year apart and a telephone interview between personal contacts (for a total of three waves of data collection). The geographic area covered by the Baltimore ECA is the eastern third of Baltimore City, an area with a population of 241,000, which is 38% black and where 19% of the population is Medicaid eligible. Formal service providers include 3 short-term general hospitals, 11 small health centers and prepaid plans, and 1 free standing community mental health center. There were approximately 0.36 primary care physicians per 1,000 population in 1981. The field survey was designed to obtain household interviews with one randomly selected person from those 18 to 64 years of age and with every household member 65 or older; proxy respondents were accepted for 2.7% of subjects who were ill or had language problems. The overall response rate in the baseline survey was 78%) which resulted in 3,481 completed interviews. The follow-up six-month telephone survey and the one-year household survey had response rates of 83 and 8 1 Vo , respectively. The longitudinal sample was compared to the baseline sample for evidence of systematic selection by respondents. Only minor differences were found between the two samples which should not influence analyses of marital stability. The questionnaire used in the household survey contained a battery of mental health and health status measures. At the core of the ECA data base is the Diagnostic Interview Schedule (DIS), a structured questionnaire administered by lay interviewers that includes data for computer generated diagnoses according to the criteria specified in the Diagnostic and Statistical Manual, Third Edition (DSM-III) of the American Psychiatric Association. The categorization of mental illness used here is based on the diagnoses generated from the DIS. We identify two categories of mentally ill individuals. The first consists of all DSM-ZZZ diagnoses except for phobia and substance abuse. The second category is made up of diagnosed substance abusers. We eliminated the phobias to be conservative in our definition of mental illness. The Baltimore site of the ECA study reported unusually high rates of phobia. Because the validity of those results have been questioned, we take a cautious approach to use of the phobia diagnosis. A second issue connected to use of DIS diagnoses relates to the timing of disorders. Because we are attempting to link current marital status with a history of mental illness we only include diagnoses that were for conditions that were more than a year old. While this does not clearly link changes to marital status to prior mental problems, it is an attempt to establish such time sequences, especially given the age (under 36) of the subsample studied. An alternative approach to the one taken here would have been to use the dates of

MENTAL HEALTH AND

onset of signs and symptoms of mental illness. The strength of this approach is the increased precision of the time of onset information. The weakness of the approach is that the signs and symptoms do not necessarily meet diagnostic criteria. We chose to use the stricter clinical criteria at the cost of less precision in time at onset information. The ECA surveys also collected information on a variety of social, demographic, and economic characteristics of the individuals questioned. This information included: income, marital status, living arrangements, family structure, education, and other demographics. The sample of individuals used here are a subsample of those who answered the baseline questionnaire. That is, we selected male and females under the age of 36 years for analysis. The major reason for this is that we were trying to measure relative recent changes in marital status and associate prior mental health status with those changes. After eliminating cases where there were missing values for key variables to be used in the analysis, we obtained samples of (a) 738 females who were either never married or had at some time been married; (b) 466 females who were either currently married or had at some time been married; and (c) 240 males who were either currently married or separated and divorced. Empirical Methods We estimate four models: a marriage and divorce model for males and females. They relate to (a) whether or not an individual has ever been married, and (b) whether or not an individual who has ever been married is now married or divorced. Since the dependent variables are dichotomous (i.e., only take on the values zero or one), ordinary least squares estimates are inappropriate. We therefore use logistic regressions to obtain parameter estimates. The Ever Married Model. The basic model of whetTLer or not an individual was ever married is quite simple. We include five regressors. They are (a) educational status measured in years of schooling; (b) age in years; (c) age squared; (d) whether or not an individual had a DIS diagnosis of a mental disorder (excluding phobias and substance abuse), and (e) whether or not an individual was diagnosed as being a substance abuser. It should be remembered that the diagnoses are for illnesses that occurred more than one year prior to the baseline interview. The education variable is used as a proxy variable for earnings potential and for household productivity. The Divorce Model. The divorce model is also quite simple. In this model we attempt to control for the basic characteristics of the individual as in the marriage model described above (age and race). Years of schooling serves to measure both market and nonmarket productivity. We also add the number of children under six years of age to represent marriage specific investments and the higher cost of divorce. We thus expect the number of young children to be negatively related to the probability of divorce. Finally, we include the presence of both a diagnosed mental disorder and a diagnosed substance abuse problem.

R. G. FRANK and P. GERTLER

382

TABLE 1 Descriptive Statistics Males

Variable Education

Age Race (nonwhite) Any DIS Substance

Abuse

No. Children

< 6

Females

Ever Married or Single

Divorced or Married

Ever Married or Single

Divorced or Married

12.48 (2.41) 26.51 (4.81) 0.39 (0.48) 0.03 (0.18) 0.10 (0.29) -

12.37 (2.48) 28.91 (3.97) 0.33 (0.47) 0.04 (0.18) 0.12 (0.32) 1.75 (2.15) 466

12.06 (2.14) 26.60 (4.67) 0.51 (0.50) 0.05 (0.22) 0.05 (0.22) -

11.95 (2.07) 28.12 (4.24) 0.39 (0.49) 0.06 (0.24) 0.05 (0.22) 2.00 (2.24) 240

788

n

Table 1 presents

descriptive

statistics

475

for the key variables

in the model.

Estimation. We estimate the four basic regression models (two sets of sex specific regressions) using logistic regression. The logistic model takes into account the dichotomous dependent variables used in this study. Moreover, the logistic model offers a convenient interpretation for estimated coefficients for the mental health indicators, that is, as the log odds ratio for the particular variable. That means that when the coefficient estimate for the DIS diagnosis in the divorce equation is exponentiated it represents an estimate of the relative risk of divorce for those with a diagnosed mental disorder. Maximum likelihood estimation is used to obtain the logistic regression coefficient estimates. Results The results for the estimation of both the sex specific marriage and divorce models are presented in Tables 2 and 3, respectively. The results in Table 2 reflect the impacts of the variables of interest on the probability of having ever been married. The impact of education for females was estimated to be negative and significantly different from zero at conventional levels (0.05). This suggests that increases in additional years of schooling may either (a) delay the time when women are available for marriage; or (b) decrease the need to marry for income-related reasons. Higher levels of education lead to better market prospects for women and therefore value less a potential mate’s income support. A very similar result appears for the males. In both sets of regressions blacks were substantially less likely to marry than whites. The mental disorder indicator suggests women with a mental problem are

MENTAL HEALTH AND MARITAL STABILITY

Logistic

TABLE 2 Regression

Results”

Female Estimates

Variable

Male Estimates

- 10.89

Intercept Education

V-k& Race (nonwhite = 1) Mental Disorder (yes = 1) Substance Abuse (yes = 1) Log Likelihood n Pseudo R*

383

(3.57) - 0.20 (4.42) 1.02 (4.34) - 0.02 (3.41) - 1.71 (8.80) 0.64 (1.41) - 0.89 (2.29) -379.01 788 0.29

- 11.57 (2.82) -0.17 (3.35) 0.84 (2.67) - 0.01 (1.78) - 0.79 (3.44) - 0.96 (1.57) - 0.31 (0.88) - 250.79 475 0.29

“Asymptotic tin parentheses Dependent

variable:

Ever Married

= 1

more likely to marry prior to age 35 (1.89 times) than are otherwise similar women without such problems. The coefficient estimate is not very precise as reflected by the relatively low t statistic (1.41). The coefficient for mental disorder in the male marriage model was negative and significant at the 10% confidence level. This suggests that males with a diagnosed mental problem are less than one-half as likely to marry than are otherwise similar males. The results for the substance abuse indicator suggest that women substance abusers get married before age 35 only 41% as often as do similar women who are not diagnosed substance abusers. For males the results were not significantly different from zero meaning that male substance abusers were neither more nor less likely to marry than other men. The results for the divorce model are reported in Table 3. The coefficient for education for both males and females indicates that higher levels of educational attainment reduce the probability of divorce. Both coefficient estimates are significantly different from zero at conventional levels. Black females are significantly more likely to get separated or divorced before age 35 compared to white women. The coefficient estimates suggests that black women who get married have a four times greater chance of divorce than do whites. The results for the number of children under five support the view of marital contracts proposed above. The estimated coefficient in both the male and female regressions were negative, suggesting that the presence of young children lowers the probability of divorce. The results for females are substantially different in magnitude and precision than are those for males. The male coeffi-

384

R. G. FRANK and P. GERTLER

TABLE 3 Logistic Regression

Female Estimates

Variable Intercept Education

PW* Race No. Children

< 5

Mental Disorder Substance

Resultsa

Abuse

Log Likelihood n Pseudo R”

- 16.75 (3.30) - 0.21 (3.83) 1.14 (3.13) - 0.02 (2.92) 1.42 (6.57) - 0.25 (1.79) 0.91 (1.96) - 0.06 (0.12) - 262.63 466 0.20

Male Estimates

- 5.29 (0.68) -0.10 (1.41) 0.31 (0.54) - 0.01 (0.42) 0.54 (1.58) -2.19 (4.63) 0.66 (0.82) 0.41 (0.79) _ 109.83 240 0.23

aAsymptotic tin parentheses Dependent

variable:

Divorced

= 1

cient is roughly nine times the size of the female coefficient (in absolute value). Moreover, the female result is significant at the 10% confidence level, whereas in the male regression the coefficient is significant at the 1 Vo confidence level. The results for the mental disorder indicator suggests that women with a diagnosed mental disorder are almost 2.5 times as likely to get divorced as are similar women with no diagnosis. This finding is significant at the 5% confidence level. Males are almost twice as likely to get divorced if they have a diagnosed mental problem. However, the coefficient estimate was quite imprecise and we therefore cannot reject the hypothesis that there is no effect of mental disorder on divorce. The impact of substance abuse was not significant in either of the sex specific models. It is worth noting that the coefficient estimate in the male model was relatively large (an odds ratio of 1.51). Finally, we experimented with interactions of mental disorder and substance abuse with the number of young children. In all specifications, the interactions were not significantly different from zero and had small estimated coefficients. Discussion There are several findings where the interpretation and implications are worthy of discussion. Perhaps the most important is the differences in the pattern of results for mental disorders and substance abuse by sex. Mental

MENTAL HEALTH AND MARITAL STABILITY

385

disorders appear to increase the probability of marriage for females and decrease the probability for males. In the case of substance abuse, being a substance abuser reduces the probability of being married prior to age 35 for both sexes. In contrast having a diagnosed mental disorder increases the chances of being divorced by age 35 for both sexes. For cases of substance abuse there is no effect on divorce rates for women and weak evidence of an increased likelihood among men. These results are only partly consistent with the hypotheses put forth above. We offer the following interpretations of the results. 1. For females who have mental disorders the sequelae involve increased dependency on others for both material and emotional support. In most segments of an American society we posit that it is more acceptable for women to be dependent than men. Thus, an individual who exhibited various effects of mental illness associated with dependence would be more likely to be able to find a male match than would a male with a similar set of attributes. 2. Males who become mentally ill suffer substantially larger income losses than do females (Frank & Gertler, 1989). We also posit that a man whose earnings capacities have been limited by illness will be particularly unattractive as a mate. This is, in part, because in most segments of society the male is still viewed as a primary provider of material goods. For this reason we might expect to find males with mental problems to have particular difficulty in finding a match in the market for marriage. 3. As hypothesized, mental illness appears to reduce the value of the marriage contract and increases the probability of divorce much as was hypothesized. However, substance abuse seems to be at most weakly related to divorce probability. This seems to vary only slightly by sex. Thus, our general conclusion from these observations is that the losses of expected household benefits (both monetary and nonmonetary) due to mental illness is lower for women than men because of smaller losses in income due to illness and a greater acceptance of dependency among women. Thus, social roles seem to be a key factor in evaluation of the social cost of mental disorders.

References Becker, G. (1974). A theory of marriage part II. Journal ofPolitical Economy, Z(2), Sl l-S26. Becker, G. (1981). A treatise on thefumily. Cambridge, MA: Harvard University Press. Eaton, W., Regier, D. A., Locke, B. Z., & Taube, C. A. (1981). The epidemiological catchment area program of the NIMH. Public Health Reports, 94(9), 3-17. Fisher, P. J., & Breakey, V. R. (1986). Homelessness and mental health: an overview. Internotional Journal of Mental Health, 14, 6-41. Frank, R., & Gertler, P. (1991). An assessment of measurement error bias in estimating the effect of mental distress on income. Journal of Human Resources, 26, 154-164. Frank, R., & Gertler, P. (1989). The effect of Medicare policy on mental health and poverty. Inquiry, 26, 283-290.

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cause, IRP, University Office of Technology Assessment (OTA). (1986). Children’s ton, DC: US GPO. Peters, H. E. (1986). Marriage and divorce: Informational Economic Review, 76,437-454.

and a partial

cure.

mental health problems

and services.

Washing-

constraints

contracting.

American

and private

Mental health and marital stability.

International Journal of Law and Psychiatry, Vol. 14. pp. 377-366. Printed in the U.S.A. All rights reserved. 1991 Mental Health and Marital 0160.2...
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