Financial Strain and Depression Among Farm ... - Wiley Online Library

Rural Sociology 55(4), 1990, pp. 475-493 Copyright © 1990 by the Rural Sociological Society

Financial Strain and Depression Among Farm Operators: The Role of Perceived Economic Hardship and Personal Control l Paula S. Armstrong and Michael D. Schulman North Carolina State University, Department of Sociology, Anthropology and Social Work, Raleigh, North Carolina 27695-8107

ABSTRACT The causal processes accounting for the relationship between farm financial strain and depression are not well understood. Using data from a statewide survey of North Carolina farm operators, we develop a covariance structure model that specifies relationships among farm financial strain, perceived economic hardship in the household, personal control, and depression. Analyses reveal that the relationship between farm financial strain and depression is mediated by perceptions of economic hardship and personal control. Results point to the importance of differential resilience to objective economic problems instead of differential exposure to these problems.

Introduction

The recent farm crisis has focused attention on the well-being of farmers and their families. Researchers have identified the key factors associated with farm enterprise economic strain (e.g., debt load, gross farm income) and have investigated the relationship between financial strain and farm operator/family distress. There is evidence that the farm crisis has increased depression, anxiety, substance abuse, interpersonal violence, and marital discord among farm families (Bultena et al. 1986; Davis-Brown and Salamon 1988; Hargrove 1986; Heffernan and Heffernan 1986; Weigel and Weigel 1987). Much of the research on the social psychological consequences of the farm crisis relies on generalized measures of distress rather than measures of specific mental health outcomes such as depression (e.g., Keating 1988; Lorenz et al. 1989; Walker and Walker 1988). In addition, the causal processes that account for the relationship between financial strain on the farm and distress are not well understood. Does farm enterprise financial strain translate directly into depression among farm operators, or do other variables mediate the

I Data collected for this research are part of the North Carolina Farm and Rural Life Study, a project of the Department of Sociology, Anthropology, and Social Work, North Carolina State University, Raleigh, North Carolina. The North Carolina Farm and Rural Life Study is supported by the North Carolina Agricultural Research Service and the North Carolina Agricultural Extension Service. Additional support was provided by the North Carolina Agricultural Foundation. The authors thank Catherine Zimmer and Michael Schwalbe for comments on previous drafts.

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relationship? This is not a trivial question. Research based upon other occupational groups has shown that economic hardship and control are key mediating variables in the stress process. In this paper, we present a covariance structure model to examine the causal relationships between farm enterprise financial strain, household economic hardship, personal control, and depression among farm operators in North Carolina. Financial strain and depression among farm operators

The stress process consists of three major components: the sources of stress (demands or stressors), the factors that reduce stress (resources or capabilities), and the outcomes of stress, commonly referred to as distress (Pearlin et al. 1981). Depression in this study is conceptualized as a consequence of the stress process (Kaplan et al. 1987; Mirowsky and Ross 1986). It refers broadly to a number of uncomfortable subjective states (e.g., feeling sad, feeling everything is a major effort) and to the absence of positive subjective states (Mirowsky and Ross 1986). Longitudinal studies suggest that depression has deleterious consequences for individuals: it can interfere with social performance, reduce the overall quality of life, and lead to illness and suicide (Murphy et al. 1986). Negative or culturally undesirable life events and chronic life strains are key factors in the development of depression (Brown and Harris 1978; Dohrenwend and Dohrenwend 1969; Thoits 1982). Among chronic life strains, the absence of adequate financial resources and the perception of economic hardship are implicated in the onset of depression (John and Weissman 1987; Lin et al. 1986; Mirowsky and Ross 1986; Ross and Huber 1985; Thoits 1987). However, the degree of distress that people exhibit cannot be adequately predicted from the intensity of its sources (Pearlin et al. 1981). Individuals confront strain-provoking situations with a wide variety of perceptions and cognitions that are capable of mediating the impact of difficult conditions. One key resource that individuals can tap to mediate chronic life strains is their perception of control (Ross and Mirowsky 1989). Farm operators are an occupational group exposed to uncontrollable and often unpredictable chronic life strains or stressors. The financial well-being of the farm operation is affected by domestic government policies, international markets, and the forces of nature (e.g., weather, plant and animal diseases) (Olson and Schellenberg 1986; Rosenblatt 1989; Rosenblatt and Anderson 1981). Though economic problems are not limited to farm operators, the threat of economic disaster is present in the lives of most farmers in ways people in other occupations rarely experience (Rosenblatt and Keller 1983). Therefore, farmers are an especially appropriate group for studying the linkages between financial difficulties and depression.

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Farming also differs from other occupations in terms of the nature of the interrelationship between the enterprise and the household. Since enterprise and household are directly linked in family-labor farming (Coleman and Elbert 1984), financial strain at the level of the farm translates directly into hardship for the household. While farmers are subject to a variety of outside forces, as petty commodity producers they have more control over the production process than most workers and actively seek to remain in farming in order to be "their own boss" (Mooney 1988). Farm financial strain and the associated threat of leaving farming impact their sense of control. Accordingly, perceived household economic hardship and control should mediate the relationship between financial strain and depression. Studies of the farm crisis tend to emphasize the effects of financial strain at the level of the farm enterprise on some outcome, such as operator or family stress levels. This emphasis flows from the assumption that an adequate financial base is essential for a farmer to stay in business and to be optimistic about his/her future (Keating et al. 1986). The debt-to-asset ratio is among the most widely used indicators of the overall financial health of a farm enterprise (Jolly et al. 1985). Several studies have found the debt-to-asset ratio to be associated with increased distress (Keating 1988; Lorenz et al. 1989; Rosenblatt and Keller 1983) and depression (Belyea and Lobao 1990; Heffernan and Heffernan 1986). The general stress literature is also concerned with the relationship between financial strain and depression. Studies confirm the positive relationship between financial difficulty and distress (Catalano and Dooley 1983; Dohrenwend and Dohrenwend 1969; Kessler 1982; Wheaton 1978). However, unlike studies of farm operators, studies of nonfarm samples pay greater attention to how an individual's perception and assessment ofeconomic difficulties affect depression. This research was suggested in part by the finding that financial difficulties are not equally stressful for all individuals. One line of inquiry into the differential impact of economic difficulties concerns the perceptions of household economic hardship. Ross and Huber (1985) show that while the perceived difficulty in meeting household obligations is influenced by economic resources stemming from the workplace, perceptions of economic hardship in the household represent an important mechanism linking occupational financial strain to depression. Pearlin et al. (1981) found chronic economic strain in the household to be one factor explaining the relationship between income and depression. Specifically, they found that decreases in income increased perceptions of economic hardship in the home, which in turn increased depression levels. Furthermore, the results of their study show that economic hardship in the household increased depression in part by decreasing self-esteem and feelings of mastery.

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Researchers who have examined differential vulnerability to depression have recognized the importance of psychological resources. A strong sense of mastery or personal control over life's problems is a resource people can draw upon to mediate chronic life strains (Kohn 1973; Pearlin and Schooler 1978; Pearlin et al. 1981; Ross and Mirowsky 1989; Thoits 1987; Turner and Noh 1983; Wheaton 1983). Rotter (1966) suggests that people differ from one another in the extent to which they perceive their environment as being under their control. There appears to be a thematic similarity in a number of related concepts including personal mastery, self-efficacy (Bandura 1977), helplessness (Seligman 1975), locus-of-control (Rotter 1966), and powerlessness (Ross and Mirowsky 1989; Seeman and Seeman 1983). Despite variations in terms, we concur with others that these concepts refer generically to a person's sense of control. Community surveys have found that depression decreases as one's sense of control increases (Kohn and Schooler 1982; Mirowsky and Ross 1983; Pearlin et al. 1981). Individuals with a high sense of subjective control should be more likely to engage in active, problemfocused coping in dealing with the demands in their environment. Conversely, the sense of powerlessness or low control over problems should decrease the propensity to use active coping responses (Ross and Mirowsky 1989). Feelings of powerlessness can arise from the inability to achieve one's ends, from inadequate resources and opportunities, andy'or from restricted alternatives (Pearlin and Schooler 1978). Their vulnerability to financial stress, the direct linkages between firm and household, and the central role that control plays in their lifestyles make farm operators fit subjects for the study of the stress process. Studies of the impact of the farm crisis demonstrate that financial strain at the level of the enterprise translates into increased stress and depression. Studies of nonfarm samples show that a sense of control and perceptions of economic hardship mediate the link between socioeconomic position and emotional well-being (Pearlin et al. 1981; Ross and Huber 1985). In order to better understand the causal processes which produce depression among farm operators, it is necessary to integrate the rural sociology literature on the consequences of the farm crisis with the medical sociology literature on economic hardship and control. Relatively few studies have explored the impacts on farm operators of household-level economic hardship and personal control as well as the impacts of farm enterprise financial strain. Keating (1988) found personal resources (using a mastery scale) to be the strongest predictor of stress among both farm men and women. Belyea and Lobao (1990) found that increased levels of farm financial strain had a direct impact upon depression and perceived stress, but that household economic hardship was the strongest determinant of depression



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among a sample of Ohio farm operators. However, Belyea and Lobao (1990) did not include personal or social resource variables which have been shown to mediate the direct impact of financial strain upon depression (Pearlin et al. 1981; Ross and Huber 1985). Do financial difficulties stemming from the farm enterprise impact farm operator depression directly, or do they cause depression indirectly because of their impact upon the perceived ability to feed, clothe, and care for the health of the household? Financial strain at the level of the farm business could give rise to depression because it lowers a farmer's sense of control. Perhaps it is the feeling of powerlessness in and of itself that leads to depression among farmers. We are interested in understanding the roles that financial strain, perceptions of economic hardship, and personal control play in the etiology of depression. A covariance structure model is the approach which will allow us to accomplish this task. Data and methods

The data for this study come from the second wave of farm operator interviews conducted as part of the North Carolina Farm and Rural Life Study. A random sample of North Carolina farm operators was selected from a list provided by a state agency. Ninety percent of the eligible respondents completed the first wave of telephone interviews in January and February of 1987. One year later, an attempt was made to re-interview all the original respondents. Ninety percent of the first-wave respondents completed the second wave of telephone interviews in January and February of 1988. The second wave of interviews produced data on 595 North Carolinians who operated farms during the 1986-1987 time period. This particular time period was fraught with much uncertainty for North Carolina farmers due to drought, changes in government programs, and general economic instability in the farm sector. Comparisons of the respondents' demographic and farm operation characteristics with data from the 1982 and the 1987 Census of Agriculture revealed that the sample and the farm population were very similar (Lilley et al. 1989). Cases with missing data on the observed indicators were deleted from the analysis, resulting in a total N of 549 farmers for the covariance structure model. 2 Based upon previous studies among both farm and nonfarm populations, we generated several hypotheses which are summarized in Figure 1. 2 The average age of farm operators in the sample was 55. Six percent of the sample was female and six percent was nonwhite. Approximately 40 percent of the farm operators had some days of off-farm labor. Median gross farm income was approximately $30,000, and median total family income was approximately $30,000. The mean percent of total family income from farming was 47 percent.

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Figure 1.

Hypothesis 1: Hypothesis 2: Hypothesis 3: Hypothesis 4: Hypothesis 5: Hypothesis 6:

Conceptual model of depression

Financial strain at the level of the farm enterprise will have a direct positive impact upon depression. Financial strain will have a direct positive impact upon perceptions of economic hardship in the household. Financial strain will have a negative impact upon a farm operator's sense of personal control. As perceptions of economic hardship increase, personal control will decrease. Personal control will have a negative impact upon depression. Perceptions of economic hardship will have a positive impact upon depression.

The structural equation model we utilize is specified and estimated within the LISREL framework ofJoreskog and Sorbom (1986). The diagram in Figure 2 presents the general form of the structural equation model based upon our hypotheses. This completely recursive model employs one exogenous latent variable-financial strain, and three endogenous latent variables-perceived economic hardship, personal control, and depression. The model specifies the relationships of each indicator to the unobserved concept, and takes into account measurement error (or unreliability) in all items measuring the individual concepts. Each indicator is understood to reflect the underlying concept, which it measures only imperfectly. This general model is intended to specify the relationships among latent variables measured at the same point in time. Substantively, it states that depression among farm operators is potentially affected by financial strain in the farm enterprise (as measured by the debtto-asset ratio), perceptions of economic hardship in the household,


Figure 2.


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General structural model of depression

and personal control. Since this analysis is cross sectional, errors of measurement are assumed to be independent.

Financial strain Financial strain was measured by dividing total farm debt by total farm assets to form the debt-to-asset ratio, which was then log-transformed to reduce the skewness of the distribution.t Total farm debt was measured by asking respondents what their total amount of debt was as of January 1, 1988. Responses could fall into one of ten categories ranging from 0 debt to debt of $200,000 or more. Assets were measured by asking respondents, "About how much would you be willing to pay for your present farm if it was on the market and you were looking to buy a farm?" Answers to this question were categorized in the same way as the debt responses. Mid-points of each category were used to code values for every respondent. ~ Several researchers have expressed doubt concerning the use of a debt-to-asset ratio on the grounds that it indicates a family's debt position and only hints at the family's ability to repay (Salant and Saupe 1986). In contrast, it has been argued that a viability ratio, a measure of debt-to-total family income, is an improvement over the debt-to-asset ratio, because it takes into account the family's ability to meet debt obligations. Because of this and other claims that the debt-to-asset ratio is problematic, we calculated a similar model with the viability ratio as the measure of financial strain. When the viability ratio was substituted for the debt-to-asset ratio, a similar structure emerged with little difference in results.

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As the debt-to-asset ratio increases, farm enterprise financial strain and the likelihood that the farm operator will become financially insolvent increases (Bultena et al. 1986; Heffernan and Heffernan 1986). According to the U.S. Department of Agriculture (1985), farmers with ratios over 70 percent have "extreme financial distress," and farmers with ratios of between 40 percent and 70 percent usually have "serious financial distress." Approximately 13 percent of the North Carolina sample had a debt-to-asset ratio of 40 percent or greater. Perceived economic hardship

Perceived economic hardship was measured using three items from an economic strain measure developed by Pearlin et al. (1981). Respondents were asked: "During the past 12 months, how often did it happen that you did not have enough money to afford the kind of ... (Y1) food you thought your household should have; (Y2) clothes you thought your household should have; (Y3) medical care you thought your household should have?" Responses were coded often (2), sometimes (1), never (0). The three items form dimensions from low to high economic hardship." Personal control

Three items from the Perceived Stress Scale (PSS) (Cohen et al. 1983) were used in the analysis as indicators of perceived control over one's problems. Items included as indicators of the latent variable personal control are designed to tap the farm operators' sense of competence in managing problems in their lives." In a recent investigation of the PSS, Lorenz et al. (1989) provide evidence for the presence of a dimension which corresponds closely with our measure of personal control. Indicators selected from the PSS include responses to the following questions: "How often in the last month have you ... (Y4) dealt successfully with irritating problems; (Y5) felt confident about your ability to handle your personal problems; (Y6) been able to control the way you spend your time?" Depression

Six questions from a modified version of the Center for Epidemiological Studies-Depression (CES-D) scale (Radloff 1977) were used as indicators of depressive symptomatology. The CES-D scale measures 4 Preliminary confirmatory-factor analysis revealed adequate reliability and that all three of these indicators measured the underlying latent structure. 5 Initially, four indicators were included as indicators for the latent construct personal control. Preliminary confirmatory-factor analysis revealed that one of the indicators was not significant. Hence, it was dropped from the covariance structure model.



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symptoms of depression in community samples and does not indicate a diagnosis of clinical depression. The CES-D scale has been found to have a high degree of validity with respect to distinguishing between psychiatric inpatient and general population samples (Husaini et al. 1980; Radloff 1977; Roberts and Vernon 1983). In addition, tests of the CES-D scale point to economic strain as an important determinant of higher rates of depression (Comstock and Helsing 1976; Craig and VanNatta 1979). The items included in the present analysis as indicators of depressive symptomatology include responses to the following questions: "How often in the past week did you ... (Y7) not feel like eating or your appetite was poor; (Y8) have trouble keeping your mind on what you were doing; (Y9) feel that everything you did required a major effort; (YI0) feel fearful; (Yll) feel sad or blue; (YI2) feel that people were unfriendly?"6 Table 1 presents the bivariate correlations, means, and standard deviations for the observed variables in the model. Results

After satisfying identification requirements concerning whether the parameters in the model were "uniquely determined" (Long 1983), we estimated the model using maximum likelihood techniques developed by Joreskog and Sorbom (1986). The objective is to find estimates of the parameters that reproduce the sample matrix of the variances and covariances of the observed variables as closely as possible (Long 1983). Our initial analytic task was to estimate the general model in Figure 2 where financial strain was hypothesized to affect all latent variables in the model. Although the model we originally specified provided an acceptable overall fit with a chi-square value of 67.79 and 60 degrees of freedom (p = .299), at the parameter level we found a different picture. The two direct effect coefficients running from financial strain to depression and to personal control were not significant. Therefore, we re-estimated the model (after constraining the nonsignificant parameters to equal zero) and present the results of our final model in Figure 3. Overall model fit Focusing first on the model as a whole, the chi-square value (71.17, with 62 degrees of freedom and probability = .199) is smaller than 6 Preliminary confirmatory-factor analysis upon a seven-item scale showed that one of the items (which asked whether the respondent felt that he/she was just as good as other people) was not a good indicator for farm operators. We dropped this item from the covariance structure model presented in this analysis.

1.16 .451

x SD

1.22 .482

.434*** .104* .147*** .155*** .176*** .218*** .151*** .139** .133*** .052 .181***

Y2

Y4

1.18 .468

t

1.54 .634

.123** .096* .314*** .121** .072 .089* .099* .133** .120** .153*** .054 .142*** .005 .174*** .081 -.009 .017 .176*** .031

Y3

1.26 .506

.162*** .092* .103* .081 .085* .132** .015 .006

Y5

1.51 .627

.056 .076 .080 .093* .005 .008 .078

Y6

1.19 .488

.204*** .206*** .243*** .267*** .099* .127**

Y7

1.47 .649

.217*** .296*** .297*** .156*** .090*

Y8

1.72 .765

.218*** .322*** .150*** .061

-

Y9

1.21 .496

.368*** .184*** .056

-

YI0

1.41 .575

.224*** .127**

-

Yll

-

Xl

1.20 .121 .460 .230

.044

-

Y12

Bivariate correlations, means, and standard deviations for observed variables (N = 549)

Note: The variables represented b YI-Y3 are measures of economic hardship in relation to food (Yl), clothing (Y2), and medical care (Y3). The variables ref-resented by 4-Y6 are measures of personal control in relation to how often re~ondents dealt successfully with irritating rroblems (Y ), how often thv; felt confident about their ability: to handle personal problems (Y ,and how often they were able to contro how the; spent their time ( 6). Those variables represented by Y7-Y12 are measures of depression based on s.;:ptoms, such as poor appetite (Y ), lack of concentration (Y8), loss of enerfi (Y9). fear (Y10), sadness \Y11). and perceptions of having en treated in an unfriendly manner (YI2). The variable represented by X is the measure for financia strain, the debt-to-asset ratio.

* p < .05. **p