Mental health in women experiencing intimate ... - Wiley Online Library

INTERNATIONAL JOURNAL OF SOCIAL WELFARE

DOI: 10.1111/j.1468-2397.2006.00442.x Int J Soc Welfare 2006: 15 (Suppl. 1): S31– S40

ISSN 1369-6866

Mental health in women experiencing intimate partner violence as the efficiency goal of social welfare functions von Eye A, Bogat GA. Mental health in women experiencing intimate partner violence as the efficiency goal of social welfare functions Int J Soc Welfare 2006: 15 (Suppl. 1): S31– S40 © 2006 The Author(s), Journal compilation © 2006 Blackwell Publishing Ltd and the International Journal of Social Welfare. In this article, we propose that psychological goal functions, such as mental health, as well as income are important elements of social welfare. We examine the relationship between income, depression, social welfare (food stamps and Medicaid) and intimate partner violence, using a personorientation in a sample from the United States. Data from four time points of a longitudinal study of intimate partner violence are analysed. Clusters of women are derived based on whether or not they received or did not receive food stamps and Medicaid at each of the four time periods. These clusters differ on income and intimate partner violence as well as the trajectory of depression. However, a series of linear models suggests that only intimate partner violence (not income and social welfare variables) predict the development and level of depression over time. The effects of the social welfare variables we examined seem to be domain specific and do not influence the mental health of women receiving these services.

In this article we propose linking two otherwise unrelated approaches to social welfare – welfare economics and mental welfare. Specifically, we propose examining the effects of welfare economics on mental welfare. Welfare economics is concerned with the distribution consequences of macroeconomy and the economic activities of individuals. Specifically, it is concerned with the welfare of individuals, as opposed to larger bodies of societies such as communities. Thus, the individual is used as the basic unit of analysis. From a psychological perspective, it is interesting to note that welfare economics assumes that individuals are the best evaluators of their own welfare, and that welfare can be adequately measured in pecuniary units or as a relative preference. It almost seems trivial to read (Wikipedia, 2005) that individuals will prefer greater welfare over less welfare. In accordance with the desiderate of using the individual as the unit of analysis is the approach of social welfare which refers to the overall utilitarian state of a society. Often, the overall state is estimated based

Alexander von Eye, G. Anne Bogat Michigan State University, East Lansing

Key words: mental health, person-oriented, women, violence, social welfare Alexander von Eye, Department of Psychology, Michigan State University, East Lansing, MI 48824-1116, USA E-mails: [email protected]; [email protected] Accepted for publication February 15, 2006

on summation over all individuals in the society. In this approach, individual-specific parameters are estimated first, and they are summed in a subsequent step. Two approaches have been proposed to conceptualising welfare economics (see Hicks, 1975; Sen, 1993). The first approach is called Neo-classical (see Henry, 1990). This approach considers utility cardinal. It thus can assess utility by summing up pecuniary units (dollars, euros, talers). The Neo-classical approach assumes that all individuals possess similar and, thus, comparable utility functions. By implication, this assumption justifies the summation of individual utility functions. In contrast with the Neo-classical approach is the New Welfare Economics Approach. This approach distinguishes between economic efficiency and income distribution. Questions concerning efficiency are dealt with using such criteria as Pareto efficiency (see below, Pareto principle). In addition, efficiency is often assessed using measures of ordinal utility (which cannot be summed). Questions concerning the income distribution are dealt with by way of specifying social welfare functions (Bergson, 1938).

© 2006 The Author(s) Journal compilation © 2006 Blackwell Publishing Ltd and the International Journal of Social Welfare. Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA

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There have been many approaches to specifying social welfare functions. Here, instead of reviewing these approaches, we present an attempt at applying social welfare function concepts to welfare in nonpecuniary units. Specifically, we ask questions concerning the effects of social welfare measures on the mental health of women who differ in the degree to which they experience violence by their romantic partners. For the psychological discussion, we assume a person-oriented research strategy (Bergman & Magnusson, 1997; von Eye & Bergman, 2003; von Eye & Bogat, 2006). That is, as in welfare economics and research on social welfare, the individual or groups of individuals are the units of analysis.

Mental health as the goal function of social welfare In this section we first redefine social welfare to include mental health in addition to pecuniary aspects. Then, we ask whether social welfare has effects on mental health. From a psychological perspective, social welfare involves more factors than the income of individuals and the distribution of income. In addition, it is proposed, mental health is a major indicator of social welfare. In other words, we widen the scope of social welfare functions to also include psychological goal functions. We begin with a definition. Social welfare is composed of a number of indicators, including, for instance, the income of individuals and their mental health. Income can be measured in pecuniary units. Mental health can be measured in units of, for instance, severity of depression, schizophrenia, post-traumatic stress or neuroticism. The indicators of social welfare are not necessarily independent of each other. For example, it is well known that poverty has broad and deleterious implications for those who live in poverty. In the United States, poverty has been associated with poor health, including cardiovascular disease (see Kaplan & Keil, 1993), reduced marital satisfaction (e.g. Conger & Elder, 1994; Conger et al., 1990) and mental health problems such as depression (see review article by Belle & Doucet, 2003). In other words, there is a partial confound such that certain levels of some indicators of social welfare are predictive of certain levels of other indicators. Societies sometimes undertake efforts to alleviate inequality. These efforts target income inequalities as well as inequalities in access to medical and psychological help. Now, if certain levels of indicators of social welfare are predictive of certain levels of other indicators, one might be tempted to propose that changing the levels of the predictors affects the levels of the criterion variables. This proposal is based on the assumption of a causal relationship between predictors and criteria. For example, if one observes that individuals living

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below the poverty line are less educated than individuals above the poverty line, one might attempt moving individuals to income levels above the poverty line, thus providing them with the opportunity of reaching higher levels of formal education. The present article entertains such a hypothesis, as will be detailed later. Consider n individuals with i = 1, . . . , n. Each of these individuals comes with a profile of K goods which now include, in addition to goods with pecuniary value, indicators of mental health, with k = 1, . . . , K. Consider also the amount of effort (labour), L. This labour is needed to produce these goods or to maintain a particular level or profile of mental health. Consider, furthermore, the production function, P. This function represents aspects of the process of the production of goods, for instance, production costs. In the present context, this function can also describe the costs of curative efforts in case an individual needs psychological help. Lastly, consider the self report (preference), Ui of individual i. This self report represents the individual’s preference structure, including statements concerning their (desired) mental health status. The goal of social welfare is then, in analogy to the goal described in Section 1.1, to maximise social (psychological) welfare, W, W = f(Ui).

Here, welfare is defined in terms of individual preference structures. These structures can be simple as in the example of absence of suffering. However, they can be complex as in the example of a social situation an individual needs to feel comfortable. In other words, we discuss the role of society in the creation of a situation in which individuals are financial and psychological contributors to the overall average. Now, we discuss the idea that the indicators of social welfare are partially dependent on each other (or predictive of each other), and we narrow the discussion by focusing on the effects of intimate partner violence. Consider again the well-documented deleterious effects of poverty (see the references listed above). If the social welfare function includes psychological variables of well-being in the form of, for example, absence of mental suffering, below poverty income will decrease the functional value in more than one way. First, the low income of individual i will make a contribution to a low population income average. Second, it will make a contribution to a low population well-being average. Other contributions to a low well-being average are made by, for instance, intimate partner violence. It is trivial to predict that victims of violence suffer from this violence and that their well-being scores are lower in comparison with non-victims; however, the wellbeing of all sufferers of intimate partner violence is not uniform. The authors’ research has often taken a personorientation in examining the effects of intimate partner

© 2006 The Author(s) Journal compilation © 2006 Blackwell Publishing Ltd and the International Journal of Social Welfare

Mental health in women

violence. We found that women with a history of exposure to intimate partner violence (retrospectively reported) as well as women exposed to intimate partner violence during pregnancy had poor mental health across a number of diagnostic categories (Bogat, Levendosky, Theran, von Eye & Davidson, 2003). The number of partners and the timing of this exposure had different effects on the women. Those women experiencing chronic intimate partner violence (across both partners and time) had the worst outcomes. Women who experienced intimate partner violence recently – during their pregnancies and in the year prior to pregnancy with their current partners – had the next worst outcomes. When we prospectively examined mental health indicators for abused and non-abused women during pregnancy and two years postpartum, we found that negative mental health symptoms tended to diminish and then moderate for most women over time (Bogat, Levendosky, DeJonghe, Davidson & von Eye, 2004). Pregnancy seems to be a difficult time for women regardless of whether they are abused or not. However, by one year postpartum, both abused and non-abused women’s mental health improves. For women experiencing abuse at all three time periods (chronic abuse), however, mental health symptoms again deteriorate by their children’s second year of life. Women who had frequent abuse (two time periods) and recent abuse also had more negative outcomes. Another of our studies found that prolonged exposure to intimate partner violence (across 3-year-long time periods) reduced the chances of finding women who did not suffer depression, a group of resilient women we call ‘survivors’ (Leahy, DeJonghe, von Eye, Bogat & Levendosky, 2005). The present research, using data from these same participants, asks whether depression is predictable from degree of violence. In addition, we ask whether societal efforts to alleviate poverty counteract, and reduce, depression, and what is the relationship between social welfare, depression and intimate partner violence.

Violence, poverty and depression The data analysed in this article were collected in the context of a longitudinal study on intimate partner violence conducted in mid-Michigan in the United States (Bogat, Levendosky & Davidson, 1999; Levendosky, Bogat, Davidson & von Eye, 2000). Two hundred and six women were first interviewed during the last trimester of their pregnancies. More than one-half of the women (60%) had experienced intimate partner violence during their pregnancies; the others had not. All participants had to be between the ages of 18 and 40, involved in a romantic relationship of at least six weeks duration during their pregnancy and sufficiently proficient in English to complete the questionnaires and laboratory assessments. At the time of pregnancy, the

mean age of the women was about 25 years (SD = 5.0). About 45 per cent had high school diplomas or less education, about 40 per cent had attended some college and the rest had either 4-year college or graduate degrees. The women were Caucasian (63%), African American (25%), Latina (5%) and other (7%) ethnic backgrounds. Most (50%) had never been married; 40 per cent were married, 5 per cent were divorced, 4 per cent were separated and 1 per cent were widowed. Their monthly income was about US$1,500 (range: US$0– 9,500). The women who had experienced intimate partner violence during their pregnancies, compared with those who had not, were about three years younger, were less educated and had a lower monthly income. Data collected from the women when their children were one, two, three and four years of age (T3, T4, T5 and T6 in the vernacular of this study) were used in the present research. Social welfare variables were not assessed during the earlier waves of the study. The number of participants with complete data at each of these time points varies and is presented in Table 1. The following sample analyses differ from those in other studies on the effects of social welfare in two respects. First, the data describe a population from the United States. Therefore, they may depict a different picture than data from other countries, in particular countries with different welfare systems such as the Scandinavian countries. Second, we do not attempt to examine all of the factors associated with intimate partner violence. For example, younger individuals (US Department of Justice, 1995), those who cohabit (e.g. Magdol, Moffitt, Caspi & Silva, 1998), persons from lower socioeconomic groups (e.g. Gelles & Cornell, 1985; Gelles & Straus, 1988), persons with antisocial traits including aggressive, deviant or criminal behaviour (e.g. Hotaling, Straus & Lincoln, 1990; Simons, Wu, Johnson & Conger, 1995), those who experienced abuse or harsh discipline as children (e.g. Doumas, Margolin & John, 1994; Downs, Miller, Testa & Panek, 1992; Dutton & Hart, 1992; Murph, Meyer & O’Leary, 1993; Simons, Wu, Johnson & Conger, 1995) and those who engage in excessive use of alcohol and drugs (e.g. Barnett & Fagan, 1993; Fagan & Browne, 1994; Magdol et al., 1997) are all more likely to be perpetrators or victims of intimate partner violence. Instead, we ask whether measures taken by the State to improve the pecuniary situation of the financially poor show a spill-over effect such that the mental health of the recipients is also improved. This goal of the analysis is intended to complement rather than replace earlier results on other factors associated with intimate partner violence. The predictor and criterion variables are presented below. 1. Income. Total family income per month (in US dollars) will be used as a continuous predictor of depression.


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2. Social welfare received. Two variables of welfare received were used – Food Stamps and Medicaid. Both variables are scaled as 1 = did not receive, and 2 = did receive. Social welfare received will also be used as predictor of depression. Food stamps are government-issued stamps used in exchange for food. In other words, they provide the recipient with free food. Medicaid provides the recipient with free basic medical services. Eligibility for both is linked to income thresholds. In the following analyses, we treat the social welfare variables as individual-level variables. Although everybody is entitled to the same amount of State contribution (given the same circumstances), the participants in our study moved in and out of recipient status in ways specific to the individual. Therefore, the trajectories describe individuals and are unique to the extent that the finite number of possible trajectories allows (see Figure 2 later in this article). 3. Violence. For all four time periods, the self-reported amount of intimate partner violence the women suffered during the year preceding the interview was available. Information for all partners during each of the four time periods was summed to result in a measure of total amount of violence suffered in the year before the interview. Violence will also be used as a continuous predictor of depression. 4. Depression. Each respondent completed the Beck Depression Inventory (BDI; Beck, Ward, Mendelson, Mock & Erbaugh, 1961). The BDI is a 21-item selfreport questionnaire that assesses symptoms of depression during the past week. Each item consists of four statements that range from neutral to severe symptoms. A total depression score is calculated by summing the participant’s responses for all items. Scores can range from 0 to 63, with higher scores indicating more depressive symptom status. This score serves as the dependent variable in our analyses. Variable analysis will proceed in the following steps. Descriptive statistics and graphical analyses are presented in the first section. In the second section, we discuss the temporal characteristics of the social welfare variables – Food Stamps and Medicaid. A cluster analytic solution is presented that represents four groups of temporal patterns of social welfare reception. This step of analysis reflects a first element of the person-orientation that is adopted for the present research. Linear models are estimated and presented in the third section. The linear models develop hypotheses by sequentially including variable groups into the model. First, a repeated measures analysis will be performed using only the dependent measures of depression. This analysis will show whether there is a general increase or decrease of depression over time. In addition, a polynomial decomposition will be performed which

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will show whether there exist quadratic or cubic trends. Second, the cluster classification will be included as a categorical predictor in the model. This step reflects a second element of the person-orientation that is adopted for the present research. The inclusion of the cluster classification will tell us whether the temporal characteristics of depression are cluster-specific. In other words, if there is an interaction with the cluster grouping, the solution that is based on the aggregated data must be qualified. Lastly, income and violence will be added to the model. This step will show whether additional individual-level variables make a contribution to the development of depression above and beyond State-controlled pecuniary social welfare.1

Descriptive and graphical analyses Table 1 displays descriptive statistics for all variables used in the present study. Table 1 shows that the mean income increased over time. The number of food stamp recipients decreased slightly and the number of Medicaid recipients increased slightly. Violence decreased over time, as either the women were able to leave violent partnerships or the violence within these partnerships abated. In contrast, depression did not decrease, thus possibly suggesting that violence may not be the only predictor of depression. Lastly, the sample size information suggests that over the reported span of four years, up to 18 respondents (9.57%) did not provide complete information. For the following statistical analyses, only cases with complete information were used. We now depict the data taking a person-oriented perspective. We present graphs that highlight characteristics of the data that support the assumption that aggregatelevel statements may need qualification. We begin with the outcome variable, depression. The two panels of Figure 1 show the temporal course of depression. The y-axes of both panels indicate the BDI score of depression. The x-axes give the variable name (BDI for depression) at the four observation points. The left hand panel of Figure 1 contains the raw data points and the distance-weighted least squares estimate of the aggregate curve. This curve describes the means given in Table 1. This panel does not allow one to identify the individual trajectories. These are discernible, in part, in the second panel which shows a parallel coordinate display. Obviously, this panel shows that many of the individual trajectories are not smooth at all, as one might conclude from the minimal changes 1

Please note that, in the present analyses, we use the food stamp and the Medicaid variables as individual-level variables also. Individuals varied in these variables over time to such a degree that their trajectories reflected all possible patterns (see below).


Mental health in women Table 1. Descriptives of the variables under study. Variable

N

Minimum

Maximum

Mean

SD

Income T3 Income T4 Income T5 Income T6 Food Stamps Medicaid T3 Food Stamps Medicaid T4 Food Stamps Medicaid T5 Food Stamps Medicaid T6 Violence T3 Violence T4 Violence T5 Violence T6 BDI T3 BDI T4 BDI T5 BDI T6

186 180 170 171 188 188 181 181 174 174 173 173 188 179 173 172 188 180 174 173

267 0 0 184 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0

10,000 18,000 12,000 13,000 2 2 2 2 2 2 2 2 120 77 66 94 29 28 46 33

2,202.55 2,478.46 2,398.91 2,513.54 1.74 1.40 1.69 1.45 1.64 1.44 1.64 1.49 6.30 4.91 3.13 3.58 5.74 6.83 6.24 6.27

1,757.054 2,216.782 1,831.720 1,963.980 .440 .492 .466 .499 .480 .497 .481 .501 15.446 13.073 9.108 10.543 5.413 6.243 6.837 6.552

T3 T4 T5 T6

Figure 1. The temporal course of depression (the labels are defined as follows: the first two digits indicate the data wave, the following seven digits indicate that depression scores are depicted).

suggested at the aggregate, mean level. In this article, we try to explain these individual differences.

The temporal characteristics of food stamp and Medicaid reception In Figure 2 we display the courses of food stamp and Medicaid over time. For both variables, we show only the parallel coordinate displays. The smoothed raw score curves would have the means given in Table 1. The left panel of Figure 2 shows the course of food stamp reception. The right panel shows the course of Medicaid reception.

Figure 2 shows two identical looking panels. The graphs suggest that for both food stamp reception and Medicaid reception, all three possible patterns of welfare reception in these two domains were observed: (1) consistent reception; (2) changes in reception; and (3) consistent non-reception. The graphs show all possible patterns, because both variables were scaled as 1 = did not receive and 2 = did receive. The number of patterns is easily calculated as follows. There are four dichotomous measures of food stamp reception. Crossed, these can form 24 = 16 patterns. The number of patterns for Medicaid is also 16. Crossing the food stamp and the Medicaid patterns yields 162 = 256 patterns. This


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Figure 2. Parallel coordinate displays of food stamp reception (left panel) and Medicaid reception (right panel), each over 4 points in time (T3–T6).

number is larger than the sample size of the present study. Therefore, it was decided to group the observed food stamp/Medicaid patterns. To group the observed patterns, we performed a longitudinal cluster analysis, a method that allows one to identify groups of longitudinal trajectories (von Eye, Mun & Indurkhya, 2004). In this analysis, the four indicators of food stamp reception and the four indicators of Medicaid reception were included. Clusters were created using Ward’s aggregation method and the Euclidean distance for a base measure (for an explanation of these decisions, see von Eye et al., 2004). Based on such criteria as between-cluster distances and cluster size, the four cluster solution was selected. Specifically, the cluster solution accounted for 49 per cent of the total distances among cases, thus leaving room for individual differences while grouping similar cases into the same clusters. The four cluster solution was, in a second step, replicated using the k-means method (also known as quick clustering method). This method maximises the F ratio instead of calculating the intercase distances, and is therefore deemed more efficient. The resulting classification was the same. The maximised F-values ranged from 44.26 to 1,036.89 (df1 = 3, df2 = 169 for all four F statistics).2 Using the same variables as in Table 1, the four social welfare clusters can be described as follows. (Note that, for the following description, we use three variables – income, intimate partner violence, and depression – that were not used for the creation of the 2

Note that these F statistics cannot be used for significance testing of hypotheses such as the cluster means are equal. Corrections would need to be performed to take into account that assignment to clusters was not random. In the present context, we use the F values to describe the obtained cluster solution.

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clusters. The description thus reflects aspects of the clusters’ external validity.) • Cluster 1 (21 complete cases). Second highest average income of all four clusters; income more than doubling over the observation period; number of food stamp recipients at or close to 100 per cent at all observation points; number of Medicaid recipients between 0 and 50 per cent at all observation points, with strong increasing trend; U-shaped development of amount of intimate partner violence, with decreasing trend; inversely U-shaped development of depression, with increasing trend. • Cluster 2 (64 complete cases). Highest average income of all four clusters (still below US$4,000 per month on average); income increasing with peak at T5; number of food stamp recipients close to 100 per cent at all observation points; number of Medicaid recipients also close to 100 per cent at all observation points, with slightly decreasing trend; decreasing development of amount of intimate partner violence (lowest level in study); inversely U-shaped development of depression. • Cluster 3 (40 complete cases). Average level of income; income slightly increasing; number of food stamp recipients beginning at 100 per cent at the first observation point, decreasing to about 30 per cent; number of Medicaid recipients close to or at 0 per cent at all observation points; amount of intimate partner violence oscillating (down-and-up pattern); oscillating development of depression. • Cluster 4 (38 complete cases). Lowest income in study; income showing development with positive trend; number of food stamp recipients increasing from 0 per cent at T3 to 33 per cent at T6; number of Medicaid recipients at or close to 0 per cent at all



Figure 3. Development of depression, by welfare cluster (labels indicate data wave and depression score; same scores were used as in Figure 1).

observation points; average level of intimate partner violence, but with strongly decreasing trend; oscillating trend in development of depression. One interesting aspect of these cluster profiles is that the group with the highest income is the one most likely to receive social welfare (Cluster 2). An explanation of this surprising result is that these women are still rather poor. However, they may have the resources to stay in their domicile. Thus, they have a home address and can be reached by mail and, perhaps, by phone. Women with no steady home address may not receive social welfare because they cannot be reached. In addition, the lives of women with extremely low incomes may be chaotic to the extent that they may be unable to get to the offices where one applies to receive welfare services. To set the stage for the following analyses, we now present the development of depression again, but this time separately for the four clusters. Figure 3 displays this graph (cf. Figure 1). Figure 3 suggests a different development of depression than does Figure 1. Instead of being flat, the curves now show up-and-down patterns. In addition, the average levels of the curves differ. In the following section, we attempt to predict the development of depression from variables of social welfare, income, violence and cluster membership.

Explaining the development of depression In this section, we attempt to explain the development of depression of the women in our study. As is obvious from the descriptive information provided in Table 1, most of the women live under financially constrained conditions. In addition, about half of them suffered from partner violence at any observation point. We first analyse the curve depicted in Figure 1. The average curve, shown in the left panel, is flat, slightly curved, and shows a very slight increasing trend. In the first

analysis, we decomposed this series of four measures per person into orthogonal polynomials of up to third order. Wilks’ Lambda for this model was 0.95 (F3, 165 = 2.92; p = 0.036; observed power = 0.69), indicating that 5 per cent of the observed variance was explained. The polynomial decomposition showed that only the quadratic element of the curve was significant, i.e. the curvature (F1, 167 = 4.337; p = 0.039; observed power = 0.54; partial η2 = 0.025). In other words, there is some evidence that a significant curvature exists. This effect, however, is weak, and it explains less than 3 per cent of the variance. As was indicated using Figures 1 and 3, our respondents seem to differ in their development of depression. Therefore, we added, in a second step, membership in the welfare clusters in the equation. The time factor alone in the new model explains 7.4 per cent of the variance, an increase of about 50 per cent over the previous model (Lambda = 0.926; F3, 162 = 4.329; p = 0.006; observed power = 0.86). Overall, Time had a significant effect (F2.84, 7223.43 = 3.521; p = 0.017 [GreenhousGeisser-adjusted]; partial η2 = 0.021; observed power = 0.765). As before, the quadratic trend was the only one that turned out significant (F1, 164 = 5.713; p = 0.018; partial η2 = 0.034; observed power = 0.66). The interaction of Time with cluster membership was not significant. The social welfare factor did, however, constitute a significant between-cases main effect, suggesting that it allows us to explain 5.4 per cent of the mean differences among the developmental curves of depression (F3, 164 = 3.132; p = 0.027; partial η2 = 0.054; observed power = 0.72). From this result, we conclude that State-provided social welfare in the form of food stamps and Medicaid affects the level of depression, but not the shape of the curves that describe the development of depression over a span of four years. We now ask whether (1) the violence that some of these women suffered and (2) their income allow us to explain variance above and beyond the variance explained by social welfare. Therefore, we estimate a third model.


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In this model, we include the information we have about violence and income at each point in time as covariates. In this model, the explanatory value of the Time factor is reduced to 0.2 per cent, and it is no longer significant (Lambda = 0.926; F3, 149 = 0.118; p = 0.95; observed power = 0.07). However, there are two interactions of Time and the covariates. Specifically, violence at T3 and T4 interacted significantly with Time (F3, 149 = 8.57; p < 0.01; partial η2 = 0.147; observed power = 0.99; and F3, 149 = 3.497; p = 0.017; partial η2 = 0.066; observed power = 0.77). None of the other interactions of covariates with Time was significant, and none of these interactions explained more than 2.4 per cent of the variance. In addition, the social welfare cluster classification, as before, has no interaction with Time. From these results, we conclude that earlier experiences of violence have lasting effects on the level of depression over a span of four years. This conclusion is corroborated by the fact that the correlations among violence at earlier points in time with depression at later points in time are, on average, higher than the crosssectional correlations (0.34 versus 0.22). In contrast, income does not seem to have an effect at all. The examination of the polynomial contrasts showed that the quadratic component of the development of depression interacted with violence at T3 (F1, 151 = 25.295; p < 0.01; partial η2 = 0.143; observed power = 0.99), thus allowing one to predict an accelerated slide of victims into depression. Violence at T4 affected the cubic component of the development of depression (F1, 151 = 10.604; p = 0.00; partial η2 = 0.066; observed power = 0.90), thus allowing one to predict a change in the developmental trend of depression such that, typically, a change to the worse is observed. Two of the between-case effects were significant. These were the effects of violence at T3 (F1, 151 = 7.446; p = 0.007; partial η2 = 0.047; observed power = 0.77) and at T5 (F1, 151 = 10.256; p = 0.002; partial η2 = 0.064; observed power = 0.89). In the presence of the covariates, cluster classification no longer had a significant effect. From these results, we conclude that violence at earlier periods of the observation interval affects the shape of the curve that describes the development of depression. In contrast, violence at both more distant and more proximal periods affects only the level of the developmental trajectories of depression, not their shape. In addition, social welfare that is provided by the State in the form of food stamps and Medicaid has no effect on the development of depression when violence suffered in the time interval during which welfare is provided is taken into account.

Discussion In this article, we proposed that psychological goal functions such as mental health are important elements

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of social welfare. We adopted a person-orientation to analyse the relationship between individual income, social welfare (food stamps and Medicaid), depression and intimate partner violence. The person-orientation was helpful in determining whether income and intimate partner violence contribute to depression above and beyond social welfare variables. The first step in answering our question was to find out whether there were changes in depression over time. Using a variable orientation, the initial examination of the mean depression scores indicated that there was no change across the four time periods. However, when the individual depression scores were plotted (a person approach, also compatible with ideographic research, see Molenaar, 2004; von Eye, 2004), it was obvious that some individuals had large fluctuations in their depression scores over time. These individual experiences of depression were obscured when only the mean of the sample at each time period was examined. The second step in our analyses, again using a personorientation, was to derive clusters of individuals based on whether they received or did not receive food stamps and Medicaid at each of the four time periods. Four clusters resulted. They were differentially related to income and experiences of intimate partner violence and, thus, showed external validity. However, the clusters failed to show differential trajectories of depression over time. Our final step was to run a series of linear models. When intimate partner violence is considered, income and social welfare (food stamps and Medicaid) do not predict the development of or the level of depression over time. Violence at earlier time periods predicts the shape of the curve that describes the development of depression. Violence at both more distal and more proximal periods affect the level of depression. These findings have important implications for the understanding of the relationships among depression, income, intimate partner violence and social welfare variables such as food stamps and Medicaid. Among women, numerous studies find that poverty is a risk factor for depression. Prospective studies indicate that poverty nearly doubles the rates of depression (e.g. Bassuk, Buchner, Peroff & Bassuk, 1998). However, women in poverty are not uniformly depressed. For example, women with young children (e.g. Gyamfi, Brooks-Gunn & Jackson, 2001) and Caucasian women (e.g. Riolo, Nguyen, Greden & King, 2005) are at higher risk than others. In addition, within various groups of poor women, not all develop depression. For example, Siefert, Bowman, Heflin, Danziger and Williams (2000) found that 25 per cent of current and recent welfare recipients are depressed. This indicates that poverty, in and of itself, cannot explain depression and its development. Our findings indicate that income was not a significant predictor of depression.



Societies assume to make a positive difference in individuals’ lives by providing social welfare services. These services are thought to enhance the quality of an individual’s life. That is, reduction in depressive symptoms results from the reduced stress and anxiety associated with procuring food and health care, and by helping individuals to hold on to more of their income. Our findings indicate that this is not always the case. Our findings do support the voluminous literature indicating that a very likely psychological response of women experiencing intimate partner violence is depression. In the present research, intimate partner violence was a good predictor of the development and level of depression. In fact, when intimate partner violence was taken into account, social welfare no longer had any predictive power. Our findings also support recent statements that social happiness, i.e. subjective well-being in a population, is unrelated to income (Di Tella & MacCulloch, 2004; this may be in contrast to results that suggest that subjective well-being and income are related; see Diener & Biswas-Diener, 2002). Our findings are also open to the conclusion that the effects of measures of social welfare are specific; i.e. they may not go beyond the domain within which they are applied. In other words, if food and basic health care are provided, the effects of these measures can be found at the level of nutrition and physical health. If governments consider their citizens’ well-being and mental health worth investing in, measures may need to be taken that specifically improve the psychological state of the recipients. Unspecific spread of effects may simply fail to take place. Lastly, we note that the causal chain to depression is highly complex. Factors such as violence, poverty, social welfare interventions, support structures and predispositions at the individual level are involved. The problem that researchers still need to solve concerns the mutual dependence of these factors. None affects depression without the simultaneous moderator and mediator effects of the others. More research is clearly needed.

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