Age and the Effect of Economic Hardship on Depression

Age and the Effect of Economic Hardship on Depression* JOHN MIROWSKY CATHERINE E . ROSS

The Ohio State University

Journal of Health and Social Behavior 2001, Vol 42 (June) : 132-150

The amount of depression associated with economic hardship among adults may depend on age . This study tests alternative hypotheses about the interaction . The first asserts that the amount of depression associated with economic hardship decreases with older age because of maturity and experience . The second, the opposite, asserts that the amount increases with older age because of increasingly limited future opportunities for recovery . The study analyzes data from 2,592 households in the 1995 and 1998 telephone survey ofAging, Status, and the Sense of Control (ASOC) . Regression analyses find that the amount of depression associated with economic hardship decreases with older age, both cross-sectionally and over time . No model shows an increase with age in the depression associated with economic hardship . However, regressions do show that not having household wage income or having a disabling or life threatening chronic disease increases the depression associated with economic hardship . Those interactions somewhat suppress the moderating effect of older age on the association between economic hardship and depression .

In this study we test the hypothesis that economic hardship's association with depression changes across adult age-groups . Economic hardship is lack of the money needed to meet household needs for food, clothing, shelter, and medical care. Economic hardship decreases in successively older age groups (Hazelrigg and Hardy 1997 ; Mayer and Jencks 1989 ; Mirowsky and Ross 1999b, 1999c) . The same attributes that help older Americans avoid economic hardship might also make them feel less daunted when faced with such problems . In particular, economic hardship might be less

* The survey of Aging, Status and the Sense of Control (ASOC) was funded by a grant from the National Institute on Aging (RO1-AG12393) . Sampling, pretesting, and interviewing for the surveys were conducted by the Survey Research Laboratory of the University of Illinois . This analysis was supported by grant RO1-AG12393 from the National Institute on Aging . Direct correspondence to John Mirowsky or Catherine Ross, Department of Sociology, Ohio State University, 190 North Oval Mall, Columbus, OH 43210-1353 (mirowsky.1@osu .edu or ross . 13 1 @osu .edu).

depressing to older persons because of their greater maturity, composure and experience outlasting the vicissitudes of life . Alternatively, constraints associated with older age might sharpen the depression stimulated by economic hardship, offsetting the gains from lower exposure to money problems . Restricted opportunity for future earnings and the presence of debilitating or life-threatening health problems among older Americans seem especially likely to make economic hardship more depressing . Through the household the larger social and economic order impinges on individuals, exposing them to varying degrees of hardship, frustration, and struggle . Daunting efforts to pay the bills and feed and clothe the family on an inadequate income exact feelings of depression-of being run-down, tired, hopeless, exhausted, and sad, with gnawing worries that make sleep restless and drain the joy from life (Ross and Huber 1985) . A number of studies find that economic hardship increases depression among adults in various age groups or age-related statuses, including seniors in the United States (Keith 1993) and several Asian 132

AGE, ECONOMIC HARDSHIP AND DEPRESSION

countries (Krause and Liang 1993 ; Ferraro and Su 1999), and also American working-age adults (Pearlin et al . 1981 ; Ross and Huber 1985), parents of adolescents (Ge et al . 1992 ; Conger et al . 1992), mothers (Brown and Moran 1997), and adults from the ages of 18 to 92 years old (Ross and Van Willigen 1997) . However, researchers have not compared the impact of hardship on depression in different age groups. Is the association the same across adult age groups? To date, no one has tested the proposition that the association of economic hardship with depression depends on age . Contrasting views of age suggest it might . Age signifies a number of different traits, including both maturity and decline (Mirowsky and Ross 1992; Riley 1987) . On the one hand, older people have more practice with living, more general experience, and more specific experience surviving similar problems in the past. Greater maturity, experience, and composure may reduce the impact of economic hardship on depression for older adults . On the other hand, older adults may have more difficulty recovering from economic hardship, especially if they have health problems or do not have any wage-earners in the household. Among those experiencing hardship, older adults may see fewer reasons to hope for a better future, producing more depression when struggling with economic difficulties .

OLDER AGE : EXPERIENCE SURVIVING OR LIMITED FUTURE? Older Age Viewed as Maturity Older Americans may have a psychological advantage over younger Americans when faced with economic hardship . Their greater maturity implies more experience and composure (Gove, Ortega, and Style 1989) . Age implies the summation of growth and development. With age, people become experienced, accomplished, and seasoned. They mature . Each human sums a lifetime of experience, composing a self of elements arranged to function efficiently and successfully . Aging increases practice with living . With growing insight and skill, social and psychological traits and tendencies merge into an increasingly harmonious whole. (Mirowsky and Ross 1992 :188) .

Greater maturity, experience, and compo-

133 sure may reduce the impact of economic hardship on depression for older adults. Older age brings a general evenness of temper. A number of observations suggest that older Americans generally evince greater equanimity. In successively older age groups adult Americans rate themselves progressively more helpful, supportive, disciplined, able, and satisfied with life, and less emotional, nervous, and frustrated (Campbell, Converse, and Rodgers 1976 ; Gove, Ortega, and Style 1989) . The reported frequency of feeling worried, tense, restless, angry, or annoyed decreases considerably in successively older adult agegroups (Mirowsky and Ross 1999a) . Emotional stability and balance may reduce the impact of stressors like economic hardship . Older persons may have experience surviving similar problems in the past that gives them an advantage over younger adults . They may consider economic hardship a transient and manageable part of life, thus feeling less demoralized or threatened by it. Other things being equal, the older the adult, the greater the number of communal and personal hard times they have weathered. Americans in their 30s were adults during the economic restructuring of the 1980s. Those in their 40s, 50s and 60s also faced the inflation and recession of the 1970s as adults . Those in their 70s also lived through the World War II years as adults. Those in their 80s and 90s also weathered the Great Depression as adults . Experience overcoming or outlasting problems in the past may help people solve or survive similar problems in the present (Elder and Liker 1982) . That specific experience may reduce the amount of depression associated with economic hardship in successively older age groups . Older age also brings greater experience in general. On a general level, age may indicate human capital, which is the productive capacity developed, embodied, and stocked in human beings themselves (Becker 1964). Work experience is one aspect of human capital . The more years someone has worked at their job, the greater their skills, knowledge, ability to solve problems, ability to communicate effectively, and so on . Practice improves performance . This principle applies to life in general, not just paid work. Age indicates life experience . Older adults may have general skills learned from solving problems in the past, may have figured strategies that work and learned to avoid those that don't, and may have more

134

JOURNAL OF HEALTH AND SOCIAL BEHAVIOR

confidence in their ability to solve problems . Just as work experience is an indicator of human capital, life experience may also indicate human capital. Older adults' greater stock of human capital may help them deal with the problems of life, including economic hardship .

O1derAge Viewed as Decline

Older age doesn't just indicate maturity . The approach of death also implies a more limited future . Aging brings decline in addition to maturity (Mirowsky and Ross 1992) . Older adults may feel less able to recover from hardships because they don't see a future in which things improve. They are more likely to have chronic illnesses and less likely to have a wage earner in the household, both of which impede recovery from hardships . The view of age as decline suggests that economic hardship is increasingly depressing in successively older age-groups. Young adults have their whole lives ahead of them. Young adults facing difficulty paying bills and buying necessities generally may expect that the situation will improve . Most can expect many future years of earnings, with household income rising for a decade or more . Other things being equal, though, each additional year of age reduces the fraction of total lifetime income yet to come, and reduces the expected annual increase in income, eventually making it an expected decline . Average levels of wealth also increase into late middle age at a progressively slowing rate, and subsequently decline (U.S . Bureau of the Census 1995) . The less the future promises escape from current economic difficulties, the more demoralizing and threatening those difficulties may feel . Among those experiencing economic hardship, the older adults may see fewer reasons to hope for a better future . Two age-related statuses seem particularly likely to influence perceptions about the likelihood of recovery from financial difficulties : the absence of a wage-earner in the home, and chronic disease . Individuals who remain employed, or couples with at least one member who remains employed, often can reasonably hope to increase household income and wealth in the future . Individuals or couples retired from the labor force generally have less reason to hope for such increases. Retired individuals facing economic hardship can try to re-enter

employment . However, they generally have fewer and less lucrative prospects than the continuously employed . Debilitating or life-threatening chronic disease also may undermine confidence in one's ability to escape economic hardship . For one thing, individuals often cite chronic disease as a reason for retiring (Ekerdt 1987) . Individuals who acquire a debilitating or life-threatening chronic disease after retirement probably realize that it diminishes their capacity for employment. Chronic disease may sharpen the depression associated with economic hardship for other reasons, too, apart from its impact on the ability and opportunity to hold a paying job . Managing a chronic disease takes money . Continuous payment of medical insurance premiums becomes critical . Medicare covers many costs for persons older than 65, but not all-particularly not the cost of medications . By persistently increasing the need for money while permanently decreasing the ability to earn it, chronic disease may create the sense that current economic troubles will not go away over time . Apart from the rational, economic desperation implied, the ominous combination of economic hardship with a debilitating or life-threatening chronic disease may overwhelm confidence and hope . A spirit that remains upright and active under one burden may falter and halt under two .

Stable, New, and Past Hardship.

The strength of economic hardship's association with depression may depend on its persistence, immediacy, or novelty . If so, then differences across age groups in economic hardship's duration may create an apparent interaction between them in their effects on depression . For example, suppose that American adults find persistent economic hardship very depressing but optimistically find new or resolved money problems of less concern. Suppose also that the persistence of economic hardship generally declines in successively older age groups because of greater skill or latitude in matching expenses with income. As a result, the effect of economic hardship on depression would diminish in older age groups because of the shift to less persistent difficulties . A strict test of the experience-surviving versus limited-future hypotheses needs to show

135


that age modifies the association of depression with economic hardship even when its persistence, immediacy, and novelty are taken into account. The simplest hypothesis is that age interacts in the same manner with all temporal categories of economic hardship that correlate significantly with depression . Figure 1 illustrates the combinations of current and past economic hardship that define the temporal categories of persistent, resolved, and new hardship . The most salient temporal issue pits immediacy, chronicity, and novelty against each other. The immediacy hypothesis assumes that current emotional well-being depends solely or primarily on current economic conditions . Today's problems worry and dishearten, but yesterday's are soon forgotten. Indeed, resolved hardships may strengthen character and confidence, improving well-being in the long run (Elder and Liker 1982) . Thus the

immediacy hypothesis implies an association of greater depression with persistent and new economic hardship, but not with resolved hardship. In contrast, the chronicity hypothesis views persistent economic problems as unique or exceptional in their emotional impact (McLeod and Shanahan 1996) . Tenacious problems may come to seem intractable, with no way out . The emotional impact of economic deficits may grow the longer they persist, perhaps through the accumulation of worrisome and discouraging consequences . By comparison, resolved or new economic hardship may seem transient and manageable-a temporary problem of less emotional significance . A third possibility exists-that a stable state has little effect but a change stimulates an emotional response . The novelty hypothesis assumes that people adjust emotionally to environmental constants. Those who live with persistent hardship harden, expecting no bet-

FIGURE 1. Temporal Categories of Economic Hardship Differing in Immediacy, Chronicity, and Novelty

Current Economic Hardship

Yes

Yes

No

Persistent

Resolved

Past Economic Hardship

c a~~ea Gr

No

New

~

Absent

136


ter ; those who live free of hardship soften, tolerating no worse . The advent of a change produces a temporary effect, decreasing depression for awhile when economic conditions improve and increasing it when they worsen . Whichever temporal hypothesis holds true, the size of the association may depend on age . The prevalence and persistence of economic hardship may correlate with the impact it has on depression. For example, the age groups best able to avoid and resolve money problems might, as a result of that ability, feel least depressed by its advent or presence . A study of young adults finds that coping styles known to reduce the emotional distress associated with stressful experiences actually shorten the duration of those experiences (Harnish, Aseltine, and Gore 2000) . The result suggests that money management ability may simultaneously reduce the prevalence and persistence of economic hardship and the amount of depression associated with the hardship . On the other hand, economic hardship could be most depressing among those for whom it is least common and persistent for two reasons . One is that individuals take extra care to avoid or resolve a more negative experience . The other is that an uncommon or unexpected problem may be more demoralizing than a common or expected one .

Summary

Several studies find an inverse association between economic hardship or material deprivation and age among adults in the United States (Mayer and Jencks 1989 ; Mirowsky and Ross 1999b,1999c) . The observation suggests that the total amount of depression due to economic hardship may be lower in successively older age groups because of the hardship's declining prevalence . However, it is possible that the association of economic hardship with depression may increase with age, thereby reducing, negating, or reversing the emotional benefits of lower prevalence . The experiencesurviving and limited-future hypotheses state the primary opposing views about differences across adult age groups in the association of hardship with depression . The first view argues that older persons have acquired a greater sense of security from experience surviving similar problems in the past, from the personal and household management skills

acquired through experience, and from the composure and balance that older age brings . The second view argues the opposite-that increasingly constrained opportunities for recovery make economic hardship more ominous and demoralizing the older the person is . Serious chronic disease or absence of a wage earner in the household might act as mechanisms of such an interaction . The association of depression with economic hardship may depend on its immediacy, chronicity, or novelty, all of which may differ in prevalence across age groups . A strict test of the experience-surviving versus limited-future hypotheses would show that age interacts with whatever temporal forms of economic hardship correlate with depression.

MODELS AND ANALYSES Our models describe and test the interaction between age (A) and economic hardship (H) as predictors of depression (D) . We estimate both cross-sectional differences and over-time changes, as described below.

Cross-Sectional Model

The first cross-sectional model describes the association between depression and hardship as a function of current age : D = [bo + b2 (A - 45)] + [b ] +b3 (A-45)]H r +Jb,X . + u

(1)

T6

Equation 1 describes the basic cross-sectional model regressing depression on economic hardship, adjusting for various X . (listed in the measurement section) . Both the intercept (in the first set of brackets) and the regression coefficient (in the second set of brackets) depend on age . Age is centered on 45, following the procedures recommended by Aiken and West (1991) . That makes b l represent the slope of depression with respect to economic hardship for persons age 45 (approximately the mean age in random samples of U .S . adults .) The coefficient b3 describes the expected

137


change in the slope with deviation from age 45 . The t-value associated with b3 tests the interaction between age and economic hardship . If significantly different from zero, the sign of b3 shows whether economic hardship is more depressing for older persons (b 3 > 0) or less depressing for older persons (b3 < 0). The second version of the cross-sectional model tests the hypothesis that a specific agerelated moderator variable (M) increases the association between depression and economic hardship : D=[bo +b2 (A-45)+b4M] + [b, + b 3 (A - 45) + b5M]H

change models adjust for baseline depression at time 1 (D), and for baseline ascribed or acquired statuses that might operate as confounders (X,j). They also adjust for any change in an acquired status that significantly predicts change in the outcome ( ). AD + [bo + b3 (A - 45) + b 6M1 ]

+ [b1 + b4 (A - 45) + b.1M1 ]H1 + [b2

+

b5 (A - 45) + b8 M1 ]A I (3)

+ b9D 1 (2)

+ b10h(Z)

k

+JbXj +u

The analyses explore two possible moderator variables : non-wage household and debilitating or life threatening chronic conditions . In both cases we hypothesize that b 5 > 0 . If b3 is positive in equation 1, then it will be closer to zero or negative in equation 2 . If it is negative in equation 1, then it will be more negative in equation 2 .

Subsequent and Concurrent Change Model

We estimate two kinds of over-time change models . The first kind, called the subsequent and concurrent change model, reiterates the cross-sectional model . The model, shown in equation 3 below, estimates the association of baseline economic hardship with subsequent changes in depression and the association of change in economic hardship with concurrent change in depression . It also tests whether the subsequent and concurrent changes in depression depend on age. The model shows whether the slope of change in depression with respect to baseline economic hardship and to change in economic hardship reiterates the cross-sectional slope of differences in depression with respect to differences in hardship . The subsequent and concurrent change model follows the form of the cross-sectional model, except that the dependent variable represents change in depression (AD = D2 - D1 ) regressed on baseline hardship at time 1 (H,) and change in economic hardship (AH = H2 - H1 ) . Our

Adjustment for the Hazard

ofAttrition

All change models in this study adjust for the hazard of having dropped out h(Z) as a correction for possible outcome-related attrition . Follow-up studies lose cases over time . Typically, most of the attrition is random with respect to the variables under study . Random losses do not bias regression coefficients, although the smaller final sample reduces statistical power somewhat (Winship and Radbill 1994). Attrition predictable from the independent variables in the model also does not bias regression coefficients (Winship and Radbill 1994). However, attrition related to residual changes in the outcome may bias the regression coefficients if the relationship is fairly strong . It seems possible that depression may lower the probability of remaining in the sample. Adjusting for the hazard of attrition can correct the bias if it exists (Winship and Mare 1992) . The Z in the hazard function is the Zscore that corresponds to the probability of remaining in the sample, as predicted in a probit regression. The Z is predicted from all of the baseline independent variables in the model, including Y1 . The hazard function is the ratio of the slope to the value of the cumulative probability P at Z (also the ratio of probability


1 38

density of the normal curve to its cumulative probability at Z) .

The Temporal-Category Change Model The temporal-category change model distinguishes four categories of economic hardship experiences : persistent (Hp) if present at both time 1 and time 2 interviews, new (HN) if absent at the time 1 interview but present at time 2, resolved (HR) if present at time 1 but absent at time 2, and absent (HA) if absent at both times, as illustrated in Figure 1 . Each type's association with change in depression may depend on age . The regression coefficients of the binary dummy variables Hp, HA , and HR represent the effects of persistent, new, or resolved economic hardship relative to the absence of hardship at both times : AD = [b0 + b4 (A - 45)] + [b 1 + b5 (A - 45)]Hp + [b2 + b6 (A - 45)]HN + [b3 + b 7 (A - 45)]HR + bgD1

(4)

+ b9 h(Z)

SAMPLE Baseline Sample Our analyses use data from the survey of Aging, Status, and the Sense of Control (ASOC) . ASOC is a national telephone probability sample of 2,592 U .S . households . A first wave of interviews was completed at the beginning of 1995 . Respondents were selected using a pre-screened random-digit dialing method that increases the hit rate and decreases standard errors compared to the standard

Mitofsky-Waksberg method while producing a sample with the same demographic profile (Lund and Wright 1994 ; Waksberg 1978) . The ASOC survey has two subsamples designed to produce an 80 percent over-sample of persons aged 60 or older at baseline (time 1) . The general sample draws from all households ; the oversample draws only from households with one or more seniors . In the general sample the adult (18 or older) with the most recent birthday was selected as respondent . In the oversample the senior (60 or older) with the most recent birthday was selected . The survey was limited to English-speaking adults . Up to 10 call-backs were made to select and contact a respondent, and up to another 10 calls were made to complete the interview once contact was made . Interviews were completed with 71 .6 percent of the eligible persons who were contacted. The ASOC baseline sample is fairly representative of the population, differing in the same way as most surveys do in that it is somewhat disproportionately more female, white, and middle-class . The following statistics compare the demographic characteristics of the ASOC baseline sample to those for the U.S . household population as a whole (U.S . Bureau of the Census 1995) . These statistics are weighted to compensate for the oversample of seniors . For ASOC and the U.S ., respectively, the percent female is 56 .2 and 51 .2, the percent white is 85 .1 and 82 .9, and the percent married (excluding cohabitors and the separated) is 55 .7 and 55 . For persons age 25 or older, the percent with a high school degree is 85 .1 and 80.9, and the percent with a college degree is 25 .6 and 22 .2 . The mean household income is $43,949 and $41,285 . Any model that adjusts for variables that distinguish members of the sample from members of the population eliminates the potential for bias in estimated regression slopes from self-selection on those variables (Winship and Radbill 1994) . All of the models in the analyses that follow adjust for age, sex, race, education, marital status, household income, and economic hardship, thus eliminating the risk of bias from self-selection contingent on those factors .

Follow-up Sample Follow-up surveys inevitably lose cases for a

139


variety of reasons. Attrition reduces the sample size, and also may create or enlarge differences between the sample and the population it represents. The random component of attrition does not bias estimates of regression coefficients . It does reduce the power of significance tests somewhat . The ASOC survey reinterviewed 1,344 members of the initial sample (or 53 .6 percent) in 1998, approximately three years after the baseline 1995 interviews . The size of the final sample determines its statistical power (regardless of the retention rate) . With 1,344 cases, small increases in explained variance can be significant . For example, a variable that increases the R2 of a 10-variable regression model by .003, from .050 to .053, will have a coefficient significant at p < .05 . The larger the R 2 to which the .003 is added, the smaller the p-value . Thus the statistical power seems generally adequate . Attrition also can affect sample representativeness . The non-random component of attrition can bias regression estimates . That raises the issue of whether self-selection makes the sample differ from the population in ways that may produce false answers to the study's questions . Regressions that adjust for the determinants of attrition produce unbiased estimates (Winship and Radbill 1994) . However, concern arises when unobserved residual changes in the outcome under study may covary with the tendency to remain in the sample, as discussed TABLE 1 .

in the section on models and analyses' subsection on subsequent and concurrent change . The probit regression predicting attrition is detailed in the Appendix. The model shows no significant effect of baseline depression on the probability of remaining in the sample, net of baseline economic hardship, age, race, education, and marital status . Thus, there is little reason to suspect bias due to self-selection on changes in depression in a model that adjusts for baseline variables and changes in hardship . Our change models nevertheless adjust for the hazard of attrition, so that readers may be reassured that outcome-dependent attrition does not bias estimated effects of age, hardship, and changes in hardship on changes in depression .

MEASURES Table I gives the means and standard deviations of all variables for the total sample in 1995 and for the follow-up sample in 1995 and 1998 .

Dependent Variable: Depression

Depression is an unpleasant emotion that people generally wish to avoid. Feelings of depression are a common type of distress . Depression correlates positively with other

Means with Standard Deviations in Parentheses Total Sample

Female Age Married White Education Household Income' Non-wage Household Chronic Conditions Economic Hardship Depression

' Income

Follow-up Sample

1995

1995

1998

.575 ( .494) 47 .600 (17 .747) .578 ( .494) .846 ( .361) 13 .399 (2.647) 43 .944 (48 .351) .211 ( .408) 1 .088 (1 .249) 1 .436 ( .674) .929 (1 .310)

.572 ( .495) 48 .913 (16 .949) .647 ( .478) .889 ( .314) 13 .585 (2.619) 45 .292 (44.229) .216 (.412) 1 .095 (1 .236) 1 .355 ( .603) .820 (1 .236)

.572 ( .495) 51 .913 (16 .949) .651 (.477) .889 (.314) 13 .665 (2 .612) 49 .769 (48 .150) .239 ( .426) 1 .239 (1 .325) 1 .320 (.570) .819 (1 .236)

in thousands . Medians respectively are 36.000, 40 .000 and 40 .000 .

1 40


unpleasant feelings such as anger and anxiety, and with clinical diagnoses of depression, and it correlates with disadvantaged statuses in the stratification systems of gender, work, and family (Mirowsky and Ross 1989, 1995 ; Pearlin 1989 ; Pearlin et al. 1981 ; Ross and Van Willigen 1997 ; Turner and Lloyd 1999) . Depression is measured as the frequency of unpleasant symptoms of depressed mood (such as feeling sad or lonely) and physiological malaise (such as trouble concentrating or sleeping) (Mirowsky and Ross 1989), using a seven-item modification of the Center for Epidemiological Studies' Depression Scale (CES-Dm) (Ross and Mirowsky 1984) . Respondents were asked to indicate on how many of the past 7 days they have: (1) had trouble getting to sleep or staying asleep ; (2) felt that everything was an effort ; (3) felt you just couldn't get going; (4) had trouble keeping your mind on what you were doing ; (5) felt sad ; (6) felt lonely ; and (7) felt you couldn't shake the blues. Responses are coded in days per week from 0 to 7 . The depression scale is the mean response to the seven items . The scale has an alpha reliability of .85 . CES-Dm correlates .92 with the full CES-D .

and coded 0 otherwise, (2) New Hardship is coded 1 if hardship is absent at time 1 and present at time 2, and coded 0 otherwise, and (3) Resolved Hardship is coded 1 if hardship is present at time 1 and absent at time 2, and coded 0 otherwise . The comparison group, coded 0 on all three dummies, reports no hardship at both interviews .

Main Independent Variables : Age and Economic Hardship

Sociodemographic Controls

Age is measured at baseline by asking the year of birth and subtracting it from the year of the interview. Economic hardship is assessed by asking respondents three questions : "During the past 12 months, how often did it happen that (1) you had trouble paying the bills ; (2) you did not have enough money to buy food, clothes, or other things your household needed ; and (3) you did not have enough money to pay for medical care?" Responses are "never" (coded 1), "not very often" (2), "fairly often" (3), and "very often" (4) . The responses are averaged to produce an index of economic hardship, which has a .819 alpha reliability. This is a modification of Pearlin's economic strain index (Pearlin et al. 1981), and it is similar to components of the material hardship index used by Mayer and Jencks (1989). Three binary dummy variables represent four temporal categories of economic hardship : (1) Persistent Hardship is coded 1 if there is some hardship present at both interviews,

Possible Explanatory Moderators : Non-Wage Household and Chronic Conditions A non-wage household is one in which neither the respondent nor the spouse (if one is present) has a paying job (binary coded 1 if true of the household and 0 if not) . The index of serious chronic conditions asks respondents, "Have you ever been diagnosed or told by a doctor that you have heart disease? Lung disease (like emphysema or lung cancer)? Breast cancer? Any other type of cancer? Diabetes? Arthritis or rheumatism? Osteoporosis (brittle bones)?" We selected the items to represent the most common seriously debilitating or life-threatening chronic conditions . The index counts the number reported .

Precursors of economic hardship that influence depression could create a spurious association between hardship and depression . Thus, our models adjust for the following possible confounders : Female is coded 1 if the respondent is female and 0 if male; white contrasts whites (coded 1) with non-whites (coded 0) ; education is coded in number of years of formal education completed ; married contrasts individuals who are married or live together with someone as married (coded 1) with those who are not married (coded 0) . We combined cohabitors with the legally married rather than the unmarried (never married, divorced and separated, and widowed) because their economic well-being is more similar to that of the married . Household income is assessed using a set of questions that maximize response while conserving precision (Ross and Reynolds 1996) . The interviewer first asks for the exact income for all members of the household from all sources . If the respondent does not report an exact household income, then the interviewer probes for approximate income ("Can

141


you tell me, is it more than X or less than X?") Annual household income equals the exact dollar amount if reported, and the categorical approximation otherwise . Reported household incomes range from $600 to $800,000 .

RESULTS Cross-Sectional Association

On balance, the cross-sectional results support the experience-surviving hypothesis more than the limited-future hypothesis, despite evidence that chronic conditions and the absence of a household wage earner magnify the depression associated with economic hardship. The cross-sectional results show a substantial association of economic hardship with depression. The top row of Table 2 shows a positive coefficient significant atp < .01 (2-tailed test) . The size of the association depends on age : The coefficient of the age-by-hardship product

term is statistically significant at p < .05 (2tailed test) . The sign of the age-by-hardship interaction term is consistent with the experience-surviving hypothesis . The coefficient of economic hardship may be written as a function of age : .588 - .005(A - 45) . The function implies that the amount of depression associated with economic hardship generally decreases in successively older age groups . According to the model, the coefficient relating depression to economic hardship at age 75 is 64 percent of its size at age 25 ([ .588 - .005(75 - 45)] / [ .588 - .005(25 - 45)] = .637) . Put the other way, the estimated coefficient is 57 percent larger at age 25 than at age 75 . Figure 2 illustrates the interaction, graphed at the mean level of all other variables . Economic hardship is associated with depression in all age groups, but its effect is greater among younger adults than among older adults. Limiting situations that increase with age, such as having no wages in the household or having a debilitating or life threatening chron-

TABLE 2 . Cross-Sectional Association : Depression Regressed on Age, Economic Hardship, the Interaction of Age by Economic Hardship, and Sociodemographic Controls . Significant Interactions with Mediators Added in Subsequent Models (metric coefficients with t-values in parentheses) Economic Hardship Age Age x Economic Hardship Non-wage Household

.588** (14 .566) .005t (1 .689) -.005* (-2 .110)

Non-wage Household x Economic Hardship Chronic Conditions Chronic Conditions x Economic Hardship Female White Education Married Household Income constant R2

.458** (8 .768) .008** (2 .684) -.008** (-3 .336) -.252t (-1 .843) .314** (3 .749)

.131** (2 .624) .022 ( .305) -.059** (-6 .290) -.283** (-5 .601) - .203E-3 (-.373) .949 .145

t p < .10; * p < .05; ** p < .01 (2-tailed tests) Data : Aging, Status and the Sense of Control Survey, 1995 . Note : Age measured as deviation score (age - 45) .

.134** (2 .684) .028 ( .399) -.057** (-6 .024) -.239** (-4 .588) - .176E-3 (- .325) 1 .006 .153

.509** (10.254) .001 ( .287) -.007** (-2 .853)

.214** (3 .035) .079t (1 .702) .101* (2.060) .025 (.362) -.054** (-5 .848) -.266** (-5 .356) .317E-3 (- .384) .845 .178

142


FIGURE 2 . The Effect of Economic Hardship on Depression at Different Ages

3 .0 Age 25 2 .5 45 Y

65

2 .0

0 V) . o

N o

85

1 .5

- . N .12 0.

1 .0

0 .5

0 .0

I never

I not very often

I fairly often

I very often

Economic Hardship is condition, cannot account for the decreasing effect of economic hardship on depression in older age groups . However, the cross-sectional results seem consistent with the hypothesis that such problems magnify the demoralizing impact of economic hardship . Table 2 shows a statistically significant coefficient for the interaction of economic hardship with not being either a wage earner or married to one (p < .05, 2-tailed) . The absence of income from wages appears to increase the effect of economic hardship on depression substantially. In the second equation in Table 2, the coefficient of economic hardship may be written as .458 .008(A - 45) + .314(Non-wage) . Thus, for example, the formula implies that the effect of economic hardship among 75-year-old adults is 2 .4 times greater if the household has no wages ([ .458 - .008(75 - 45) + .314] / [ .458 .008(75 - 45)] = 2 .4). The model that includes a term for the interaction of economic hardship with chronic conditions shows a similar pattern, except that the coefficient is of borderline significance (p < .10) . (Attempts to put both product terms in the same model created multi-

collinearity problems .) Adjusting for the interaction of economic hardship with either being in a non-wage household or having serious chronic disease increases the negative effect of age on the association between depression and economic hardship. For example, the magnitude of the coefficient representing the interaction of age with economic hardship increases about 60 percent with adjustment for the interaction of age with no wages in the household (going from -.005 to -.008) . In other words, were it not for the absence of wages and the presence of chronic disease in older age groups, older age would reduce the association of depression with economic hardship even more than it does . In summary, the cross-sectional results are consistent with the experience-surviving hypothesis more than with the limited-future hypothesis. Older age decreases the association of economic hardship with depression . The significant positive coefficient associated with economic hardship and the significant negative coefficient associated with the interaction between age and economic hardship

143


indicate that economic hardship is associated with depression for all age groups, but the association is smaller among older persons . There is no evidence that economic hardship is more depressing for older persons despite the fact that the absence of wages and the presence of chronic conditions magnify the depression associated with economic hardship .'

Subsequent and Concurrent Change

The subsequent and concurrent changes also support the idea that older age signals experience surviving more than a limited future . The change regressions in Table 3 show results consistent with the cross-sectional ones . Economic hardship at baseline predicts a subsequent increase in depression from baseline levels. The significant interaction with age

implies that the size of the subsequent increase in depression associated with baseline hardship gets smaller with older age . Likewise, change in economic hardship over the period predicts concurrent change of the same sign in depression . Again a significant interaction with age implies that the change in depression associated with the concurrent change in hardship gets smaller with older age . Table 3 does not test interactions of economic hardship with being in a non-wage household or with having chronic conditions for two reasons . First, the cross-sectional results indicate that those interactions cannot explain the interaction of hardship with age . (Because they may suppress it somewhat, the estimates in Table 3 are conservative .) Second, the parallel regressions require many interactions with age, which creates multicollinearity problems.

Change in Depression Regressed on Economic Hardship, Age, and Control Variables, with Age Interactions Added in the Second Model (Metric coefficients with t-values in parentheses)

TABLE 3 . Subsequent and Concurrent Change :

Age & Economic Hardship Economic Hardship,, Change in Economic Hardship(, - ti) Age

.277** (3 .912) .469** (7.194) .001 (.391)

.355** (4 .898) .510** (7 .858) .022** (4 .653) - .017** (-4 .973) - .019** (-4.678)

.039 ( .653) .101 ( .775) - .026t (-1 .812) - .112 (-1 .004) -.382** (-3.806) -.128E-3 (-.147) -.151 (-.309) -.481** (-18 .972) .465 .272

.024 (.409) .057 ( .442) -.028* (-1 .982) -.128 (-1 .156) - .351** (-3 .526) .100E-3 ( .116) - .306 (-.628) -.472** (-18 .791) .528 .289

Age x Economic Hardship,, Age X Change in Economic Hardship Sociodemographics, Hazard of Attrition & Depressiontl Sex (I = female) Race (1 = white) Education Marital status (1 = married) Change in Marital Status

(o

_ tt)

Household Income Hazard of Attrition Depression,, constant R2 t p < .10; *p < .05 ; **p < .01 (2-tailed tests) Data: ASOC, 1995 and 1998. Note: Age measured as deviation score (age - 45) .


1 44

The results in Table 3 prefigure the temporal-category models described next . Changes in economic hardship over the period have larger estimated effects on depression than do past differences in hardship at baseline . This suggests fading effects of past difficulties . The models in Table 3 treat the effects of increases and decreases as mirror images of one another. For example, the models assume that a unit increase in economic hardship increases depression by the same amount that a unit decrease in hardship decreases it . They also assume that the effect of a change does not depend on the initial or final levels of hardship . The temporal-category models that follow provide an alternate view.

Temporal Category of Change The older the adult, the less likely they are to experience any economic hardship at either

time point . Young adults, in contrast, experience more persistent and new hardship, but a large percentage also show resolved economic hardship . Figure 3 illustrates the association between age and temporal economic hardship . The temporal-category models point to hardship's immediacy rather than chronicity or novelty as the depressing aspect. The models in Table 4 indicate that past differences in depression associated with past differences in economic hardship fade over time . Compared to no hardship at either time, stable and new hardship predict greater increases in depression net of baseline levels, but resolved hardship has no significant direct effect. The coefficient of baseline depression, which represents regression to the mean, captures all of the effect that the old, resolved hardship has on the subsequent change in depression . The models imply that, were it not for baseline differences in depression, there would be no difference in the final level of depression between those

Temporal Hardship

persistent Mnew resolved none 1r,

sQ

00

>-

610 0

0

0 /1

1 61 1-

01

Age Group

145


who had hardship that resolved and those who had no hardship at either time . However, persons with economic hardship in the past generally had higher depression in the past . The coefficients of baseline depression imply that about half of the past differences between those with resolved hardship and those with none at either time vanished over the threeyear period . Persistent and new economic hardship have essentially the same effect on changes in depression. Persistent economic hardship, present at both points in time, and new economic hardship, present at time 2 but not at time 1, have equivalent effects (b = .365 and .359, respectively). Alternative analyses (not shown) using new hardship as the comparison catego-

ry reveal that persistent hardship does not predict significantly greater increases in depression . Interactions of the temporal categories of hardship with age continue to support the experience-surviving hypothesis and contradict the limited-future hypothesis . The increase in depression associated with stable or new economic hardship gets smaller with older age . The interactions also continue to support the immediacy hypothesis . Resolved economic hardship has no direct effect on changes in depression at any age . Figure 4 illustrates the interaction, graphed at the mean level of all other variables . Among young adults, new and persistent hardship greatly increase depression (compared with no hardship or resolved hard-

Change in Depression Regressed on Persistent, New, and Resolved Economic Hardship (Compared to None), Age, and Control Variables, with Age Interactions Added in the Second Model (metric coefficients with t-values in parentheses)

TABLE 4 . Temporal Category:

Age & Economic Hardship

Persistent Economic Hardship' New Economic Hardship' Resolved Economic Hardship' Ageb

.365** (4 .385) .359** (3 .807) -.012 (-.125) .001 ( .386)

Age x Persistent Hardship ,, b Age x New Hardship'-' Age x Resolved Hardship', b

.414** (4.696) .430** (4 .213) .018 (.187) .003 (1 .342) -.009* (-2.204) -.009t (-1 .641) .003 (.558)

Sociodemographics, Hazard of Attrition & Depressions ,

Sex (1 = female) Race (1 = white) Education Marital status (1 = married) Change in Marital Status (t2 -

0)

Household Income Hazard of Attrition Depression,, constant R2 t p < .10; * p < .05 ; ** p < .01 (2-tailed tests) Data : ASOC, 1995 and 1998 . 'Compared to stable absence of economic hardship . bAge measured as deviation score (age - 45).

.023 ( .387) .012 ( .094) - .033** (-2 .390) - .183t (-1 .729) - .415** (-4 .112) .309E-3 ( .371) -.532 (-1 .206) -.473** (-18.594) .791 .261

.018 (.293) -.013 (-.100) -.035** (-2 .554) -.199t (-1 .872) -.394** (-1 .641) .431E-3 ( .514) -.658 (-1 .466) -.467** (-18 .270) .893 .265

1 46


FIGURE 4 . The Effect of Temporal Economic Hardship on Change in Depression in Different Age Groups. .6

c a) a) c co U

0 .0 -

jjji

0 U m a.

Temporal Hardship

I

j

none

M resolved

- .2 -

Mnew persistent

- .4 G

(P

Z

S

6'

0

Age Group

ship), whereas new and persistent hardship have much smaller effects among older adults . Figure 4 also shows that it is the immediacy rather than chronicity of economic hardship that is depressing . New and persistent hardship have about the same impact on changes in depression . If anything, new hardship is slightly worse . Furthermore, for adults under the age of 40, it is slightly better to have resolved past economic hardship than to have never experienced it at all .

DISCUSSION Taken together, our results support the experience-surviving hypothesis . The cross-sectional model, the subsequent and concurrent change model, and the temporal category model reveal consistent findings about economic hardship and depression . Economic

hardship demoralizes American adults of all ages, but it demoralizes older persons less than younger ones . Economic hardship is less prevalent, persistent, and dispiriting in older Americans . Taken together, these observations suggest more adroit management by older adults of household economic strains. Older persons may have more human capital in the form of personal and household management skills acquired through experience and practice .' Experience overcoming or outlasting problems in the past may bolster confidence about solving or surviving similar problems in the present . Older adults have weathered more economic adversity in the past because older age implies more time for prior exposure and resolution, and because the historical trend toward greater prosperity implies greater economic adversity in the lives of Americans who came into adulthood further in


the past . Future studies may find additional ways of testing these ideas. Although the interactions of age with economic hardship clearly support the experiencesurviving hypothesis over the limited-future hypothesis, some results suggest a sensible origin of the limited-future hypothesis . Having no household wages or having a debilitating or life threatening chronic disease makes economic hardship more depressing . Those interactions suppress the moderating effect of older age on the association between economic hardship and depression, although the moderating effect of older age remains significant and substantial despite the suppression . The overtime results, in addition, show that the depression associated with economic hardship fades over time, unless the hardship persists . The models of subsequent and concurrent change show a larger effect of a unit change in hardship than of a unit baseline difference in hardship on change in depression over the period (net of baseline levels) . The temporal category models show similar increases in depression associated with persistent or new economic hardship compared to no hardship at either time . Resolved economic hardship has no effect on changes in depression net of baseline levels . Those baseline elevations of depression fade considerably but not entirely over the three-year period. Economic hardship declines with age, so older people have good reason to be confident about their ability to overcome it . The view of age as maturity implies that, as people age, they should be increasingly able to handle life strains such as economic hardship, but it is not clear whether or not these results generalize beyond economic hardship . To date, no one has shown that older people are generally less depressed by problems than are younger people, and the opposite view of age as decline gives reason to think that they might not be . Older persons have a lower sense of control over their lives than younger, in part because of poor physical health, which impairs the ability to cope actively with problems (Mirowsky and Ross 1992 ; Mirowsky 1995 ; Rodin 1986) . Thus, when faced with some types of problems, older people might be more depressed . Future research may uncover the types of problems that are more or less depressing to people

147

of different ages, and the reasons for the differences .

APPENDIX. Predicting the Hazard ofAttrition

If baseline depression influences the probability of remaining in the sample, then unobserved changes in depression may too . According to statistical theory, adjusting for the hazard of attrition corrects the potential bias (Winship and Mare 1992) . The correction is described in the modeling subsection on subsequent and concurrent change . The table to this appendix shows the probit regression of remaining in the sample at time 2 on sociodemographic attributes, economic hardship, and depression at time 1 . The pseudo-R 2 of .047 indicates that the large majority of attrition is random with respect to the model's baseline measures . The table also shows no significant effect of baseline depression on the probability of remaining in the sample, net of sociodemographic attributes and baseline economic hardship . Thus, the probit regression gives little reason to suspect selection bias in our regression models . Nevertheless, our models adjust for the hazard of attrition as a precaution. APPENDIX TABLE. Probit Regression of Remaining in the Sample at Time 2 on Time 1 Depression, Economic Hardship, and Sociodemographic Characteristics (N = 2,507)8 b

t

Depressiont1 -.030 -1 .197 Economic Hardship,, -.129** -2.888 .777E-2*** 4.164 Age,, -45 (Age,,-45)2 -.031E-2*** -3 .547 Female .061 1 .163 White .271*** 3 .531 Education„ 2 .471 .025* Married„ .242*** 4 .358 .000 Household Income„ -.303 Intercept -.361 pseudo-R 2 .047 * p < .05 ** ; p < .01 *** ; p < .001 (2-tailed tests) 8 Listwise deletion . Metric coefficients with r-values are shown. Aging, Status and the Sense of Control Survey, 1995 and 1998 .

148


NOTES

ship (Mirowsky and Ross 1999b), but children do not appear to increase the amount of depression associated with economic hardship .

1 . We also examined a lagged-outcome model, a fixed-effects model, and a latent growth-curve model . These alternative models differ from our primary analysis as follows : (1) The lagged-outcome model REFERENCES predicts depression at time 2 rather than change in depression over the period, (2) the Aiken, Leona S . and Stephen G . West. 1991 . Multiple Regression : Testing and Interpreting fixed-effects model does not adjust for Interactions . Newbury Park, CA: Sage . baseline depression or baseline hardship ; and (3) the latent growth-curve model Allison, Paul D . 1994 . "Using Panel Data to Estimate the Effects of Events ." Sociological adjusts for a person's mean depression at Methods and Research 23(2) :174-99 . both times rather than for baseline depresBecker, George S. 1964 . Human Capital . New York: sion. The alternative models parallel our Columbia University Press. model of subsequent and concurrent Brown, George W and Patricia M . Moran . 1997 . change . The time-2 outcome model substi"Single Mothers, Poverty and Depression ." tutes for AY as the dependent variable. It Psychological Medicine 27 : 21-33 . is mathematically equivalent to our model Campbell, Angus, Philip E . Converse, and Willard except for two things : (1) the effect of Y I on L . Rodgers . 1976 . The Quality ofAmerican Life. Y2 equals 1 plus the effect of Yl on AY, and New York : Russell Sage . Conger, Rand D., Katherine J. Conger, Glen H. (2) the R 2 is larger because of the stability Elder, Frederick O . Lorenz, Ronald L . Simons, of Y over time . The fixed-effects model and Les B . Whitbeck . 1992 . "A Family Process regresses AY on the OX. (Allison 1994) . It Model of Economic Hardship and Adjustment of assumes two things : (1) there are no lagged Early Adolescent Boys ." Child Development effects, and (2) the factors that remain con63 :526-41 . stant within an individual over time have Ekerdt, David J. 1987 . "Why the Notion Persists that approximately equal effects at time 1 and Retirement Harms Health ." Journal of time 2, which then subtract out . The theoGerontology 27 :454-57. retical advantage of the fixed-effect model Elder, Glen H . and Jeffrey K. Liker. 1982 . "Hard is that it implicitly adjusts for any individual times in women's lives : Historical influences constant with respect to time, whether meaAcross forty years ." American Journal of sured or not . The growth-curve model estiSociology 88 :241-66 . Ferraro, Kenneth F. and Ya-oing Su. 1999 . mates effects on AY, adjusting for the indi"Financial Strain, Social Relations, and vidual's characteristic level of Y over time, Psychological Distress among Older People : A measured as (Y1 + Y2)/2, rather than adjustCross-Cultural Analysis ." Journal of ing for Y1 . Its theoretical advantage is that it Gerontology : Social Sciences 54B(1) :S3-15 . shows the relationship between the characGe, Xiaojia, Rand D . Conger, Frederick O . Lorenz, teristic levels of Y and AY, rather than simGlen H. Elder, Ruth B . Montague, and Ronald L. ply the degree of regression to the mean . Simons . 1992 . "Linking Family Economic (McArdle and Epstein 1987) . The alternaHardship to Adolescent Distress ." Journal of tive models all show statistically significant Research on Adolescence 2 :351-78 . negative interactions between age and ecoGove, Walter R ., Suzanne T. Ortega, and Carolyn nomic hardship at time 1 and between age Briggs Style. 1989. "The Maturational and Role Perspectives on Aging and Self through the and a change in economic hardship, in supAdult Years : An Empirical Evaluation ." port of the experience-surviving hypothesis American Journal of Sociology 94(5) :1117-45 . (analyses available on request from the Harnish, Jennifer D., Robert A . Aseltine, and Susan authors) . Gore . 2000 . "Resolution of Stressful 2 . We also thought that older persons would Experiences as an Indicator of Coping find economic hardship less demoralizing Effectiveness in Young Adults : An Event History because of fewer family obligations . Analysis ." Journal of Health and Social However, interactions of economic hardship Behavior 41(2) :121-36 . with dependent children in the home proved Hazelrigg, Lawrence E . and Melissa A . Hardy. insignificant . Children in the home greatly 1997 . "Perceived Income Adequacy among increase the prevalence of economic hardOlder Adults : Issues of Conceptualization and

r

149

AGE, ECONOMIC HARDSHIP AND DEPRESSION Measurement, with an Analysis of Data ." Research on Aging 19(1) :69-107 . Keith, Verna M . 1993 . "Gender, Financial Strain, and Psychological Distress among Older Adults ." Research on Aging 15(2) :123-47 . Krause, Neil and Jersey Liang . 1993 . "Stress, Social Support, and Psychological Distress among the Chinese Elderly." Journals of Gerontology 48 :P282-91 . Lund, Laura, and William E . Wright . 1994 . "Mitofsky-Waksberg vs . Screened Random Digit Dial : Report on a Comparison of the Sample Characteristics of Two RDD Survey Designs ." Presented at the Center for Disease Control's 11th Annual Behavioral Risk Factor Survey Conference, June, Atlanta, GA . Mayer, Susan E . and Christopher Jencks . 1989 . "Poverty and the Distribution of Material Hardship." The Journal of Human Resources 24 :88-114 . McArdle, J.J. and David Epstein. 1987 . "Latent Growth Curves within Developmental Structural Equation Models ." Child Development 58 :110-33 . McLeod, Jane D. and Michael J. Shanahan . 1996. "Trajectories of Poverty and Children's Mental Health .' Journal of Health and Social Behavior 37 :207-20 . Mirowsky, John . 1995 . "Age and the Sense of Control ." Social Psychology Quarterly 58(1) :31-43 . Mirowsky, John and Catherine E. Ross . 1989 . Social Causes of Psychological Distress . New York : Aldine de Gruyter. . 1992 . "Age and Depression ." Journal of Health and Social Behavior 33 :187-205 . 1995 . "Sex Differences in Distress : Real or Artifact?" American Sociological Review 60 :449-68 . . 1999a . "Well-Being Across the Life Course ." Pp. 328-47 in The Sociology ofMental Health and Illness, edited by Allan V Horowitz and Teresa L. Scheid . New York : Cambridge University Press. . 1999b. "Economic Hardship across the Life Course." American Sociological Review 64 :548-69 .

. 1999c . "Economic Hardship Declines with Age ." American Sociological Review 64 :577-84. Pearlin, Leonard 1 . 1989 . "The Sociological Study of Stress ." Journal of Health and Social Behavior 30 :241-56 . Pearlin, Leonard I ., Elizabeth G . Menaghan, Morton A . Lieberman, and Joseph T. Mullan. 1981 . "The Stress Process:' Journal of Health and Social Behavior 22 :337-56 . Riley, Matilda White . 1987 . "On the Significance of Age in Sociology." American Sociological Review 52 :1-14 . Rodin, Judith . 1986. "Aging and Health : Effects of the Sense of Control." Science 233 :1271-76 . Ross, Catherine E . and Joan Huber. 1985 . "Hardship and Depression ." Journal of Health and Social Behavior 26(4) :312-27 . Ross, Catherine E . and John Mirowsky. 1984 . "Components of Depressed Mood in Married Men and Women : The Center for Epidemiologic Studies' Depression Scale ." American Journal of Epidemiology 119(6) :997-1004. Ross, Catherine E . and John R . Reynolds . 1996 . "The Effects of Power, Knowledge and Trust on Income Disclosure in Surveys" Social Science Quarterly 77 :899-911 . Ross, Catherine E. and Marieke Van Willigen . 1997 . "Education and the Subjective Quality of Life"

Journal

of Health and Social Behavior

38:275-97 . Turner, R. Jay and Donald Lloyd . 1999 . "The Stress Process and the Social Distribution of Depression." Journal of Health and Social Behavior 40 :374-404 . U.S . Bureau of the Census . 1995 . The Statistical Abstract of the United States 1995. Washington, DC . : U.S . Government Printing Office . Waksberg, Joseph. 1978 . "Sampling Methods for Random Digit Dialing ." Journal ofthe American

Statistical Association 73 :40-46. Winship, Christopher and Robert D. Mare. 1992. "Models for Sample Selection Bias ." Annual Review of Sociology 18 :327-50 . Winship, Christopher and Larry Radbill . 1994. "Sampling Weights and Regression Analysis ."

Sociological

Methods

and

Research

22(2):230-57 .

John Mirowsky is Professor of Sociology at the Ohio State University . In addition to the "Aging, Status and the Sense of Control" survey, funded by the National Institute on Aging, he is principal investigator project funded by the National Institute of Mental Health to study "Children, Child-Care and Psychological Well-Being" (Catherine E . Ross is co-principal investigator on both) . He is a member of a scientific review panel of the National Institutes of Health. His recent publications include "Economic Hardship Across the Life Course," (with Catherine E . Ross, American Sociological Review, 1999), and "Age, Depression and Attrition in the National Survey of Families and Households" (with John R . Reynolds, Sociological Methods and Research, 2000) . Catherine E. Ross is a Professor in the Department of Sociology at the Ohio State University . She studies the effects of socioeconomic status, work, family and community on men's and women's physical and men-

150


tal health, and their sense of control versus powerlessness . Recent publications include "Does Medical Insurance Contribute to Socioeconomic Differentials in Health?" Milbank Quarterly, 2000 (with John Mirowsky); and "The Contingent Meaning of Neighborhood Stability for Residents' Psychological Wellbeing" American Sociological Review, 2000 (with John Reynolds and Karlyn Geis) .