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a Department of Agricultural Economics and Rural Sociology and Population Research Institute. The Pennsylvania State University, University Park, PA
Correspondence: Diane K. McLaughlin, PhD, 110C Armsby Building, University Park, PA 16802. E-mail: dkk{at}psu.edu.
Decision Editor: Vernon L. Greene, PhD
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Abstract |
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 TOP Abstract Elders' Sociodemographic...Status Attainment, Cumulative...Data and MethodsResultsDiscussionReferences |
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Key Words: Residential differences • Event history analysis
Despite improved economic well-being of elders as a group, poverty remains prevalent among selected subgroups of elders—minorities, women, and rural residents (McLaughlin and Jensen 1993
; Smeeding 1990; Warlick 1985). O'Rand 1996 article in this journal, and earlier work by Crystal and Shea 1990, have outlined the links between social institutions and life course processes and how these accumulate over a lifetime to place some groups of older people in relatively advantaged or disadvantaged circumstances. We refine these ideas by incorporating work history and residential context in a study of transitions into poverty among older Americans.
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Elders' Sociodemographic Attributes, Recent Life Events, and Transitions into Poverty |
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TOPAbstractElders' Sociodemographic...Status Attainment, Cumulative...Data and MethodsResultsDiscussionReferences |
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; McLaughlin and Holden 1993; McLaughlin and Jensen 1993). This cross-sectional research has been complemented by studies that have found that rates of transition into poverty during older ages are higher for women, Blacks, those with lower educational attainment, and those living in nonmetro areas (Coe 1988; Holden, Burkhauser, and Feaster 1988; McLaughlin and Jensen 1995). The same factors are found to inhibit the escape from poverty among elders (Jensen and McLaughlin 1997). These studies have provided strong evidence that residence does affect poverty, even when controls for important characteristics such as race, education, and age are included. However, most of these studies have not included work history in explaining poverty or poverty transitions.
Burkhauser, Holden, and colleagues (Burkhauser, Butler, and Holden 1991; Holden et al. 1988) have examined transitions into poverty after retirement for always-married couples compared with eventual widows or eventual widowers. Higher total preretirement income and a higher percentage of income contributed by the wife decrease the risk of becoming poor for intact couples and eventual widows. Although useful, these studies do not tell us how work history, which determines both pre- and postretirement income, influences poverty. We suggest that occupation and work history affect poverty risks beyond the wages earned. Occupations that demand more complex thinking, decision making, and intellectual challenges may not only pay better, they may better prepare people for making decisions and planning for retirement. Some occupations, or coverage by a union, may also affect the nonpay benefits of employment such as retirement and health care plans. We explore these more complex relationships by linking poverty transitions back to the work history of elders, while controlling for educational attainment and preretirement wages.
Changes in marital status, whether before or during older ages, influence the risks of becoming poor differently for men and women. These differences are partly rooted in men's and women's disparate labor force participation and wages and in noncovered spouses' access to pension and Social Security benefits. Studies of eventual widows revealed that 52.6% of women who eventually are widowed experience poverty (Holden, Burkhauser, and Myers 1986). The events most often associated with falls in income-to-needs ratios of eventual widows are the death of the spouse and declines in non-wage income (Bound, Duncan, Laren, and Oleinick 1991; Burkhauser, Holden, and Feaster 1988; Smith and Zick 1986). Share of assets in housing, wife's contribution to household income, and higher preretirement income protected eventual widows from poverty (Burkhauser et al. 1991; Holden and Burkhauser 1986; Holden et al. 1988). For eventual widowers, the wife's death reduced the risk of poverty (Burkhauser et al. 1988).
This literature has identified sociodemographic attributes and recent life events that correspond with drops in income among elders, as well as a strong relationship between higher preretirement income levels and lower poverty risks. Generally, these studies have not attempted to incorporate information about actual labor force experiences and work history before retirement. Individuals with similar preretirement incomes could have arrived at those incomes through quite different career paths. These differential career paths may influence the ability to save and plan for retirement and, ultimately, affect poverty risks.
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Status Attainment, Cumulative Advantage, and Structural Explanations for Elders' Economic Well-Being |
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TOPAbstractElders' Sociodemographic...Status Attainment, Cumulative...Data and MethodsResultsDiscussionReferences |
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; Henretta and Campbell 1976). More recently, this line of research has been expanded to argue that local opportunity structures affect the status and income attainment processes.
Those who do well during their working life do even better once they retire—advantages experienced during younger ages accumulate to increase relative well-being during older ages (Crystal and Shea 1990; Crystal, Shea, and Krishnaswami 1992; Henretta and Campbell 1976; Leon 1985a, Leon 1985b). O'Rand 1996 took this argument further to suggest that researchers need to incorporate more information on the lifetime experiences of individuals in understanding their ability to avoid poverty at older ages.
Despite a focus on income levels rather than poverty, these studies have documented a direct tie between individual characteristics, work experiences, and work history prior to retirement and economic well-being after retirement. The important implication is that education and work history directly and separately affect elders' postretirement income and thus their poverty risks. It is less clear whether these factors remain important predictors of post-retirement well-being after controlling for preretirement income.
Recent studies of earnings attainment have argued that structural characteristics of labor markets can constrain or enable individuals' labor market success. Although this approach has been applied broadly to studies of working-age people, it has seldom been used to examine poverty transitions or income levels of elders. Using the broad concept of place of residence that captures differences in labor market structure and operation, prior research has documented higher poverty prevalence, higher rates of transition into poverty, and lower probability of leaving poverty among nonmetro elders (Jensen and McLaughlin 1997; McLaughlin and Jensen 1995). This pattern is argued to result from the more limited employment opportunities available in nonmetro areas (Colclough 1988; Glasgow, Holden, McLaughlin, and Rowles 1993; McGranahan 1988), different occupational and industrial structures (Tienda 1986), lower pay for similar jobs (McLaughlin and Perman 1991), fewer opportunities for upward mobility (Doeringer 1984; McGranahan 1988), seasonality of employment (Lucas 1971; Tomaskovic-Devey 1987), and low unionization rates and few pension (Erickson 1981) or health benefits. Prior studies have documented differences in the poverty prevalence, type, and amount of income received by elders in metro and nonmetro areas (McLaughlin and Jensen 1993), providing indirect evidence of the differences in employment opportunities and how they influence elders' economic well-being.
Prior studies that have found higher rates of transition into poverty among nonmetro elders have not included detailed work history information or preretirement income. Thus, it is important to determine whether higher nonmetro poverty and transitions into poverty can be explained by lower wages or poorer employment histories of workers in nonmetro areas. Bringing these lines of research together requires that we estimate models of transitions into poverty that include work history and an indicator of preretirement wage levels. Finding that both work history and education influence poverty risks with preretirement income controlled for indicates that there are other aspects of employment and characteristics of occupations that may provide resources beyond those reflected in preretirement income alone. Additionally, it has long been argued that the higher poverty prevalence of nonmetro residents is due to poorer human capital and work opportunities. Incorporating these factors into models estimating transitions of elders into poverty will determine whether these factors completely explain the differential in poverty transition probabilities of metro and nonmetro elders.
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Data and Methods |
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TOPAbstractElders' Sociodemographic...Status Attainment, Cumulative...Data and MethodsResultsDiscussionReferences |
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). Individuals in families have been surveyed annually since 1968 with an emphasis on income, poverty, and work-related activities. The PSID data are collected annually, and the panel is one of the longest running studies available for examining income; thus, poverty transitions and geographic identifiers are available, making comparisons by residence possible. Enough older persons were included in the original sample or have aged during the panel to provide an adequate sample size of those aged 55 and over. When appropriate weighting procedures are used, the PSID sample provides representative estimates of the U.S. (nonimmigrant) population (Becketti, Gould, Lillard, and Welch 1988). Although the lack of data on immigrants to the United States in the first 20 years of the PSID is regrettable, ancillary analysis of 1990 U.S. Census Public Use Microdata sample data indicates that only 2.7% of the 1990 population aged 53 and older had immigrated to the United States since 1965. These immigrants do not constitute a large percentage of the population in this age group. We created a person-year file of transitions into poverty in which individuals contribute 1 year of data for each year they are observed in the study and are at risk of becoming poor. Among those in the PSID who were heads or wives and aged 55 and older, 7.6% were poor at age 55 or when first observed, and so were excluded from the analysis. Of those in nonmetro areas, 11.8% were poor when first observed compared with 5.6% of metro persons (see Table 1 ). We selected age 55 as the starting point so that we could examine transitions into poverty as people approach and move through retirement ages. Less orderly transitions to retirement and continued labor force participation past "typical" retirement age make the role of work history and current work effort particularly interesting for examining factors that influence transitions into poverty among older Americans. We use annual, rather than monthly, poverty ratios because the PSID reports income information annually. The resulting transition probabilities are likely to be understated compared with an analysis using monthly income information, but the factors influencing transitions into poverty would change little. We also argue that persons reporting annual incomes below poverty face much more severe income restrictions than would persons who report only 1 month in poverty, making this a more stringent criteria for becoming poor.
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We examined only first observed transitions into poverty after the person reached age 55, so those who were poor at age 55 or when first observed after age 55 were eliminated from the analysis. Second and higher transitions into poverty had much higher probabilities of occurring in a given year than did first transitions into poverty (McLaughlin and Jensen 1995), and the effects of covariates differed for first and later transitions into poverty (analysis not shown).
We restricted our analysis to elders who were heads or wives in households headed by persons aged 55 and over. The PSID contained very few elders (5.6% of the weighted sample) who were neither heads nor wives. These persons were excluded because they frequently lived in households with younger household heads who contributed significant amounts of income, thus any transitions into poverty reflect the experiences of the younger head combined with any contributions made by the elder. In addition, there was little employment information available in the PSID for those who were not heads or wives. There were 3,438 sample individuals in the PSID who met our selection criteria between 1968 and 1992 (weighted Ns). They contributed 39,952 nonpoverty person-years (weighted) to the file.
Measurement
A transition into poverty was the dependent variable of interest. Our definition of poverty closely followed the official U.S. Bureau of the Census definition. Families (or individuals in families) are in absolute poverty if the family's annual cash income is less than a minimum threshold needed for subsistence as determined by the U.S. Department of Agriculture's economy food plan (Orshansky 1965). In 1979, for example, the poverty threshold was $4,389 for a two-person family with a head aged 65 or older.
Admittedly, this poverty measure has been criticized for a number of reasons (Citro and Michael 1995; Ruggles 1990). First, it has been criticized because it is an absolute rather than a relative measure of poverty (Ruggles 1990). A relative measure of poverty bases the poverty level on the economic well-being of the population rather than on a minimum-needs level. We also estimated models using transitions into relative poverty (measured as 50% of the median income-to-needs ratio for the population), but the differences were negligible so these results are not presented.
Other criticisms stem from the measure's not incorporating the value of fungible non-cash benefits (food stamps), the housing service value of owner-occupied housing and the spending down of assets, and not adjusting for geographic cost-of-living differentials. Ruggles 1990 and Citro and Michael 1995 discussed each of these measurement issues at length, but Citro and Michael (p. 72) recommended that it is more appropriate to define resources as disposable income from all sources, including any income from assets. They recommended against adjusting income for spending down of assets and for housing service value because it is not fungible income. We did adjust income for the cash value of food stamps as reported in the PSID, to create new poverty transition indicators. Models estimated using the food-stamp–adjusted transition indicators were similar to those in Table 3 , so they are not shown.
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, is not feasible with current data). The appropriateness of the basket of goods on which cost-of-living differentials are based and their representativeness of the regular purchases of elders, the poor, or persons in different geographic regions can also be questioned. One criterion for making adjustments to income is whether the adjustments make the data more or less accurate. Until accurate cost-of-living differentials are determined, it is not clear that adjustments using the limited data available improve the accuracy of the income data.
Current sociodemographic attributes of family heads, disability status, current work effort, occupation, and work history were measured as indicated in Table 2 . The mean values for the person-year data are also presented in Table 2 . We discuss only those measures that are not self-explanatory. The value of the house is included as a proxy for the full set of financial assets of the elders. Although the 1984, 1989, and 1994 waves of the PSID included a supplement that focused on financial and other assets held, these data were not available in the 16 survey years prior to 1984. Thus, we used only the information on the value of owned housing available in every year of the PSID as a proxy for financial assets. We also included two recent life events that have been found to be important for transitions into poverty—a decline in hours worked and changes in marital status. Changes in marital status are indicated by a dummy variable in the year following the event. This is consistent with findings in earlier research that most of the largest income declines following widowhood or divorce occur in the year immediately after the death or divorce (Zick and Smith 1991). Disability status was measured as a time-varying covariate so that the separate influence of current disability status in any given year could be assessed.
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The occupational categories corresponded to those used by Featherman and Hauser 1978 and included professional and managerial workers; clerical, sales, and service workers; and operative and craft occupations. Those respondents who did not have a reported occupation were divided into two groups: those who had worked full time since age 18 and those who had never worked full time since age 18. This captured some work history information for those who were not observed working during the PSID (this generally included those who were older than age 65 in 1968 or who were never in the labor force). The reference group in this set of occupation-related variables was laborers (except farm); farmers, farm managers, and farm workers; and other occupations not mentioned.
The context within which persons live and work influences their ability to earn adequate incomes and to accumulate assets. We used metro and nonmetro residence in each year, based on the 1970 metro designation of the individual's county of residence. This measure slightly overstated the proportion of elders in nonmetro areas today because of county reclassification (McLaughlin and Jensen 1998). Because we were interested in examining whether nonmetro residence during late working age and old age influences poverty risks, the 1970 nonmetro designation is appropriate. Because respondents move, we did examine patterns of moving to or from nonmetro areas. Out of the roughly 39,000 person-years, there were 344 in which people who had lived in nonmetro areas when they were first observed later moved to metro areas and 1,559 in which those living in metro areas when first observed moved to nonmetro areas. Variables measuring these moves were included in preliminary models but were not statistically significant. We included a county wage rate as a proxy for cost-of-living differences, but it was not statistically significant in any model and so was eliminated from the final models. To capture the influence of the direction in which the national economy was headed, we used a national composite index of coincident economic indicators for June of each year. We tested models with the actual measure but found that a dummy variable that identified years during which the national economy was in decline was more predictive of transition into poverty. It was only significant in the men's model, however.
Using the person-year file, we estimated discrete time logistic regression hazard models of the first observed transition into poverty after reaching age 55 (Allison 1982, Allison 1984; Yamaguchi 1991). We used discrete time models because the income data and independent time-varying covariates were based on annual reports. We give the coefficients and standard errors in Table 3 . Following Petersen 1986, we did not make adjustments for the nonindependence of observations in the person-year data. Such an adjustment was not necessary because only records that correspond with a transition into poverty enter the calculation of the likelihood function, thus the number of observations does not affect the standard errors.
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Results |
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TOPAbstractElders' Sociodemographic...Status Attainment, Cumulative...Data and MethodsResultsDiscussionReferences |
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Characteristics from each of the variable groups influenced older men's chances of becoming poor. Focusing on the work history measures, having been employed in a professional or managerial occupation significantly decreased the probability of becoming poor such that these persons were 29% (e-1.244) as likely to become poor as laborers or farm workers (the reference group). Those who worked in a crafts or operative occupation were half as likely to make a transition into poverty, whereas men who held a job that was covered by a union were 41% as likely to become poor. Having a wife who never worked full-time decreased poverty risks, as did each year of full-time work reported for the wife. This suggests a nonlinear relationship of wives' employment and poverty transitions, with households where wives never worked less likely to become poor as were those where the wife worked more years full time. Preretirement wage of the head was not statistically significant. The work history measures were significant even after controlling for preretirement wages, and education also remained statistically significant. Having less than a high school education doubled the probability of becoming poor compared with graduating from high school even after controlling for work history and wages. Counter to findings in other studies, becoming newly widowed or divorced increased the likelihood that these older male household heads became poor. A head who worked fewer hours was 1.8 times more likely to become poor in that year than a head who worked the same or more hours.
After controlling for sociodemographic attributes, recent life events, and work history, metro men were 70% as likely to become poor as nonmetro men. The higher probability of becoming poor for nonmetro residents found in prior research was not explained by differences in preretirement wages or work history as measured in this study. We also tested two-way interactions between nonmetro residence and education and occupation. None of those coefficients were statistically different from zero. We found little evidence of any multiplicative effects of nonmetro residence for older men making a transition into poverty.
Women's Models of Transitions Into Poverty
Women's economic well-being was tightly tied to their marital status. Because of this, we estimated models separately for female household heads who experienced no marital status changes over the course of the study and for women who did experience marital status changes. Models for wives who remained married throughout the study were estimated using the wives' characteristics and only a few of the spouses' characteristics to determine if the women's attributes were predictive of transitions into poverty. Models for wives were also estimated using the head's attributes, but they are not shown because these models were essentially the same as those for the elderly men who headed households (in 92% of the person-years the men were married).
Beginning with the model for female heads with no marital status change, fewer coefficients were statistically significant in this model than in the men's model, possibly because of the smaller sample and number of events. Focusing on the work history variables, only the variable indicating that the woman did not work full time and had no occupation increased the probability of becoming poor. Looking at current work patterns, each additional hour worked reduced the probability of becoming poor, and a reduction in work hours in a given year made female heads 2.8 times more likely to become poor. Having more than a high school education made these female heads 38% as likely to become poor as women with a high school education. Single women were 2.4 times more likely to make a transition into poverty in a given year than were widows.
Even after controlling for work history and wages, female heads from metro areas were 42% as likely to become poor as female heads in nonmetro areas. Nonmetro x Education and Nonmetro x Occupation interactions revealed that metro women with less than high school educations, professional or managerial occupations, and clerical or sales occupations were especially less likely to make a transition into poverty than their nonmetro counterparts. Thus, metro residence was especially protective for female heads with these characteristics compared with nonmetro female heads with the same attributes.
The third model in Table 3 identifies characteristics associated with transitions into poverty of older women who experienced changes in marital status. These are women who were married and became widowed or divorced or who were not married and married during the survey period. Again looking at the work history variables, women in households where the head reported working in a professional or managerial occupation, who worked full time but had no reported occupation, or whose job was unionized were less likely to make a transition into poverty. This also was true if, when the woman was a wife, she reported working full time. A higher preretirement wage reported by the household head reduced these older women's poverty risks, as did more current hours worked by the head. A reduction in hours worked compared with the prior year increased the probability of becoming poor, such that women in households where the head reported working fewer hours this year than last were twice as likely to make a transition into poverty.
Women living in metro areas were 65% as likely to become poor, and those residing in the South had poverty risks 1.4 times those of women who lived in other regions of the United States. Having more than a high school education decreased, and having less than a high school education increased, the likelihood of making a transition into poverty. Newly widowed women were 2.7 times more likely to fall into poverty, and newly divorced women were six times more likely to make a transition into poverty the year following the divorce. None of the Nonmetro x Education or Nonmetro x Occupation interactions were statistically significant in explaining the transitions into poverty of this group of older women.
The final model in Table 3 shows the estimated coefficients for women who remained wives through 1992. As indicated earlier, this model includes measures of the wife's own work and educational characteristics, as well as a few indicators of the spouse's work effort, to assess how well women's own characteristics predicted their transitions into poverty as part of a married couple. Among the work history variables, wives who had worked full time but had no reported occupation and those with some full-time work experience were less likely to make a transition into poverty. The preretirement wage of the wife was also associated with lower risks of becoming poor. A wife who still worked, as evidenced by her reporting hours worked in the current year, was less likely to make a transition into poverty. Metro wives were neither more nor less likely to become poor than their nonmetro counterparts. Testing for Metro x Education and Metro x Occupation interactions revealed that metro women in clerical and service occupations, or who reported no occupation whether they worked full time or not, were less likely to make a transition into poverty than their nonmetro counterparts with similar characteristics.
Two of the questions addressed in this research are whether the metro and nonmetro differences in poverty risks can be explained by differences in the work histories of people living in these areas and whether information on work history adds explanatory power once controls for preretirement wages are included. Table 4 shows the estimated coefficients for metro residence and preretirement wage of the head as different groups of control variables were added to the models. The first model includes the basic controls for age, birth cohort, years nonpoor, economic decline, and left-censoring. Metro residence had essentially the same coefficient in each of the models, and although the coefficient in the wives' model was smaller it was not statistically different. The coefficient for metro became smaller in all but the model for female heads when controls for demographic characteristics (race, marital status, education, house value, hours worked, and working fewer hours in that year) were added. None of these changes were statistically significant, using a two-tailed test. Adding preretirement wage to the model caused only minor changes in the metro coefficient; adding the full set of work history variables resulted in the metro coefficient in the wives' model becoming insignificant, but there were no statistically significant changes in the other models.
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Discussion |
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TOPAbstractElders' Sociodemographic...Status Attainment, Cumulative...Data and MethodsResultsDiscussionReferences |
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These results suggest that using education or preretirement wage as a proxy for work history does not adequately capture the influence of work experiences on transitions into poverty. The importance of education and occupation or hourly wage combined with the influence of union coverage and years worked full time by the spouse in the men's model supported the idea that personal and labor market advantages continue in older age to help prevent moves into poverty. Older men at the lower end of the status spectrum—those who had less than a high school education and held nonunion laborer jobs—were 16 times more likely to become poor than older men at the upper end of the status spectrum—those with more than a high school education and held union, professional, or managerial jobs. Each of these attributes of older men—education, work history, and residence—should be incorporated to fully understand how a lifetime of experiences translates into higher probability of making a transition into poverty.
This research verifies the importance of including work history when attempting to understand falls into poverty among older individuals. As hypothesized, those in professional or managerial occupations while of working age were less likely to become poor in old age, but the influence of education, and for women with changes in marital status, pre-retirement wages remained. Life experiences affect poverty risks in complex ways and do not funnel exclusively through preretirement earnings. The influence of professional occupations may occur because these individuals are likely to have had high earnings throughout their careers, or it may result from the cognitive demands and decision-making skills required in these jobs that then give the individuals improved capacity for retirement planning. The advantages of union coverage for older men suggest that population subgroups with less union coverage may face poorer prospects. For both men and women, current employment protects against becoming poor, while decreasing hours worked, perhaps as part of a retirement process, increases the probability of making a transition into poverty. This indicates the tenuous economic status of elders who rely on current work effort to stay out of poverty.
The limited explanatory power of women's own sociodemographic and work history variables in these models may occur because their economic well-being was determined more by their complex marital histories and by their former spouse's occupations and work histories than by their own (especially for the widowed or divorced women; Holden and Kuo 1996). Some of these formerly married women may have been able to retain income sources and assets generated during their marriages. In addition, women's own information on occupation and work history may look nothing like that of their former spouses, which may weaken the explanatory power of work history and education. The limited influence of work history and education on poverty risks for women may also reflect the very limited diversity of labor market opportunities available to women in these cohorts. Depending on the women's marital status, models incorporating former husbands' characteristics, as well as those of the women, might do a better job of identifying the probability of making a transition into poverty.
Another explanation may lie with the special characteristics of women with no marital status change. Many women who were widowed, divorced, or single when they reached age 55 (or started the study at ages above 55) were already poor as a result of their prior marital and labor force histories (Holden and Kuo 1996; Zick and Smith 1986). Many of these women were not at risk of becoming poor in our study.
For those older widowed, divorced, and separated women who do become poor, the transition into poverty may result from declines in interest income or the gradual erosion of assets rather than any particular event that triggers a drop in income. Such gradual changes are more difficult to capture using individual human capital and work history characteristics.
Even after controlling for work history differences that we hypothesized would explain higher transitions into poverty among nonmetro elders, those higher poverty transitions remained. Work history, as measured in this study, human capital, and changes in work hours and marital status do not explain the residential difference in transitions into poverty. And residence interacted with education and occupation for female heads to further reduce metro women's probability of making a transition into poverty. Occupation x Residence interactions were also protective of metro wives, reducing transitions into poverty relative to their nonmetro counterparts. Thus, we found some evidence of differential effects of occupation and education for women's transitions into poverty in metro and nonmetro areas.
The persistence of the main effect of residence indicates that other factors that operate differently across residence and that would influence transitions into poverty need to be identified. One such factor is job mobility. We measured last reported full-time occupation and wages when available, but this did not capture a succession of job or employer changes during a career that may have influenced earnings, pensions, and benefits, nor did it capture bridge jobs as workers left the labor force (Quinn and Kozy 1996). Early labor force exit or long spells of unemployment due to plant closings or downsizing were also not measured in this study. These processes are likely to have varied by residence and may have helped explain differential transitions into poverty and some of the remaining metro/nonmetro gap. What is clear from these models is that better information on lifetime employment and earnings patterns, assets accumulated, and on the patterns by which men and women leave the labor force are essential to improving the ability to explain elders' transitions into poverty.
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Acknowledgments |
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Received for publication June 28, 1999. Accepted for publication January 28, 2000.
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