Although socioeconomic status is strongly linked to health, many research questions remain about the nature of this linkage and the contribution of socioeconomic status to racial and ethnic health differences.
Research Need 6: Clarify the degree to which socioeconomic status accounts for racial and ethnic differences in health outcomes over the life course. Some differences are not explained by socioeconomic status, or even run counter to the expected. Would better measures of education, or other aspects of status, provide clarification? Would incorporating measures of earlier socioeconomic status, perhaps status prior to immigration, explain more of the differences?
Is better modeling needed of presumed nonlinear relationships? What differences in health outcomes would still not be explained even if all these questions could be answered? The possibility that the effects of socioeconomic factors are misestimated because of differential survival by race and ethnic group also requires consideration. One complication is that dimensions of socioeconomic status are not identical in their effects on racial and ethnic health differences.
Analysts need to know the most appropriate aspect of status to consider—income, wealth, education, or occupation. Health differences by race or ethnicity will look different if one or the other indicator is controlled.
Policy makers need to know which aspect of status matters most. It makes a great deal of difference to policy whether differences are largely due to income, in which case increasing the income of the poor gains greater weight from its possible.
That identical levels on the same indicators may have different implications across groups also requires attention. Variability in the effect of socioeconomic status over the life course is an additional complication. In late life, which aspects of status have the most influence on health? Children may also acquire from their parents habits and personal characteristics that directly affect health. How intergenerational transmission of all these factors is patterned by race or ethnicity may be important, given the influence of early life factors on late-life health.
Reciprocal causation between socioeconomic status and health is an important aspect of the lifelong effect of status, and whether it operates similarly across the life course for different racial and ethnic groups needs study. Research Need 7: Identify the mechanisms through which socioeconomic status produces racial and ethnic differences in health among the elderly, and identify other factors that complicate its effects.
Socioeconomic status may have an effect because of its links to commonly recognized health behaviors, other psychosocial factors, multiple dimensions of access to health care, geographic residence, environmental conditions, and nativity and duration of residence, especially for Hispanics and other immigrant groups.
In what circumstances, or for which subgroups, are racial and ethnic differences robust to controls for such variables? Which controls are most important and why? If none of them adequately explain the effects of status, how does it come to modify health outcomes?
This analysis will require attending not just to socioeconomic variation in disease prevalence but to variation in the disease process: the onset of conditions, their severity, duration, and effects on survival Crimmins et al. The relevant mechanisms may differ at each stage. Whether macrolevel mechanisms are important is another aspect worth studying. Can aggregate effects be verified, and is income inequality the most appropriate aggregate indicator? If such aggregate effects exist, how do they work—at the local, regional, or societal levels, or even at the workplace level, and through what mechanisms?
How are such aggregate macromarkers related to other aggregate variables, such as social capital and group cohesion, and how do such factors vary by race and ethnicity? As the population of older Americans grows, it is becoming more racially and ethnically diverse. Differences in health by racial and ethnic status could be increasingly consequential for health policy and programs. Such differences are not simply a matter of education or ability to pay for health care.
For instance, Asian Americans and Hispanics appear to be in better health, on a number of indicators, than White Americans, despite, on average, lower socioeconomic status. The reasons are complex, including possible roles for such factors as selective migration, risk behaviors, exposure to various stressors, patient attitudes, and geographic variation in health care. This volume, produced by a multidisciplinary panel, considers such possible explanations for racial and ethnic health differentials within an integrated framework.
It provides a concise summary of available research and lays out a research agenda to address the many uncertainties in current knowledge. It recommends, for instance, looking at health differentials across the life course and deciphering the links between factors presumably producing differentials and biopsychosocial mechanisms that lead to impaired health.
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Similarly, Tan et al. Their results not only confirmed that the estimated subjective SES—well-being association was significantly larger almost twice than that of the objective SES—well-being association, but also illuminated differences that depended on the type of objective and subjective SES measure.
In particular, in terms of objective SES, their data showed that the meta-analytic effect size corresponding to the relationship of income with well-being was higher than that of education. However, to the best of our knowledge, no empirical investigation has yet addressed whether the subjective placement within three distinctive graphical social ladders based on income, education, and occupation, rather than within a unique social ladder that considers these three empirically distinguishing dimensions of SES together, would uniquely account for psychological well-being scores.
Adding to the growing literature on the determination of the ability of various SES indicators to predict well-being, we surmise that these new exploratory approaches could refine subjective SES measurement by elucidating the particular role of such differentiated SES components. Thus, such an approach would facilitate the gathering of comprehensible information pertaining to the need or absence thereof for further research to evaluate subjective SES by considering income, education, and occupation using separate social ladders.
A total of A snowball sampling procedure via online administration was used to recruit the participants. Specifically, before the questionnaire was distributed, undergraduate students at a university in southeastern Spain were trained in sampling methods. Afterward, they contacted potential respondents e.
Once the participants agreed to participate in the study, they were given access to the online survey. At the beginning of the survey, the participants received information that emphasized the principles of confidentiality and anonymity in this research, their voluntary participation, and the estimated duration. In addition, they were given the e-mail address of one of the researchers in the case they needed to resolve any issues arising from their participation.
After signing an informed consent form, the participants completed the questionnaire. Finally, the undergraduate students in charge of distributing the online survey among potential respondents received partial academic credit in exchange for their participation.
The study was approved by the ethical committee of the southeastern Spanish university and carried out in compliance with the ethical standards of the Declaration of Helsinki. It consists of 29 items rated on a 6-point Likert scale ranging from 1 strongly disagree to 6 strongly agree that covered six subscales: self-acceptance e.
High scores indicated high levels of psychological well-being. The PWBS has six subscales grouped into a second-order factor called global psychological well-being Ryff and Keyes, Participants were asked to select the rung that represented their position in the social hierarchy relative to others in society in terms of income, educational level, and occupation. High numbers were indicative of higher placement on this social ladder.
Thus, respondents were presented three adapted rung social ladders, one for each SES indicator: a Income ladder. Participants were asked to indicate the rung of this ladder on which they believed they stood, considering that individuals at the top of the ladder would have the most income-money, whereas those at the bottom would have the least income-money; b Education ladder.
Participants were asked to select the rung of this ladder on which they perceived they stood, taking into account that people at the top of the ladder would have the most education, whereas those at the bottom would have the least education; c Occupation ladder.
Participants had to select the rung of this ladder on which they perceived they stood, considering that individuals at the top of the ladder would have the best jobs, whereas those at the bottom would have the worst jobs or no job.
Participants indicated the highest level of education they had completed. Educational level was coded into eight categories, from 1 primary school to 8 doctoral degree. Participants indicated which professional occupation best described the type of work they do European Social Survey, In this research, occupational status was coded into ten categories, from 1 unemployed to 10 technical professional occupations.
First, frequency distribution analyses and reliabilities were obtained. Second, Pearson product-moment correlations were performed to test the relationships among the objective and subjective SES indicators and the various psychological well-being dimensions. Before we conducted the hierarchical regression analyses, age, and objective and subjective SES measures were standardized. Then, as an initial check, we confirmed that the collinearity statistics did not exceed the recommended values Akinwande et al.
Afterward, we performed the hierarchical regression analyses, in which we entered common sociodemographic factors i. Then, we added objective SES indicators as predictors in Step 2 method: enter. Lastly, we entered the new proposed ladders for income, educational level, and occupation in Step 4 method: enter to estimate their added value in explaining variance in the criterion variables and to determine their potential unique contribution to psychological well-being above and beyond demographics, objective SES, and the MacArthur SSS scale.
We separately introduced self-acceptance, positive relationships with others, autonomy, environmental mastery, purpose in life, personal growth, and global psychological well-being as criteria throughout each regression analysis. The frequency distribution of the subjective SES indicators is represented graphically in Figure 1. As this figure illustrates, some differences in the distribution rate of the traditional MacArthur SSS scale compared to each novel ladder for income, education, and occupation, as well as between these new ladders, can be observed.
Responses ranged from 1 to 10 for all ladders. Pearson correlations among objective and subjective SES indicators are given in Table 1. Objective SES indices i. In addition, the traditional MacArthur SSS scale was also positively related to the novel income, education, and occupation ladders.
Nonetheless, the coefficients were lower than 0. This pattern of correlations seems to indicate that although the traditional MacArthur SSS scale and the new proposed ladders for income, educational level, and occupation undoubtedly share components, they also differ.
This supports the existence of differences between such subjective SES measures. Finally, the income, education, and occupation ladders exhibited significant and positive weak-to-moderate relationships with objective SES factors i. Pearson correlations among objective and subjective SES indicators and psychological well-being scales are given in Table 2. However, this measure of subjective SES was positively related to self-acceptance, positive relationships with others, environmental mastery, purpose in life, personal growth, and global psychological well-being.
Table 2. Bivariate correlations of objective and subjective SES indicators with psychological well-being. Nevertheless, these associations were stronger for the subjective income ladder. A different pattern of associations was found for occupation as an objective indicator of SES.
Table 3 through 9 give the findings from the set of multiple hierarchical regression analyses predicting each component of psychological well-being as well as its general indicator from demographic factors i. Thus, the higher the income, the greater the levels of self-acceptance. When we focused on the income, education, and occupation ladders see Table 3 , our results showed that, after controlling for demographics, objective metrics of SES i.
Thus, participants who placed themselves higher on the MacArthur SSS scale reported increased positive relationships. Importantly, the addition of the new social ladders i. Therefore, higher income was indicative of greater scores on environmental mastery. Unlike the cases described above, we found none of the objective indices of SES i. Similar to the previous dimensions, our results corroborated the predictive utility of the occupation and education social ladders regarding purpose in life above and beyond demographics, indicators of objective SES, and the traditional MacArthur SSS scale.
In line with abovementioned results concerning the new proposed ladders for income, educational level, and occupation, our results yielded a significant contribution of the occupation and education social ladders to the prediction of personal growth.
Table 9. Hierarchical regression analysis predicting global psychological well-being. Lastly, we graphically illustrate the standardized beta coefficients of the different measures of objective and subjective SES in Figure 2. Figure 2. Visual comparison of standardized beta coefficients of the various objective and subjective SES measures.
Prior research has widely converged on the notion that SES is a rather complex and multifaceted construct determined by relatively independent objective indicators e. In an attempt to address this gap, the present research revisited subjective SES measurement by a proposing a novel method of assessing subjective SES, namely an adaption of the MacArthur SSS scale, resulting in three independent ladders based on income, educational level, and occupation, and b empirically testing the role of these three subjective SES measures in psychological well-being while examining in conjunction objective SES and the traditional MacArthur SSS scale.
Hence, this investigation provides the first preliminary data on the empirical contribution of distinctive social ladders focused on income, education, and occupation, as an innovative and broader way of evaluating the effects of subjective SES on various components of psychological well-being.
In aligning with notions recognized in earlier studies, our results clearly confirmed that subjective assessments of SES are better predictors of well-being-related aspects than are objective SES metrics e. Indeed, among the various components of psychological well-being i. In particular, higher educational level predicted greater scores on positive relationships, and higher income predicted increased autonomy.
However, it is important to mention that the education ladder, compared to income, exhibited a higher predictive utility regarding autonomy. Moreover, the traditional MacArthur SSS scale was not a significant predictor of global psychological well-being. In particular, our findings indicated that the novel indicators of subjective SES were stronger predictors of all measures of psychological well-being except self-acceptance than the conventional MacArthur SSS scale.
In addition, their inclusion in the hierarchical regression analyses significantly accounted for incremental criterion variance except in positive relationships beyond demographics, objective SES, and the MacArthur SSS scale. Interestingly, of these new social ladders, the one linked to income levels, did not emerge as a significant predictor of any of the criterion indicators. Taking into account that income, when compared to other objective facets of SES e.
However, according to our data, the education and occupation ladders were identified as consistent predictors of psychological well-being. The education ladder was significantly related to all indicators of psychological well-being except self-acceptance and environmental mastery, which were explained better by the occupation ladder.
These results concerning the education ladder could help to elucidate the role of this facet of SES in psychological well-being. Although the contribution of education to well-being has been shown to be relatively limited when it is objectively assessed Tan et al.
Previous works have posited that education may precede higher income levels or more prestigious occupations Snibbe and Markus, Furthermore, the role of the occupation ladder was almost comparable to that of the education ladder. In particular, we found the occupation ladder predicted all measures of psychological well-being except positive relationships and autonomy, which were explained better by the education ladder. In this case, a similar interpretation to that proposed for the education ladder might be extrapolated for the occupation last.
However, the characteristics of the socioeconomic context should not be overlooked. Specifically, this research was conducted in southeastern Spain. Together, our findings revealed that the novel education and occupation ladders are unique predictors of psychological well-being beyond objective SES, and the traditional MacArthur SSS scale.
Although the current findings allow open up a new strand of research in the psychological literature on SES and well-being, some limitations should be acknowledged while suggesting further research directions. First, it is worth mentioning that this study used non-probabilistic sampling i.
Overall, future research should use probabilistic sampling procedures to collect samples that are as representative as possible. In addition, we followed a non-experimental methodology in this research. Hence, causal inferences regarding our findings must not be made. Thus, further research should use experimental or longitudinal designs to determine the potential causal effects of the proposed subjective SES measures on psychological well-being.
The associations between household income and individual income in relation to late-life health were similar. Unfortunately, we were not able to assess wealth. Wealth is an indicator of financial resources accumulated over the life course including inheritances , and the patterning of wealth in old age might therefore differ substantially from the patterning of incomes.
In , Paul Lazarsfeld found that indices constructed with different information that is, indices based on whether a person paid income tax, owned a car, or whether a person had a telephone in his or her home resulted in approximately the same distribution of economic status throughout the population.
He concluded that it is both theoretically and empirically probable that indices of economic status are interchangeable [ 52 ].
Geyer et al. They found that each indicator had an independent effect on the outcomes but that the effect sizes and strengths of the associations varied by indicator. Education was the strongest predictor for diabetes and income strongest predicted all-cause mortality, while results were mixed for myocardial infarction morbidity and mortality.
Thus, they argued that indicators of SES are not interchangeable in relation to health. In contrast, our results showed no independent effects by education, social class or occupational complexity on health in old age. Only income was independently associated with health in old age. In addition, Geyer and colleagues found that different indicators were differently associated to different outcomes.
In contrast, our results showed that income was most strongly associated to all health outcomes, except compared with the SES-index. Torssander and Erikson [ 33 ] found similar results as Geyer and colleagues in relation to mortality risk in the Swedish population aged 35— Each indicator of SES was clearly associated with risk of death for both women and men.
On the other hand, the authors argue that if each indicator has an independent function, using them to map a latent construct would result in a loss of information [ 33 ]. This idea, that using one of these indicators interchangeably to indicate a latent concept of SES may result in a loss of information relevant for social stratification and health and policy implications, have been supported by others [ 6 , 53 ].
Few studies have explored this issue in relation to health in old age, but Avlund et al. Our results show that income was the only indicator independently associated to late-life health, and that the indicators are otherwise statistically interchangeable. The indicators had approximately the same association to the outcomes, and their contribution to the model fits were comparable.
In line with previous studies [ 14 , 15 , 16 ], we also found that the most direct measure of economic differences in this case, income was most strongly and robustly associated with adverse health in old age. A novel contribution of our study was the introduction of occupational complexity as an alternative indicator of SES in studies of health inequalities in old age.
However, our results did not suggest that occupational complexity was a stronger determinant of late-life health than education, social class, income or a composite measure of SES the SES-index. Further research is needed to confirm the robustness of these findings and to explore the causal mechanisms underlying the associations between different aspects of socioeconomic status and late-life health.
This study investigated the most commonly used indicators of SES in health research in relation to three health outcomes in old age.
We also included a less traditional measure strongly associated with SES occupational complexity , and a composite measure based on several indicators of SES the SES-index. The study contributes to the literature by doing an in depth investigation of how the SES indicators relate to each other, and to late-life health, and by testing the predictive value of two novel measures of SES.
In sum, our results suggests that income explain more variance in late-life health than any of the other SES indicators, with a predictive capacity that is equal, or even better, than that of a composite measure including a range of indicators. In sum, our results suggests that if the primary objective of including an indicator of SES, in studies of health in old age, is to merely adjust the model for socioeconomic differences income may be the preferred choice. If, on the other hand, the primary objective of the study is to examine health inequalities or the mechanisms that drive health inequalities in old age per se, then the choice of indicator should be made on the basis of a theoretical model that considers the unique properties of the different indicators.
Health inequalities among older adults in Sweden — Eur J Pub Health. Article Google Scholar. Fors S, Thorslund M. Enduring inequality: Educational disparities in health among the oldest old in Sweden Int J Public Health. Article PubMed Google Scholar. Socioeconomic inequalities in morbidity among the elderly; a European overview. Soc Sci Med. Hoffmann R. Socioeconomic differences in old age mortality. Education, income, and occupational class cannot be used interchangeably in social epidemiology.
Empirical evidence against a common practice. Community Dent Health. Google Scholar. Goldthorpe JH. Analysing social inequality: a critique of two recent contributions from economics and epidemiology.
Eur Sociol Rev. Lifelong socio economic position and biomarkers of later life health: Testing the contribution of competing hypotheses. Class, occupation, wages, and skills: The iron law of labor market inequality.
Emerald Group Publishing Limited. Vertical differentiation of work tasks: conceptual and measurement issues. Empir Res Vocat Educ Train. Class clues. Mirowsky J, Ross CE. Education, social status, and health. New York: Transaction Publishers; Dupre ME. J Health Soc Behav. Social position and health in old age: the relevance of different indicators of social position.
Scand J Soc Med. Optimal indicators of socioeconomic status for health research. Am J Public Health. Grundy E, Holt G. The socioeconomic status of older adults: How should we measure it in studies of health inequalities? J Epidemiol Community Health. On sociology. Illustration and retrospect. Standford: Stanford University Press; Rose D, Harrison E. Social class in Europe: An introduction to the European socio-economic classification. NewYork: Routledge; The economic basis of social class.
Class and poverty: cross-sectional and dynamic analysis of income poverty and life-style deprivation. London: Routledge; Do socioeconomic health differences persist in nonagenarians? The association between income and life expectancy in the United States, Rehnberg J, Fritzell J.
The shape of the association between income and mortality in old age: A longitudinal Swedish national register study. SSM-Popul Health. Marmot M. The influence of income on health: views of an epidemiologist. Health Aff. Aday LA, Andersen R. A framework for the study of access to medical care. Health Serv Res. Andersen RM. Revisiting the behavioral model and access to medical care: does it matter?
Muennig P. Health selection vs. Health Care Poor Underserved. Measuring socioeconomic position in health research. Br Med Bull. J Aging Health. Education, cumulative advantage, and health. Ageing Int. Dannefer D. Ben-Shlomo Y, Kuh D.
A life course approach to chronic disease epidemiology: conceptual models, empirical challenges and interdisciplinary perspectives. Torssander J, Erikson R. Stratification and mortality—A comparison of education, class, status, and income.
Sex differences in the association of physical function and cognitive function with life satisfaction in older age: The Rancho Bernardo Study. Mobility limitations in the Swedish population from to age, gender and social class differences. Aging Milan Italy. CAS Google Scholar. Are public care and services for older people targeted according to need?
Applying the behavioural model on longitudinal data of a Swedish urban older population. Eur J Ageing. Mental health symptoms in relation to socio-economic conditions and lifestyle factors—a population-based study in Sweden. BMC Public Health. Psychological distress and risk of peripheral vascular disease, abdominal aortic aneurysm, and heart failure: pooling of sixteen cohort studies. Association between psychological distress and mortality: individual participant pooled analysis of 10 prospective cohort studies.
Proneness to psychological distress and risk of Alzheimer disease in a biracial community. Fritzell J, Lundberg O. Health inequalities and welfare resources: continuity and change in Sweden.
Bristol: Policy Press; Int J Epidemiol. Erikson R, Goldthorpe JH. The constant flux: A study of class mobility in industrial societies. Worker functions and work traits for the U. Miller Ed. Mood C. Logistic regression: Why we cannot do what we think we can do, and what we can do about it. Williams R. Stata J.
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