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European Journal of Ageing

, Volume 16, Issue 1, pp 121–128 | Cite as

It’s “the Joneses”: the influence of objective and subjective socioeconomic status on subjective perceptions of aging

  • Alaina N. English
  • Jennifer A. BellingtierEmail author
  • Shevaun D. Neupert
Original Investigation

Abstract

Positive subjective perceptions of aging predict various well-being, physical health, mental health, and longevity outcomes. Thus, it is important to consider what factors contribute to their formation. Socioeconomic status (SES) has been hypothesized to be one such factor, but past research has been mixed. We propose that subjective assessments of SES may better predict subjective perceptions of aging than traditional objective measures (e.g., income and education). The purpose of this study was to examine the effects of objective and subjective indicators of SES on subjective perceptions of aging. Participants (n = 296) from the Mindfulness and Anticipatory Coping Everyday study were recruited with a Human Intelligence Task on Amazon’s Mechanical Turk. Participants reported on their SES (i.e., income, education, and subjective social status) and subjective perceptions of aging (i.e., attitudes toward own aging, subjective age, and awareness of age-related gains and losses). Data were analyzed via hierarchical multiple regression with demographic and health variables entered first, followed by income and education, then subjective social status. Results demonstrated that although objective measures of SES did not contribute to predicting subjective perceptions of aging, those who rated themselves subjectively higher in their community social standing were more likely to possess positive aging attitudes, younger subjective ages, more awareness of age-related gains, and fewer awareness of age-related losses. These findings suggest that perceptions of doing better than one’s neighbors (i.e., “the Joneses”) may matter more for understanding subjective perceptions of aging than objective indicators of social status.

Keywords

Subjective age Socioeconomic status Subjective social status Aging attitudes 

Introduction

Older age does not necessarily entail feeling worse. Instead, well-being depends, in part, on individuals’ subjective perceptions of the aging process (Mock and Eibach 2011). Positive aging attitudes predict beneficial outcomes, whereas more negative attitudes take a toll on an individual’s health, physically and cognitively (Levy and Myers 2004). Additionally, positive subjective perceptions of aging are associated with additional years of longevity (Levy et al. 2002). Individuals who feel better about their aging are more likely to take better care of their bodies, practice preventive medicine, and have a healthier diet (Levy and Meyers 2004). When aging attitudes are less favorable, older subjective age predicts overall lower life satisfaction (Mock and Eibach 2011). Negative outcomes that have been shown as a result of less favorable attitudes include overall impaired cognition (Levy 1996; Levy and Langer 1994), health problems (Levy et al. 2000, 2009), and increased reactivity to stressors (Bellingtier and Neupert 2016). Ultimately, there is a reduced desire to live (Levy et al. 1999) and greater mortality rates (Levy et al. 2002) with negative perceptions of aging. Thus, subjective perceptions of aging are important for subsequent well-being, but there are gaps in our knowledge about predictors that precede these perceptions.

Subjective perceptions of aging include multiple related constructs. Attitudes toward own aging (ATOA) refer to feelings and beliefs regarding one’s individual aging process (Lawton 1975). Subjective age refers to how old or young individuals seem to themselves (Kastenbaum et al. 1972). Awareness of age-related change (AARC) is a relatively new construct that captures “all those experiences that make a person aware that his or her behavior, level of performance, or ways of experiencing his or her life have changed as a consequence of having grown older (i.e., increased chronological age)” (Diehl and Wahl 2010, p. 340). This awareness can reflect awareness of gains associated with aging (e.g., feeling more free to be oneself) as well as losses associated with aging (e.g., feeling that the pace of one’s thoughts has slowed with age). Past work has found small to moderate correlations among these constructs, suggesting that they tap related, but not identical constructs (Brothers et al. 2017).

Diehl et al. (2014) proposed a framework organizing these and other subjective aging constructs in relation to their predictors and outcomes (Diehl et al. 2014). The subjective aging constructs are arranged on a spectrum from preconscious/implicit (e.g., ATOA) to conscious/explicit (e.g., subjective age and AARC). Among the hypothesized influences are developmental and cultural influences, and psychological and socioeconomic resources. In regards to the latter, they argue that individuals take their material and social resources into account when thinking about their own aging (Diehl et al. 2014). As this is a conscious assessment, it is thought to have the largest influence on the conscious/explicit end of the spectrum of subjective aging constructs (i.e., subjective age and AARC), although the relationship has been examined on the preconscious/implicit end of the spectrum as well (Hickey et al. 2010). However, previous research on socioeconomic resources has not simultaneously examined multiple subjective aging constructs, and thus we do not yet know if socioeconomic resources have a differential impact on these constructs and if the preconscious/implicit vs. conscious/explicit distinction helps to understand any differences that emerge.

Previous work suggests that those with higher incomes do indeed report a higher quality of life, which is associated with more positive aging attitudes (Hickey et al. 2010). Lower income has also been associated with lower levels of psychological well-being (Arendt 2005), lower levels of satisfaction with aging (Lucchetti et al. 2008), and more positive perceptions of the aging experience (Steverink et al. 2001). However, some previous work suggests that income may not influence subjective perceptions of aging. Connidis (1989) found no association between income and liking aspects of old age, disliking aspects of old age, or worrying about growing older. Rubin and Berntsen (2006) found minimal effects of socioeconomic status (measured as education and income) on individuals’ subjective ages (i.e., how old one feels to him or herself). Similarly in two studies, Henderson et al. (1995) found no association between income and subjective ages (i.e., felt-age, look-age, interests-age, do-age), suggesting that income may not always be a key factor used to subjectively evaluate aging.

The mixed findings regarding the impact of economic status on perceptions of aging may reflect the general challenges of using income and education to measure SES. Specifically, there are local variations in income required to meet acceptable standards of living and discrepancies between income earned and income available for spending or accumulated wealth (Braveman et al. 2005). Additionally, the same education level may have different social implications depending on one’s ethnicity, gender, and age (Braveman et al. 2005) or hold various weights, depending on various demographic locations and incomes in that area (Andersson 2018). In addition, education does not necessarily indicate specific job training or networking connections that may influence SES (Duncan et al. 2002).

One method to circumvent the challenges of objective SES indicators is to allow participants to self-report their social standing in reference to those in their community (i.e., the MacArthur Scales of Subjective Social Status). The expression “keeping up with the Joneses” refers to the tendency of individuals to compare themselves to their neighbors as a way to judge their social standing. This method of capturing SES asks individuals to identify their position on a pictorial representation of a ladder where the bottom rung indicates the lowest social standing and the tenth or top rung indicates the highest social standing in their community (Adler et al. 2000). This method allows participants to consider multiple factors such as current occupation, income, wealth, education, and location that may influence their current social standing (Andersson 2015). Importantly, it allows participants to consider their status in regards to other members of their community. For example, the income or education that indicates high status in one neighborhood may be considered low status in another. This may be especially important for older adults as aging can denote a loss of social status (Weiss and Weiss 2016). Thus, subjective SES allows for a comparison with “the Joneses” that is missing in objective indicators (Wolff et al. 2010).

Research using subjective social status ladders has found that after controlling for objective measures of SES (e.g., income and education) subjective SES independently and significantly predicted self-rated health and engagement with physical activities (Hu et al. 2005). Furthermore, lower subjective SES is associated with higher incidents of depression, diabetes, and cholesterol (Demakakos et al. 2008), as well as higher levels of anxiety and stress (Ghaed and Gallo 2007). We propose that in line with these past findings regarding physical and mental health, subjective perception of social status may be an important predictor of subjective perception of aging above and beyond objective indicators of social status.

In the current study, we simultaneously assessed objective and subjective indicators of SES in order to assess their ability to predict four indicators of subjective perceptions of aging (i.e., ATOA, subjective age, AARC gains, and AARC losses). Our hypotheses were as follows:

H1:

Income and education would be significantly associated with subjective perceptions of aging.

H2:

Individuals’ subjective social status would explain significant additional variance beyond the effects of income and education.

H3:

Subjective SES would have a larger effect size when modeling the conscious/explicit constructs (i.e., subjective age, AARC gains, and AARC losses) and a smaller effect size when modeling the preconscious/implicit construct (i.e., ATOA).

Method

Participants and procedures

A total of 296 participants from the Mindfulness and Anticipatory Coping Everyday (MACE) study (Neupert and Bellingtier 2017) were recruited from a Human Intelligence Task (HIT) entitled “Well-Being Survey for adults 60+” on Amazon’s Mechanical Turk (mTurk). Recruitment on mTurk involved posting a HIT that utilized filters that restricted participants to adults living in the USA. We requested participants with a minimum age of 60 years which we confirmed by comparing their numerical age to birth date which were reported on separate screens and separated by other study questions. When participants clicked the link to the survey, they were redirected to Qualtrics where they provided informed consent and then completed the survey. Participants were compensated $1.

The participants were near evenly divided between men (n = 149) and women (n = 147). The majority (86%) identified as White. The mean reported income was 5.76 which corresponds to approximately $40,000–$59,999, and the mean level of education was 8.07 which corresponds to some college or an associate’s degree.

Measures

Aging attitudes were assessed using the attitudes toward own aging (ATOA) subscale of the Philadelphia Geriatric Center Morale Scale (Lawton 1975). The measure includes five items assessing overall views of the aging process (e.g., “I am as happy now as I was when I was younger”). Items were answered on a 5-point scale from “1—does not apply to me” to “5—applies very well.” When averaging the scores, obtaining a higher score indicated that the participant had an overall more positive attitude toward aging (Cronbach’s α = .75).

Subjective age was measured with 1 item assessing felt-age (cf. Kastenbaum et al. 1972). We asked, “Many people feel older or younger than they actually are. What age do you feel most of the time?” Participants indicated their response by typing in the appropriate number of years. Proportional discrepancy scores were created by subtracting chronological age from subjective age than dividing by chronological age (Rubin and Berntsen 2006). These scores represent what percent younger (negative scores) or older (positive scores) an individual subjectively feels.

Awareness of age-related change (AARC; Diehl and Wahl 2010) was assessed via a short 20-item version of the AARC Questionnaire (AARC-20; Brothers et al. 2017). There were two subscales reflecting perceptions of age-related gains (e.g., “With my increasing age, I realize that I have more freedom to live my days the way I want”) and age-related losses (e.g., “With my increasing age, I realize that I have to limit my activities”). Questions were answered on a 5-pt Likert scale ranging from “1—not at all” to “5—very much.” Sum scores were created for each subscale with higher scores reflecting greater perceptions of age-related gains (Cronbach’s α = .88) and losses (Cronbach’s α = .89), respectively.

Subjective social status was measured with the MacArthur Scales of Subjective Social Status (Adler et al. 2000). The community ladder was used. Participants indicated which rung of a 10-rung ladder corresponded most to their economic standing in comparison with other individuals within their community (Rung 1 = lowest standing; Rung 10 = highest standing).

Income Participants reported their current total household income per year in US dollars. There were 11 options ranging from “between $0 and $11,999” and “$100,000 or more.”

Education Participants were asked to report the highest grade of school or year of college they completed. Response options ranged from “1— no school/some grade school” to “12—PhD, MD, or other professional degree.”

Covariates We included the following covariates as they have previously been associated with SES and subjective perceptions of aging: age, gender (coded as men = 1, women = 0), race (coded as White = 1, all others = 0), and health (Schöllgen et al. 2010; Wurm et al. 2008).1 Health was assessed using the chronic conditions checklist from the National Survey of Midlife Development in the USA (MIDUS; Brim et al. 1996; Ryff et al. 2006). Participants indicated “yes” or “no” to experiencing or being treated for any of 29 chronic health problems (e.g., asthma, arthritis, and migraine headaches) in the past 12 months. Sum scores were created by totaling the number of “yes” responses.

Results

Table 1 displays the descriptive statistics and correlations for the study variables. Participants reported feeling 19% younger than their chronological age (M = − 0.19, SD = 0.17), similar to previous research recruiting US adults via community advertising (M = − 0.19, SD = 0.14, Brothers et al. 2017) and in large representative surveys such as the Midlife in the USA (MIDUS; wave 1 M = − 0.15, SD = 0.19 and wave 2 M = − 0.17, SD = 0.17, Stephan et al. 2015). Our participants’ ATOA was near the middle of the scale (M = 3.32, SD = 0.86) indicating an average to slightly positive view of aging, similar to our previous research with a community-recruited sample (M = 3.77, SD = 0.76; Bellingtier and Neupert 2016) and similar to previous US community samples (Brothers et al. 2017). Our participants reported more awareness of age-related gains (M = 35.81, SD = 7.97) than losses (M = 23.67, SD = 8.02) similar to previous US community samples (Brothers et al. 2017). The bivariate relationships indicate that subjective SES was positively correlated with education r (294) = .23, p < .01, and income r (294) = .47, p < .01, and that education and income were positively correlated r (294) = .37, p < .01. Higher levels of education were associated with the perception of fewer AARC losses r (294) = − .16, p < .01, but none of the other subjective aging constructs. Higher income was associated with more positive aging attitudes r (294) = .13, p < .05, but none of the other subjective aging constructs. Subjective SES was associated with more positive ATOA r (294) = .27, p < .01, younger subjective ages r (294) = − .25, p < .01, and the perception of fewer AARC losses r (294) = − .12, p < .05, but not the perception of AARC gains r (294) = .10, p = .09.
Table 1

Descriptive statistics for study variables

Variable

M

SD

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

1. Age

64.67

4.36

          

2. Gender

0.50

0.50

.004

         

3. Race

0.86

0.35

− .15**

− .03

        

4. Health

3.47

3.56

.02

− .19**

.04

       

5. Education

8.07

2.21

.06

.08

− .01

− .18**

      

6. Income

5.76

2.97

− .01

.10

− .02

− .18**

.37**

     

7. Subj. SES

5.39

1.91

.12*

.07

− .03

.06

.23**

.47**

    

8. ATOA

3.32

0.86

.02

.04

.01

− .18**

.08

.13*

.27**

   

9. Subjective age

− 0.19

0.17

− .05

.01

.004

.07

− .07

− .08

− .25**

− .40**

  

10. AARC gains

35.81

7.97

.02

− .24**

.10

.09

− .07

− .02

.10

.40**

− .17**

 

11. AARC losses

23.67

8.02

.04

− .06

− .13*

.31**

− .16**

− .08

− .12*

− .67**

.30**

− .15**

The reported income mean of 5.76 corresponds to approximately $40,000–$59,999. Gender is coded as “0 = female” and “1 = male.” Race is coded as “1 = White” and “0 = all others”

ATOA attitudes toward own aging, Subj. SES subjective social status

**Correlation is significant at the .01 level (2-tailed). *Correlation is significant at the .05 level (2-tailed)

To address hypotheses H1 and H2, we conducted four hierarchical multiple regressions (see Table 2). In Step 1, we entered the demographic and health variables. In Step 2, we entered income and education, followed by subjective SES in Step 3.
Table 2

Predictors of subjective perceptions of aging constructs

Variable

ATOA

Subjective age

AARC gains

AARC losses

R 2

β

R2

R 2

β

R2

R 2

β

R2

R 2

β

R2

Step 1

.03°

  

.008

  

.07**

  

.114**

  

 Age

 

.03

  

− .05

  

.03

  

.01

 

 Gender

 

.01

  

.025

  

− .23**

  

− .00

 

 Race

 

.02

  

− .01

  

.10°

  

− .14*

 

 Health

 

− .18**

  

.08

  

.04

  

.31**

 

Step 2

.04°

 

0.01

.014

 

.006

.07*

 

.003

.127**

 

.013

 Age

 

.03

  

− .05

  

.03

  

.02

 

 Gender

 

<.001

  

.03

  

− .23**

  

.000

 

 Race

 

.02

  

− .01

  

.10°

  

− .14*

 

 Health

 

− .16**

  

.06

  

.033

  

.29**

 

 Income

 

.09

  

− .05

  

.03

  

.02

 

 Education

 

.02

  

− .04

  

− .055

  

− .12*

 

Step 3

0.11**

 

0.07**

.076**

 

.062*

.09**

 

.02**

.148**

 

.021**

 Age

 

− .01

  

− .01

  

.02

  

.04

 

 Gender

 

− .02

  

.04

  

− .24**

  

.01

 

 Race

 

.02

  

− .01

  

.10°

  

− .14*

 

 Health

 

− .21**

  

.11°

  

.01

  

.32**

 

 Income

 

− .05

  

.08

  

− .04

  

.09

 

 Education

 

− .01

  

− .01

  

− .07

  

− .11°

 

 Subj. SES

 

.31**

  

− .29**

  

.15*

  

− .17**

 

N =296. °p < .10 *p < .05 **p < .01. Gender is coded as “0 = female” and “1 = male.” Race is coded as “1 = White” and “0 = all others”

ATOA attitudes toward own aging, Subj. SES subjective social status

In the ATOA model, after controlling for age, gender, race, and health in Step 1, the addition of income and education in Step 2 did not add significantly to the variance explained (Step 1 R2 = 3%, Step 2 R2 = 4%). However, the addition of subjective SES in Step 3 significantly added to the explained variance (Step 3 R2 = 11%), indicating that those who perceived themselves to be of higher status in their communities had more positive views of aging.

Similarly in the subjective age model, the addition of income and education in Step 2 did not significantly add to the explained variance (Step 1 R2 = 1%, Step 2 R2 = 1%); however, the addition of subjective SES in Step 3 significantly added to the explained variance (Step 3 R2 = 6%), indicating that those who perceived themselves to be of higher status in their communities had younger subjective ages.

In the AARC gains model, the addition of income and education in Step 2 did not significantly add to the explained variance (Step 1 R2 = 7%, Step 2 R2 = 7%); however, the addition of subjective SES in Step 3 significantly added to the explained variance (Step 3 R2 = 9%), indicating that those who perceived themselves to be of higher status in their communities also perceived higher levels of AARC gains.

The AARC loss model followed a similar pattern regarding a lack of significant change in R2 from Step 1 to Step 2 (Step 1 R2 = 11%, Step 2 R2 = 13%), but higher education was independently associated with the perception of fewer AARC losses. The addition of subjective SES in Step 3 significantly increased the explained variance (Step 3 R2 = 15%), indicating that those who perceived themselves to be of higher status in their communities perceived fewer AARC losses.

Thus, all four models are inconsistent with H1 (i.e., income and education do not predict subjective perceptions of aging), but are consistent with H2 (i.e., subjective SES significantly predicts subjective perceptions of aging after controlling for age, gender, race, health, income, and education).

To test H3 that subjective SES would have a larger effect size when modeling the conscious/explicit constructs (i.e., subjective age, AARC gains, and AARC losses) and a smaller effect size when modeling the preconscious/implicit construct (i.e., ATOA), we computed 95% confidence intervals (Soper 2018) around the observed R2 change values in Step 3 of Table 2. When confidence intervals do not overlap, we can conclude that the R2 values are significantly different from each other. When confidence intervals do overlap, we can conclude no evidence for significant differences between the R2 values. The results indicate that all of the confidence intervals overlap ([ATOA model 95% CI: .016 to .12] [subjective age model 95% CI: .01 to .11] [AARC gains model 95% CI: − .01 to .05] [AARC losses model 95% CI: − .01 to .05]). Thus, in contrast to H3, the observed R2 values are not significantly different from one another.

Discussion

The goal of this study was to examine the relative predictive value of subjective and objective measures of SES on subjective perceptions of aging. We considered four related constructs: ATOA, subjective age, AARC gains, and AARC losses (Brothers et al. 2017). The overall pattern of relationships was similar for each construct. Specifically, the addition of the objective SES indicators income and education did not contribute significant variance to any of our models, whereas the addition of subjective SES significantly contributed to each model. In contrast to Hypothesis 1, but in line with Hypothesis 2, our findings suggest that it is not the objective amount of money or education one possesses, but how one perceives their social status in relationship to their community (i.e., “the Joneses”) that best predicts subjective perceptions of aging.

The small to moderate correlations between our subjective and objective measures of SES suggest that these measures cannot easily be substituted for one another (Braveman et al. 2005). Our findings can help address previous concerns about the measurement and utility of education and income as SES markers (Duncan et al. 2002) by providing evidence of the predictive utility of a simple single-item assessment of subjective SES (Adler et al. 2000). In line with previous research demonstrating the advantage of subjective SES for predicting physical and mental health (Demakakos et al. 2008; Ghaed and Gallo 2007), our findings indicate that subjective SES may also be useful for researchers predicting subjective perceptions of aging.

Our findings could also be interpreted as reflecting the “relative income hypothesis” (Clark et al. 2008) which suggests that happiness is determined not by level of income, but by how much income one possesses relative to one’s community. Past research does find that those with household incomes above that typical for their county report higher levels of happiness (Firebaugh and Schroeder 2009). Our findings suggest that the subjective perception of higher relative income matters for understanding subjective perceptions of aging. Future research should investigate if these perceptions reflect an objective economic reality (i.e., those who perceive higher subjective social status actually possess relatively more income than others in their community) or if it is merely the perception that one has higher standing (perhaps erroneously) that predicts more positive aging attitudes.

Our findings also help to clarify the previous mixed findings regarding the relationship between SES and subjective aging constructs (e.g., Henderson et al. 1995; Lucchetti et al. 2008). In populations where objective and subjective perceptions of SES align closely or in studies drawn from a single geographic community, income and education may be good predictors of subjective aging. However, when samples are drawn from many different communities, subjective SES is likely to diverge from income and education (Duncan et al. 2002), and thus the objective SES indicators may fail to predict subjective perceptions of aging. This study highlights the importance of subjective SES for a national sample, but this consideration may be even more important to consider when conducting cross-cultural research.

Interestingly, we found that subjective SES was the best of the SES predictors for all four subjective aging constructs. The subjective aging framework proposed by Diehl et al. (2014) hypothesized that more conscious/explicit constructs (e.g., subjective age and AARC), as opposed to more preconscious/implicit constructs (e.g., ATOA), would be more influenced by socioeconomic resources. In our study, we found slightly larger effect sizes for ATOA (Step 3 ∆R2 = .07) and subjective age (Step 3 ∆R2 = .06) than for AARC gains (Step 3 ∆R2 = .02) and AARC losses (Step 3 ∆R2 = .02), although these differences were not significant. In the case of ATOA and subjective age, subjective SES was the strongest predictor in our models outweighing demographics, health, and objective SES, whereas the strongest predictor of AARC gains was identifying as a woman. Identifying as non-White and reporting more chronic health conditions were the strongest predictors of AARC losses. However, subjective SES was still a significant predictor in both AARC models. Thus, our findings do not support Hypothesis 3 that preconscious/implicit constructs are less influenced by socioeconomic resources in comparison with conscious/explicit constructs.

Future research should investigate the mechanisms through which subjective SES affects subjective perceptions of aging. Diehl et al. (2014) suggest that individuals consider their material and social resources when thinking about their own aging. Perceiving one’s resources to be relatively better than others in the community may foster the feeling that one is still performing at the peak of their abilities, which could give way to more positive perceptions of aging. Additionally, it may be that perceiving a lower social status is a reflection of a relatively more stress-filled life. Stressors can produce a weathering effect whereby those exposed to more stressors tend to have both an older biological and subjective age (Bellingtier et al. 2017; Geronimus 1992; Newman 2003). Financial stressors in particular have been associated with an older subjective age (Agrigoroaei et al. 2017). Stressors have also been identified as one pathway by which social health inequalities get under the skin and lead to premature aging (Keith 2014). Perceiving oneself to be higher on the social ladder may also be associated with having greater access to resources that allow one to maintain a youthful identity, for example, access to health, exercise, and leisure opportunities. Finally, lower subjective SES could reflect a lack of integration with one’s community, which could predict lower social support and well-being (Siedlecki et al. 2014).

There were some limitations associated with the current study. Although mTurk allowed for the recruitment of participants from across the USA, our sample was mostly White and contained more young-old adults than old-old adults. Additionally, the use of mTurk to collect data may have created selection effects when compared to community-recruited participants, although our participants were similar to community-recruited samples regarding their subjective perceptions of aging. Older adults from different countries may consider different factors when determining their subjective SES, and thus our pattern of findings cannot be assumed to apply outside the USA (Andersson 2015). Subjective SES has been shown to shift in older adults across the years (Cornman et al. 2012), as have measures of subjective perceptions of aging (Westerhof and Wurm 2015). It is also possible that the relationship between subjective SES and subjective perceptions of aging was influenced by a positive response bias such that some participants tended to respond more favorably on subjective questions in general. Although our work is an important first step in understanding the relationships between multiple measures of subjective perceptions of aging and SES, future longitudinal research across years (Bodner et al. 2017) and days (Bellingtier et al. 2017; Neupert and Bellingtier 2017) could further illuminate the pattern of covariation between subjective SES and subjective aging. Finally, our findings suggest that future researchers interested in the determinants of subjective perceptions of aging include subjective measures of SES.

In conclusion, our study is the first to demonstrate the importance of considering community standing (i.e., “the Joneses”) when assessing the association of SES and subjective perceptions of aging. The contribution of subjective SES outweighed objective indicators of SES when predicting ATOA, subjective age, AARC gains, and AARC losses. Thus, it is older adults’ perception of their socioeconomic standing, as opposed to their income and education, that best predicts their view of aging.

Footnotes

  1. 1.

    In addition, we ran the same models including retirement status as a control variable in Step 1. The pattern of results remained unchanged.

Notes

Acknowledgements

This research was supported by a Scholarship and Research Award to Shevaun D, Neupert from North Carolina State University. The authors wish to thank Hari Iyer for guiding us to the confidence interval test for R squared.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Alaina N. English
    • 1
  • Jennifer A. Bellingtier
    • 1
    • 2
    Email author
  • Shevaun D. Neupert
    • 1
  1. 1.Department of PsychologyNorth Carolina State UniversityRaleighUSA
  2. 2.Department of Developmental PsychologyFriedrich Schiller UniversityJenaGermany

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