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. 2022 Jul 6;97(3):267–288. doi: 10.1177/00914150221112294

Age, Individual Resources, and Perceived Expectations for Active Aging: General and Domain-Specific Effects

Maria K Pavlova 1,*,✉, Sonja Radoš 1,*, Klaus Rothermund 2, Rainer K Silbereisen 2
PMCID: PMC10363939  PMID: 35791630

Abstract

Exposure to expectations for active aging may be modulated by age and individual resources (socioeconomic status, social integration, and health) via multiple pathways. Using a cross-sectional, nationally representative sample of adults aged 17 to 94 (N = 2,007), we investigated the relations between age, individual resources, and perceived expectations for active aging (PEAA) in three domains (physical health, mental health, and social engagement). Across domains, young adults and individuals aged 70+ reported slightly lower PEAA than emerging adults did; no other age differences emerged. Multiple regression showed that a higher subjective socioeconomic status, better perceived general health, and partnership (in older adults) predicted higher PEAA (almost) across domains, whereas church attendance, employment status, and occupational prestige yielded domain- and age-specific effects, which were not always positive. We conclude that the effects of individual resources on PEAA are limited in general but vary depending on life domain and age.

Keywords: active aging, domain-specific views on aging, prescriptive age stereotypes, social expectations, social inequality

Introduction

In aging societies, older individuals face increasing social expectations that they remain active, maintain good health, and participate in society (Pavlova & Silbereisen, 2012; World Health Organization [WHO], 2002, 2015). Perceived expectations for active aging (PEAA) refer to older adults’ recognizing expectations of this kind that are directed at them personally and perceptibly in everyday life contexts (Pavlova & Silbereisen, 2012, 2016). Such expectations recall the prescriptive age stereotypes described by North and Fiske (2013) but emphasize activation rather than disengagement (de Paula Couto et al., 2022). Pavlova and Silbereisen (2016) have shown that some young-old adults might benefit from PEAA. However, no benefits can accrue to older adults who do not experience such expectations in the first place. Unequal exposure to expectations for active aging (Pavlova & Silbereisen, 2012) may not only reflect but also exacerbate unequal opportunities to age well. In the present study, we examined how age and individual resources might account for interindividual differences in PEAA in three domains that are highly relevant for aging well: physical health, mental health, and social engagement.

Expectations for Active Aging as Prescriptive Age Stereotypes

Age stereotypes are beliefs about the process of aging, and about older people in general, pertaining to perceived age-related losses and gains (Meisner & Levy, 2016). Negative (self)stereotypes predict numerous outcomes in old age, such as mental health issues, poorer physical and functional health, less preparation for old age, and declining well-being (Kornadt & Rothermund, 2015; Kornadt et al., 2015; Meisner & Levy, 2016). Prior research centered on descriptive age stereotypes (i.e., perceptions of what older people are like). Recently, North and Fiske (2013) introduced prescriptive age stereotypes (i.e., ideas about how older people should behave) in three domains (Succession, Consumption, and Identity) that reflect intergenerational tensions and ultimately aim to limit older people's resources.

However, evidence points to the recent rise in positively framed prescriptive beliefs on aging that focus on older people's productive potential (de Paula Couto et al., 2022; Pavlova & Silbereisen, 2012, 2016). To combat possible undesirable consequences and capitalize on some benefits of having an aging population, a paradigm of active aging has introduced new policy goals for older individuals, such as health preservation, lifelong learning, civic engagement, and extended paid work (WHO, 2002, 2015). Such changes challenged researchers to understand how older adults perceive, experience, and cope with emerging expectations (Pinquart & Silbereisen, 2004). In this vein, Pavlova and Silbereisen (2012, 2016) investigated the perceived growth in expectations for active aging (e.g., staying physically and mentally fit, keeping up with technical developments, and contributing to the public good) and appraisals of those expectations (i.e., as a threat or a challenge) among young-old Germans (age 56–75). Pavlova and Silbereisen (2012) showed that, on average, young-old individuals were aware of these higher expectations and appraised them as a challenge. Moreover, among the young-old who were neither volunteering nor working, PEAA predicted better psychological adjustment (Pavlova & Silbereisen, 2016). Thus, PEAA may represent positively framed prescriptive age stereotypes (or self-stereotypes; de Paula Couto et al., 2022), although they were not originally conceptualized that way.

Socio-Structural and Individual Differences in PEAA

Social inequalities have a notorious impact on aging (WHO, 2015). For instance, childhood conditions, socioeconomic and sociodemographic indicators (income, education, gender, race, etc.), and health can predict who is able to age actively and who is not (Brandt et al., 2012; Hess et al., 2016). Expectations for active aging, especially those stemming from individuals’ immediate environments, could help overcome such inequalities by fostering a better image of old age and ultimately a positive change in lifestyles. However, exposure to these expectations (i.e., who has a chance to perceive them?) may vary along the same dimensions of inequality. Pavlova and Silbereisen (2012) found that older, retired, widowed, and more disadvantaged individuals (e.g., those with worse health or SES) reported less pronounced PEAA or experienced them more often as a threat. Similar findings emerged for descriptive age stereotypes: A younger age and higher education predicted more positive views on aging (Beyer et al., 2017).

Age and individual resources may modulate the exposure to expectations for active aging through multiple pathways. Chronological age may be important because the older individuals are, the likelier they are to become targets of negative age stereotypes (Weiss & Zhang, 2020), which presumably go along with reduced expectations for active aging (the stereotyping pathway). In turn, individual resources, such as SES, social integration, and health (Pillemer & Glasgow, 2000; WHO, 2002, 2015), may be important for several reasons. They enable active aging (the enabling pathway; WHO, 2002, 2015); make individuals more valued in major activity settings such as the workplace or voluntary organizations (the demand pathway; Hess et al., 2016; Verba et al., 1995); provide access to stimulating environments (the challenge pathway); and embed individuals into social interactions that may serve as sources of social control (Umberson et al., 2010) and, through sheer numbers, increase the likelihood of exposure to various social expectations (the social control and connectedness pathways).

A Domain-Specific Perspective

Pavlova and Silbereisen (2012, 2016) found indications that PEAA might not be a unitary construct. Item-specific analyses revealed that the mean endorsement varied considerably across items (e.g., the perceived growth in expectations to stay physically and mentally fit was higher than in expectations for contributing to the public good; Pavlova & Silbereisen, 2012) and yielded domain-specific relationships with certain predictors (e.g., volunteering predicted an increase in the perceived expectations for contributing to the public good; Pavlova & Silbereisen, 2016). Domain-specific approaches to views on aging may be more valid and informative than unidimensional ones (Kornadt & Rothermund, 2015). For instance, Fiske and colleagues proffered two major dimensions of social stereotypes, warmth, and competence, and showed that older people are generally perceived as warm but incompetent (Cuddy et al., 2005).

Furthermore, Kornadt and Rothermund (2011, 2015) proposed differentiating views on aging across the domains of family, friends, religion, leisure, lifestyle, money, work, and health. This approach takes the multidimensionality of lifespan development into account and captures the affordances and restrictions of different aging domains. Domain-specific views on aging were shown to vary by age and gender (Kornadt & Rothermund, 2011, 2015) and to correspond to domain-specific behaviors: for example, positive future self-views on aging predicted preparation for age-related changes in the matching domain (Kornadt et al., 2015).

Study Aims and Hypotheses

Previously, research has addressed the level and appraisal of perceived growth in expectations for active aging, their sociodemographic and health predictors, and well-being outcomes of PEAA in German adults aged 56–75 (Pavlova & Silbereisen, 2012, 2016). It remains unclear whether PEAA are domain-specific or relevant only to the young-old. In the present study, we used an independent cross-sectional sample from a nationally representative survey, German Socio-Economic Panel (SOEP), to compare the current level (rather than perceived growth) of PEAA across a broad age range (17–94), to relate them to the major categories of individual resources (socioeconomic, social, and health) that are relevant to aging well (Pillemer & Glasgow, 2000; WHO, 2002, 2015) and that may modulate exposure to social expectations via various pathways, and to investigate domain differences.

We considered three life domains: (a) physical health, (b) mental health, and (c) social engagement (i.e., contributing to the common good). In general, health and social participation are mutually dependent: health is a prerequisite for social participation, but the latter is also known to foster health (Bennett, 2005). However, research suggests that the health domain is more salient to older adults (Pavlova & Silbereisen, 2012) and more strongly permeated by negative stereotypes (Kornadt & Rothermund, 2011) than is the domain of social engagement.

Table 1 summarizes the theoretical pathways linking age and specific resources (selected on the basis of their availability in the SOEP dataset) to PEAA in the three domains. Regarding the stereotyping pathway, negative age stereotypes mainly target individuals above 60 (Weiss & Zhang, 2020), whereas self-views on aging are most negative in those aged 70+ (Beyer et al., 2017). In a young-old sample, Pavlova and Silbereisen (2012, 2016) found relatively older age to predict lower PEAA. Simultaneously, younger and middle-aged individuals are increasingly aware of the need to prepare for age-related changes (Hahn & Kinney, 2021; Kornadt et al., 2015); thus, PEAA may be relevant across adulthood. Accordingly, we expected that, irrespective of domain, the average level of PEAA would not differ significantly between emerging adults (17–24), younger adults (25–39), the middle-aged (40–54), and young-old adults (55–69; Hypothesis 1a). However, we expected that in comparison, the middle-old group (age 70+) would report lower PEAA across domains (Hypothesis 1b).

Table 1.

Conceptual Model Linking Age and Individual Resources to PEAA.

Predictors Pathways of exposure Expected outcomes
Age 70+ Stereotyping Lower PEAA across domains
Socioeconomic resources
 Education Demand Higher PEAA across domains
 Occupational prestige Demand, challenge Higher PEAA: mental health
 Income Enabling Higher PEAA across domains
 Subjective socioeconomic status Enabling, demand, challenge, social connectedness Higher PEAA across domains
Social integration
 Employment Challenge, social connectedness In adults aged 55+: higher PEAA across domains
 Partnership Enabling, social control In adults aged 55+: higher PEAA across domains
 Church attendance Social control/connectedness Higher PEAA: social engagement
Challenge, social control In adults aged 55+: higher PEAA across domains
Health
 General health Enabling Higher PEAA across domains
 Domain-specific functional status Enabling Higher domain-specific PEAA
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Note. PEAA = perceived expectations for active aging.

Prior findings on the roles of education and income are mixed. Previously, better educated middle-aged and older individuals reported more positive views on aging (Beyer et al., 2017), whereas better educated young-old adults appraised PEAA more positively without reporting higher PEAA (Pavlova & Silbereisen, 2012). Thus, educational attainment appears to foster more positive views on aging or reactions to PEAA rather than PEAA as such. Nevertheless, it may be argued that activity settings, which may endorse expectations for active aging, actively recruit better-educated individuals (the demand pathway; Hess et al., 2016; Verba et al., 1995). Furthermore, income has shown inconsistent associations with indicators of successful aging (Depp & Jeste, 2006) and was not associated with PEAA (Pavlova & Silbereisen, 2012). Still, having a high income makes it possible to take part in activities such as attending cultural events (the enabling pathway; WHO, 2002, 2015). We therefore expected both education and income to have positive effects on PEAA across domains (Hypothesis 2).

Occupational complexity may foster intellectual flexibility (Kohn & Schooler, 1978) and was found to protect against cognitive decline in old age (Greenfield et al., 2021). Hence, occupational prestige may not only make individuals more sought-after (the demand pathway, which is already covered by general education) but also entail more cognitive demands on the job (the challenge pathway; Kohn & Schooler, 1978). Thus, we hypothesized that occupational prestige would have a uniquely positive effect on PEAA in the mental health domain (Hypothesis 3).

When assessing their subjective socioeconomic status (SSS), individuals consider multiple unmeasurable aspects and points of comparison with other people (Adler et al., 2000). Over and above SES, SSS has been linked to positive subjective perceptions of aging, awareness of age-related gains, and younger subjective age (English et al., 2019). Assigning a higher rating to oneself than to others may reflect access to means of aging well (the enabling pathway), being in demand or operating in a stimulating environment (the demand and challenge pathways), and social integration (the social connectedness pathway). Thus, we expected SSS to be positively related to PEAA across domains (Hypothesis 4).

As specific indicators of social integration, we considered embeddedness in major social roles (employment and partnership) and church attendance. Paid work is particularly central to younger individuals, and those who do not work (especially the unemployed) are typically exposed to various “activation” policies, such as retraining measures or sanctions for not accepting job offers. We therefore hypothesized that up to middle age (17–54), nonworking individuals would report higher PEAA across domains than their working counterparts (Hypothesis 5a; cf. Pavlova & Silbereisen, 2012). In older age, remaining in paid work can expose individuals to a challenging environment (the challenge pathway; Rohwedder & Willis, 2010) and maintain their social network (the social connectedness pathway; Kauppi et al., 2021). Thus, we expected that in young-old and middle-old groups (age 55+), working individuals would report higher PEAA across domains than their nonworking counterparts (Hypothesis 5b).

A (marital) partner is a major source of social support (the enabling pathway; Pillemer & Glasgow, 2000) and social control regarding, for instance, health behaviors (Umberson et al., 2010). Furthermore, steady partnerships are associated with more positive attitudes toward aging (Bryant et al., 2012) and higher PEAA (Pavlova & Silbereisen, 2012) in older adults. As younger and middle-aged individuals are typically embedded in multiple roles and social relationships (Antonucci et al., 2010), they are exposed to many sources of social expectations, and a partner may not make much of a difference. Thus, up to middle age (17–54), we expected no difference in PEAA by partnership status (Hypothesis 6a), but we hypothesized that young-old and middle-old individuals (age 55+) without a steady partner would report lower PEAA than those with one (Hypothesis 6b).

Church attendance signals belonging to a community that reinforces religious values, including the norm of contributing to the welfare of others (Lewis et al., 2013). Thus, via the social control and connectedness pathways, we expected church attendance to be positively related to PEAA in the social engagement domain (Hypothesis 7a). Additionally, attending church may have a broader significance to older adults, stimulating them to remain active (the challenge pathway) and, through the social control pathway, to maintain a healthy lifestyle (Krause, 2008; Pillemer & Glasgow, 2000). Therefore, we hypothesized that in young- and middle-old adults (age 55+), church attendance would predict higher PEAA in the domains of mental and physical health (Hypothesis 7b).

Finally, health is essential to active aging (the enabling pathway; WHO, 2002, 2015). General health has been linked to higher PEAA (Pavlova & Silbereisen, 2012). Furthermore, Bryant et al. (2016) found that physical and mental health/functioning were associated with more positive attitudes to aging in a domain-specific manner. Hence, we hypothesized that general health would be positively related to PEAA across domains (Hypothesis 8), whereas physical, mental, and social functional status would have domain-specific positive associations with the corresponding PEAA (Hypotheses 9a–9c).

Method

Participants and Procedure

We used data from the Innovation Sample of the German Socio-Economic Panel (SOEP-IS; Socio-Economic Panel, 2020), a multi-disciplinary representative annual survey of adults (age 17+) residing in private households in Germany. The Innovation Sample runs since 2011 and is intended for user-designed research projects (Richter & Schupp, 2015). Trained interviewers select households via a random-walk algorithm and conduct standardized computer-assisted personal interviews (CAPI; 92.7 min on average). The SOEP strictly adheres to ethical guidelines in conducting research (Richter & Schupp, 2015). In the 2016 SOEP-IS wave, our items on PEAA, age stereotypes, and preparation for old age (see Online Supplement 1 for the original German version) were administered to a randomly drawn subsample of 2,007 individuals aged 17–94, which we used in the present study. According to the fieldwork report (Zweck & Glemser, 2018), 3,049 out of the 3,550 households approached (i.e., 85.9%) participated; at the individual level, 4,802 out of 5,376 eligible participants (89.3%) took part in the survey. Participants received monetary incentives. For descriptive statistics, see Table 2.

Table 2.

Descriptive Statistics.

Variable Total sample
N = 2,007
Age, M (SD) 52.7 (18.2)
PEAA: physical health (1–7), M (SD) 5.1 (1.7)
PEAA: mental health (1–7), M (SD) 5.4 (1.6)
PEAA: social engagement (1–7), M (SD) 3.8 (1.8)
Female, n (%) 1,039 (51.8%)
Educational attainment in years (7–18), M (SD) 12.2 (2.6)
Equivalised household income in Euro (176–10,000), M (SD) 1,787.6 (871.1)
Occupational prestige a (1–11), M (SD) 7.7 (2.9)
Subjective socioeconomic status b (1–10), M (SD) 6.0 (1.4)
Working, n (%) 1,086 (54.1%)
Cohabiting with a partner, n (%) 1,313 (65.4%)
Church attendance (1–5), M (SD) 1.7 (0.9)
General health (1–5), M (SD) 3.4 (0.9)
FS: physical component (1–5), M (SD) 4.0 (1.2)
FS: mental component (1–5), M (SD) 4.5 (0.9)
FS: social component (1–5), M (SD) 4.4 (0.9)
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Note. Item missings did not exceed 5% in any variable except for church attendance (6%). PEAA = perceived expectations for active aging. FS = functional status. For all ordinal or continuous variables, higher values indicate higher levels of functioning or simply higher levels of the respective dimension (for details, see the Method section).

a

Was assessed in working persons only.

b

Was administered to an independently selected random subsample of participants in the same wave. As a result, only 909 persons in our subsample responded to these items.

Measures

Perceived Expectations for Active Aging

We assessed PEAA with three single items using these instructions: “In the following, we want to ask you about aging and social expectations. Please think of your everyday life. In our society, we often face certain expectations of other people. The following questions relate to these expectations.” Each item referred to one domain: physical health (“It is expected of me to keep myself physically fit”), mental health (“It is expected of me to keep myself mentally fit”), and social engagement (“People have high expectations that I get involved in social and non-profit-making activities”), with a 7-point rating scale (1 = does not apply at all; 7 = applies completely). Item formulations were adapted from the Jena Study on Social Change and Human Development (Pavlova & Silbereisen, 2012). Note that the items did not explicitly refer to aging, but the topic of aging was introduced at the beginning. The convergent validity of these items was supported by their moderate-to-high intercorrelations (r = .4–.7). Discriminant validity (domain specificity) was indicated by the somewhat higher within-domain correlations than across-domain correlations between PEAA and preparation for old age, most evident for the social engagement domain (see Online Supplement 2 for intercorrelations between all items in our module). Overall, bivariate analyses indicated that these three items could be used separately to measure specific domains.

Sociodemographic and Socioeconomic Indicators

We defined age groups on the basis of participants’ chronological ages in 2016: 17–24 years (emerging adults), 25–39 years (young adults), 40–54 years (middle-aged adults), 55–69 years (young-old adults), and 70+ (middle-old adults). Gender was a binary variable (0 = male; 1 = female). Educational attainment was a generated variable delivered with the dataset, measured in years of education or training. Furthermore, we considered (logged) equivalised disposable household income in Euros, taking household size and composition into account. For occupational prestige, we used a generated variable based on the Erikson and Goldthorpe class scheme (Erikson & Goldthorpe, 1993), here comprised of 11 occupational categories that pertained to working persons only; higher values indicated higher occupational prestige.

To assess SSS, we used a social ladder measure (Adler et al., 2000): “Please imagine this ladder shows where people are situated in their social environment. At the top/bottom, we can find people (1) who have the highest/lowest social importance to their social environment; (2) who are the best/worst off—who own the most/least money, have the highest/lowest education, and the most/least prestigious occupations. Where would you place yourself on the ladder?” (1 = lowest social importance, lowest standing; 10 = highest social importance, highest standing; r = .56). We used the mean of two items. The validity of this measure was supported by moderate intercorrelations between SSS and SES measures (r = .3–.4; see Online Supplement 3; cf. English et al., 2019).

Social Integration

All participants who had some form of employment, even if marginal, were coded with 1 = working; all others were coded with 0. The partnership was operationalized via cohabitation (1 = cohabiting with a partner, irrespective of marital status; 0 = not). For church attendance, we used a 5-point item from a set of questions on leisure activities (“In which of the following activities do you take part during your free time? … Attending church/religious events,” 1 = never and 5 = at least once a week). This was the only variable taken from the 2015 wave because it was not available in 2016.

Health and Functional Status

We assessed general health with one item (“How would you describe your current health?”; 1 = bad; 5 = very good). We used three functional status items from the Medical Outcomes Short Form 12 (SF-12; Jenkinson & Layte, 1997): physical health (one item: “Please think about the last four weeks. During this time, did you always, often, sometimes, almost never, or never feel that due to physical health problems, you were limited in some way at work or in everyday activities?”), mental health (two items, r = .83; we used a mean score), and social activity (one item). Items were measured on a 5-point scale whereby higher scores reflected better functional status (1 = always and 5 = never).

Analytical Approach

To test for age differences in PEAA (Hypotheses 1a and 1b), we conducted a factorial repeated measures ANOVA in SPSS (26.0) with the domain as a within-subjects factor and age group as a between-subjects factor. To test other hypotheses, we conducted multiple regression analyses using Mplus 8.5 (Muthén & Muthén, 2017). We regressed all PEAA domains on socioeconomic and health indicators, SSS, social integration indicators, age, and gender (as a control). Wherever a predictor had a significant effect in at least one domain, we compared its standardized regression coefficients between the domains using a z-test. To test for the moderating effects of age (Hypotheses 5a, 5b, 6a, 6b, and 7b), we dichotomized the age variable (under 55 and 55+) and computed its statistical interactions with employment, partnership, and church attendance. Following up on significant interaction effects (p < .05), we computed simple slopes of the predictor variable in the two age groups. For planned tests (i.e., where we had hypotheses), we used a conventional alpha level of .05, whereas for other (post-hoc) tests, we used a .01 level. We corrected for nonindependence of observations within households and for nonnormality of variable distributions by using a robust sandwich estimator (Muthén & Muthén, 2017). All missing values were handled using the full information maximum likelihood estimation, which uses all available information without imputing missing values (Enders, 2001).

Results

Mean Comparisons

For means of PEAA items and other descriptive statistics separated by age group, see Online Supplement 4. Repeated measures ANOVA with a Greenhouse–Geisser correction showed significant main effects of age, F (4, 1,972) = 4.04, p = .003, ηp2 = .008, and domain, F (1.6, 3,154.8) = 757.26, p < .001, ηp2 = .277. An interaction between age and domain was not significant: F (6.4, 3,154.8) = 0.83, p = .554. As illustrated in Figure 1, across age groups, the PEAA scores were highest for the mental health domain and lowest for the social engagement domain. Pairwise comparisons with a Bonferroni correction showed that the three domain means were significantly different from each other. Age differences were much less pronounced. With Bonferroni correction applied, the mean of emerging adults was significantly higher than those of young and middle-old adults; no further significant age differences emerged. Thus, Hypothesis 1a (no age differences in PEAA up to young-old age) was not supported because we found significant mean differences between young and emerging adults. Hypothesis 1b (lower PEAA endorsement in the middle-old) was not fully supported either, since the middle-old participants reported significantly lower PEAA only in comparison to the emerging adults.

Figure 1.

Figure 1.

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Means of PEAA domains across age groups.

Note. PEAA = perceived expectations for active aging.

Multiple Regression Analyses

The main effects of all predictors are depicted in Table 3. In the mental health domain only, the female gender was significantly associated with higher PEAA (β = .09). Regarding socioeconomic variables, neither educational attainment nor income was significantly associated with PEAA in any domain, thus refuting Hypothesis 2. Contrary to Hypothesis 3, occupational prestige had no significant effect on PEAA in the mental health domain. However, according to the alpha level we used for post-hoc tests (.01), it had a marginally negative significant effect on PEAA in the physical health domain (β = −.11), significantly more negative than in the mental health domain (p < .001).

Table 3.

Multiple Regression Analyses Results.

Predictor PEAA: physical health PEAA: mental health PEAA: social engagement
Age 17–24 a 0.29 (0.18) 0.47** (0.16) 0.62** (0.19)
Age 25–39 a −0.20 (0.16) −0.16 (0.15) 0.03 (0.17)
Age 40–54 a 0.02 (0.15) 0.04 (0.14) 0.28 (0.17)
Age 55–69 a 0.05 (0.13) 0.00 (0.12) 0.31* (0.14)
Female 0.12 (0.07) 0.15* (0.07) 0.07 (0.08)
Education −0.02 (0.02) −0.02 (0.02) 0.03 (0.02)
Equivalised income (logged) 0.17 (0.13) 0.11 (0.12) 0.04 (0.13)
Occupational prestige −0.04* (0.02) 0.02 (0.02) −0.02 (0.02)
Subjective socioeconomic status 0.09† (0.05) 0.11* (0.05) 0.15** (0.06)
Employment 0.39* (0.17) −0.05 (0.16) 0.17 (0.18)
Partnership 0.33** (0.10) 0.28** (0.09) 0.26** (0.10)
Church attendance in 2015 −0.03 (0.05) −0.01 (0.05) 0.16** (0.05)
General health 0.12* (0.05) 0.11* (0.05) 0.07 (0.06)
FS: physical component 0.00 (0.05) −0.05 (0.04) 0.02 (0.05)
FS: mental component 0.01 (0.07) 0.05 (0.06) −0.03 (0.07)
FS: social component −0.02 (0.06) 0.02 (0.06) −0.04 (0.07)
R 2 .030 .038 .042
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Note. Cells show unstandardized regression coefficients with standard errors in parentheses. N = 2,007. All variables except for church attendance were measured in 2016. PEAA = perceived expectations for active aging. FS = functional status.

a

Reference group: Age 70+.

†p < .10. *p < .05. **p < .01.

In contrast, SSS showed a significant positive association with PEAA in the social engagement (β = .11) and mental health domains (β = .10) and a marginally significant positive effect in the physical health domain (β = .08), thus supporting Hypothesis 4. When SSS was excluded from the model (see Online Supplement 5), income and educational attainment yielded only marginally significant positive effects on PEAA. For income in the physical and mental health domains, B(SE) = 0.24 (0.12), p = .043, β = .06, and B(SE) = 0.20 (0.11), p = .065, β = .06, respectively; for educational attainment in the social engagement domain, B(SE) = 0.04 (0.02), p = .054, β = .05. Thus, even with SSS excluded, objective SES measures had no substantial association with PEAA.

The main effect of employment was positive and significant only in the physical health domain (β = .22) and significantly more positive than in the mental health domain (p = .001). Interactions proposed by Hypotheses 5a and 5b were significant in the mental health and social engagement domains, B(SE) = 0.46 (0.17), p = .005, and B(SE) = 0.45 (0.20), p = .024, respectively, but not in the physical health domain: B(SE) = 0.25 (0.18), p = .167. As shown in Figure 2a, working participants under 55 reported marginally lower PEAA in the mental health domain than their nonworking counterparts did: B(SE) = −0.31 (0.18), p = .083. The difference between working and nonworking participants under 55 was not significant for the social engagement domain: B(SE) = 0.01 (0.21), p = .977 (see Figure 2b). Thus, Hypothesis 5a was supported for the mental health domain only. In those aged 55+, the opposite direction of effects was observed (i.e., working individuals perceived higher PEAA than nonworking individuals did). These differences were not significant in the mental health domain, B(SE) = 0.15 (0.18), p = .396, but significant in the social engagement domain: B(SE) = 0.46 (0.20), p = .025 (see Figures 2a and 2b). Thus, Hypothesis 5b was supported for the social engagement domain only.

Figure 2.

Figure 2.

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Significant interactions between age and employment status, partnership, and church attendance.

Note. PEAA = perceived expectations for active aging. Expected PEAA values are presented with all other predictors held at zero (continuous predictors were grandmean-centered). (Marginally) significant simple slopes are shown (all interaction effects were significant at p < .05 or lower). Low/high church attendance refers to M ± 1SD. †p < .10. *p < .05. **p < .01.

The partnership showed a positive and significant effect on all PEAA domains, qualified by a significant interaction with age across domains: physical health, B(SE) = 0.58 (0.17), p = .001; mental health, B(SE) = 0.71 (0.16), p < .001; and social engagement, B(SE) = 0.55 (0.19), p = .003. As shown in Figure 2c for the physical health domain, in participants under 55, there was no significant difference in PEAA by partnership status: B(SE) = 0.00 (0.11), p = .974. However, in those aged 55+, cohabiting participants reported higher PEAA in the physical health domain than their non-cohabiting counterparts: B(SE) = 0.58 (0.14), p < .001. The pattern of effects was the same in the domains of mental health and social engagement (see Figures 2d and 2e). Thus, Hypotheses 6a and 6b were supported.

In line with Hypothesis 7a, church attendance was significantly and positively associated with PEAA in the domain of social engagement (β = .08), significantly more than in the physical (p < .001) and mental health domains (p = .002). Furthermore, the interaction between church attendance and age was significant (p < .05) in the physical health domain, but the reverse of what was expected: B(SE) = −0.22 (0.09), p = .016. As depicted in Figure 2f, among those under 55, more and less frequent church attendees did not differ significantly, B(SE) = 0.10 (0.07), p = .122, but among participants aged 55+, higher church attendance was marginally significantly related to lower PEAA in the physical health domain: B(SE) = −0.11 (0.06), p = .066. The interaction was not significant in the mental health and social engagement domains: B(SE) = −0.09 (0.09), p = .299, and B(SE) = 0.12 (0.10), p = .218, respectively. Thus, Hypothesis 7b was not supported.

In partial support of Hypothesis 8, general health was significantly related to higher PEAA in the physical (β = .07) and mental health domains (β = .06), but not in the social engagement domain. Finally, contrary to Hypotheses 9a–9c, none of the functional status components were associated with any of the PEAA domains (see Table 3).

To explore whether the predictors of PEAA might differ in the subsample of older individuals, we repeated the analyses of the main effects in those aged 55+ (see Online Supplement 6). The only notable additional finding pertained to functional status, whose physical component was significantly associated with lower PEAA in the mental health domain: B(SE) = −0.16 (0.06), p = .006, β = −.11. However, the corresponding bivariate correlation had the opposite sign (r = .05, p = ns), suggesting a suppression effect. Additionally, a marginally significant positive effect of equivalised income on PEAA in the social engagement domain emerged: B(SE) = 0.40 (0.19), p = .035, β = .09.

Discussion

Starting with the premises that individuals are unequally exposed to expectations for active aging and that such expectations may be domain-specific, we investigated the links between age, individual resources, and PEAA in the domains of physical and mental health and social engagement. We argued that age and resources might modulate exposure to expectations for active aging via various pathways: stereotyping, enabling, demand, challenge, social control, and social connectedness (see Table 1). In a nutshell, the effects that we found indicated some support for the enabling and social control pathways. The (almost) domain-general predictors of higher PEAA were SSS, perceived general health, and (in older adults) partnership, whereas employment, occupational prestige, and church attendance yielded more domain-specific (and not always positive) associations with PEAA.

Participants’ age as such was a weak predictor of PEAA. As expected, middle-old adults reported somewhat lower PEAA across domains, but the age difference was significant only in comparison to emerging adults, a finding that did not corroborate the stereotyping pathway (i.e., individuals aged 70+ would be less exposed to expectations for active aging as negative age stereotypes target mainly this group; Weiss & Zhang, 2020). Overall, without a direct reference to aging in the PEAA items, adults from different age groups gave similar answers and coincided in perceiving higher expectations in health-related domains than in the social engagement domain (cf. Pavlova & Silbereisen, 2012). Interestingly, PEAA in the mental health domain were higher on average than those in the physical health domain. This unexpected finding may indicate the profound importance ascribed to mental fitness in a rapidly changing, competitive, and technology-rich environment that requires constant upgrading of human capital, irrespective of age.

We used gender only as a control variable and found females to report slightly higher PEAA in the mental health domain. It is known that dementia prevalence is higher in females, and German females were shown to experience a higher fear of dementia than their male counterparts did (Hajek & König, 2020). In supplementary analyses, we found that in females only, fear of dementia (assessed in 2012) and PEAA in the mental health domain (2016) were significantly and positively correlated (r = .11, p < .05). Thus, females or their significant others may feel more concerned about the maintenance of their cognitive fitness.

We found no significant positive associations between PEAA and education or occupational prestige, factors that make individuals more valued in formal settings requiring activity (e.g., workplaces, Hess et al., 2016; voluntary organizations, Verba et al., 1995). Thus, the demand pathway does not appear to expose individuals to greater expectations for active aging. We expected occupational prestige to reflect cognitive challenge at work and therefore to predict PEAA in the mental health domain, but its only effect was in the physical health domain, and it was negative. This finding may support the challenge pathway, but in a negative sense: white-collar occupations are typically more prestigious, but they are not physically demanding and are even associated with sedentary lifestyles (Smith et al., 2016).

In contrast to income, which was assumed to enable active aging (WHO, 2002, 2015) and thereby increase exposure to the corresponding expectations, but did not yield significant effects, SSS (Adler et al., 2000) was associated with somewhat higher PEAA across domains. Seeing oneself as more resourceful or prominent in the social hierarchy may imply many pathways of exposure to expectations for active aging (see Table 1). However, as objective SES measures were hardly relevant, it is worthwhile to consider the subjective nature of SSS. For instance, individuals high in SSS may overestimate their social importance by way of self-enhancement (Wills, 1997) and be quick to perceive expectations that reinforce their self-views.

We expected employment to expose older (but not younger) individuals to more expectations for active aging via the challenge and social connectedness pathways. However, the pattern of effects that we found did not quite fit with these pathways. First, a positive effect of employment on PEAA in the physical health domain did not vary with age. Second, in the mental health domain, no differences by employment status emerged in older adults. By contrast, up to age 55, nonworking participants perceived higher expectations to stay mentally fit than their working counterparts. As staying away from work may lead to skill depreciation and affect subsequent labor market chances (Edin & Gustavsson, 2008), these individuals may be under particular pressure to stay intellectually up to date. Finally, in the social engagement domain only, working individuals aged 55+ reported higher PEAA than their nonworking counterparts. This domain-specific effect may imply that older employed individuals perceive heightened expectations to remain productive and consider social engagement (e.g., volunteering) as a possible continuation of their occupational careers.

As expected, a steady partnership played a role in older adults: There were no differences in PEAA by partnership status in adults under 55, but participants aged 55+ perceived somewhat higher PEAA across domains if they were cohabiting with a (marital) partner (cf. Pavlova & Silbereisen, 2012). Along with adult children, a partner in old age is a primary person who provides social support (the enabling pathway; Pillemer & Glasgow, 2000) and is likely to “nudge” the other partner toward more self-care and activity (the social control pathway; Umberson et al., 2010). The connection between social control and PEAA was also suggested by the unique effect of church attendance on PEAA in the social engagement domain, which was independent of age (and expected). However, we also expected church attendance in older adults to promote PEAA in the physical and mental health domains. Surprisingly, in participants aged 55+, we found a marginally negative effect of church attendance on PEAA in the domain of physical health. The church may have a benevolent but outdated notion of old age, focusing on assisting the frail but not making tailored offers to older adults who (want to) remain fit and healthy (Bengtson et al., 2018), thus downplaying expectations for physical fitness in old age.

Finally, although perceived general health did have positive associations with PEAA in the physical and mental health domains, supporting the enabling pathway (cf. Pavlova & Silbereisen, 2012; WHO, 2002, 2015), the role of domain-specific functional status in PEAA was not confirmed. Overall, for significant predictors, effect sizes were rather small, suggesting that the classical dimensions of inequality in opportunities to age well (Brandt et al., 2012; Hess et al., 2016) play only a minor role in PEAA. This may be good news because an (almost) equal exposure to expectations for active aging can help to combat social inequalities in aging. However, interpretations of what it means to age actively may already be influenced by one's age and resources (cf. Teater & Chonody, 2020). For instance, a working-class person may interpret staying active as remaining employed until retirement, while a highly educated person may interpret it as founding a start-up after retirement. Similarly, people with different health status may apply different standards of mental or physical fitness. Such underlying differences in evaluation standards may mask the effects of age and individual resources on PEAA.

Limitations

The cross-sectional design of our study precluded us from making causal inferences or judgments about change over time. Longitudinal studies are needed to understand how and why PEAA change throughout the life course. Another limitation was our reliance on self-report measures that are subject to social desirability bias and common method bias. Although self-reported data was appropriate for the central construct of interest (perceived social expectations), future studies would benefit from adding measures such as actual expectations or age stereotypes held by significant others. Our PEAA items did not explicitly mention aging, although the topic of aging was introduced in the beginning of this interview section. This enabled a valid comparison of currently experienced social expectations across age groups, but strictly speaking, this measure reflected expectations for active aging (rather than simply being active) only in the older participants. Furthermore, particularly because we opted for a domain-specific approach, many variables were assessed with single items. Two PEAA items were highly correlated (physical and mental health); nonetheless, meaningful domain-specific effects emerged.

Conclusions and Future Directions

Our study demonstrated that almost irrespective of age, German adults perceived social expectations to maintain physical and especially mental fitness and, to a lesser extent, to remain socially engaged. The effects of some individual resources on PEAA depended on age and life domain, whereas others (health and SSS) demonstrated generally positive effects on PEAA. We conclude that exposure to expectations for active aging may occur mainly via the enabling pathway (e.g., better health enables active aging) and the social control pathway (e.g., partners influence self-care). As certain contexts, such as church or prestigious jobs, may inadvertently dampen expectations for active aging in the physical health domain, critical reflection upon the hidden messages older adults receive may be required. Future researchers might investigate pathways of exposure more directly, by measuring the mediating variables such as partner communication, or examine more psychological determinants of PEAA, such as attitudes toward (one's own) aging or age identification, personality, personal goals, and self-efficacy, along with domain-specific outcomes (e.g., health behaviors or volunteering).

Supplemental Material

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Acknowledgements

Data from the Innovation Sample of the German Socio-Economic Panel (SOEP-IS) were made available by DIW Berlin via a data distribution contract. We thank DIW Berlin for including our items in the SOEP-IS 2016.

Author Biographies

Maria K. Pavlova is a professor of Psychological Gerontology at the University of Vechta. Her research interests cover lifespan psychology, active aging, civic and political engagement, and paid work and mental health.

Sonja Radoš is a research associate at the Department of Psychological Gerontology, the University of Vechta. Her research focus is on expectations for active aging, their predictors and psychological outcomes in old age.

Klaus Rothermund is a professor of Psychology at the Friedrich Schiller University of Jena. His research interests cover views on aging (age stereotypes, age norms) as well as ageism and age discrimination, and their influence on development in old age. He is the chairman of the Aging-as-Future project.

Rainer K. Silbereisen is a research professor Emeritus of Developmental Psychology at the Friedrich Schiller University of Jena. His research has been focused on human development across the life span and on psychological consequences of social, political, and cultural change. He was President of various international science organizations.

Footnotes

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article. This work was supported by the Deutsche Forschungsgemeinschaft, German Research Foundation (DFG) to Pavlova and Rothermund (grant numbers: PA 2704/5-1 and RO 1272/15-1).

ORCID iD: Maria K. Pavlova https://orcid.org/0000-0002-2074-1063

Supplemental Material: Supplemental material for this article is available online.

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