A Latent Deprivation Perspective: Mechanisms Linking Volunteering to Mental Health in Later Life

Abstract

This study tests the hypothesis that the latent deprivation model (LDM) can be extended to volunteer work, by exploring the extent to which two potential latent benefits of volunteer work—purpose in life and perceived social status—mediate the negative relationship between volunteerism and mental health (measured as depressive symptoms). Structural equation modeling with the full-information maximum likelihood (FIML) was adopted to model a sample of 5887 respondents from the Health and Retirement Study (HRS). The outcome was depressive symptoms; the independent variable was volunteering; and the mediators were “purpose in life” and “perceived social status.” Findings show that purpose in life and perceived social status partially mediated the relationship between volunteering and depressive symptoms, with purpose in life having a more substantial effect than perceived social status. Implications for future research and practice are also discussed.

Keywords

mediation older adults depressive symptoms purpose in life social status

Increases in longevity have given way to the emergence of a new life stage during older adulthood. Some have called this the “third age,” as it arrives after midlife (often times seen as the career/family phase or second age) and before significant functional decline begins to occur (the fourth age; Carr & Gunderson, 2016; James & Wink, 2007). Individuals in the third age are typically considered “the young old” and are in relatively good health. While those in the second age may lack adequate discretionary time for engaging in various activities due to family and employment obligations, the third age is seen as a possible window of opportunity in the life span to finally engage in roles and activities that were not possible at other stages in life. Volunteerism is one of those frequently cited activities (Corporation for National Community and Service, 2016). Clearly, the need to find purpose and meaning and to form and maintain meaningful personal relationships surely does not diminish with age; in fact, from a developmental standpoint, some suggest that these goals become more salient in later life (e.g., Carstensen et al., 2000). Thus, volunteerism is a pathway toward these ends for many older adults in the third age.

Significant empirical evidence has accumulated demonstrating that formal volunteering is positively associated with older adults’ psychological well-being, including reduced depressive symptoms (e.g., Musick & Wilson, 2003) and increased life satisfaction (e.g., Borgonovi, 2008; Van Willigen, 2000). While most of this work has been observational, some experimental studies have also shown the effect of a formal volunteering program (i.e., Experience Corps [EC]) on improvements in depressive symptoms and physical health among older adults (Carlson et al., 2008; Fried et al., 2004; Hong & Morrow-Howell, 2009; Tan et al., 2006). However, few empirical studies have examined the mechanisms through which this protective benefit of volunteering operates, despite multiple calls in the literature emphasizing the need for such work (e.g., Carr et al., 2015; Jenkinson et al., 2013).

Some studies point out that the mechanisms may be threefold, including physical, cognitive, and social pathways (Fried et al., 2004). Although few studies have directly tested these potential mediators, existing empirical studies have supported the physical, cognitive, and social benefits of volunteering. For example, analyzing the data from EC, a model civic engagement program for older adults, Tan et al. (2009, 2006) found that volunteers were more physically active and the increased activity level was sustained after 3 years. Regarding cognitive benefit, Carlson et al. (2009) found that volunteers at EC demonstrated neural gains corresponding to improvements in executive function compared with nonvolunteers. For the social pathway, using an Australian cross-sectional sample, Pilkington et al. (2012) found that social support and positive/negative social exchanges mediated the link between volunteer status and well-being.

Given that the theoretical base for EC was that “it would attract older adults because of the opportunity for generativity, and that ongoing generativity would retain volunteers while they received regular doses of physical, social, and cognitive activity” (Fried et al., 2004, p. 74), Matz-Costa et al. (2016) tested “benefit to others” (i.e., emotional exchange) as a fourth possible mediator in their empirical test of the mechanisms through which productive activity affects emotional and physical health. They found significant indirect effects for social interaction and benefit to others on emotional health (depressive symptoms) and indirect effects for use of body (physical) and benefit to others on physical health (frailty). They also found support for all four of these mechanisms working in synergy to mediate the activity–health relationship. These results are supported by Roman (2018) who examined generativity as a possible pathway through which the health effects of volunteerism could be occurring in a sample of adults aged 25–74, finding that the greater well-being in those who volunteer more over time partially occurs through more positive self-perceptions of generativity.

Generativity can be broadly defined as “the impulse to build and create something lasting, something that will connect you to future generations” (Alboher, 2013, p. 212). It has been found to be a motivator for work (Mor-Barak, 1995) as well as volunteerism (Son & Wilson, 2011) in later life. Studies have found that generativity is important to healthy aging (e.g., Carlson et al., 2000) and that it is substantially associated with purpose in life and experienced meaningfulness (Kruse & Schmitt, 2012; Schnell, 2011). Matz-Costa et al. (2019) found that on days where older adults engaged in generative behaviors (i.e., sharing information about one’s work and encouraging/inviting others into one’s work), they had higher perceptions of the meaningfulness of their work that lasted up to 5 hr later that day.

Several other concepts that are similar to generativity have been studied in the volunteerism literature—including the altruistic nature of volunteering and its meaning-making function, which can also be beneficial to older adults’ well-being (Carr et al., 2015; Morrow-Howell, 2010). A related concept is “mattering,” which includes three dimensions: awareness, importance, and reliance (Elliott et al., 2004). The underlying logic is that when people feel they matter or they have a meaningful life, they fare well psychologically. Using data from the Wisconsin Longitudinal Study, Piliavin and Siegl (2007) found that mattering mediated the link between volunteering and well-being. Using a cross-sectional convenience sample (N = 458) of adults aged 41–91 who volunteered in a nonprofit organization, Thoits (2012) found that purpose in life and mattering mediated the relationship between the role-identity salience of being a volunteer and respondents’ well-being. Yet another concept is beneficence, which refers to the sense of being able to give (Weinstein & Ryan, 2010). Beneficence has been found to mediate between pro-social behavior and general well-being (Martela & Ryan, 2016).

While the work summarized above offers important insight, we are still in an early stage of understanding the full picture of the mechanisms by which volunteering confers beneficial effects on older adults’ well-being. Specifically, the literature lacks a well-tested theory to guide this line of research. Although several theoretical positions pertaining to the mediating role of physical, cognitive, and social benefits have been postulated, few empirical studies have directly tested the formal mediation hypotheses. Although Matz-Costa et al. (2016) tested four potential mediating factors as discussed above, they did not specifically hone in on volunteerism but looked at productive activity profiles more broadly—including 28 activities such as working, traditional leisure, and activities of different intensity levels. Examining volunteering individually and separating it from other forms of productive activity is key because an aggregated effect may not apply for individual activities. Moreover, Matz-Costa et al.’s (2016) results showed partial mediation, indicating there may be additional mechanisms that can explain the relationship, and highlighted a need for more comprehensive theoretical approaches.

Theoretical Framework

The latent deprivation model (LDM; Jahoda, 1981, 1982, 1997) is a model that can guide hypotheses around the potential mechanisms linking volunteering and older adults’ mental health. The LDM was developed by Marie Jahoda, a social psychologist who specialized in unemployment, and the model is considered the most influential theoretical approach in understanding the mechanisms through which unemployment leads to the deterioration of mental health (Creed & Bartrum, 2006; Ezzy, 1993; Muller & Waters, 2012). Grounding her theory on the empirical findings of people’s unemployment experiences during the 1930s depression and on unemployment experiences in the 1970s and 1980s, Jahoda (1981, 1982) argued that employment provides both manifest and latent benefits to workers, whereas unemployment deprives people of both the manifest and latent benefits of employment, thus leading to poor mental health. Manifest benefits refer to financial gains, and latent benefits refer to time structure, regular activities, social contact, collective purpose, and social status. Although people work for the manifest benefit, Jahoda (1982) argued that it is the deprivation of these latent benefits that leads to the adverse effect on psychological well-being.

The LDM is, by design, a mediation model—the latent and manifest benefits serve as mediators linking employment status to people’s mental health. We propose that it may be possible to extend the latent benefits portion of the LDM to unpaid work—or volunteerism. By definition, volunteer work does not offer the manifest benefits proposed in the LDM; however, it can provide important latent benefits, and it could be that these latent benefits are accounting for its health-promoting effects. Indeed, formal volunteerism can provide time structure, regular activities, social contact with colleagues, and a sense of purpose. Further, some studies suggest that formal volunteering can serve as a buffer to people’s mental health (e.g., Greenfield & Marks, 2004), which indicates volunteering may have a substituting effect over paid work. In this sense, the LDM is an excellent theoretical guide for us to understand the potential mechanism linking volunteering and its effect on older adults’ mental health.

The LDM is empirically supported by a number of studies in the research on unemployment. The first line of empirical evidence supports the proposition that unemployed people have, in general, less access to those latent benefits compared to their employed counterparts (e.g., Creed & Muller, 2006; Paul & Batinic, 2010). The second line of empirical evidence supports the proposition of the LDM that the lack of access to those latent benefits negatively influences the unemployed persons’ mental health. For example, using the full latent and manifest benefits (LAMBs) scale, Selenko et al. (2011) found that activity and social contacts are two latent benefits that are significantly correlated with mental health. Some studies found an association between collective purpose and better mental health outcomes (see Evans & Haworth, 1991; Haworth & Ducker, 1991). Low status was found to be a significant predictor of psychological distress in a German sample (Paul & Batinic, 2010). Creed and Macintyre (2001) found that status was the only latent benefit that was associated with well-being. However, to date, we are not able to identify any study that has adopted the LDM to study the mechanism linking volunteering specifically to mental health among older adults.

The Current Study

The current study tested whether “purpose in life” and “perceived social status” at least partially mediate the relationship between formal volunteering and mental health (measured as depressive symptoms) among older adults. We chose these two mediators for several reasons. First, although it is ideal to examine all five latent benefits as mediators together, the current data that we used from the Health and Retirement Study (HRS) lack proper proxies of some of the latent benefits such as time schedule. Yet, the data have their advantages, which are illustrated in the “Methods” section. Second, although “purpose in life” differs from “collective purpose” in that the latter concept stresses a sense of collectivism while the former is more a general concept of having a purpose in life, the two concepts overlap significantly in operationalization. For example, Kovacs et al. (2017). defined collective purpose as the feeling of participation in a larger group or goal, whereas one of the items measuring purpose in life reads “I have a sense of direction and purpose in my life” (Ryff, 1989). Last, purpose in life is also close to the concept of mattering that is discussed in the earlier section and has been linked to volunteering in particular.

With regard to perceived social status, Singh-Manoux et al. (2003) suggest that perceived social status reflects an individual’s impressions of their current circumstances with regard to factors they perceive to be valued by society, such as occupational position, education, household income, satisfaction with standard of living, and feelings of financial security regarding the future. Similar to how employment provides individuals with a valued social status that affects their overall sense of identity and, in turn, their health and well-being (Ezzy, 1993; Jahoda, 1981; Wang & Matz-Costa, 2019), it is plausible to suggest that volunteering may offer a similarly valued social status. Studies have found that those with more human and social capital are more likely to volunteer, with the thought being that such resources increase access to volunteer opportunities (Morrow-Howell, 2010). However, it is unclear whether volunteering may also enhance participants’ perceived social status—especially if volunteering is serving as a substitute for work or if it is seen as a pathway to work or new work opportunities. Participating in volunteer activities has been found to be associated with increased job opportunities (e.g., Spera et al., 2013). Given the fact that a large portion of older adults are working past traditional retirement ages either because they want to or they have to (Morrow-Howell et al., 2001), increased access to job opportunities is often desirable. Further, volunteering has been found to be associated with increased social networks among older volunteers (Rook & Sorkin, 2003), which might also lead to increased perceived social status. To our understanding, no previous study has examined the potential mediating role of perceived social status in the relationship between volunteering and mental health.

We propose two hypotheses:

Hypothesis 1: Purpose in life partially mediates the relationship between formal volunteering and older adults’ depressive symptoms.

Hypothesis 2: Perceived social status partially mediates the relationship between formal volunteering and older adults’ depressive symptoms.

Methods

Data and Sample

We analyzed two waves (2008 and 2010) of the HRS data, a nationally representative study of individuals over the age of 50 in the United States. Since 2006, a rotating (random) 50% of the core panel participants who completed an enhanced face-to-face interview were given a psychosocial and lifestyle questionnaire to fill out and mail back. This questionnaire was referred to as the Leave-Behind Questionnaire (LBQ). The LBQ contained the mediator variables used in the current study. The RAND HRS Longitudinal File is a cleaned, easy-to-use, and streamlined data product containing information from Core and Exit Interviews of the HRS. We merged the LBQ data in the 2008 wave with the RAND HRS and variables related to volunteering in the core data of the 2008 wave as well as the outcome variable from the 2010 wave. Because the volunteer questions asked about respondents’ volunteering activities during the past 12 months, the volunteer variable preceded the mediators in the temporal order, making the mediation possible. Therefore, we did not need to merge a volunteering variable from the 2006 wave. The response rates of the HRS in 2008 and 2010 are 88% and 81%, respectively. For sample selection, we excluded those who were either age-ineligible (<age 51) spouses, out of the country, or institutionalized. The final sample size was 5887.

Measures

Outcome Variable

Although volunteerism has been found to relate to a whole host of health and well-being outcomes, the Jahoda model primarily focuses on mental health due to its rooting in the unemployment literature; therefore, we focus on mental health for the purpose of this study. The HRS contains two measures of mental health: (a) an inventory of psychological distress, or symptoms of depression; and (b) a measure of major depressive episodes (Steffick, 2000). The measure of symptoms of depression used a subset of items from the Center for Epidemiologic Studies Depression (CES-D) scale (Radloff, 1977), which measures a continuum of psychological distress (symptoms of depression and anxiety), rather than determining the presence or absence of recognized psychiatric disorders. We felt this was the more appropriate measure for our study and in line with the approaches used to measure mental health in prior studies testing the Jahoda model (e.g., Sheeran et al., 1995). We used depressive symptoms in the 2010 wave to establish the temporal order with mediators and the independent variable. The items asked whether respondents, “for much of the time during the past week prior to the interview,” (a) had feelings of depression, (b) felt everything is an effort, (c) sleep was restless, (d) felt alone, (e) felt sad, (f) could not get going, (g) felt happy (reverse-coded), and (h) enjoyed life (reverse-coded). Each item yielded an answer of “yes” or “no.” The CESD scores range from 0 to 8, with higher scores indicating a greater number of depressive symptoms. Cronbach’s α of depressive symptoms was 0.83.

Independent Variable

Volunteer status was a dichotomous variable indicating whether the respondent had volunteered for religious, educational, health-related, or other charitable organizations during the last 12 months (coded as 1) or not (coded as 0).

Mediators

Two mediators were used, and both were from the LBQ 2008. To measure purpose in life, an index was created using the seven-item purpose in life subscale of the Ryff Psychological Well-Being Scale (Ryff, 1989). The items ask respondents the extent to which the following statements describe them: (a) “I enjoy making plans for the future and working to make them a reality,” (b) “My daily activities often seem trivial and unimportant to me” (reverse-coded), (c) “I am an active person in carrying out the plans I set for myself,” (d) “I don’t have a good sense of what it is I’m trying to accomplish in life” (reverse-coded), (e) “I sometimes feel as if I’ve done all there is to do in life” (reverse-coded), (f) “I live life 1 day at a time and don’t really think about the future,” and (g) “I have a sense of direction and purpose in my life.” The coding for each item ranges from 1 = strongly disagree to 6 = strongly agree. Scores were averaged with a higher score indicating higher levels of purpose in life. The Cronbach’s α of this scale was 0.76.

Perceived social status is measured using a single-item proxy measure on a 1–10 scale based on the Cantril Self-Anchoring Striving Scale with a higher score indicating higher social status (Cantril, 1965). In the LBQ of the HRS, respondents were asked how they would rate their perceived social status on a vertical ladder with the bottom rung being 1 and the top rung being 10. This measure is purposely broad and vague, leaving it in the hands of the respondent to decide what social status means to them. Social status was square-transformed to correct its skewness.

Control Variables

We controlled for physical health variables due to their association with mental health, including self-reported health (a single-item question with responses options ranging from 1 = poor to 5 = excellent); Instrumental Activities of Daily Living (IADLs, ranging 0–5 indicating whether respondents have any difficulty in using the phone, managing money, taking medications, shopping for groceries, and preparing hot meals); and doctor-diagnosed conditions (coded as number of conditions such as high blood pressure and diabetes, ranging 0–8). Several demographic variables were also included in models. Marital status had four categories: married/partnered, separated/divorced, widowed, and never married. Age was a continuous and time-varying variable ranging from 51 to 101. Gender was a dichotomous variable. Race/ethnicity was a four-category variable including non-Hispanic Whites, non-Hispanic Blacks, non-Hispanic other races, and Hispanics. Education was a continuous variable with a range of 0–17 (17+ was coded as 17). Total household income was the sum of all income in a household and was log-transformed due to its skewness. Labor force status was a five-category variable including full-time working, working part-time/partly retired, unemployed, retired, and not in labor force.

We further controlled for personality variables because personality is often considered a potentially unobserved confounder and not available in most secondary datasets. Personality is measured using the “Big 5” (neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness) personality traits obtained from the LBQ in 2008. The “Big 5” personality traits were assessed using 31 items asking how much respondents agree with the adjectives that describe their personality (coded as 4 = a lot; 3 = some, 2 = a little, or 1 = not at all), based on Lachman and Weaver (1997). The Cronbach’s αs were 0.66 for conscientiousness, 0.78 for agreeableness, 0.72 for neuroticism, 0.79 for openness, and 0.74 for extraversion.

Analysis Strategy

We first examined the missingness of the data using the user-developed packaged misspattern in Stata. The percentage of missingness for all variables range from 0 to 9.5. Older respondents tended to have more missing values than younger respondents, which indicates a missing at random pattern. For the mediation analysis, we adopted structural equation modeling with the full-information maximum likelihood (FIML) option. The FIML option in structural equation modeling allows the maximum utilization of each observation’s data information without excluding the case, and it has been proven to be an excellent method for handling missing data in a wide variety of situations and is more efficient compared to the multiple imputation approach (Allison, 2003, 2012). A path model was estimated. The initial model includes correlations among exogenous variables (i.e., volunteer status and covariates) and uncorrelated disturbances among endogenous variables (mediators and the outcome). However, modification indices suggested several paths for improvement. The most theoretically justifiable one was to allow the error for the mediators to covary, as we could not rule out potential unobserved confounders in our model. This would be expected in a fully specified mediation model, so as Kenny (2018) suggested, we used the Bayesian inference criterion (BIC) and Akaike inference criterion (AIC) to assess model fit. Both fit statistics use model comparisons and favor parsimony (Burnham & Anderson, 2004). The model with the smallest BIC/AIC value has the best fit, with the difference between the model BICs/AICs indicating the strength of the evidence for improved fit. We used bootstrapping with 200 replications to obtain standard errors and confidence intervals for direct and indirect effects. Stata 15 was used for all the analyses.

Results

Table 1 present the unweighted descriptive statistics of the sample. Around 35% of respondents reported having volunteered. The sample contained 77% non-Hispanic Whites, 12% non-Hispanic Blacks, 2% non-Hispanic other races, and 8% Hispanics.

Table 1

Unweighted Descriptive Statistics (N = 5887).

	N	Mean (SD) or %	Min-Max	Missing (%)
Variables
Outcome depressive symptoms	5598	1.31 (1.88)	0–8	4.91
Baseline depressive symptoms	5772	1.33 (1.91)	0–8	1.95
Volunteer	5885		0–1	0.03
Yes	2099	35.67
No	3786	64.33
Mediators
Purpose in life	5738	4.64 (0.92)	1–6	2.53
Social status	5326	6.46 (1.73)	1–10	9.53
Controls
Self-reported health	5885	2.84 (1.07)	1–5	0.03
Doctor-diagnosed conditions	5886	2.14 (1.40)	0–8	0.02
IADLs	5887		0–1	0
Yes	653	11.09
No	5234	88.91
Age	5887	69.56 (9.09)	51–101	0
Gender	5887		0–1	0
Female	3507	59.57
Male	2380	40.43
Race and ethnicity	5885			0
Non-Hispanic Whites	4534	77.04	0–1
Non-Hispanic Blacks	731	12.42	0–1
Non-Hispanic other races	126	2.14	0–1
Hispanics	494	8.39	0–1
Education (years)	5882	12.62 (3.10)	0–17	0
Marital status	5886			0
Married/partnered	3750	63.71	0–1
Separated/divorced	738	12.54	0–1
Widowed	1215	20.64	0–1
Never married	183	3.11	0–1
Household income	5887	64,820 (121,485)	0–5, 310, 391	0
Labor force status	5887			0
Working full-time	1136	19.30	0–1
Working part-time/partly retired	825	14.01	0–1
Unemployed	83	1.41	0–1
Retired	573	56.41	0–1
Not in labor force	522	8.86	0–1
Big 5 personality
Neuroticism	5803	2.86 (0.37)	1–4	1.43
Extraversion	5807	3.28 (0.50)	1–4	1.35
Agreeableness	5804	2.73 (0.38)	1–4	1.41
Conscientiousness	5789	2.95 (0.30)	1–4	1.66
Openness	5793	2.89 (0.35)	1–4	1.60

Note. IADLs = Instrumental Activities of Daily Living.

Table 2 presents the direct and indirect effects of volunteering. As discussed earlier, it was a saturated model with 0 degrees of freedom and a χ² of 0.00, suggesting that the model-implied covariance matrix perfectly reproduced the observed data; AIC = 240,120 and BIC = 241,449. When compared to the AICs/BICs for each of these models—no direct effect, no effect from causal variable to the mediators, and no effect from the mediators to outcome—the saturated model had the lowest AIC and BIC values, and therefore was the best fitting.

Table 2

Direct and Indirect Effects of Volunteering on Depressive Symptoms (N = 5887).

Variables	Direct effect			Indirect effect
Variables	B	95% CI	β	B	95% CI	β
Volunteering	−0.06	[−0.15, 0.03]	−0.01	−0.07***	[−0.09,–0.05]	−0.02
Purpose in life	−0.37***	[−0.44, –0.31)	−0.18	—	—	0.00
Social status (transformed)	0.00**	[−0.01, 0.00]	−0.05	—	—	0.00
Self-reported health	0.33***	[0.28, 0.39]	0.19	0.05***	[0.04, 0.07]	0.03
Doctor-diagnosed conditions	0.15***	[0.11, 0.19]	0.11	0.01	[0.00, 0.01]	0.00
IADLs	0.51***	[0.32, 0.69]	0.08	0.10***	[0.07, 0.14]	0.02
Age	−0.01**	[−0.02, 0.00]	−0.05	0	[0.00, 0.00]	0.00
Male	−0.17***	[−0.26, –0.07]	−0.04	−0.02*	[−0.04, –0.01]	−0.01
Race and ethnicity (Non-Hispanic Whites = base)
Non-Hispanic Blacks	0.07	[−0.07, 0.21]	0.01	−0.12***	[−0.15, –0.09]	−0.02
Non-Hispanic other races	0.43**	[0.11, 0.75]	0.03	0.01	[−0.05, 0.06]	0.00
Hispanics	0.15	[−0.03, 0.34]	0.02	−0.07***	[−0.11, –0.04]	−0.01
Marital Status (married/partnered = base)
Separated/divorced	0.38***	[0.21, 0.55]	0.07	0.05**	[0.02, 0.08]	0.01
Widowed	0.15*	[0.11, 0.75]	0.03	0.03	[0.00, 0.06]	0.01
Never married	0.25	[−0.03, 0.34]	0.02	0.04	[−0.01, 0.09]	0.00
Education	−0.03**	[−0.05, –0.01]	−0.05	−0.01***	[−0.02, –0.01]	−0.02
Household income (transformed)	−0.03	[−0.08, 0.02)	−0.02	−0.04***	[−0.05, –0.02]	−0.02
Neuroticism	−0.01	[−0.16, 0.13]	0.00	−0.12***	[−0.15, –0.09]	−0.02
Extraversion	−0.03	[−0.16, 0.09)	−0.01	−0.13***	[−0.16, –0.10]	−0.03
Agreeableness	0.74***	[0.60, 0.87]	0.15	0.03*	[0.00, 0.06]	0.01
Conscientiousness	−0.44***	[-0.63, –0.24]	−0.07	−0.26***	[−0.31, –0.20]	−0.04
Openness	−0.11	[−0.31, 0.10]	−0.02	−0.03	[−0.07, 0.01]	−0.01
Labor force status (unemployed = base)
Working full-time	−0.79**	[−1.30, –0.27]	−0.16	−0.09	[−0.18, 0.00]	−0.02
Working part-time/partly retired	−0.80**	[−1.32, –0.27]	−0.15	−0.09	[−0.19, 0.00]	−0.02
Retired	−0.73**	[−1.25, –0.21]	−0.19	−0.08	[−0.17, 0.01]	−0.02
Not in labor force	−0.68*	[−1.22, –0.14]	−0.10	−0.1	[−0.19, 0.00]	−0.01

Note. CI = confidence interval, CIs and significance levels were generated using bootstrapping.

*p < .05, **p < .01, ***p < .001.

Figure 1 presents all standardized direct, indirect, and total effects for the model. Both indirect (β = −0.02, p < .001) and total effects (β = −0.03, p < .01) of volunteering were detected. The direct effect was not statistically significant (β = −0.01, p = .191), which indicated that there might be a complete mediation through purpose in life and perceived social status between volunteering and depressive symptoms. However, one should be cautious in interpreting this result as complete mediation because Kenny and Judd (2014) demonstrated that the test of the indirect effect is much more powerful than the test of the direct effect especially when the effect size is small. The rest of the direct paths were all statistically significant. Volunteering was positively associated with purpose in life (β = 0.09, p < .001) and perceived social status (β = 0.03, p < .05). Purpose in life was negatively associated with depressive symptoms (β = −0.18, p < .001), and perceived social status was also negatively associated with depressive symptoms (β = −0.05, p < .01). The total indirect effect was 67% of the total effect. The indirect effect of purpose in life (β = −0.016) was five times larger than the indirect effect of perceived social status (β = −0.003).

Figure 1

Mediation analysis results. *p < .05, **p < .01, ***p < .001.

Discussion

The findings of this study showed that purpose in life and perceived social status together partially mediated the negative relationship between volunteering and depressive symptoms. The effect size was moderate, counting about two-thirds of the total effect. Notably, purpose in life exerted a much larger influence compared to perceived social status, which is in line with the findings from multiple studies demonstrating the meaning-making, mattering, and altruistic characteristic of volunteering activity (e.g., Greenfield & Marks, 2004; Kahana et al., 2013). Considering the developmental importance of generativity and related concepts in the third age (Carr & Gunderson, 2016; Carstensen et al., 2000; Morrow-Howell, 2010), it makes sense that purpose in life serves as a significant pathway through which volunteering casts its positive benefits on mental health in later life.

On the other hand, perceived social status as a mediator is a novel finding, albeit the effect size was relatively small compared to purpose in life. Since volunteering, by definition, does not provide much economic reward, it is interesting to see that older adults may still boost their perceived social status through engaging in noneconomic but productive activities. As we discussed earlier, volunteering may enhance older adults’ perceived social status through increased job opportunities and social networks (Spera et al., 2013). In addition, these findings are in line with the research findings on older adults’ motivations for volunteering. For example, Clary and Snyder (1999) adopted a functional approach and proposed six motivations for seniors’ volunteering: values like humanitarianism, understanding about others, psychological enhancement, gaining career-related experience, strengthening social relationships, and reducing negative feelings. Hence, the enhanced perceived social status could derive from the gained social networks and potential job opportunities, which are particularly important as increasing numbers of older adults are engaging in nontraditional pathways in and out of work in later life (e.g., delaying retirement, career changes, bridge jobs, leaving and then rejoining the workforce, etc.; Gonzales et al., 2015).

However, there could be alternative ways that social status may be relevant for older adults’ volunteering, besides job opportunities and social networking. For instance, it could be that volunteering enhances older adults’ perceived social status because volunteers feel like others will view them more favorably (e.g., as someone who is active, vital, and still contributing to society) or as more altruistic. Volunteering also may provide reinforcement to an individual that their lifetime experience is still valued and needed—that they have skills to offer. Wright and Steptoe (2005) argue that in older adults, subjective social status may be particularly useful in providing an aggregate estimate of lifetime social experience. Given the broad/vague measure of social status employed in this this study, it seems possible that perceived social status could be rooted in multiple domains of social life. Future research should continue to explore these issues by testing and refining the true mechanisms through which the positive benefits of volunteering are obtained. The current study makes a contribution to the theorization of the mechanism of volunteering.

Implications for Research

The findings of this study have implications for future theory-building regarding the mechanisms of how volunteering benefits older adults’ mental health and well-being. Scholars have been calling for theory-building to find pathways through which formal volunteering exerts benefits (e.g., Jenkinson et al., 2013), and we have demonstrated that the LDM could be used to partially explain why formal volunteering may be beneficial to people’s mental health. A result of partial mediation from this study indicates that more latent factors may contribute to the mediation process. Future studies could utilize other data sources to fill this gap. As mentioned, beyond the two mediators examined here, other latent benefits including time structure, social contact, and collective activities may also mediate the volunteer well-being path. Moreover, as compelling and well-tested as the LDM is, Jahoda (1982) does admit that the list of mediators might not be exhaustive. This implies that researchers can further explore other possible pathways guided by the LDM.

Examining the mechanisms through which volunteering exerts its benefit to older adults is particularly useful because the nature of volunteering activities vary significantly—from participating in one’s church community to volunteering at a nursing home. When researchers demonstrate that volunteering is beneficial, practitioners may not know exactly what type of volunteering is helpful or why. This knowledge gap fosters an uncertainty as to what type of volunteer work could actually have positive benefits for older volunteers. For example, when older adults are involved in volunteering activities that are not personally meaningful for them, they may not receive the positive benefit from volunteering (Matz-Costa et al., 2016). Therefore, the current study demonstrated the importance of examining the specific mediators linking volunteering to different health outcomes; by doing so, researchers may be able to pinpoint exactly what type or component of a volunteering program is optimal and thus further help develop such programs.

Implications for Practice

The discovery of the underlying mediation processes could not only delineate why volunteering is beneficial to older adults’ mental health, but also shed light on the potential intervening strategies for any volunteering opportunities to better enhance older participants’ mental health. For example, nonprofit organizations such as retirement communities can reach out to older volunteers by advertising the significance of the volunteer activity and how the volunteer’s work will contribute to the mission of the organization, thus facilitating a sense of value and purpose. Health-care professionals can also educate the benefits of volunteering and encourage their clients to take on some volunteering activities targeting their health outcomes. Depending on the future findings of other significant mediators such as time structure and regular social activities, nonprofit organizations can strengthen those components in order to better attract older volunteers as well. On the other hand, any interventions involving a volunteer component can also purposefully design the volunteering activity to enhance these latent benefits (mediators) in order to optimize volunteer roles for both individuals and organizations.

Limitations and Future Research

This study has several limitations. First, despite controlling for personality variables and multiple other covariates, this study may still suffer from selection bias because we were not able to use change scores from one wave to another to model the mediation process. In other words, it may be that those who volunteer may have had more resources (e.g., social status) and better mental health to begin with, compared with those who did not volunteer. Another limitation is that formal volunteering was measured as a dichotomous variable, which lacks nuance. This is due to the sheer size of the longitudinal survey and its lack of capacity to incorporate detailed questions on every subject of research interest. Future primary data collection can obtain more nuanced data regarding volunteering types, intensity, and duration. A third limitation is that there is a 2-year interval between each wave of longitudinal HRS data. A time lapse may indicate a threat to internal validity when the variables are not truly continuous. However, again, considering the sheer amount of data that the HRS purports to collect, a 2-year interval is the shortest compared to other commonly used longitudinal data. Finally, only one specific indicator of mental health was examined (i.e., depressive symptoms); future research should explore how these processes work for a wider range of measures of mental health (e.g., anxiety, loneliness) and well-being (e.g., life satisfaction) as well as for physical health outcomes.

Conclusions

Adopting the LDM, the current study demonstrated the mediating role of purpose in life and perceived social status in the relationship between volunteering and depressive symptoms among a nationally representative sample of community-dwelling older adults in the United States. The findings provide evidence that, similar to how employment provides individuals with a sense of purpose and valued social status that affect their overall sense of identity and, in turn, their mental health, volunteering may also offer these latent benefits to some extent. Future research should explore additional latent benefits proposed by the Jahoda model to assess whether these factors can further explain the positive effect of volunteering on health and well-being. Findings of the current study may also inspire potential intervention development targeting the mediators of volunteering that support mental health.

Footnotes

Acknowledgments

The author wishes to thank her dissertation committee members for their support and guidance: Dr. Christina Matz, Dr. Sara Moorman, and Dr. James Lubben.

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Jie Yang

Author Biographies

Jie Yang is an assistant professor at East Carolina University School of Social Work. She is interested in examining social determinants of health in later life, particularly productive engagement (e.g., aging workforce, volunteerism) and social isolation among older adults.

Christina Matz is an associate professor at the Boston College School ofSocial Work. Her areas of research include productive engagement of olderadults, aging workforce, quality of employment, healthy aging, volunteer andcivic engagement, and elder caregiving.

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