Health Outcomes and Volunteering: The Moderating Role of Religiosity

Abstract

In this paper, we examine whether and what extent public and private forms of religiosity act as moderators of the volunteering and well-being relationship in mid- to later-life. We use data from the second wave of the National Survey of Midlife Development in the United States (n = 1,805). We analyzed the relationships between volunteering and indicators of well-being (self-rated physical and mental health), and tested the moderating effects of public and private religiosity on the volunteering and well-being relationship. Our findings suggest that salubrious effects of volunteering on the self-perceived physical and mental health of middle- aged and older- aged adults varied by their participation in different forms of religiosity. In particular, volunteers who engaged in more public forms of religiosity reported significantly better physical and mental health than non-volunteers who engaged in these forms of religiosity. In other words, individuals who were actively engaged public forms of religious practices and who volunteered, maximized the associated health benefits.

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Notes

  1. 1.

    There is a difference between volunteering for a religious organization (e.g., teaching church school; serving in the temple soup kitchen) and volunteering through a religious organization (e.g., signing up for a Habitat build with congregation members.

  2. 2.

    Although scholars have identified a positive link between religiosity and well-being, findings from this research have been far from conclusive (see, Thoresen and Harris 2002).

  3. 3.

    Although we focus here on relationships between volunteering and health outcomes of both middle- and older- aged adults, it should be noted that some research suggests that the well-being benefits derived from volunteering may actually be stronger for older adults than for their middle-aged (and even younger) adult counterparts (e.g., Musick and Wilson 2003; van Willigen 2000). Volunteering in mid-life, however, still remains an important area of research as functional limitations and poor health may reduce the ability to volunteer in older age.

  4. 4.

    For reviews of the literature on volunteering and health, see Grimm et al. (2007) Oman (2007).

  5. 5.

    Correlation between the dependent variables suggests that the measures, although related, are distinct indicators of well-being (r = .55).

  6. 6.

    Given that the volunteering questions in the MIDUS survey were not mutually exclusive, it is uncertain whether or not individuals responded positively to a single volunteer experience by selecting multiple response options. For example, someone who volunteered with an environmental youth services organization may have indicated that he/she participated in “school or youth-related volunteer work” as well as “volunteer work for any other organization, cause or charity.” Thus, although some scholars have used total volunteer hours from the MIDUS surveys as a variable in their analyses (see for instance, Einolf 2009), it is uncertain whether these values truly reflect the total time respondents actually spent participating in volunteer activities. As such, total number of volunteer hours was not included in this analysis.

  7. 7.

    Although other researchers (e.g., Einolf 2013; Son and Wilson 2011) have controlled for race when using MIDUS data, 90 per cent of the sample (in MIDUS II, in particular) is white; therefore, we excluded race from our analysis.

  8. 8.

    We also estimated the models using the natural log of the income variable. The results (not shown) from these estimations did not alter the substantive findings.

  9. 9.

    OLS regression can often be problematic when estimating a model on an ordinal outcome variable (Long 1997). However, with five or more categories OLS results (which are considered linear probability models, in this instance) can allow for simpler interpretation. Still, in order to verify that our results were not altered by model specification, we also estimated each of the models in this analysis using logit regression analyses. Only our models assessing predictors of mental health status passed the proportionality of odds assumption for ordered logit analysis. Therefore, we estimated our models of physical health status using multinomial regression techniques. In general, most substantive results held, thus leading us to believe that model specification is not a significant concern.

  10. 10.

    Missing values for most variables were <3.5 %, and in these cases respondents were excluded from the analysis. However, as is common in survey research, the household income variable had a relatively higher amount of missing data (approximately 8 %). As recommended by Allison (2001) the use of listwise deletion is sufficient for missing data <15 %.

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Acknowledgments

Partial support for this research was gratefully received from a grant by the Robert Wood Johnson Foundation Health & Society Scholars Program at the University of Pennsylvania.

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Correspondence to Femida Handy.

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McDougle, L., Handy, F., Konrath, S. et al. Health Outcomes and Volunteering: The Moderating Role of Religiosity. Soc Indic Res 117, 337–351 (2014). https://doi.org/10.1007/s11205-013-0336-5

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Keywords

  • Volunteering
  • Religiosity
  • Health outcomes