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Does Religion Make You Healthier and Longer Lived? Evidence for Germany

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Abstract

Researchers in the US have consistently reported substantial—not just statistically significant—links between religious belief and practice, and improved health and longevity. In this paper we report evidence for Germany, using data from the long-running, nationally representative German Socio-Economic Panel (SOEP 1984). The SOEP dataset includes multiple measures of health, plus many ‘controls’ which it is appropriate to use in assessing links between religious practice, health and longevity. These controls include personality traits known to be associated with better health (notably conscientiousness), and also the age of death of parents of the survey respondents. Initial results suggested that religious practice (church attendance) may be linked only to subjective (self-rated) measures of health, not to more objective measures. It seemed possible that results in some previous research could be due to what may be termed satisfaction bias or positivity bias; the known tendency of religious people to report higher than average satisfaction with almost all aspects of life. Further investigation indicated that relationships between church attendance and subjective measures of health were weaker, when a control for satisfaction bias was in place. However, there was countervailing evidence that the subjective measures in SOEP may actually be more not less valid than the objective measures; they are better not worse predictors of mortality. It was also clear that religious belief and church attendance are associated with health-protective behaviors and attitudes, including taking more exercise, not smoking and higher life satisfaction. At the end of the paper we estimate a structural equation model which maps links between religious practice, these protective behaviors and attitudes, and improved health outcomes.

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Notes

  1. These figures are for 2007; the latest year in which denomination was asked in SOEP.

  2. Pcs and mcs stand for physical component summary and mental component summary.

  3. The longer ago a doctor visit occurred, the more likely it is that it will be forgotten and not reported, especially if the health problem was minor. Recall over a 3-month period is likely to be subject to a small degree of downward bias (Ayhan and Isiksal 2004).

  4. Data have also been collected in 2010 and 2012 but are not yet available.

  5. However, the correlation of pcs with self-reported health cannot be considered in this context because the self-reported health measure (a single item) is included in the pcs index. It remains the case that pcs has the highest mean correlation with the other 3 items.

  6. Regression analysis is essentially a single equation technique. Regression estimates derived from multi-equation systems are likely to be biased, due to correlations between explanatory variables and error terms in some or all equations. A key assumption of OLS regression is that such correlations are zero.

  7. ML estimates are usually consistent and asymptotically normal under the (not very restrictive) assumption of conditional normality (STATA 2011). Only paths or covariances linking conditioning (i.e. control) variables may not be consistent and asymptotically normal (even then, the main problem lies just with estimates of standard errors). These paths are not usually of substantive interest; substantive interest lies in paths (1) linking exogenous with endogenous variables and (2) between endogenous variables.

  8. From a mathematical standpoint, a model can be viewed as a set of constraints—or a set of restricted paths—limiting the possibilities of simply reproducing the input data. Attempts by a researcher to improve his/her model involve modifying these constraints to improve model fit…subject to the theory/hypotheses underlying the model.

  9. The pcs health measure (0–100 scale) and life satisfaction (0–10 scale) both have quasi-normal distributions.

  10. However, omitted variables which vary within-person over time can still bias coefficients.

  11. A large number of respondents reported never have been to the doctor in the period in question, and a few reported going hundreds of times.

  12. Because some individuals have very strong grips, the variable is not normally distributed, but is approximately log-normal.

  13. Again using the subjective measures, no differences in the church attendance-health link were found between Protestants and Catholics. Nor did church attendance appear to benefit older people, or specifically older widows, more than younger people.

  14. It has been suggested to us that this may be partly because the subjective measures, as well as being self-assessments of health, may also to some extent measure optimism or cheerfulness, which could itself be related to longevity.

  15. (Ln) Grip strength of dominant hand proved to be a better predictor than change in grip strength (Bohannon 2008).

  16. Without making this assumption, we would have had to leave an implausible number of apparent centenarians in the dataset!

  17. Age of father’s death (but not age of mother’s death) appears significantly related to men’s own longevity in alternative equations in which personality traits are omitted. Further, the results linking women’s longevity to parental longevity are strengthened if traits are omitted. It may be noted that personality traits were not included as controls in the Israeli and Icelandic studies cited in the “Introduction”, which showed statistically significant but weak intergenerational longevity linkages.

  18. In this table only the coefficients for church attendance and the pcs health measure are of interest. The other variables are worth inclusion as ‘controls’, but their coefficients are somewhat misleading because the causal ordering is inappropriate. For example, the variables measuring age, partnership status and years of education should be regarded as causally antecedent to the pcs health measure. See Table 5 for preferable estimates.

  19. Note, however, the Norwegian study referenced above which indicated that attending religious services lowers blood pressure (Sorensen et al. 2011).

  20. The grip strength measure was first taken in 2006, so it is not possible to make an 8-year prediction. However, grip strength performs less well than life satisfaction for 2- and 4-year predictions.

  21. Initially, Granger’s reasoning only applied to pairs of variables and took no account of possible 3rd, 4th etc confounding variables which might be associated with both x and y. However, the approach was later extended to relationships involving multiple variables by using vector autoregression.

  22. In practice, when 6- and 8-year lags were included, these ML models began to show symptoms of multicollinearity. This was another reason for preferring 3-wave panel models to models with 4 or 5 waves.

  23. It can be technically feasible to estimate 3+ wave models which include both lagged and simultaneous links. However, it is difficult to achieve identification for such models, so few examples are found in the literature.

  24. The simultaneous causation model included 2-year lagged links between the same variable at different time points, but not 4-year (Granger-style) lags.

  25. The sensitivity tests are all the more stringent because two lags of the “mechanism” variables are included in estimates of links between church attendance lagged by 2 years and contemporaneous (time t) “mechanisms”.

  26. Our alternative simultaneous causation model is also a good fit: CFI = 0.993; TLI = 0.985; RMSEA = 0.020; SRMR = 0.013 and CD = 0.627 (p < 0.001).

  27. In our alternative simultaneous equation model the coefficients (bs) for links between church attendance and exercise, smoking and life satisfaction are a bit larger: exercise b = 0.066 (p < 0.001), smoking b = −0.019 (p < 0.001) and life satisfaction b = 0.196 (p < 0.001).

  28. The reason for preferring the simultaneous model at this juncture is largely technical. In the simultaneous model it is possible to obtain estimates of the total effect of contemporaneous church attendance on pcs (given in the text), as well as church attendance lagged by 2 years and 4 years. In the Fig. 1 model, solely because of the Granger-style specification, there are neither direct nor indirect links between pcs and contemporaneous church attendance, or church attendance lagged by 2 years. The only available estimate is between pcs and church attendance lagged by 4 years. This estimate is 0.075 (p < 0.001) which, given the 4-year lag, seems certain to be downwardly biased.

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Acknowledgments

We are grateful for comments and suggestions from Ruud Muffels of Tilburg University and Stephen Headey of Monash University.

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Authors and Affiliations

  1. Melbourne Institute, University of Melbourne, Parkville, 3052, Australia

    Bruce Headey

  2. German Institute for Economic Research (DIW), Berlin, Germany

    Bruce Headey & Gert G. Wagner

  3. Berlin University of Technology, Berlin, Germany

    Gerhard Hoehne & Gert G. Wagner

  4. Max Planck Institute for Human Development, Berlin, Germany

    Gert G. Wagner

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  1. Bruce Headey
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  2. Gerhard Hoehne
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  3. Gert G. Wagner
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Correspondence to Bruce Headey.

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Headey, B., Hoehne, G. & Wagner, G.G. Does Religion Make You Healthier and Longer Lived? Evidence for Germany. Soc Indic Res 119, 1335–1361 (2014). https://doi.org/10.1007/s11205-013-0546-x

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