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A Two-edged Sword: The Differential Effect of Religious Belief and Religious Social Context on Attitudes towards Democracy

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Abstract

Different components of the religious experience have differing effects on attitudes towards democracy. Using heteroskedastic maximum likelihood models and data from the fourth wave of the World Values Survey for 45 democratic countries, we show that as a personal belief system, religiosity contrasts with democratic principles, generating opposition to democracy while increasing ambivalence towards democratic principles among religious people. Nevertheless, at the group level, religion also serves as a social institution which increases the homogeneity of one’s social network, leading to lower ambivalence, and makes for an active minority group which benefits from the democratic framework, consequently increasing support overall for a democratic regime. This double-edged sword effect explains the mixed results currently found in the literature on religiosity and democracy, and clearly illustrates the multidimensionality of religiosity.

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Notes

  1. The final countries included in the analysis and their Freedom House scores are: Austria (1), Canada (1), Denmark (1), Finland (1), Iceland (1), Ireland (1), Luxembourg (1), Malta (1), Netherlands (1), United States (1), Belgium (1.5), Czech Republic (1.5), Estonia (1.5), France (1.5), Germany (1.5), Great Britain (1.5), Hungary (1.5), Italy (1.5), Japan (1.5), Latvia (1.5), Lithuania (1.5), Poland (1.5), Slovakia (1.5), South Africa (1.5), Spain (1.5), Bulgaria (2), Chile (2), Croatia (2), Greece (2), Peru (2), Romania (2), India (2.5), Mexico (2.5), Philippines (2.5), Argentina (3), Republic of Moldova (3), Serbia (3), Montenegro (3), Albania (3.5), Bangladesh (3.5), Macedonia (4), Ukraine (4), Tanzania (4), Russian Federation (5), Uganda (5).

  2. Since the measure is composed of two items, which does not allow for the utilization of CFA, we use a summary index.

  3. Unfortunately, the WVS questionnaire does not include many items that could be used as anchors for other scales.

  4. Missing values in the dataset lead to a serious decline in the number of observations available due to listwise deletion. To overcome such problems, for measures derived from confirmatory factor analysis, we use the MPLUS software’s missing command, which uses full information of maximum likelihood from all available data unless all items in the factor model are missing.

  5. Independent Christianity may mean different things in different contexts depending on the broader institutional arrangements. Although we coded “Christians” (14 observations from Canada only) as belonging to the Catholic tradition, Manza and Brooks (1997) code unaffiliated Christians in the “Protestant” category, while Mueller (2009) codes them into the "other" category (coding scheme obtained via personal communication, December, 2010). However, because the number of observations for "Christian" identifiers in our dataset is small, recoding them into an "other/independent" category or dropping them from the analysis leads to no substantive changes in our results.

  6. The average of the score in our dataset is 1.22, and the range is 1–5.5. Since non-democratic countries were not considered in our analysis, the average is lower than the world average of 3.4.

  7. We reran the models using binary measures that specify a country’s dominant religious tradition (as in Norris and Inglehart 2004) rather than the reported percentage of each denomination (as in Barro and McCleary 2003a, b, and Durlauf et al. 2005). The results are robust to this alteration in measure.

  8. Effect sizes are comparable, as all individual level variables are coded to vary 0–1, with the exception of age, measured in years (see frequencies table in the appendix).

  9. Note that we took the logs in order to constrain the variance to positive values; thus changes in the independent variables predict changes in the logs of the variance.

  10. Each model included one religious tradition at a time such that the baseline is all others. For example, the multilevel model that tests for the effect of being a Protestant on support for democracy conditional on the dominance of Protestant tradition in a country is: The multilevel model for attitudes towards democracy is \( {\text{Y}}_{\text{ij}} = \beta_{{0{\text{j}}}} + \beta_{{ 1 {\text{j}}}} \times {\text{X}}_{{ 1 {\text{ij}}}} + \beta_{{2{\text{j}}}} \times {\text{X2}}_{\text{j}} + \sum\nolimits_{q = 3}^{p} {\beta {\text{qj}} + \varepsilon_{\text{ij}} ;} \) where Yij is support for democracy for individual i country j, β0j is the country-level intercept, X1ij Protestant dummy for individual i in country j, X2j is percent Protestant in country j, Since we control for the effect of Protestant identification on support for democracy as Protestantism becomes more dominant, the slope of the Protestant identification (β1j) variable is defined by the equation: β1j = γ10 + γ11 × X2j + δ1j. In alternative models, we also included the “no denomination” dummy, so that the baseline is “all other identifiers”, which did not lead to substantive changes in the results.

  11. In fact, a number of predominantly Orthodox countries are also characterized by restricted religious freedoms and by high levels of regulation of religious activity.

  12. The multilevel model for attitudes towards democracy is \( {\text{Y}}_{\text{ij}} = \beta_{{0{\text{j}}}} + \beta_{{ 1 {\text{j}}}} \times {\text{X}}_{{ 1 {\text{ij}}}} + \beta_{{2{\text{j}}}} \times {\text{X2}}_{\text{j}} + \beta_{{3{\text{j}}}} \times {\text{X3}}_{\text{j}} \sum\nolimits_{q = 4}^{p} {\beta {\text{qj}} \times {\text{X}}_{\text{qij}} + \varepsilon_{\text{ij}} ;} \) where Yij is support for democracy for individual i country j, β0j is the country-level intercept, X1ij Orthodox dummy for individual i in country j, X2j is percent Orthodox in country j, X3j is religious freedom index value for country j. Since we are interested in the level of support of Orthodox identifiers conditional on the level of religious freedoms in countries as percent orthodox increases, the slope of Orthodox identification β1j is random and is defined by the equation: β1j = γ10 + γ11 × X2j + γ12 × X3j + γ13 × X2j × X3j + δ1j.

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Acknowledgment

We would like to thank two anonymous reviewers and the editors for their insightful readings and constructive suggestions. All remaining errors are our own.

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  1. Department of Political Science, The Hebrew University of Jerusalem, Mount Scopus, Jerusalem, Israel

    Pazit Ben-Nun Bloom

  2. Department of Sociology, Yeditepe University, Istanbul, Turkey

    Gizem Arikan

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Appendix

Appendix

See Appendix Tables 3, 4.

Table 3 Summary statistics by religious affiliation and country
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Table 4 Key summary statistics by country
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Ben-Nun Bloom, P., Arikan, G. A Two-edged Sword: The Differential Effect of Religious Belief and Religious Social Context on Attitudes towards Democracy. Polit Behav 34, 249–276 (2012). https://doi.org/10.1007/s11109-011-9157-x

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