Abstract
Using data from the 1984–2014 General Social Survey, we employ cross classified random effects models to examine age-period-cohort changes in political tolerance by religion, incorporating religious belief, belonging, and behavior into the analyses. The results show large cohort and period based changes in political tolerance of the various religious traditions. The results also suggest that the primary driver of changes over time in political tolerance is best attributed to period effects, not cohort replacement, although there is some evidence of a cohort effect among Roman Catholics. Finally, our results demonstrate that aggregate data analyses can lead to noticeably different results and thus can mask substantial differences between groups.
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Notes
To help the reader visualize the influence of age-period-cohort, we offer an example of what each effect might look like. For religion, it is commonly observed that individuals become more religious as they age (an age effect). Given the link between doctrinal orthodoxy (a type of increased religiosity), the influence of age might, due to increased religiosity, lead to decreased political tolerance. However, different generations might experience religion differently (a cohort influence). Thus, individuals born in 1940 will have a different experience with Christianity (for example) than those born in 1980. There are all sorts of cohort trends, such as in fashion, music, or social norms; it works similarly with religion and political tolerance. Finally, there are period effects, which means there are historical changes that effect everyone regardless of age or cohort. Recently, there is significant data suggesting that the “no religion” category is growing (the none’s). This is a potential period/historical effect as it occurred rapidly. Thus, if there are a significant number of individuals who do not affiliate with religion, formally, the change happened rapidly at a particular point in time, and it endures, then this is a period effect with enduring consequences for both religion and political tolerance.
Methodologically, age effects are integral to age-period-cohort analysis and are included in the analyses. However, since our interest is in identifying period and cohort effects, age effects are largely excluded from the discussion. In short, while it is necessary to simultaneously estimate age, period, and cohort effects, we estimate but do not discuss the effects associated with age.
It is important to recognize that a long history of the extant literature in social science research suggests religion (belief, belonging, and/or behavior) is a predictor of political tolerance (including, but not limited to: Nunn et al. 1978; Sullivan et al. 1982; Smidt and Penning 1982; McClosky and Brill 1983; Wilcox and Jelen 1990; Green et al. 1994; Gibson 2010). Yet, some recent research suggests a more complex relationship between political attitudes and religion wherein political attitudes may drive changes in religious tradition (on this point, see Putnam and Campbell 2010; Husser 2012). Is it possible that tolerance attitudes are driving religious belonging instead of religious belonging driving tolerance attitudes? We do not believe that theoretical or empirical debate is consequential to our analysis here for two reasons. First, the measures for (various) political attitudes are distinctly different than our measure of political tolerance. Second, as one will see in our results, whether looking at our data for all respondents or for each religious tradition, either by cohort or by period, there is an overall increase in political tolerance. Regardless of why religious sorting occurs, our results suggests the trends are the same for all traditions.
The GSS has modified its list of target groups in order to adjust for changes in attitudes toward out-groups over time. Differences in attitudes toward target groups, in part, compelled the path-breaking work of Sullivan et al. (1982) introducing a least-liked measure of political tolerance, which allows individuals to select their least-liked group (or target group). Nevertheless, in many cases, individuals will choose groups like the Ku Klux Klan, which provides little differentiation from the GSS target-groups (Eisenstein 2006; Djupe and Calfano 2013). That is, while the “least liked” approach gives respondents the opportunity to name a group from the opposite side of the political spectrum, few choose that option. For example, in one dataset only 6.5% of respondents chose gay rights activists and just 5.1% chose pro-choice advocates as their least liked group (Djupe and Calfano 2013). In another dataset, 64% of all respondents chose the Ku Klux Klan as their least-liked groups; in contrast only 6.3% chose homosexuals and 3.3% chose pro-choice advocates as their least-liked groups (Eisenstein 2006). In 1987, Gibson’s national survey using the least-liked approach shows 61 percent of his respondents selected the Ku Klux Klan as either their first or second least-liked group. In 2005, Howard et al. conducted a national survey using the last-liked approach and 57.6% of their respondents selected the Ku Klux Klan as either their first or second least-liked group. Essentially, a majority of respondents from across the political and religious spectrum, even when given a chance to choose their least-liked group, choose the Ku Klux Klan. While we recognize the issue of target-group selection is at the core of political tolerance measurement, and we certainly recognize that out-groups can and do change over time, the data suggests target group selection is stable and consistent.
In the GSS, these 15 tolerance items are coded in various ways. For anti-religionists: the speech options are 1 allowed/2 not allowed; the teach options are 4 allowed/5 not allowed; the library options are 1 remove/2 not remove. Thus, when recoding the anti-religionist tolerance items, the options 1 and 4 for the speech and teach items are both coded as 1 (tolerance), and 2 and 5 are both coded as 0 (intolerance); the library item is recoded so 1 is 0 (intolerance) and 2 is 1 (tolerance). For communists: the speech options are 1 allowed/2 not allowed; the teach options are 4 fired/5 not fired; the library options are 1 remove/2 not remove. When recoding the communist tolerance items, the speech item is 1 (tolerance) and 2 is changed to 0 (intolerance); both teach and library items are coded so that 4 and 1 are 0 (intolerance) and 5 and 2 are 1 (tolerance). For homosexuals: the speech options are 1 allowed/2 not allowed; the teach options are 4 allowed/5 not allowed; the library options are 1 remove/2 not remove. When recoding the homosexual tolerance items, the options 1 and 4 for the speech and teach items are coded as 1 (tolerance), and 2 and 5 are coded as 0 (intolerance); the library item is recoded so that 1 is 0 (intolerance) and 2 is 1 (tolerance). For racists: the speech options are 1 allowed/2 not allowed; the teach options are 4 allowed/5 not allowed; the library options are 1 remove/2 not remove. When recoding the racist tolerance items, the options 1 and 4 for speech and teach items are coded as 1 (tolerance), and 2 and 5 are coded as 0 (intolerance); the library item is recoded so that 1 is 0 (intolerance) and 2 is 1 (tolerance). For militarists: the speech options are 1 allowed/2 not allowed; the teach options are 4 allowed/5 not allowed; the library options are 1 remove/2 not remove. When recoding the militarist tolerance items, the options 1 and 4 for the speech and teach items are both coded as 1 (tolerance), and 2 and 5 are both coded as 0 (intolerance); the library item is recoded so 1 is 0 (intolerance) and 2 is 1 (tolerance).
The tolerance index, 0–15, has a mean of 9.98 and a standard deviation of 4.675.
In order to further support our treatment of political tolerance as a single variable, we ran separate analyses on the five outgroups while controlling for religious tradition. Our results suggest that there are no meaningful differences, when accounting for religious tradition, on the political tolerance of each outgroup, in comparison to our combined tolerance measure. For example, for anti-religionists, the influence of mainline Protestants, Catholics, and black Protestants (with evangelicals as our reference) exhibited the same pattern of influence on tolerance toward anti-religionists as on our combined tolerance index. The same is true for the influence of religious tradition on racists, communists, and militarists in comparison to our combined tolerance index; the pattern of influence by religious tradition is similar when tolerance is aggregated or disaggregated by outgroup. For tolerance toward homosexuals, all religious traditions had almost identical increased levels of tolerance toward homosexuals (with evangelicals, again, as our reference). Taken as a whole, we believe this supports our usage of tolerance as a single index. There is either no difference in the pattern of influence exhibited by religious tradition on tolerance (aggregated or disaggregated), or all religious traditions behave the same in relationship to the object of tolerance (homosexuals) with the exception of evangelical Protestants, which is not unexpected. Further, a factor analysis of the 15 tolerance items does not support any group-based (anti-religionists, racists, etc.) attitude structure (results available upon request).
Because we are looking at the political tolerance of distinct religious traditions, we can only analyze evangelical Protestants, mainline Protestants, and Catholics. That is, when we do not look at the aggregate political tolerance data but instead look at it for each religious tradition, we reduce the number of respondents we have in each category. For example, political tolerance by black Protestants produces 1160 black Protestants, but there are 18 time periods that we assess, which means there are an average of 64 respondents for each survey year for black Protestants. In fact, some survey years have as few as 23 black Protestants. There are simply not enough respondents in each category (period, cohort) to analyze black Protestants. This same problem also prevents us from separately analyzing the Jewish tradition, the unaffiliated, and the category for other religious groups.
The GSS does not distinguish among respondents ages 90 and above and simply assigns these individuals an age score of 89.
As noted by Yang and Land (2008) the definition of the width of the time intervals is somewhat arbitrary, but the use of five-year birth cohorts is the norm in age-period-cohort analyses. However, due to the small number of respondents from older and younger cohorts, we group respondents born between 1898 and 1910 into a single cohort category and respondents born between 1986 and 1996 into a single cohort category for more stable estimates.
In a comparison of fixed and random period and cohort effects, Yang and Land (2008) show that the random-effects models are more statistically efficient when using unbalanced data that contains unequal numbers of respondents in year-by-cohort cell.
Please see footnote 8 above; because we are analyzing respondents by period (18 of them) and cohort (17 of them), we do not have enough respondents in other groupings to estimate across period and cohort effects.
It was suggested we run our models without the religious belief and behavior variables for a robustness check. Our results are stable across all respondents if these variables are excluded. Given that, and the importance of the three b’s (belief, belonging and behavior) in the empirical study of religion, we do not report those results.
As our empirical results showed, Roman Catholics exhibit some distinctive cohort effects with respect to political tolerance. The proportion of the Catholic population in the United States descended from European ancestors has become smaller, and the Latino percentage of the Catholic population has witnessed a corresponding increase. Unfortunately, the GSS do not contain sufficiently precise measures of racial identification to permit us to analyze the effects of these demographic changes.
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Appendices
Appendix 1: Dependent Variable Wording
There are three questions addressing each of the five target groups, which pertain to freedom of expression: making a public speech, teaching in a college or university, and having a book in the public library. Responses are dichotomous and coded 0 for intolerance and 1 for tolerance.
See Table 2.
Appendix 2
Individual level determinants of political tolerance fall into three sources: social, psychological and political. Due to availability constraints in GSS, we are unable to include any psychological determinants of political tolerance.
POLITICAL VARIABLES
Political Ideology (Polviews)
We hear a lot of talk these days about liberals and conservatives. I’m going to show you a seven-point scale on which the political views that people might hold are arranged from extremely liberal–point 1–to extremely conservative– point 7. Where would you place yourself on this scale?
Extremely liberal
Liberal
Slightly liberal
Moderate
Slightly conservative
Conservative
Extremely conservative
SOCIAL VARIABLES
Religious Tradition: (Relig) Religious tradition has (minimally) six religious traditions: evangelical Protestant, mainline Protestant, black Protestant, Roman Catholic, Jewish, and non-affiliated. Religious tradition is categorized using the Steensland et al. (2000) coding scheme for GSS data with corrections suggested by Stetzer and Burge (2015).
Religious Belief: (Bible)
Which of these statements comes closest to describing your feelings about the Bible?
-
a.
The Bible is the actual word of God and is to be taken literally, word for word.
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b.
The Bible is the inspired word of God but not everything in it should be taken literally, word for word.
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c.
The Bible is an ancient book of fables, legends, history, and moral precepts recorded by men.
Religious Behavior: (attend & pray)
1. How often do you attend religious services?
Never.
Less than once a year.
Once a year.
Several times a year.
Once a month.
2–3 times a month.
Nearly every week.
Every week.
More than once a week.
DK/NA.
2. About how often do you pray?
Several times a day.
Once a day.
Several times a week.
Once a week.
Less than once a week.
Never.
DK/NA.
Age: Measured by date of birth.
Gender: We measure this with a dummy variable for female respondents.
Race: We measure this as a dummy variable white/non-white, with non-white as the reference category.
Region of Residence: We measure this with dummy variables for the Southern, Midwestern, Northeastern, and Western regions.
Education: This is coded as years of education and is centered on the grand mean.
Income: We use the GSS variable ‘realinc”, which is a measure of family income in constant dollars (base = 1986), and convert it to its natural log.
Married: This is measured as a dummy variable.
Minor child(ren) in the home: This is measured as a dummy variable.
Urbanity: Urban (100 largest SMSAs), suburban (suburb of 100 largest SMSAs), and rural (with “other urban” as the reference category).
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Eisenstein, M.A., Clark, A.K. & Jelen, T.G. Political Tolerance and Religion: An Age-Period-Cohort Analysis, 1984–2014. Rev Relig Res 59, 395–418 (2017). https://doi.org/10.1007/s13644-017-0295-4
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DOI: https://doi.org/10.1007/s13644-017-0295-4