Abstract
In this study, we analyze the relationship between county-level religious adherence rates and county-level cohabitation rates in the year 2000. Based on the concept of moral communities, we test hypotheses that higher rates of affiliation with certain religious traditions and lower levels of religious heterogeneity will correlate with lower rates of cohabitation. The analysis consists of data from two sources: the 2000 U.S. Census and the 2000 Religious Congregations and Membership Study. In well-controlled models, spatial regression analysis indicates significant negative relationships between evangelical adherence rates and percent cohabiting. Our results also show that, although rates of adherence and heterogeneity also matter outside the South, the effects are consistently stronger in the South and among Christian denominations. This study advances research on cohabitation by conducting a county-level examination of the impact of religion and moral communities on the increasingly high levels of cohabitation that we are seeing in the United States.
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Notes
Economists and readers familiar with the religious pluralism literature will recognize the similarity between our index and the Herfindahl index. The Herfindahl index was frequently used by religious economies theorists during the 1980 s and 1990 s, but subsequently has become the object of much debate (Chaves and Gorski 2001; Finke and Stark 1989; Voas et al. 2002). Voas et al. (2002) make it clear that the problems with the index involve assessing its correlation with other religiosity measures such as religious participation. Since our dependent variable is a measure of cohabitation, the problem does not apply to this analysis. Voas et al. (2002: 215) point out that, despite its misuse in religious economies studies, “the index of pluralism has much to recommend it as an indicator of diversity.”.
The same calculations were performed for the second index of heterogeneity, which includes only Christian denominations.
Moran’s I (Moran 1950) is a weighted correlation coefficient used to identify non-random patterns in spatial data. A significant positive value indicates high covariance between proximate counties, thus helping to confirm the presence of spatial autocorrelation.
Standardized estimates were calculated using the following formula, recommended for maximum likelihood regression in Allison (1999): βj* = βj*σj/σd, j = l,…..,k,
Where βj* represents the standardized estimate, βj represents the unstandardized estimate, and σj represents the standard deviation of independent variable Xj. σd represents the standard deviation of the dependent variable. By holding this denominator constant in computing the standardized coefficients, the actual size of the coefficients is altered while the relative size for comparing coefficients remains the same. This method permits comparison of standardized coefficients.
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The authors would like to thank David Voas and Charles Tolbert for their reviews of and suggestions for this manuscript.
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Gault-Sherman, M., Draper, S. What Will the Neighbors Think? The Effect of Moral Communities on Cohabitation. Rev Relig Res 54, 45–67 (2012). https://doi.org/10.1007/s13644-011-0039-9
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DOI: https://doi.org/10.1007/s13644-011-0039-9