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Inequality and Religiosity: The Gini ~ Religion Matters Vector, with Correlations and Plot

Responses to a post on the correlation between country-average IQ and responding yes to a question on if religion matters are inversely correlated, but not strongly so, prompted me to dig up a more significant issue, the relationship between religiosity and inequality, as measured by the Gini coefficient.

The correlation is quite high, at about .7, although this really says nothing about the cause, if religious countries tend toward inequality because of general tendencies, or if inequality drives people to religion, as a salve against suffering. In truth, they could both be reflective of some other aspect of a country, and not in any way causative.

Example Code


 # Correlations on ReligionMatters and Gini Coeficients  
 oecdData <- read.table("OECD - Quality of Life.csv", header = TRUE, sep = ",")  
 #names(oecdData)  
   
 religionMattersVector <- oecdData$ReligionMatters  
 giniVector <- oecdData$Gini  
   
 cor.test(giniVector, religionMattersVector)  
 lm2 <- lm(giniVector ~ religionMattersVector)  
 summary(lm2)  
   
 # Plot the chart.  
 plot(giniVector, religionMattersVector, col = "blue", main = "Gini Coefficient ~ Religion Matters Vector"
     , abline(lm(religionMattersVector ~ giniVector)), xlab = "Religion Matters", ylab = "Gini")  
   

Example Results

   
 Pearson's product-moment correlation  
   
 data: giniVector and religionMattersVector  

 t = 4.4676, df = 20, p-value = 0.0002359  

 alternative hypothesis: true correlation is not equal to 0  

 95 percent confidence interval:  
  0.4061694 0.8693247  

 sample estimates:  
    cor   
 0.7067507   
   
 Call:  
 lm(formula = giniVector ~ religionMattersVector)  
   
 Residuals:  
   Min   1Q Median   3Q  Max   
 -5.400 -2.174 -1.629 1.626 7.054   
   
 Coefficients:  
                       Estimate Std. Error t value  Pr(>|t|)    
 (Intercept)            23.597   1.989      11.866   1.66e-10 ***  
 religionMattersVector  19.810   4.434      4.468    0.000236 ***  
 
 Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  
   
 Residual standard error: 3.513 on 20 degrees of freedom  
  (2 observations deleted due to missingness)  
 
Multiple R-squared: 0.4995,     Adjusted R-squared: 0.4745   
 F-statistic: 19.96 on 1 and 20 DF, p-value: 0.0002359  


Sample Data

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Example Code
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Example Code
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