ANOVA with Hofstede's Cultural Values and Economic Outcomes

While in B-School, back around 2002, I considered trying to get published, partially because I had done well enough in economics and finance - A's in everything, and disturbed the curve in Financial Economics - to be a member of the International Economics Honor Society, which came with a subscription to American Economics, and that came with an opportunity to publish. Nothing came of it, other than highly illuminating correlations between income inequality (Gini Coefficient) and negative social outcomes, but I kept the data, and can now use it with R. To make the data more relevant to ongoing political discourse, it was limited to developed countries in the OECD. The data file is linked via Google Drive at the end of this post.

Countries
 
[1] Australia      Austria        Belgium        Canada         Denmark       
[6] Finland        France         Germany        Greece         Iceland       
[11] Ireland        Italy          Japan          Korea          Luxembourg    
[16] Netherlands    New Zealand    Norway         Portugal       Spain         
[21] Sweden         Switzerland    United Kingdom United States   


Data Columns
 
 [1] "Country"                       "HofstederPowerDx"             
 [3] "HofstederIndividuality"        "HofstederMasculinity"         
 [5] "HofstederUncertaintyAvoidance" "Diversity_Ethnic"             
 [7] "Diversity_Linguistic"          "Diversity_Religious"          
 [9] "ReligionMatters"               "Protestantism"                
[11] "Religiosity"                   "IQ"                           
[13] "Gini"                          "Employment"                   
[15] "Unemployment"                  "EduReading"                   
[17] "EduScience"                    "TertiaryEdu"                  
[19] "LifeExpectancy"                "InfantDeath"                  
[21] "Obesity"                       "HoursWorked"                  
[23] "Prison"                        "Carvandalism"                 
[25] "Cartheft"                      "Theftfromcar"                 
[27] "Motorcycletheft"               "Bicycletheft"                 
[29] "Assaultsandthreats"            "Sexualincidents"              
[31] "Burglaries"                    "Robberies"                    


Example Code
 
 #ANOVA  
 rm(list = ls())  
 
 oecdData <- read.table("OECD - Quality of Life.csv", header = TRUE, sep = ",")  
 
 print(is.data.frame(oecdData))  
 print(ncol(oecdData))  
 print(nrow(oecdData))  
 print(summary(oecdData))  
 print(names(oecdData))  
 
 v1 <- aov(Gini ~ HofstederIndividuality * HofstederMasculinity, data = oecdData)  
 v2 <- aov(Gini ~ HofstederIndividuality + HofstederMasculinity, data = oecdData)  
 result <- anova(v1, v2)  
 print(summary(v1))  
 print(summary(v2))  
 print(result)  
  


Example Results

 > v1 <- aov(Gini ~ HofstederIndividuality * HofstederMasculinity, data = oecdData)  
 + v2 <- aov(Gini ~ HofstederIndividuality + HofstederMasculinity, data = oecdData)  
 + result <- anova(v1, v2)  
 + print(summary(v1))  
 + print(summary(v2))  
 + print(result)  
                                               Df Sum Sq Mean Sq F value Pr(>F)  
 HofstederIndividuality                        1  4.00   4.00    0.283   0.60097  
 HofstederMasculinity                          1 46.87   46.87   3.318   0.08519  
 HofstederIndividuality:HofstederMasculinity   1 188.07  188.07  13.315  0.00184  
 Residuals                                    18 254.26  14.13          
 HofstederIndividuality              
 HofstederMasculinity            .   
 HofstederIndividuality:HofstederMasculinity **  
 Residuals                     
 ---  

 Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  

 3 observations deleted due to missingness  

                            Df Sum Sq Mean Sq F value Pr(>F)  
 HofstederIndividuality     1  4.0    4.00    0.172   0.683  
 HofstederMasculinity       1  46.9   46.87   2.013   0.172  
 Residuals                 19  442.3  23.28          

 3 observations deleted due to missingness  

 Analysis of Variance Table  
 Model 1: Gini ~ HofstederIndividuality * HofstederMasculinity  
 Model 2: Gini ~ HofstederIndividuality + HofstederMasculinity  
 
 Res.Df  RSS    Df  Sum of Sq   F      Pr(>F)    
 1   18  254.26                  
 2   19  442.33 -1  -188.07     13.315 0.001836 **  
 ---  
 
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  



Sample Data

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