Happiness score and tax rates for 148 countries

Dataset on subjective happiness, tax rates, population sizes, continent, and major religion for 148 countries

Format

A data frame with 148 observations on the following 6 variables.

country

a factor with 148 levels that contain the country names

happy

a numeric vector with the average subject happiness score (on a scale from 0-10)

tax

a numeric vector showing the tax revenue as percentage of GDP

religion

a factor with levels Buddhist Christian Hindu Muslim None or Other

continent

a factor with levels AF, AS, EU, NA, OC, SA, corresponding to the continents Africa, Asia, Europe, North America, Ocenaia, South American, respectively

population

a numeric vector showing the population (in millions)

Source

Data collected by Ellen Ekstroem.
Population sizes are from Wikipedia per August 2nd, 2012 https://en.wikipedia.org/wiki/List_of_countries_by_population
Major religions are from Wikipedia per August 2nd, 2012 https://en.wikipedia.org/wiki/Religions_by_country
Tax rates are from Wikipedia per August 2nd, 2012 https://en.wikipedia.org/wiki/List_of_countries_by_tax_revenue_as_percentage_of_GDP
Average happiness scores are from "Veenhoven, R. Average happiness in 148 nations 2000-2009, World Database of Happiness, Erasmus University Rotterdam, The Netherlands". Assessed on August 2nd, 2012 at: https://worlddatabaseofhappiness-archive.eur.nl/hap_nat/findingreports/RankReport_AverageHappiness.php

Examples

data(happiness)
with(happiness, symbols(tax, happy, circles=sqrt(population)/8, inches=FALSE, bg=continent))


#
# Make a prettier image with transparent colors
#

newcols <- rgb(t(col2rgb(palette())),
               alpha=100, maxColorValue=255)

with(happiness, symbols(tax, happy, circles=sqrt(population)/8,
                inches=FALSE, bg=newcols[continent],
                xlab="Tax (% of GDP)", ylab="Happiness"))


#
# Simple analysis
#
res <- lm(happy ~ religion + population + tax:continent, data=happiness)
summary(res)
#> 
#> Call:
#> lm(formula = happy ~ religion + population + tax:continent, data = happiness)
#> 
#> Residuals:
#>      Min       1Q   Median       3Q      Max 
#> -1.96338 -0.67564 -0.05445  0.59088  2.26032 
#> 
#> Coefficients:
#>                     Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)        5.5427302  0.3739363  14.823  < 2e-16 ***
#> religionChristian -0.8019944  0.4014948  -1.998  0.04786 *  
#> religionHindu     -0.8993147  0.8097789  -1.111  0.26880    
#> religionMuslim    -0.3065812  0.3611458  -0.849  0.39749    
#> religionNone      -0.7014654  0.4929272  -1.423  0.15711    
#> religionOther     -1.3999987  0.6335210  -2.210  0.02886 *  
#> population         0.0003362  0.0005979   0.562  0.57484    
#> tax:continentAF   -0.0210612  0.0137894  -1.527  0.12911    
#> tax:continentAS    0.0383673  0.0140178   2.737  0.00707 ** 
#> tax:continentEU    0.0470980  0.0081429   5.784 5.16e-08 ***
#> tax:continentNA    0.1069641  0.0164712   6.494 1.62e-09 ***
#> tax:continentOC    0.0869860  0.0218046   3.989  0.00011 ***
#> tax:continentSA    0.0831271  0.0148874   5.584 1.32e-07 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Residual standard error: 0.9362 on 130 degrees of freedom
#>   (5 observations deleted due to missingness)
#> Multiple R-squared:  0.5331,	Adjusted R-squared:   0.49 
#> F-statistic: 12.37 on 12 and 130 DF,  p-value: < 2.2e-16
#>