The Data behind the World Happiness Report 2016
WHR = read.csv("data/2016.csv")Introduction
The 2016 World Happiness Report is the fourth of its kind and consists of data obtained from the Gallup World Poll, containing data from thousands of participants across over 160 countries. The report allows for a globally recognised quantifiable measure of happiness and through analysing this data we can derive a unique outlook on the international perception of happiness within a range of diverse populations.
Variable Classification:
Country: Qualitative Nominal Multivariate
Region: Qualitative Nominal Multivariate
Happiness Rank: Qualitative Discrete
Happiness Score: Qualitative Continuous
Lower Confidence Interval: Qualitative Continuous
Upper Confidence Interval: Qualitative Continuous
Economy (GDP per Capita): Qualitative Continuous
Family: Qualitative Continuous
Health (Life expectancy): Qualitative Continuous
Freedom: Qualitative Continuous
Trust (Government Corruption): Qualitative Continuous
Generosity: Qualitative Continuous
Dystopia residual: Qualitative Continuous
Research Questions
1. What does this data say about the extensiveness of global inequality between countries?
2. Is there a trend in the inequality between regions?
3. Which of the six accompanying factors (economy (GDP per Capita), family, health, freedom, trust (Government Corruption), generosity) have the highest correlation to happiness? Account for this trend.
4. Was the data able to measure “Happiness” in an effective manner?
5. How useful is the data in practical applications?
What does this data say about the extensiveness of global inequality between countries?
The data exhibits that there is a vast inequality in the measurement of “Happiness” across the 156 countries which were surveyed. By calculating the population standard deviation of the data, it becomes evident that the happiness scores are quite spread out since its at a high 1.138008. Thus, reflecting the large gaps or inequality between each country that they represent. This extensiveness of inequality is further supported by the box plot below which contain outliers, since the lowest country has a mean happiness score rating of 2.905 whilst the highest had 7.526. The range difference of 4.621 is almost half of the maximum 10 difference that can be attained on the Cantrell Ladder.
sd(WHR$Happiness.Score) * sqrt((155)/156)## [1] 1.138008range(WHR$Happiness.Score)## [1] 2.905 7.526boxplot(WHR$Happiness.Score, horizontal = TRUE, xlab = "Happiness Score")
The conclusion that there is high inequality between the countries is supported by data from the Maddison Project, a scholarly group which aims to measure the economic performance of different regions through time periods and subtopics, on the large gap between the richest and poorest countries . In accordance with this data since the 1960’s there has been a 135% increase in the wealth gap between 135% which may have a significant impact on the quality of life and therefore, levels of happiness.
Is there a trend in the inequality between regions?
Several noticeable trends exist across the 10 regions categorised in the data. The region of Sub-Saharan Africa, for example has the lowest mean happiness score at 4.14 and for which its countries occupy 17 of the bottom 20 ranks for comparative happiness. This trend follows that of recorded GDP per capita with a regional median of 0.39 as opposed to the international median of 1.0. The clear economic issues present in the region show a strong correlative trend with the relative unhappiness recorded by its citizens.
In contrast the region of Western Europe occupies 7 out of the top 10 ranks for comparative global happiness and boasts a mean happiness score for 6.69 across the region. Additionally this is paired with a a regional median GDP of 1.43, well above the 1.0 global average. This positive trend reflects the negative trend seen in the data from the region of Sub-Saharan Africa and provides statistical evidence to support a strong correlation between relative economic prosperity and perceived happiness.
The bar plot below exhibits the inequality in mean happiness scores across five of the most represented regions in the data. The clear difference in the Sub-Saharan Africa region and that of Western Europe is indicative of the opposing trends identified above.
Hscore_NA = mean(WHR$Happiness.Score[WHR$Region == "North America"])
Hscore_SEA = mean(WHR$Happiness.Score[WHR$Region == "Southeastern Asia"])
Hscore_SA = mean(WHR$Happiness.Score[WHR$Region == "Southern Asia"])
Hscore_ANZ = mean(WHR$Happiness.Score[WHR$Region == "Australia and New Zealand"])
Hscore_EA = mean(WHR$Happiness.Score[WHR$Region == "Eastern Asia"])
Hscore_WE = mean(WHR$Happiness.Score[WHR$Region == "Western Europe"])
Hscore_MENA = mean(WHR$Happiness.Score[WHR$Region == "Middle East and Northern Africa"])
Hscore_SSA = mean(WHR$Happiness.Score[WHR$Region == "Sub-Saharan Africa"])
Hscore_CEE = mean(WHR$Happiness.Score[WHR$Region == "Central and Eastern Europe"])
Hscore_LAC = mean(WHR$Happiness.Score[WHR$Region == "Latin America and Caribbean"])revec = c(Hscore_LAC, Hscore_CEE, Hscore_MENA, Hscore_SSA, Hscore_WE)
barplot(revec, main = "Mean Happiness Score of 5 Largest Regions", ylab = "Happiness Score", xlab = "Region",col = c("lightblue", "lightgreen", "yellow", "red", "purple"), legend = c("Latin America and Caribbean", "Central and Eastern Europe", "Middle East and Northern Africa", "Sub-Saharan Africa", "Western Europe"), ylim = c(0,8), xlim = c(0,7), width = 0.5,)
3. Which of the six accompanying factors (economy (GDP per Capita), family, health, freedom, trust (Government Corruption), generosity) have the highest correlation to happiness? Account for this high correlation.
cor(WHR$Happiness.Score, WHR$Economy..GDP.per.Capita.)## [1] 0.790322cor(WHR$Happiness.Score, WHR$Family)## [1] 0.7392516cor(WHR$Happiness.Score, WHR$Health..Life.Expectancy.)## [1] 0.7653843cor(WHR$Happiness.Score, WHR$Freedom)## [1] 0.5668267cor(WHR$Happiness.Score, WHR$Trust..Government.Corruption.)## [1] 0.4020322cor(WHR$Happiness.Score, WHR$Generosity)## [1] 0.1568478plot(WHR$Economy..GDP.per.Capita., WHR$Happiness.Score, ylab = "Happiness", xlab = "Economy (GDP per Capita)", main = "The Relationship between Happiness & Economy")
L = lm(WHR$Happiness.Score ~ WHR$Economy..GDP.per.Capita.)
abline(L)
plot(WHR$Family, WHR$Happiness.Score, ylab = "Happiness", xlab = "Family", main = "The relationship between Happiness and Family")
O = lm(WHR$Happiness.Score ~ WHR$Family)
abline(O)
plot(WHR$Health..Life.Expectancy., WHR$Happiness.Score, ylab = "Happiness", xlab = "Health", main = "The relationship between Happiness and Health")
Z = lm(WHR$Happiness.Score ~ WHR$Health..Life.Expectancy.)
abline(Z)
Economy = 0.79032
Family = 0.73925
health = 0.76538
Freedom = 0.56683
Trust = 0.40203
Generosity = 0.15685
Noticeable Trends:
Economy (GDP) Per capita has the strongest positive correlation. Family and Health similarly follow and have strong positive correlations to happiness whilst Freedom, Trust and Government only have weak positive correlation to Happiness score.
As can be seen in the correlation coefficient and the graphs above, Economy (GDP) Per Capita has the strongest positive linear correlation to happiness (0.79032). Theoretically this can be attributed to how a greater financial pool can improve the material quality of life for individual households and how it can improve the immaterial aspects of life for the community as a whole. Practically, this is supported by the OECD Better life Index. Countries who have higher GDP per capita have a general tendency to have better well-being outcomes.
Greater GDP per capita is reflective of a higher average income and therefore higher taxation revenue for the government, thus there is a larger monetary budget which can be spent on communal facilities such as healthcare centres, educational institutions or transport. In terms of individual households, a higher income allows for more money to be spent on goods and activities. This increase in consumption spending stimulates economic activity and has a positive multiplier effect which leads to higher demand for retail and to a certain extent, employment in this industry. However, it is important to note that although economic growth has such advantageous effects for the economy, the better life index is not entirely hand in hand with the GDP per capita with countries having a higher economic rate but lower better life index. Similarly, the correlation between “Happiness” and “Economy (GDP Per Capita)” isn’t a perfect “1” since, some people choose to exchange a lower work productivity or lower in come in exchange for leisure time with family and friends.
4. How was the data measured? Was the data able to measure “Happiness” in an effective manner?
One can argue several limitations on how “Happiness” was measured. For one, the sample size of 1000-2000 people sampled per year per country is incredibly small in comparison to the hundreds of thousands if not millions of people who are in the actual population. This brings into question the statistical significance and accuracy of the data in representing conclusions or trends about the population. Thus one can argue, that the data being measured isn’t entirely effective since it doesn’t portray an accurate image. However, the data maintains accuracy by maintaining a 95% accurate confidence interval to ensure that the population mean is included in the data. This is evident in their calculation of the Happiness Score as the mean between the upper and lower confidence interval.
Another area of contention in the effectivity of how data was measured is the use of the Cantrell scale due to its high subjectivity. It asks the individual to rate their life on a ladder from “1” (being the “Worst” point of their life) to 10 “(being the “best life they’ve lived)”. In measuring happiness, there were no clear parameters on what “Happiness” entailed or did not entail since they were based on an individuals own perception. This creates a discrepancy as to whether the same variable of “Happiness” was actually measured across the different individuals and between different countries. This discrepancy may weaken the validity of the conclusions which can be founded upon analyzing this data. Thus it can be argued, that “Happiness” was not effectively measured. This argument similarly applies to vague nature in which “Family”, “Health”, “Freedom”, “Trust” and “Generosity” were recorded on the data set.
Overall however, this difficulty in accurately measuring “Happiness” as a consistent across different groups of people is a further reflection of the difficulty in capturing highly subjective constructs.
5. How useful is the data in practical applications?
The World Happiness Report 2016 aims to encourage and catalyse the conception of numerous national policies that could benefit from data compiled by statistical indicators, such as “happiness”, “health” and “freedom”. Due to the vague nature of some of the indicators, there is doubt on whether the measurements (happiness, health, freedom) provide insight that adequately determines the amendment or creation of any national policy. For example, the measurement of “health” singularly refers to the life expectancy of a given population. However, there is no consideration given to the statistics surrounding disease, cancer, chronic physical conditions and mental health, all of which are extremely significant to an individual’s quality of life.
The findings of the report, though surveyed with good intentions and supported by numerous altruistic organisations, are extremely volatile as the data is subject to the individual societal and governmental characteristics of each country. The results provide good statistical insights and reviews into various “happiness” related statistics, and this can be helpful in identifying societal issues and raising awareness to specific national obstacles. However, there is currently limited evidence to suggest that the results alone have provided practical solutions to the issues highlighted.
It is imperative to understand that the reports are relatively new. Positive feedback, constructive criticism and worldwide discussions from various international bodies suggest that the survey methods and reports are continuously evolving with each release. Therefore, there is potential for more concrete, practical applications in the future.
Source Analysis
Although the data set used was obtained from Kaggle.com, an online platform where data scientists and statisticians post datasets and compete to analyse, the original data was published in the World Happiness Report. Since data sets from Kaggle.com is widely used and scrutinised by statisticians, this increases the credibility of the data since any obvious errors would have been identified and highlighted in the discussions section.
Furthermore, the data’s original publication in the World Happiness is supported by renowned and credible international organisations such as the United Nations, the Sustainable Development Solutions Network and the Global Happiness Council. Consequently, it can be inferred that the data must have gone through numerous checks before being published thus further increasing the credibility and reliability of the data.
The report cites that the raw data used was sourced from the Gallup World Poll. This 100-question poll studies approximately 1000-2000 individuals for each of the 160+ countries it surveys each year, using the exact same questions each year. Although one can argue that this sample size is small in comparison to the actual enormity of the population of each country, Gallup attempts to increase its accuracy and statistical significance by weighting its samples against unequal selection probability to minimise the margin of error. Thus, increasing the accuracy of the data.
However, the Gallup Poll uses the Cantrell Ladder to measure happiness which creates some issues. The Cantrell Ladder of measurement is highly subjective as it asks you about life contentment on a scale of “1” to “10”, with 1 being “the worst possible life” and 10 as “the best possible life”. The lack of parameters of what “worst” and “best” entails, partially diminishes the clarity of the data produced as each person has a different understanding of these terms. Consequently, this lack of clarity somewhat decreases the accuracy and validity of the conclusions one can make when analysing the data. Thus, albeit the source where the data was found and published is highly reliable, the method in which the data was measured partially impedes its usefulness.
StakeHolders: Who gains value from this data?
The data provided in this report offers the most use to governments who wish to make informed policy decisions to target problematic issues within their population. As the data is sourced from subjective self reporting, the information derived from such a report can be useful in modelling a general consensus regarding the quality of life of citizens. Unlike normal measurements taken into account during policy-making, this further accommodates for the social perceptions of citizen regarding their quality of life. It provides an insight on the over-all mental well-being of the population and the areas which the population believes needs more attention but may not necessarily be on the government’s agenda. Thus, it is of great interest to governments of each nation as it can be used in conglomeration with other measurements of quality of life, to accurately illustrate the state of “well being” for each nation.
Additionally the importance of the report is pushed by The Organisation for Economic Co-operation and Development (OECD) which consists of 35 developed countries including Australia. The OECD is an official United Nations observer and commits itself “to redefine the growth narrative to put people’s well-being at the centre of governments’ efforts”. (OECD 2016 Strategic Orientations of the Secretary-General: For 2016 and beyond). In this sense the data offered by this report is fundamental to the OECD in building strategies to model and build strategies to improve the happiness of its member countries.
Furthermore, organisations which seek to diminish global inequality such as Oxfam and Caritas have great use for this data as it allows them to perceive and then portray inequality from a more social and humanistic standpoint. Unlike traditional measurements such as the human development index, by using happiness as a measurement it allows them to illustrate the true social effects of inequality across different nations. It further allows these organisations to draw parallels between rates of happiness and economic status, to emphasise the consistent drive to provide less-privileged countries new sources of income. Thus, its of crucial of importance as it enables such organisations to more accurately illustrate the rate of inequality and the importance to combat it.
Dataset:
head(WHR, n = 157)## Country Region Happiness.Rank
## 1 Denmark Western Europe 1
## 2 Switzerland Western Europe 2
## 3 Iceland Western Europe 3
## 4 Norway Western Europe 4
## 5 Finland Western Europe 5
## 6 Canada North America 6
## 7 Netherlands Western Europe 7
## 8 New Zealand Australia and New Zealand 8
## 9 Australia Australia and New Zealand 9
## 10 Sweden Western Europe 10
## 11 Israel Middle East and Northern Africa 11
## 12 Austria Western Europe 12
## 13 United States North America 13
## 14 Costa Rica Latin America and Caribbean 14
## 15 Puerto Rico Latin America and Caribbean 15
## 16 Germany Western Europe 16
## 17 Brazil Latin America and Caribbean 17
## 18 Belgium Western Europe 18
## 19 Ireland Western Europe 19
## 20 Luxembourg Western Europe 20
## 21 Mexico Latin America and Caribbean 21
## 22 Singapore Southeastern Asia 22
## 23 United Kingdom Western Europe 23
## 24 Chile Latin America and Caribbean 24
## 25 Panama Latin America and Caribbean 25
## 26 Argentina Latin America and Caribbean 26
## 27 Czech Republic Central and Eastern Europe 27
## 28 United Arab Emirates Middle East and Northern Africa 28
## 29 Uruguay Latin America and Caribbean 29
## 30 Malta Western Europe 30
## 31 Colombia Latin America and Caribbean 31
## 32 France Western Europe 32
## 33 Thailand Southeastern Asia 33
## 34 Saudi Arabia Middle East and Northern Africa 34
## 35 Taiwan Eastern Asia 34
## 36 Qatar Middle East and Northern Africa 36
## 37 Spain Western Europe 37
## 38 Algeria Middle East and Northern Africa 38
## 39 Guatemala Latin America and Caribbean 39
## 40 Suriname Latin America and Caribbean 40
## 41 Kuwait Middle East and Northern Africa 41
## 42 Bahrain Middle East and Northern Africa 42
## 43 Trinidad and Tobago Latin America and Caribbean 43
## 44 Venezuela Latin America and Caribbean 44
## 45 Slovakia Central and Eastern Europe 45
## 46 El Salvador Latin America and Caribbean 46
## 47 Malaysia Southeastern Asia 47
## 48 Nicaragua Latin America and Caribbean 48
## 49 Uzbekistan Central and Eastern Europe 49
## 50 Italy Western Europe 50
## 51 Ecuador Latin America and Caribbean 51
## 52 Belize Latin America and Caribbean 52
## 53 Japan Eastern Asia 53
## 54 Kazakhstan Central and Eastern Europe 54
## 55 Moldova Central and Eastern Europe 55
## 56 Russia Central and Eastern Europe 56
## 57 Poland Central and Eastern Europe 57
## 58 South Korea Eastern Asia 57
## 59 Bolivia Latin America and Caribbean 59
## 60 Lithuania Central and Eastern Europe 60
## 61 Belarus Central and Eastern Europe 61
## 62 North Cyprus Western Europe 62
## 63 Slovenia Central and Eastern Europe 63
## 64 Peru Latin America and Caribbean 64
## 65 Turkmenistan Central and Eastern Europe 65
## 66 Mauritius Sub-Saharan Africa 66
## 67 Libya Middle East and Northern Africa 67
## 68 Latvia Central and Eastern Europe 68
## 69 Cyprus Western Europe 69
## 70 Paraguay Latin America and Caribbean 70
## 71 Romania Central and Eastern Europe 71
## 72 Estonia Central and Eastern Europe 72
## 73 Jamaica Latin America and Caribbean 73
## 74 Croatia Central and Eastern Europe 74
## 75 Hong Kong Eastern Asia 75
## 76 Somalia Sub-Saharan Africa 76
## 77 Kosovo Central and Eastern Europe 77
## 78 Turkey Middle East and Northern Africa 78
## 79 Indonesia Southeastern Asia 79
## 80 Jordan Middle East and Northern Africa 80
## 81 Azerbaijan Central and Eastern Europe 81
## 82 Philippines Southeastern Asia 82
## 83 China Eastern Asia 83
## 84 Bhutan Southern Asia 84
## 85 Kyrgyzstan Central and Eastern Europe 85
## 86 Serbia Central and Eastern Europe 86
## 87 Bosnia and Herzegovina Central and Eastern Europe 87
## 88 Montenegro Central and Eastern Europe 88
## 89 Dominican Republic Latin America and Caribbean 89
## 90 Morocco Middle East and Northern Africa 90
## 91 Hungary Central and Eastern Europe 91
## 92 Pakistan Southern Asia 92
## 93 Lebanon Middle East and Northern Africa 93
## 94 Portugal Western Europe 94
## 95 Macedonia Central and Eastern Europe 95
## 96 Vietnam Southeastern Asia 96
## 97 Somaliland Region Sub-Saharan Africa 97
## 98 Tunisia Middle East and Northern Africa 98
## 99 Greece Western Europe 99
## 100 Tajikistan Central and Eastern Europe 100
## 101 Mongolia Eastern Asia 101
## 102 Laos Southeastern Asia 102
## 103 Nigeria Sub-Saharan Africa 103
## 104 Honduras Latin America and Caribbean 104
## 105 Iran Middle East and Northern Africa 105
## 106 Zambia Sub-Saharan Africa 106
## 107 Nepal Southern Asia 107
## 108 Palestinian Territories Middle East and Northern Africa 108
## 109 Albania Central and Eastern Europe 109
## 110 Bangladesh Southern Asia 110
## 111 Sierra Leone Sub-Saharan Africa 111
## 112 Iraq Middle East and Northern Africa 112
## 113 Namibia Sub-Saharan Africa 113
## 114 Cameroon Sub-Saharan Africa 114
## 115 Ethiopia Sub-Saharan Africa 115
## 116 South Africa Sub-Saharan Africa 116
## 117 Sri Lanka Southern Asia 117
## 118 India Southern Asia 118
## 119 Myanmar Southeastern Asia 119
## 120 Egypt Middle East and Northern Africa 120
## 121 Armenia Central and Eastern Europe 121
## 122 Kenya Sub-Saharan Africa 122
## 123 Ukraine Central and Eastern Europe 123
## 124 Ghana Sub-Saharan Africa 124
## 125 Congo (Kinshasa) Sub-Saharan Africa 125
## 126 Georgia Central and Eastern Europe 126
## 127 Congo (Brazzaville) Sub-Saharan Africa 127
## 128 Senegal Sub-Saharan Africa 128
## 129 Bulgaria Central and Eastern Europe 129
## 130 Mauritania Sub-Saharan Africa 130
## 131 Zimbabwe Sub-Saharan Africa 131
## 132 Malawi Sub-Saharan Africa 132
## 133 Sudan Sub-Saharan Africa 133
## 134 Gabon Sub-Saharan Africa 134
## 135 Mali Sub-Saharan Africa 135
## 136 Haiti Latin America and Caribbean 136
## 137 Botswana Sub-Saharan Africa 137
## 138 Comoros Sub-Saharan Africa 138
## 139 Ivory Coast Sub-Saharan Africa 139
## 140 Cambodia Southeastern Asia 140
## 141 Angola Sub-Saharan Africa 141
## 142 Niger Sub-Saharan Africa 142
## 143 South Sudan Sub-Saharan Africa 143
## 144 Chad Sub-Saharan Africa 144
## 145 Burkina Faso Sub-Saharan Africa 145
## 146 Uganda Sub-Saharan Africa 145
## 147 Yemen Middle East and Northern Africa 147
## 148 Madagascar Sub-Saharan Africa 148
## 149 Tanzania Sub-Saharan Africa 149
## 150 Liberia Sub-Saharan Africa 150
## 151 Guinea Sub-Saharan Africa 151
## 152 Rwanda Sub-Saharan Africa 152
## 153 Benin Sub-Saharan Africa 153
## 154 Afghanistan Southern Asia 154
## 155 Togo Sub-Saharan Africa 155
## 156 Syria Middle East and Northern Africa 156
## 157 Burundi Sub-Saharan Africa 157
## Happiness.Score Lower.Confidence.Interval Upper.Confidence.Interval
## 1 7.526 7.460 7.592
## 2 7.509 7.428 7.590
## 3 7.501 7.333 7.669
## 4 7.498 7.421 7.575
## 5 7.413 7.351 7.475
## 6 7.404 7.335 7.473
## 7 7.339 7.284 7.394
## 8 7.334 7.264 7.404
## 9 7.313 7.241 7.385
## 10 7.291 7.227 7.355
## 11 7.267 7.199 7.335
## 12 7.119 7.045 7.193
## 13 7.104 7.020 7.188
## 14 7.087 6.999 7.175
## 15 7.039 6.794 7.284
## 16 6.994 6.930 7.058
## 17 6.952 6.875 7.029
## 18 6.929 6.861 6.997
## 19 6.907 6.836 6.978
## 20 6.871 6.804 6.938
## 21 6.778 6.680 6.876
## 22 6.739 6.674 6.804
## 23 6.725 6.647 6.803
## 24 6.705 6.615 6.795
## 25 6.701 6.601 6.801
## 26 6.650 6.560 6.740
## 27 6.596 6.515 6.677
## 28 6.573 6.494 6.652
## 29 6.545 6.456 6.634
## 30 6.488 6.409 6.567
## 31 6.481 6.384 6.578
## 32 6.478 6.397 6.559
## 33 6.474 6.396 6.552
## 34 6.379 6.287 6.471
## 35 6.379 6.305 6.453
## 36 6.375 6.178 6.572
## 37 6.361 6.288 6.434
## 38 6.355 6.227 6.483
## 39 6.324 6.213 6.435
## 40 6.269 6.073 6.465
## 41 6.239 6.154 6.324
## 42 6.218 6.128 6.308
## 43 6.168 5.950 6.386
## 44 6.084 5.973 6.195
## 45 6.078 5.996 6.160
## 46 6.068 5.967 6.169
## 47 6.005 5.921 6.089
## 48 5.992 5.877 6.107
## 49 5.987 5.896 6.078
## 50 5.977 5.898 6.056
## 51 5.976 5.880 6.072
## 52 5.956 5.710 6.202
## 53 5.921 5.850 5.992
## 54 5.919 5.837 6.001
## 55 5.897 5.823 5.971
## 56 5.856 5.789 5.923
## 57 5.835 5.749 5.921
## 58 5.835 5.747 5.923
## 59 5.822 5.740 5.904
## 60 5.813 5.734 5.892
## 61 5.802 5.723 5.881
## 62 5.771 5.670 5.872
## 63 5.768 5.683 5.853
## 64 5.743 5.647 5.839
## 65 5.658 5.580 5.736
## 66 5.648 5.507 5.789
## 67 5.615 5.406 5.824
## 68 5.560 5.486 5.634
## 69 5.546 5.442 5.650
## 70 5.538 5.453 5.623
## 71 5.528 5.427 5.629
## 72 5.517 5.437 5.597
## 73 5.510 5.315 5.705
## 74 5.488 5.402 5.574
## 75 5.458 5.362 5.554
## 76 5.440 5.321 5.559
## 77 5.401 5.308 5.494
## 78 5.389 5.295 5.483
## 79 5.314 5.237 5.391
## 80 5.303 5.187 5.419
## 81 5.291 5.226 5.356
## 82 5.279 5.160 5.398
## 83 5.245 5.199 5.291
## 84 5.196 5.138 5.254
## 85 5.185 5.103 5.267
## 86 5.177 5.083 5.271
## 87 5.163 5.063 5.263
## 88 5.161 5.055 5.267
## 89 5.155 5.037 5.273
## 90 5.151 5.058 5.244
## 91 5.145 5.056 5.234
## 92 5.132 5.038 5.226
## 93 5.129 5.031 5.227
## 94 5.123 5.030 5.216
## 95 5.121 5.017 5.225
## 96 5.061 4.991 5.131
## 97 5.057 4.934 5.180
## 98 5.045 4.965 5.125
## 99 5.033 4.935 5.131
## 100 4.996 4.923 5.069
## 101 4.907 4.838 4.976
## 102 4.876 4.742 5.010
## 103 4.875 4.750 5.000
## 104 4.871 4.750 4.992
## 105 4.813 4.703 4.923
## 106 4.795 4.645 4.945
## 107 4.793 4.698 4.888
## 108 4.754 4.649 4.859
## 109 4.655 4.546 4.764
## 110 4.643 4.560 4.726
## 111 4.635 4.505 4.765
## 112 4.575 4.446 4.704
## 113 4.574 4.374 4.774
## 114 4.513 4.417 4.609
## 115 4.508 4.425 4.591
## 116 4.459 4.371 4.547
## 117 4.415 4.322 4.508
## 118 4.404 4.351 4.457
## 119 4.395 4.327 4.463
## 120 4.362 4.259 4.465
## 121 4.360 4.266 4.454
## 122 4.356 4.259 4.453
## 123 4.324 4.236 4.412
## 124 4.276 4.185 4.367
## 125 4.272 4.191 4.353
## 126 4.252 4.164 4.340
## 127 4.236 4.107 4.365
## 128 4.219 4.151 4.287
## 129 4.217 4.104 4.330
## 130 4.201 4.127 4.275
## 131 4.193 4.101 4.285
## 132 4.156 4.041 4.271
## 133 4.139 3.928 4.350
## 134 4.121 4.030 4.212
## 135 4.073 3.988 4.158
## 136 4.028 3.893 4.163
## 137 3.974 3.875 4.073
## 138 3.956 3.860 4.052
## 139 3.916 3.826 4.006
## 140 3.907 3.798 4.016
## 141 3.866 3.753 3.979
## 142 3.856 3.781 3.931
## 143 3.832 3.596 4.068
## 144 3.763 3.672 3.854
## 145 3.739 3.647 3.831
## 146 3.739 3.629 3.849
## 147 3.724 3.621 3.827
## 148 3.695 3.621 3.769
## 149 3.666 3.561 3.771
## 150 3.622 3.463 3.781
## 151 3.607 3.533 3.681
## 152 3.515 3.444 3.586
## 153 3.484 3.404 3.564
## 154 3.360 3.288 3.432
## 155 3.303 3.192 3.414
## 156 3.069 2.936 3.202
## 157 2.905 2.732 3.078
## Economy..GDP.per.Capita. Family Health..Life.Expectancy. Freedom
## 1 1.44178 1.16374 0.79504 0.57941
## 2 1.52733 1.14524 0.86303 0.58557
## 3 1.42666 1.18326 0.86733 0.56624
## 4 1.57744 1.12690 0.79579 0.59609
## 5 1.40598 1.13464 0.81091 0.57104
## 6 1.44015 1.09610 0.82760 0.57370
## 7 1.46468 1.02912 0.81231 0.55211
## 8 1.36066 1.17278 0.83096 0.58147
## 9 1.44443 1.10476 0.85120 0.56837
## 10 1.45181 1.08764 0.83121 0.58218
## 11 1.33766 0.99537 0.84917 0.36432
## 12 1.45038 1.08383 0.80565 0.54355
## 13 1.50796 1.04782 0.77900 0.48163
## 14 1.06879 1.02152 0.76146 0.55225
## 15 1.35943 1.08113 0.77758 0.46823
## 16 1.44787 1.09774 0.81487 0.53466
## 17 1.08754 1.03938 0.61415 0.40425
## 18 1.42539 1.05249 0.81959 0.51354
## 19 1.48341 1.16157 0.81455 0.54008
## 20 1.69752 1.03999 0.84542 0.54870
## 21 1.11508 0.71460 0.71143 0.37709
## 22 1.64555 0.86758 0.94719 0.48770
## 23 1.40283 1.08672 0.80991 0.50036
## 24 1.21670 0.90587 0.81883 0.37789
## 25 1.18306 0.98912 0.70835 0.48927
## 26 1.15137 1.06612 0.69711 0.42284
## 27 1.30915 1.00793 0.76376 0.41418
## 28 1.57352 0.87114 0.72993 0.56215
## 29 1.18157 1.03143 0.72183 0.54388
## 30 1.30782 1.09879 0.80315 0.54994
## 31 1.03032 1.02169 0.59659 0.44735
## 32 1.39488 1.00508 0.83795 0.46562
## 33 1.08930 1.04477 0.64915 0.49553
## 34 1.48953 0.84829 0.59267 0.37904
## 35 1.39729 0.92624 0.79565 0.32377
## 36 1.82427 0.87964 0.71723 0.56679
## 37 1.34253 1.12945 0.87896 0.37545
## 38 1.05266 0.83309 0.61804 0.21006
## 39 0.83454 0.87119 0.54039 0.50379
## 40 1.09686 0.77866 0.50933 0.52234
## 41 1.61714 0.87758 0.63569 0.43166
## 42 1.44024 0.94397 0.65696 0.47375
## 43 1.32572 0.98569 0.52608 0.48453
## 44 1.13367 1.03302 0.61904 0.19847
## 45 1.27973 1.08268 0.70367 0.23391
## 46 0.87370 0.80975 0.59600 0.37269
## 47 1.25142 0.88025 0.62366 0.39031
## 48 0.69384 0.89521 0.65213 0.46582
## 49 0.73591 1.16810 0.50163 0.60848
## 50 1.35495 1.04167 0.85102 0.18827
## 51 0.97306 0.85974 0.68613 0.40270
## 52 0.87616 0.68655 0.45569 0.51231
## 53 1.38007 1.06054 0.91491 0.46761
## 54 1.22943 0.95544 0.57386 0.40520
## 55 0.69177 0.83132 0.52309 0.25202
## 56 1.23228 1.05261 0.58991 0.32682
## 57 1.24585 1.04685 0.69058 0.45190
## 58 1.35948 0.72194 0.88645 0.25168
## 59 0.79422 0.83779 0.46970 0.50961
## 60 1.26920 1.06411 0.64674 0.18929
## 61 1.13062 1.04993 0.63104 0.29091
## 62 1.31141 0.81826 0.84142 0.43596
## 63 1.29947 1.05613 0.79151 0.53164
## 64 0.99602 0.81255 0.62994 0.37502
## 65 1.08017 1.03817 0.44006 0.37408
## 66 1.14372 0.75695 0.66189 0.46145
## 67 1.06688 0.95076 0.52304 0.40672
## 68 1.21788 0.95025 0.63952 0.27996
## 69 1.31857 0.70697 0.84880 0.29507
## 70 0.89373 1.11111 0.58295 0.46235
## 71 1.16970 0.72803 0.67602 0.36712
## 72 1.27964 1.05163 0.68098 0.41511
## 73 0.89333 0.96372 0.59469 0.43597
## 74 1.18649 0.60809 0.70524 0.23907
## 75 1.51070 0.87021 0.95277 0.48079
## 76 0.00000 0.33613 0.11466 0.56778
## 77 0.90145 0.66062 0.54000 0.14396
## 78 1.16492 0.87717 0.64718 0.23889
## 79 0.95104 0.87625 0.49374 0.39237
## 80 0.99673 0.86216 0.60712 0.36023
## 81 1.12373 0.76042 0.54504 0.35327
## 82 0.81217 0.87877 0.47036 0.54854
## 83 1.02780 0.79381 0.73561 0.44012
## 84 0.85270 0.90836 0.49759 0.46074
## 85 0.56044 0.95434 0.55449 0.40212
## 86 1.03437 0.81329 0.64580 0.15718
## 87 0.93383 0.64367 0.70766 0.09511
## 88 1.07838 0.74173 0.63533 0.15111
## 89 1.02787 0.99496 0.57669 0.52259
## 90 0.84058 0.38595 0.59471 0.25646
## 91 1.24142 0.93164 0.67608 0.19770
## 92 0.68816 0.26135 0.40306 0.14622
## 93 1.12268 0.64184 0.76171 0.26228
## 94 1.27607 0.94367 0.79363 0.44727
## 95 1.01930 0.78236 0.64738 0.27668
## 96 0.74037 0.79117 0.66157 0.55954
## 97 0.25558 0.75862 0.33108 0.39130
## 98 0.97724 0.43165 0.59577 0.23553
## 99 1.24886 0.75473 0.80029 0.05822
## 100 0.48835 0.75602 0.53119 0.43408
## 101 0.98853 1.08983 0.55469 0.35972
## 102 0.68042 0.54970 0.38291 0.52168
## 103 0.75216 0.64498 0.05108 0.27854
## 104 0.69429 0.75596 0.58383 0.26755
## 105 1.11758 0.38857 0.64232 0.22544
## 106 0.61202 0.63760 0.23573 0.42662
## 107 0.44626 0.69699 0.50073 0.37012
## 108 0.67024 0.71629 0.56844 0.17744
## 109 0.95530 0.50163 0.73007 0.31866
## 110 0.54177 0.24749 0.52989 0.39778
## 111 0.36485 0.62800 0.00000 0.30685
## 112 1.07474 0.59205 0.51076 0.24856
## 113 0.93287 0.70362 0.34745 0.48614
## 114 0.52497 0.62542 0.12698 0.42736
## 115 0.29283 0.37932 0.34578 0.36703
## 116 1.02416 0.96053 0.18611 0.42483
## 117 0.97318 0.84783 0.62007 0.50817
## 118 0.74036 0.29247 0.45091 0.40285
## 119 0.34112 0.69981 0.39880 0.42692
## 120 0.95395 0.49813 0.52116 0.18847
## 121 0.86086 0.62477 0.64083 0.14037
## 122 0.52267 0.76240 0.30147 0.40576
## 123 0.87287 1.01413 0.58628 0.12859
## 124 0.63107 0.49353 0.29681 0.40973
## 125 0.05661 0.80676 0.18800 0.15602
## 126 0.83792 0.19249 0.64035 0.32461
## 127 0.77109 0.47799 0.28212 0.37938
## 128 0.44314 0.77416 0.40457 0.31056
## 129 1.11306 0.92542 0.67806 0.21219
## 130 0.61391 0.84142 0.28639 0.12680
## 131 0.35041 0.71478 0.15950 0.25429
## 132 0.08709 0.14700 0.29364 0.41430
## 133 0.63069 0.81928 0.29759 0.00000
## 134 1.15851 0.72368 0.34940 0.28098
## 135 0.31292 0.86333 0.16347 0.27544
## 136 0.34097 0.29561 0.27494 0.12072
## 137 1.09426 0.89186 0.34752 0.44089
## 138 0.27509 0.60323 0.29981 0.15412
## 139 0.55507 0.57576 0.04476 0.40663
## 140 0.55604 0.53750 0.42494 0.58852
## 141 0.84731 0.66366 0.04991 0.00589
## 142 0.13270 0.60530 0.26162 0.38041
## 143 0.39394 0.18519 0.15781 0.19662
## 144 0.42214 0.63178 0.03824 0.12807
## 145 0.31995 0.63054 0.21297 0.33370
## 146 0.34719 0.90981 0.19625 0.43653
## 147 0.57939 0.47493 0.31048 0.22870
## 148 0.27954 0.46115 0.37109 0.13684
## 149 0.47155 0.77623 0.35700 0.31760
## 150 0.10706 0.50353 0.23165 0.25748
## 151 0.22415 0.31090 0.18829 0.30953
## 152 0.32846 0.61586 0.31865 0.54320
## 153 0.39499 0.10419 0.21028 0.39747
## 154 0.38227 0.11037 0.17344 0.16430
## 155 0.28123 0.00000 0.24811 0.34678
## 156 0.74719 0.14866 0.62994 0.06912
## 157 0.06831 0.23442 0.15747 0.04320
## Trust..Government.Corruption. Generosity Dystopia.Residual
## 1 0.44453 0.36171 2.73939
## 2 0.41203 0.28083 2.69463
## 3 0.14975 0.47678 2.83137
## 4 0.35776 0.37895 2.66465
## 5 0.41004 0.25492 2.82596
## 6 0.31329 0.44834 2.70485
## 7 0.29927 0.47416 2.70749
## 8 0.41904 0.49401 2.47553
## 9 0.32331 0.47407 2.54650
## 10 0.40867 0.38254 2.54734
## 11 0.08728 0.32288 3.31029
## 12 0.21348 0.32865 2.69343
## 13 0.14868 0.41077 2.72782
## 14 0.10547 0.22553 3.35168
## 15 0.12275 0.22202 3.00760
## 16 0.28551 0.30452 2.50931
## 17 0.14166 0.15776 3.50733
## 18 0.26248 0.24240 2.61355
## 19 0.29754 0.44963 2.15988
## 20 0.35329 0.27571 2.11055
## 21 0.18355 0.11735 3.55906
## 22 0.46987 0.32706 1.99375
## 23 0.27399 0.50156 2.14999
## 24 0.11451 0.31595 2.95505
## 25 0.08423 0.24180 3.00559
## 26 0.07296 0.10989 3.12985
## 27 0.03986 0.09929 2.96211
## 28 0.35561 0.26591 2.21507
## 29 0.21394 0.18056 2.67139
## 30 0.17554 0.56237 1.99032
## 31 0.05399 0.15626 3.17471
## 32 0.17808 0.12160 2.47440
## 33 0.02833 0.58696 2.57960
## 34 0.30008 0.15457 2.61482
## 35 0.06630 0.25495 2.61523
## 36 0.48049 0.32388 1.58224
## 37 0.06137 0.17665 2.39663
## 38 0.16157 0.07044 3.40904
## 39 0.08701 0.28808 3.19863
## 40 0.12692 0.16665 3.06852
## 41 0.23669 0.15965 2.28085
## 42 0.25772 0.17147 2.27405
## 43 0.01241 0.31935 2.51394
## 44 0.08304 0.04250 2.97468
## 45 0.02947 0.13837 2.61065
## 46 0.10613 0.08877 3.22134
## 47 0.09081 0.41474 2.35384
## 48 0.16292 0.29773 2.82428
## 49 0.28333 0.34326 2.34638
## 50 0.02556 0.16684 2.34918
## 51 0.18037 0.10074 2.77366
## 52 0.10771 0.23684 3.08039
## 53 0.18985 0.10224 1.80584
## 54 0.11132 0.15011 2.49325
## 55 0.01903 0.19997 3.38007
## 56 0.03586 0.02736 2.59115
## 57 0.05500 0.14443 2.20035
## 58 0.07716 0.18824 2.35015
## 59 0.07746 0.21698 2.91635
## 60 0.01820 0.02025 2.60525
## 61 0.17457 0.13942 2.38582
## 62 0.16578 0.26322 1.93447
## 63 0.03635 0.25738 1.79522
## 64 0.05292 0.14527 2.73117
## 65 0.28467 0.22567 2.21489
## 66 0.05203 0.36951 2.20223
## 67 0.10339 0.17087 2.39374
## 68 0.08890 0.17445 2.20859
## 69 0.05228 0.27906 2.04497
## 70 0.07396 0.25296 2.16091
## 71 0.00679 0.12889 2.45184
## 72 0.18519 0.08423 1.81985
## 73 0.04294 0.22245 2.35682
## 74 0.04002 0.18434 2.52462
## 75 0.31647 0.40097 0.92614
## 76 0.31180 0.27225 3.83772
## 77 0.06547 0.27992 2.80998
## 78 0.12348 0.04707 2.29074
## 79 0.00322 0.56521 2.03171
## 80 0.13297 0.14262 2.20142
## 81 0.17914 0.05640 2.27350
## 82 0.11757 0.21674 2.23484
## 83 0.02745 0.04959 2.17087
## 84 0.16160 0.48546 1.82916
## 85 0.04762 0.38432 2.28136
## 86 0.04339 0.20737 2.27539
## 87 0.00000 0.29889 2.48406
## 88 0.12721 0.17191 2.25531
## 89 0.12372 0.21286 1.69626
## 90 0.08404 0.04053 2.94891
## 91 0.04472 0.09900 1.95473
## 92 0.13880 0.31185 3.18286
## 93 0.03061 0.23693 2.07339
## 94 0.01521 0.11691 1.53015
## 95 0.07047 0.23507 2.08947
## 96 0.11556 0.25075 1.94180
## 97 0.36794 0.51479 2.43801
## 98 0.08170 0.03936 2.68413
## 99 0.04127 0.00000 2.12944
## 100 0.13509 0.25998 2.39106
## 101 0.03285 0.34539 1.53586
## 102 0.22423 0.43079 2.08637
## 103 0.03050 0.23219 2.88586
## 104 0.06906 0.20440 2.29551
## 105 0.05570 0.38538 1.99817
## 106 0.11479 0.17866 2.58991
## 107 0.07008 0.38160 2.32694
## 108 0.10613 0.11154 2.40364
## 109 0.05301 0.16840 1.92816
## 110 0.12583 0.19132 2.60904
## 111 0.08196 0.23897 3.01402
## 112 0.13636 0.19589 1.81657
## 113 0.10398 0.07795 1.92198
## 114 0.06126 0.22680 2.51980
## 115 0.17170 0.29522 2.65614
## 116 0.08415 0.13656 1.64227
## 117 0.07964 0.46978 0.91681
## 118 0.08722 0.25028 2.18032
## 119 0.20243 0.81971 1.50655
## 120 0.10393 0.12706 1.96895
## 121 0.03616 0.07793 1.97864
## 122 0.06686 0.41328 1.88326
## 123 0.01829 0.20363 1.50066
## 124 0.03260 0.21203 2.20020
## 125 0.06075 0.25458 2.74924
## 126 0.31880 0.06786 1.87031
## 127 0.09753 0.12077 2.10681
## 128 0.11681 0.19103 1.97861
## 129 0.00615 0.12793 1.15377
## 130 0.17955 0.22686 1.92630
## 131 0.08582 0.18503 2.44270
## 132 0.07564 0.30968 2.82859
## 133 0.10039 0.18077 2.10995
## 134 0.09314 0.06244 1.45332
## 135 0.13647 0.21064 2.11087
## 136 0.14476 0.47958 2.37116
## 137 0.10769 0.12425 0.96741
## 138 0.18437 0.18270 2.25632
## 139 0.15530 0.20338 1.97478
## 140 0.08092 0.40339 1.31573
## 141 0.08434 0.12071 2.09459
## 142 0.17176 0.20970 2.09469
## 143 0.13015 0.25899 2.50929
## 144 0.04952 0.18667 2.30637
## 145 0.12533 0.24353 1.87319
## 146 0.06442 0.27102 1.51416
## 147 0.05892 0.09821 1.97295
## 148 0.07506 0.22040 2.15075
## 149 0.05099 0.31472 1.37769
## 150 0.04852 0.24063 2.23284
## 151 0.11920 0.29914 2.15604
## 152 0.50521 0.23552 0.96819
## 153 0.06681 0.20180 2.10812
## 154 0.07112 0.31268 2.14558
## 155 0.11587 0.17517 2.13540
## 156 0.17233 0.48397 0.81789
## 157 0.09419 0.20290 2.10404