Data
World-Happiness-Ranking

World-Happiness-Ranking

active ARFF GPL 2 Visibility: public Uploaded 24-03-2022 by Dustin Carrion
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Context The World Happiness Ranking focuses on the social, urban, and natural environment. Specifically, the ranking relies on self-reports from residents of how they weigh the quality of life they are currently experiencing which englobes three main points: current life evaluation, expected future life evaluation, positive and negative affect (emotion). Half of the underlying data comes from multiple Gallup world polls which asked people to give their assessment of the previously mentioned points, and the other half of the data is comprised of six variables that could be used to try to explain the individuals perception in their answers. Content The data sources datasets were obtained in two different formats. The World Happiness Ranking Dataset is a Comma-separated Values (CSV) file with multiple columns (for the different variables and the score) and a row for each of the analyzed countries. The rankings of national happiness are based on a Cantril ladder survey. Nationally representative samples of respondents are asked to think of a ladder, with the best possible life for them being a 10, and the worst possible life being a 0. They are then asked to rate their own current lives on that 0 to 10 scale. The report correlates the results with various life factors. GDP per capita is in terms of Purchasing Power Parity (PPP) adjusted to constant 2011 international dollars, taken from the World Development Indicators (WDI) released by the World Bank on November 28, 2019. See Statistical Appendix 1 for more details. GDP data for 2019 are not yet available, so we extend the GDP time series from 2018 to 2019 using country-specific forecasts of real GDP growth from the OECD Economic Outlook No. 106 (Edition November 2019) and the World Banks Global Economic Prospects (Last Updated: 06/04/2019), after adjustment for population growth. The equation uses the natural log of GDP per capita, as this form fits the data significantly better than GDP per capita. The time series of healthy life expectancy at birth are constructed based on data from the World Health Organization (WHO) Global Health Observatory data repository, with data available for 2005, 2010, 2015, and 2016. To match this reports sample period, interpolation and extrapolation are used. See Statistical Appendix 1 for more details. Social support is the national average of the binary responses (0=no, 1=yes) to the Gallup World Poll (GWP) question, If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not? Freedom to make life choices is the national average of binary responses to the GWP question, Are you satisfied or dissatisfied with your freedom to choose what you do with your life? Generosity is the residual of regressing the national average of GWP responses to the question, Have you donated money to a charity in the past month? on GDP per capita. Perceptions of corruption are the average of binary answers to two GWP questions: Is corruption widespread throughout the government or not? and Is corruption widespread within businesses or not? Where data for government corruption are missing, the perception of business corruption is used as the overall corruption-perception measure. Positive affect is defined as the average of previous-day affect measures for happiness, laughter, and enjoyment for GWP waves 3-7 (years 2008 to 2012, and some in 2013). It is defined as the average of laughter and enjoyment for other waves where the happiness question was not asked. The general form for the affect questions is: Did you experience the following feelings during a lot of the day yesterday? See Statistical Appendix 1 for more details. Negative affect is defined as the average of previous-day affect measures for worry, sadness, and anger in all years. Acknowledgements The World Happiness Report is a publication of the Sustainable Development Solutions Network, powered by data from the Gallup World Poll, and supported by the Ernesto Illy Foundation, illycaff, Davines Group, Blue Chip Foundation, the William, Jeff, and Jennifer Gross Family Foundation, and Unilevers largest ice cream brand Walls. Inspiration Find the relationship between the ladder score and the other pieces of data.

20 features

Country_namestring153 unique values
0 missing
Regional_indicatorstring10 unique values
0 missing
Ladder_scorenumeric153 unique values
0 missing
Standard_error_of_ladder_scorenumeric153 unique values
0 missing
upperwhiskernumeric153 unique values
0 missing
lowerwhiskernumeric153 unique values
0 missing
Logged_GDP_per_capitanumeric152 unique values
0 missing
Social_supportnumeric153 unique values
0 missing
Healthy_life_expectancynumeric152 unique values
0 missing
Freedom_to_make_life_choicesnumeric153 unique values
0 missing
Generositynumeric153 unique values
0 missing
Perceptions_of_corruptionnumeric153 unique values
0 missing
Ladder_score_in_Dystopianumeric1 unique values
0 missing
Explained_by:_Log_GDP_per_capitanumeric152 unique values
0 missing
Explained_by:_Social_supportnumeric153 unique values
0 missing
Explained_by:_Healthy_life_expectancynumeric152 unique values
0 missing
Explained_by:_Freedom_to_make_life_choicesnumeric153 unique values
0 missing
Explained_by:_Generositynumeric153 unique values
0 missing
Explained_by:_Perceptions_of_corruptionnumeric153 unique values
0 missing
Dystopia_+_residualnumeric153 unique values
0 missing

19 properties

153
Number of instances (rows) of the dataset.
20
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
18
Number of numeric attributes.
0
Number of nominal attributes.
0.13
Number of attributes divided by the number of instances.
90
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.

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