The Better Life Index is an initiative created by the OECD to compare the well-being priorities of people around the world. It consists of 11 social indicators: “housing, income, jobs, community, education, environment, governance, health, life satisfaction, safety, work-life balance” that contribute to well-being in OECD countries. This initiative aims to involve citizens in the debate on measuring the well-being of societies, and to empower them to become more informed and engaged in the policy-making process that shapes all our lives.
The 11 indicators in turn are composed of 20 sub-indicators through averaging and normalization. The visualization tool is available here. By selecting a set of weights to the sub-indicators, a user can rank countries according to their weighted sum.
The OECD's Better Life Index allows users to compare wellbeing across countries based on 11 topics identified as determinants for material living conditions and quality of life: housing, income, jobs, community, education, environment, civic engagement, health, life satisfaction, safety, and work-life balance. Each topic is based on one to three indicators, and the indicators are averaged with equal weights.
This is the Better Life Index for 2017 gathered from the OECD stats page. Grouping labels have been removed and the row for units of measurment for each column has been removed with the units added to the end of each column label as such: (Percentage: 'as pct'; Ratio: 'as rat'; US Dollar: 'in usd'; Average score: 'as avg score'; Years: 'in years'; Micrograms per cubic metre: 'in ugm3'; Hours: 'in hrs'). Also, although included in the report, Brazil, Russia, and South Africa are non-OECD economies at the time of reporting
OECD stats page For full index and others please visit: http://stats.oecd.org/Index.aspx?DataSetCode=BLI
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We provide a matlab script which computes rank-optimal weights for a given data matrix using the free SCIP optimization suite. Rank-optimal weights are weights which are used to make a weighted sum of all columns such that the value in a particular row achieves the highest possible rank. As an example, consider the OECD Better Life Index: We want to know weights for the eleven dimensions of a better life such that a particular countries jumps to the top of the ranking (or as high as possible). Datasets for the OECD Better Life Index 2013 and 2014 are provided to replicate the tables in the paper 'Rank-optimal weighting or "How to be best in the OECD Better Life Index?"'.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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SICCRED (Societal Impacts of Culture and Creativity Regional European Dashboard) is an interactive platform allowing to consult, for 209 regions in European OECD countries, the impact of employment in cultural and creative sectors (CCS) on the eleven dimensions of well-being considered in the OECD's regional Better Life Index: access to services (share of households with broadband access), civic engagement (voter turnout), community (percentage of people who believe they can rely on a friend in case of need), education (share of population aged 15-64 with educational level 3 or higher), environment (average concentration of particulate matter PM2.5 in the air in µg/m3), health (life expectancy at birth), housing (average number of rooms per person in a dwelling), income (net disposable income per capita in PPS), jobs (employment rate for the population aged 15-64), safety (homicide rate per 100,000 inhabitants) and life satisfaction (on a scale 0-10). In addition, the impact on the average labour productivity (gross added value per employee at constant prices Euro 2015) is added, given that this defines the possibilities for economic performance and material well-being, and on the number of tourist overnight stays (in all tourist accommodations), given the intertwining of the CCS with the tourism sector and its role in the tourist attractiveness of the regions. The estimates are made using Causal Forest, a novel and sophisticated technique that combines machine learning with causal inference. You can access the platform at this URL: https://www.mesoc-project.eu/resources/SICCRED
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This dataset is extracted from https://en.wikipedia.org/wiki/OECD_Better_Life_Index. Context: There s a story behind every dataset and heres your opportunity to share yours.Content: What s inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. Acknowledgements:We wouldn t be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.Inspiration: Your data will be in front of the world s largest data science community. What questions do you want to see answered?
Library of Wroclaw University of Science and Technology scientific output (DONA database)
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The Better Life Index is an initiative created by the OECD to compare the well-being priorities of people around the world. It consists of 11 social indicators: “housing, income, jobs, community, education, environment, governance, health, life satisfaction, safety, work-life balance” that contribute to well-being in OECD countries. This initiative aims to involve citizens in the debate on measuring the well-being of societies, and to empower them to become more informed and engaged in the policy-making process that shapes all our lives.
The 11 indicators in turn are composed of 20 sub-indicators through averaging and normalization. The visualization tool is available here. By selecting a set of weights to the sub-indicators, a user can rank countries according to their weighted sum.