There is more to life than the cold numbers of GDP and economic statistics. This dataset contains the 2018 data of the Better Life Index which allows you to compare well-being across countries as well as measuring well-being, based on 11 topics the OECD has identified as essential, in the areas of material living conditions and quality of life.

The database "How’s Life? Well-being" is the one-stop shop for the 80+ indicators of the OECD Well-being Dashboard, providing information on current well-being outcomes, well-being inequalities and the resources and risks that underpin future well-being. The 11 dimensions of current well-being relate to material conditions that shape people’s economic options (income and wealth, housing, work and job quality) and quality-of-life factors that encompass how well people are (and how well they feel they are), what they know and can do, and how healthy and safe their places of living are (health, knowledge and skills, environmental quality, subjective well-being, safety). Quality of life also encompasses how connected and engaged people are, and how and with whom they spend their time (work-life balance, social connections, civic engagement). The distribution of current well-being is taken into account by looking at three types of inequality: gaps between population groups (horizontal inequalities); gaps between those at the top and those at the bottom of the achievement scale in each dimension (vertical inequalities); and deprivations (i.e. the share of the population falling below a given threshold of achievement). The systemic resources that underpin future well-being over time are expressed in terms of four types of capital: Economic, Natural, Human and Social.

Comparable data on the distribution of household income provide both a point of reference for judging the performance of any country and an opportunity to assess the role of common drivers as well as drivers that are country-specific. They also allow governments to draw on the experience of different countries in order to learn "what works best" in narrowing income disparities, inequality and poverty. But achieving comparability in this field is also difficult, as national practices differ widely in terms of concepts (inequality), measures (GINI), and statistical sources. This database includes GINI and income inequality data; it replaces the datasets "Income distribution: inequality" ( http://dx.doi.org/10.1787/data-00199-en ) and "Income distribution: poverty" ( http://dx.doi.org/10.1787/data-00200-en ).

Time-use surveys are the primary statistical vehicle for recording information on how people precisely allocate their time across different day-to-day activities. Typically, a large number of people keep a diary of activities over one or several representative days for a given period. Respondents describe their activities in their own words in a time diary and these are then re-coded by national statistical agencies into a set of descriptive categories. A well-designed survey classifies activities across a total duration of 24 hours (or 1 440 minutes) per day. Interest in time-use studies has grown considerably over the last 30 years and an increasing number of national statistical agencies have been conducting large-scale time-use surveys