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[Measure] Big Data & Well-Being

This line of research reinterprets happiness theories using behavioural indicators of well-being based on Big Data.

If we want to identify the keys to well-being, we first have to know how it can be measured. Yet, the measures of well-being available so far are not reliable, particularly when it comes to the subjective well-being felt by the citizens. Current measures are based on declarative surveys in which individuals are asked to evaluate their levels of satisfaction on a scale of 0 to 10. But what does it mean to have a level of well-being of 4 rather than one of 5? Can these figures mean anything concerning individual’s most critical concerns? And if people are already at a very high or very low level on these scales, how can they indicate a change in their well-being over time? Besides, these surveys are conducted on small samples of citizens, with a rather narrow timeframe and geographic coverage.

If we really want to evaluate the impact of public policies with regard to their effects on subjective well-being, it is high time we had much more reliable measures. Part of the research project aims to improve measures of well-being and social attitudes by leveraging the millions of signs now available on the web and social networks.

The study conducted on the US shows for example that Google searches relating to health issues (for instance, how to treat a headache) or economic and social issues (such as searches concerning job search sites) can serve as indicators for subjective well-being in different US cities, and could be useful to implement health policies, employment policies, etc. As a rule, all searches or discussions on Google, Facebook, and Twitter are traces of our concerns. They also measure the ways in which individuals are affected by economic, social and political shocks, and allow to understand how they view public policies or institutions. Big Data offer a major advantage in so far as they can be precisely located in time and space.

You can explore well-being in France or the United States with interactive tools, created in partnership with CEPREMAP and Sciences Po Medialab.

Research article