How digital data is changing how we measure well-being and happiness
What is well-being, and how can we measure it? This complex question has fascinated philosophers and thinkers since ancient times. For example, Aristotle has expressed his interest on the topic claiming that human well-being, labeled as eudaimonia (greek: ευδαιμονία: Eu=Good, Daimon=spirit), is an activity of the soul expressing complete virtue [11].
In modern times, economists and policy-makers have traditionally considered Gross Domestic Product (GDP) as a good indicator of well-being in society. Unfortunately, GDP cannot measure many aspects of what makes people’s life worth living, and lately researchers of various backgrounds have started instead measuring well-being considering it as an index of societal progress and an effective indicator for public policing [1].
However, talking about well-being generally can be misleading, given the complexity that this concept conveys. For this reason, researchers generally distinguish between objective well-being and subjective well-being [2, 3, 4, 5]. Both definitions, and their relevant dimensions, have been traditionally captured with self-report surveys [6]. Although traditional data have been considered accurate and valid, they bring some considerable disadvantages, such as time limitations and high costs. Therefore, taking advantage of the big data revolution, researchers and nonprofit organisations have started to use many novel data sources to eliminate the limitations brought from traditional data and to contribute to the exploration of well-being and its relevant dimensions.[1]
Measuring objective well-being
Objective well-being investigates the objective dimensions of a good life, and therefore researchers have focused on determining and exploring its dimensions. In fact, the Organisation for Economic Co-operation and Development (OECD) [2], the United Nations Development Programme (UNDP) [3], and the Italian Statistics Bureau (ISTAT) [4] work in this direction. Thus, taking into consideration their documentation, we can identify six major objective and observable dimensions for its measurement: health, job opportunities, socioeconomic development, environment, safety, and politics. All these dimensions together represent the objective well-being, which is assessed through the extent to which these “needs” are satisfied.
The last few years have witnessed a change in the way of measuring objective well-being. We identify eight important novel data sources that are lately used for the exploration of objective well-being and its relevant dimensions: CDRs, GPS and transportation, Social Media, Health and Fitness, News, Retail Scanners, Web Search, and Crowdsourcing. The figure below describes the new data sources (left) that have been used to estimate one or more dimensions of objective well-being (right). The presence of a link in the figure between a data source and a dimension indicates that there are papers in the literature on monitoring that dimension with that data source. For example, Pappalardo et al. [7] use CDRs to capture the employment rate of French cities (A2).
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Sources of data (left) and dimensions (right) of objective well-being
Measuring subjective well-being
Subjective well-being examines people’s subjective evaluations of their own lives. In 2013 the OECD [8] recognized the importance of taking into consideration people’s perceived well-being, labeled as subjective well-being or happiness, when investigating overall well-being. Studies using traditional data to measure happiness have identified the main determinants of well-being (see e.g. [9]) that we divide in five main dimensions: the role of human genes, which seem to be fairly heritable, universal needs, meaning basic and psychological needs, social environment, such as education and health, economic environment, including a lot of research on income, and political environment, such as democracy and political freedom.
Similarly to objective well-being, researchers use novel data sources to explore subjective well-being. In particular, Social Media, Google Trends, Crowdsourcing, and News are used from researchers for the exploration of subjective well-being and its relevant dimensions. The figure below describes the new data sources (left) that have been used to estimate one or more dimensions of subjective well-being (right). The presence of a link in the figure between a data source and a dimension indicates that there are papers in the literature on monitoring that dimension with that data source. For example, Dodds et al. [10] construct the Hedonometer to measure temporal patterns of societal happiness, as influenced by basic needs (a2), social (a3), economic (a4) and political (a5) determinants.
Sources of data (left) and dimensions (right) of the subjective well-being
Why frequent measurement of well-being is important
At this critical moment that the global society is under socioeconomic and political crisis and instability, policy-makers and social good organisations need frequent updates of well-being. This is the reason they are attracted by the intellectual opportunities that novel data sources offer to explore well-being. In particular, novel data sources supplement the traditional data by making the estimation of well-being cost-efficient and almost real-time. Only by having frequent well-being estimations, policy-makers can timely react on applying the right policies to prevent detrimental societal effects and contribute to societal progress.
This post is based on a paper*, supported by SoBigData, which provides the theoretical background on objective and subjective well-being, including their relevant dimensions. Additionally, it presents to researchers the new data sources used for capturing well-being, discusses indicative existing studies, and sheds light on still barely unexplored dimensions and data sources that constitute opportunities for future research on well-being.
*V. Voukelatou, L. Gabrielli, I. Miliou, S. Cresci, R. Sharma, M. Tesconi, and L. Pappalardo, “Measuring objective and subjective well-being:dimensions and data sources,”International Journal of Data Science and Analytics (JDSA), 2020, https://doi.org/10.1007/s41060-020-00224-2
Written by: Vasiliki Voukelatou
Revised by: Luca Pappalardo
REFERENCES
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[3] UNDP. Sustainable Development Goals.https://sustainabledevelopment.un.org/sdgs, 2015. (Online;accessed October 2019).
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[8] Organisation for Economic Co-operation and Development (OECD).OECD guidelines on measuring subjectivewell-being. OECD Publishing, 2013.
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