Suchergebnisse

The identification of well-being measures is a matter that has been facing for a long time. In the past, the most commonly employed index was the Gross Domestic Product (GDP), but its adequacy has been questioned for quite all the 20th century, concluding that its use can lead to misleading conclusions (Fleurbaey, 2009). To overcome GDP, have been developed alternative approaches that are still nowadays a core topic. Following the capability approach proposed by Sen (2003), the European Commission, European Parliament, Club of Rome, Organization for Economic Co-operation and Development (OECD) and WWF, in 2007 hosted the conference titled "Beyond GDP". In August 2009, the European Commission released its road map, the Communication "GDP and beyond: Measuring progress in a changing world", when the so-called Stiglitz Commission (Stiglitz et al., 2009) suggested to build a complementary statistical system, focused on social well-being and suitable for measuring sustainability. The advice is to develop indicators that are as clear and appealing as GDP, but more inclusive of environmental and social aspects of progress (OECD, 2013). The proposed measure accounts a set of indicators representing both objective and subjective assessment, and including also people's perception of quality of life. Following the Commission's prompts a huge number of well-being indices have been developed, with different structures, considering a great variety of dimensions and for many purposes. International examples are: the Human Development Index, the Better Life Index, the Happy Planet Index, the Canadian Index of Well-being and the Gross National Happiness Index. In the field of subjective well-being, new measures have been recently proposed using big data, Facebook or Twitter data, to predict life satisfaction based on lexical and topical features (Schwartz et al., 2016, Dodds et al., 2011, Abdullah et al., 2015). In Italy, Iacus et al. (2017) developed the Social Well-being Index (SWBI), a multidimensional well-being composite indicator relying on Twitter data but derived from a new human supervised technique of sentiment analysis. All these new proposals try to overcome the lack in official statistics, however, for this kind of measures, the methodological challenges are still many and open. This work proposes a review of the traditional indices used to measure well-being in Italy, introducing their purposes, limitations, and their progressive evolution. The aim is to provide a detailed image, also to policy-makers, describing the accuracy, timeliness and territorial coverage. All these characteristics are a challenge for a good planning, for a correct international comparison or to integrate well-being indices with new data, represented, as example, by big data.

With the increase of social media usage, a huge new source of data has become available. Despite the enthusiasm linked to this revolution, one of the main outstanding criticisms in using these data is selection bias. Indeed, the reference population is unknown. Nevertheless, many studies show evidence that these data constitute a valuable source because they are more timely and possess higher space granularity. We propose to adjust statistics based on Twitter data by anchoring them to reliable official statistics through a weighted, space-time, small area estimation model. As a by-product, the proposed method also stabilizes the social media indicators, which is a welcome property required for official statistics. The method can be adapted anytime official statistics exists at the proper level of granularity and for which social media usage within the population is known. As an example, we adjust a subjective wellbeing indicator of "working conditions" in Italy, and combine it with relevant official statistics. The weights depend on broadband coverage and the Twitter rate at province level, while the analysis is performed at regional level. The resulting statistics are then compared with survey statistics on the "quality of job" at macro-economic regional level, showing evidence of similar paths.

Nella nostra ricerca applichiamo una nuova tecnica di analisi dei dati provenienti da Twitter per proporre un nuovo indicatore di benessere percepito e soggettivo: L'SWBI considera molte dimensioni della vita individuale e sociale. Per indagare se l'SWBI e i suoi singoli componenti possano rappresentare in modo adeguato la reazione di una comunità ai cambiamenti delle condizioni di vita di tutti i giorni, proponiamo un'analisi comparativa, tra le province italiane, del benessere percepito, misurato con l'SWBI, e del benessere oggettivo, misurato con l'indice della qualità della vita de il Sole 24 Ore. L'idea `e di creare un indicatore composito di benessere che integri le statistiche ufficiali e i dati provenienti dai social media. ; In our research we apply a new technique of opinion analysis over Twitter data to propose a new indicator of perceived and subjective wellbeing: The SWBI examines many dimension of individual and social life. In the purpose to investigate whether SWBI and its single components may adequately represent the reaction of a community to changes in everyday life conditions, we propose a comparative analysis, among the Italian provinces, of perceived well-being, measured with SWBI, with objective well-being, measured with the Il Sole 24 Ore QoL Index. The idea is to create a composite well-being indicator which mixes stable official statistics and fluctuating social media data.

The literature on well being measurement seems to suggest that "asking" for a self-evaluation is the only way to estimate a complete and reliable measure of well being. At the same time "not asking" is the only way to avoid biased evaluations due to self-reporting. Here we propose a method for estimating the welfare perception of a community simply "listening" to the conversations on Social Network Sites. The Social Well Being Index (SWBI) and its components are proposed through to an innovative technique of supervised sentiment analysis called iSA which scales to any language and big data. As main methodological advantages, this approach can estimate several aspects of social well being directly from self-declared perceptions, instead of approximating it through objective (but partial) quantitative variables like GDP; moreover self-perceptions of welfare are spontaneous and not obtained as answers to explicit questions that are proved to bias the result. As an application we evaluate the SWBI in Italy through the period 2012-2015 through the analysis of more than 143 millions of tweets.