2018
DOI: 10.1016/j.techfore.2018.06.044
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What matters most to people around the world? Retrieving Better Life Index priorities on Twitter

Abstract: Better Life Index (BLI), the measure of well-being proposed by the OECD, contains many metrics, which enable it to include a detailed overview of the social, economic, and environmental performances of different countries. However, this also increases the difficulty in evaluating the big picture. In order to overcome this, many composite BLI procedures have been proposed, but none of them takes into account societal priorities in the aggregation. One of the reasons for this is that at the moment there is no re… Show more

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Cited by 23 publications
(19 citation statements)
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“…In recent years, several researchers and international organizations have begun to explore the potential for 'big data' sources to overcome challenges associated with limited data coverage on development indicators, particularly in developing countries (Weber, Kashyap, and Zagheni 2018;di Bella, Leporatti, and Maggino 2016;IUSSP 2015;IEAG 2014;Letouze and Jutting 2014). This work has used diverse big data sources, ranging from the use of mobile call log data to predict income in African countries (Blumenstock, Cadamuro, and On 2015;Mao et al 2015), mobile transport apps to predict social disadvantage (Benita 2019;Tan, Zhao, and Huang 2019), nighttime satellite data to measure poverty (Elvidge et al 2009), and web search and public social media posts to predict unemployment and health outcomes (Resce and Maynard 2018;Nuti et al 2014;Choi and Varian 2012). Despite weaknesses in big data sources, such as issues of non-representativeness and limited metadata to understand the datagenerating process, a strength of these data sources is their higher frequency or realtime measurement, which make them promising for 'nowcasting' (Salganik 2017;di Bella, Leporatti, and Maggino 2016).…”
Section: Monitoring Development Indicators With 'Big Data'mentioning
confidence: 99%
“…In recent years, several researchers and international organizations have begun to explore the potential for 'big data' sources to overcome challenges associated with limited data coverage on development indicators, particularly in developing countries (Weber, Kashyap, and Zagheni 2018;di Bella, Leporatti, and Maggino 2016;IUSSP 2015;IEAG 2014;Letouze and Jutting 2014). This work has used diverse big data sources, ranging from the use of mobile call log data to predict income in African countries (Blumenstock, Cadamuro, and On 2015;Mao et al 2015), mobile transport apps to predict social disadvantage (Benita 2019;Tan, Zhao, and Huang 2019), nighttime satellite data to measure poverty (Elvidge et al 2009), and web search and public social media posts to predict unemployment and health outcomes (Resce and Maynard 2018;Nuti et al 2014;Choi and Varian 2012). Despite weaknesses in big data sources, such as issues of non-representativeness and limited metadata to understand the datagenerating process, a strength of these data sources is their higher frequency or realtime measurement, which make them promising for 'nowcasting' (Salganik 2017;di Bella, Leporatti, and Maggino 2016).…”
Section: Monitoring Development Indicators With 'Big Data'mentioning
confidence: 99%
“…First announced in July 2006 by the U.K.‐based New Economics Foundation, the Happy Planet Index (HPI) synthesizes subjective life satisfaction, life expectancy, ecological footprint, and others (Bondarchik et al., 2016). From May 2011, the Organisation for Economic Co‐operation and Development (OECD) evaluated through the Better Life Index (BLI) a total of 11 sections by country, including housing, income, employment, community education, the environment, citizen participation, health, safety, work–life balance, and life satisfaction (Balestra et al., 2018; Resce & Maynard, 2018). As early as 1972, Bhutan started to measure Gross National Happiness by conducting a survey on nine specific indicators (psychological happiness, quality of life, governance, health, education, community vitality, cultural diversity, time use, and ecological diversity) (Givel, 2015).…”
Section: Literature Reviewmentioning
confidence: 99%
“…The hedonic approach suggests deriving weights by the correlation between different dimensions to be included into the composite index and a proxy of self‐reported outcome (Nardo et al , 2008). Stated preferences weights are usually derived by a representative group of individuals in the society (see Resce and Maynard, 2018; Greco et al , 2019).…”
Section: Literature Review On Composite Indicatorsmentioning
confidence: 99%
“…Although equal weighting is often supported by the argument that all indicators and all goals are equally important (formally in the 2030 Agenda), some countries might still value some dimensions more than others, especially when it comes to prioritizing policies over time. Assuming fixed weights for all countries does not grasp the contextual heterogeneity present in different countries and the importance of autonomy in setting priorities (Resce and Maynard, 2018; Greco et al , 2019). Hence, different weightings of individual SDGs can have important implications on countries’ performance and relative rankings (Booysen, 2002).…”
Section: Introductionmentioning
confidence: 99%