Measuring well-being through OECD Better Life Index: mapping the gaps

Azevedo, Glaucia Da Costa; Costa, Helder Gomes; Filho, José Rodrigues de Farias

doi:10.14488/ijcieom2020_full_0006_37403

Cited by 5 publications

(4 citation statements)

References 27 publications

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“…The containment of all variables to show associations to happiness directly or indirectly provides much support for the Better Life framework hypothesized by the OECD. Such support is also declared by other related research in other countries [14,15]. This supporting evidence provides muchneeded influence and capacity to social policymakers to convince community members of the value of their strategies associated with advancing happiness in their community.…”

Section: Discussionsupporting

confidence: 68%

“…According to the How’s Life report from the Organization for Economic Co-operation and Development [ 11 , 12 ], many researchers in their analyses of people’s happiness use data collected through quality-of-life surveys, which have been a significant data source for many well-being studies [ 13 , 14 ]. However, some international research usually concentrates on selected social indicators that do not include health-related indicators [ 6 , 13 ], whereas an ideal set of health indicators could provide useful information about physical and mental health outcomes [ 15 , 16 ]. Self-perception of health status is relatively well covered in research projects and regularly collected across OECD countries, using a standard answering scale.…”

Section: Introductionmentioning

confidence: 99%

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Understanding the Interactions of Happiness, Self-Rated Health, Mental Feelings, Habit of Eating Healthy and Sport/Activities: A Path Model for Abu Dhabi

Badri

Alkhaili²,

Aldhaheri³

et al. 2021

Nutrients

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Understanding the interactions between happiness, self-perception of health, healthy eating behaviors, physical activities, and psychological feelings or symptoms of mental health provides necessary inputs for social policymaking. Using data from the second cycle of the Abu Dhabi Quality of Life survey, this study examined a path analysis of Abu Dhabi residents’ nature of assimilations between these variables. The results point to the significant association between happiness and self-rated health. In addition, the results portray significant direct paths to happiness from three mental feeling variables—"feeling calm and peaceful”, “having lots of energy”, and “feeling downhearted and depressed”. The variable of “often feeling rushed or pressed for time” shows a direct path to self-rated health only. Eating healthy food is significantly associated with both happiness and self-perception of health. In addition, “often doing physical activities” positively influences happiness. The present study enhances and refines policymakers’ understanding of the considered factors on happiness and self-rated health with further elaborations of the mediating roles of specific well-being determinants. Limitations and future research directions are also discussed.

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Section: Discussionsupporting

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Understanding the Interactions of Happiness, Self-Rated Health, Mental Feelings, Habit of Eating Healthy and Sport/Activities: A Path Model for Abu Dhabi

Badri

Alkhaili²,

Aldhaheri³

et al. 2021

Nutrients

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show abstract

“…Social indicators deal with broad categorical concerns of well-being (e.g., OECD's survey "How is life?" [172]), values (e.g., human values [173]), agency (demographic and health surveys [174] and inequality (often calculated via Gini coefficients) [175]). Economic indicators are statistics that allow an analysis of the economic performance of a country and are classified into two main categories: economic structure (e.g., gross domestic product (GDP), investment share in GDP, the balance of trade in goods and services) and consumption and production patterns (e.g., consumer price index (CPI), producer price index (PPI), household consumption, etc.)…”

Section: Assessing Eo Contribution To Sdgs Indicators Using a Maturit...mentioning

confidence: 99%

Using Data from Earth Observation to Support Sustainable Development Indicators: An Analysis of the Literature and Challenges for the Future

et al. 2022

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The Sustainable Development Goals (SDG) framework aims to end poverty, improve health and education, reduce inequality, design sustainable cities, support economic growth, tackle climate change and leave no one behind. To monitor and report the progress on the 231 unique SDGs indicators in all signatory countries, data play a key role. Here, we reviewed the data challenges and costs associated with obtaining traditional data and satellite data (particularly for developing countries), emphasizing the benefits of using satellite data, alongside their portal and platforms in data access. We then assessed, under the maturity matrix framework (MMF 2.0), the current potential of satellite data applications on the SDG indicators that were classified into the sustainability pillars. Despite the SDG framework having more focus on socio-economic aspects of sustainability, there has been a rapidly growing literature in the last few years giving practical examples in using earth observation (EO) to monitor both environmental and socio-economic SDG indicators; there is a potential to populate 108 indicators by using EO data. EO also has a wider potential to support the SDGs beyond the existing indicators.

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“…The BLI is also a composite index including eleven topics, where some of them are a single variable and some others measured with an average of more than one related variables. The final database in Table 1 contains 11 topics from 36 countries [3], [4]. They also evaluate which nations have the best performances and, therefore, are references.…”

Section: Introductionmentioning

confidence: 99%

Grouping OECD Countries According to Better Life Index Indicators by Multidimensional Scaling Analysis

Altintaş

2022

CJMS

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The aim of this study is to group OECD countries using the better life index indicators through multidimensional scaling analysis. The Better Life Index consists of thirty-three variables under the dimensions of material living conditions (income and wealth, work and earnings, housing conditions) and quality of life (health status, work-life balance, education and skills, social connections, civic engagement and governance, environmental quality, personal safety and life satisfaction). These thirty-three indicators constitute the variables of the multidimensional scaling analysis. As a result of the analysis, the positions of countries in two-dimensional space were obtained and it was observed that some countries formed distinct clusters. In this way, OECD countries are compared not on the basis of a selected indicator, but on the basis of an approach that encompasses all the indicators of the Better Life Index.

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Measuring well-being through OECD Better Life Index: mapping the gaps

Cited by 5 publications

References 27 publications

Understanding the Interactions of Happiness, Self-Rated Health, Mental Feelings, Habit of Eating Healthy and Sport/Activities: A Path Model for Abu Dhabi

Understanding the Interactions of Happiness, Self-Rated Health, Mental Feelings, Habit of Eating Healthy and Sport/Activities: A Path Model for Abu Dhabi

Using Data from Earth Observation to Support Sustainable Development Indicators: An Analysis of the Literature and Challenges for the Future

Grouping OECD Countries According to Better Life Index Indicators by Multidimensional Scaling Analysis

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