Calculating a Prefecture-Level Well-Being Index in Japan: Applying the framework of the OECD's Better Life Index.

Objectives Well-being serves as a crucial indicator of national governance and societal advancement. Consequently, the Better Life Index (BLI) developed by the Organisation for Economic Co-operation and Development (OECD) has emerged as a pivotal multidimensional measure of well-being, surpassing traditional indicators such as Gross Domestic Product (GDP). However, current well-being indicators predominantly focus on national measurements and do not effectively evaluate well-being in smaller regions such as states or prefectures. This study aimed to calculate a Regional Well-Being Index (RWI) tailored to localized areas in Japan.Methods Japanese official statistics, publicly available as open data, were analyzed, focusing on 11 domains similar to those in the BLI: "Income," "Jobs," "Housing," "Health," "Work-Life Balance," "Education," "Community," "Civic Engagement," "Environment," "Safety," and "Life Satisfaction." The RWI scores were calculated for each prefecture in 2010, 2013, 2016, and 2019 using standard normalization techniques. To represent the overall well-being of each prefecture in each year, scores were aggregated across all domains; this aggregate is referred to as the Integrated RWI. The reliability and validity of RWI were assessed by examining time-series changes and Pearson's correlation coefficients.Results Median Integrated RWI scores for Japanese prefectures remained relatively stable across the study period, with slight variations observed: median = 0.67 (Interquartile range [IQR]: -2.48-2.71) in 2010, median = 0.00 (IQR: -2.85-2.76) in 2013, median = 0.13 (IQR: -3.05-2.49) in 2016, and median = 0.19 (IQR: -2.75-3.06) in 2019. Geographical analysis showed lower scores in regions such as Western Kyushu and Shikoku, and higher scores in Chubu and Eastern Kinki. The RWI and the BLI demonstrated construct validity, with Pearson's correlation coefficients ranging from 0.58 to 0.99 across various domains.Conclusion The RWI, based on the OECD's BLI, proved to be a reliable and valid tool for assessing comprehensive well-being at the regional level in Japan. It offers foundational data for identifying challenges to regional well-being and shaping targeted policies, thereby contributing to evidence-based policymaking. Moreover, this methodology has potential applicability in evaluating comprehensive well-being beyond GDP at the regional level in other countries using official statistics.

Authors

Institutions

Publication Details

Journal
PubMed
Published
2024-10-03
DOI
https://doi.org/10.11236/jph.24-002
Citations
2
Primary Topic
Health disparities and outcomes
Type
article
Field-Weighted Citation Impact
1.06
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Calculating a Prefecture-Level Well-Being Index in Japan: Applying the framework of the OECD's Better Life Index.

Kazuya Taira, Yang Myung
2 citations
PubMed
Health disparities and outcomes
1.06
article

Calculating a Prefecture-Level Well-Being Index in Japan: Applying the framework of the OECD's Better Life Index.

Kazuya Taira, Yang Myung
article en
2 citations

Abstract

Objectives Well-being serves as a crucial indicator of national governance and societal advancement. Consequently, the Better Life Index (BLI) developed by the Organisation for Economic Co-operation and Development (OECD) has emerged as a pivotal multidimensional measure of well-being, surpassing traditional indicators such as Gross Domestic Product (GDP). However, current well-being indicators predominantly focus on national measurements and do not effectively evaluate well-being in smaller regions such as states or prefectures. This study aimed to calculate a Regional Well-Being Index (RWI) tailored to localized areas in Japan.Methods Japanese official statistics, publicly available as open data, were analyzed, focusing on 11 domains similar to those in the BLI: "Income," "Jobs," "Housing," "Health," "Work-Life Balance," "Education," "Community," "Civic Engagement," "Environment," "Safety," and "Life Satisfaction." The RWI scores were calculated for each prefecture in 2010, 2013, 2016, and 2019 using standard normalization techniques. To represent the overall well-being of each prefecture in each year, scores were aggregated across all domains; this aggregate is referred to as the Integrated RWI. The reliability and validity of RWI were assessed by examining time-series changes and Pearson's correlation coefficients.Results Median Integrated RWI scores for Japanese prefectures remained relatively stable across the study period, with slight variations observed: median = 0.67 (Interquartile range [IQR]: -2.48-2.71) in 2010, median = 0.00 (IQR: -2.85-2.76) in 2013, median = 0.13 (IQR: -3.05-2.49) in 2016, and median = 0.19 (IQR: -2.75-3.06) in 2019. Geographical analysis showed lower scores in regions such as Western Kyushu and Shikoku, and higher scores in Chubu and Eastern Kinki. The RWI and the BLI demonstrated construct validity, with Pearson's correlation coefficients ranging from 0.58 to 0.99 across various domains.Conclusion The RWI, based on the OECD's BLI, proved to be a reliable and valid tool for assessing comprehensive well-being at the regional level in Japan. It offers foundational data for identifying challenges to regional well-being and shaping targeted policies, thereby contributing to evidence-based policymaking. Moreover, this methodology has potential applicability in evaluating comprehensive well-being beyond GDP at the regional level in other countries using official statistics.

PubMedVol. 71(9)
Kyoto University (JP)
Decent work and economic growth
Openalex Percentile: Top 23%
Health disparities and outcomes
1.06
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.