Comparing Wellbeing Economy with other OECD Nations: A Predictive Analysis of Subjective Well-Being Score using SDGs

Focusing on the difference among Wellbeing Economy governments (WEGo), WEGo Hub and non-WEGo OECD countries, this study investigates the relationship between Sustainable Development Goals (SDGs) and subjective well-being score (Gallup's World Poll data on life evaluations used for annual happiness rankings for countries). First, after confirming normality, Analysis of Variance (ANOVA) was conducted to compare the means of the groups and with a p-value of 0.00108, a highly statistically significant difference between the groups was confirmed; the variation in subjective well-being scores is unlikely to have occurred by chance and WEGo members had a higher mean. Next, based on the Shapiro-Wilk normality test, Kruskal-Wallis rank sum test was conducted instead of ANOVA to compare median scores of the overall SDG goal score for the three groups: with a p-value (0.4492), it failed to reject the null hypothesis: There was no statistically significant difference between the groups being compared. However, WEGo countries still had higher minimum, mean, median, and maximum scores than the other two groups in 2024. WEGo countries had 11 out of 17 SDG goal mean and median scores higher than non-WEGO OECD countries, out of which 3 were statistically significant: Goal 3 (Good Health and Well-Being), Goal 5 (Gender Equality), and Goal 11 (Sustainable Cities and Communities). WEGo countries have statistically significant differences from non-WEGO countries in both subjective well-being scores and Sustainable Development Goal 3 (Good Health and Well-Being) scores. Would they also have different results when it comes to predicting the subjective well-being of their citizens? Predictive models were trained and tested to extract and assess variable importance results using data from 2018–2024 for WEGo, WEGo Hub, and non-WEGo OECD countries. The Extreme Gradient Boosting model results showcased that only 3 SDG indicators were present in all three models: ‘n_sdg8_unemp’ (Unemployment rate), ‘n_sdg1_lmicpov’ (Poverty headcount ratio at $3.65/day), and ‘n_sdg16_rsf’ (Press Freedom Index). For WEGo countries, SDG indicator ‘n_sdg2_obesity’ (The percentage of the adult population that has a body mass index of 30kg/m² or higher) emerged as the strongest predictor, followed by SDG indicator ‘n_sdg3_matmort’ (The estimated number of women who die from pregnancy-related causes) and ‘n_sdg7_renewcon’ (The share of renewable energy such as wind/solar in the total final energy consumption). For WEGo Hub countries in the OECD, ‘n_sdg8_unemp’ (Unemployment rate % of total labor force), ‘n_sdg12_explastic’ (The average annual amount of plastic waste exported), and ‘n_sdg16_admin’ (Timeliness of administrative proceedings) had significant coefficients. For Non-WEGo OECD countries, SDG indicators ‘n_sdg8_rights’ (Rating whether fundamental labor rights are effectively guaranteed), ‘n_sdg12_pollimp’ (Air pollution associated with imports), and ‘n_sdg3_traffic’ (Traffic deaths per 100,000 population) stood out. While WEGo country model had 5 SDG indicators for Goal 3 (Good Health and Well-being), WEGo hub country model and Non-WEGo country model had 2 each. Findings contribute to understanding how SDG performance correlates to subjective well-being with variable importance results varying depending on the OECD country’s association to the Wellbeing Economy initiative.