Does Geographic Proximity Matter for Happiness? Spatial Panel Evidence from the European Union (2015–2024)

This study analyzes the determinants and spatial dependence of happiness across the 27 European Union Member States during 2015–2024. It uses a panel of 270 observations based on the Cantril Ladder and the six explanatory factors from the World Happiness Report: GDP per capita, social support, healthy life expectancy, freedom, generosity, and perceptions of corruption. The empirical strategy combines panel models and spatial econometrics. Specification tests support a model with country random effects and year fixed effects. Spatial autocorrelation is assessed using Moran’s I, and random effects (RE), spatial autoregressive random effects (SAR-RE), the spatial error model with random effects (SEM-RE), and the spatial autoregressive model with spatially autocorrelated errors and random effects (SARAR-RE) are considered, using a three-nearest-neighbor matrix. The results show a strong and persistent spatial concentration of happiness. However, the non-spatial model absorbs most of this dependence, leaving no significant residual spatial autocorrelation for the full period. Consistently, neither SAR-RE nor SEM-RE significantly improves model fit, and the information criteria, likelihood-ratio tests, and Baltagi–Song–Koh tests support the non-spatial RE model with year fixed effects. GDP per capita, social support, and freedom show positive associations with happiness, while perceptions of corruption show a negative association. Healthy life expectancy and generosity are not statistically significant. Moreover, all six WHR determinants exhibit significant spatial autocorrelation, suggesting that the geographic clustering of happiness largely reflects the spatial concentration of its underlying socioeconomic, social, health, and institutional determinants rather than a direct contagion of happiness between neighboring countries. The study underscores the importance of considering the spatial dimension in wellbeing analysis and of accounting for shared regional socioeconomic and institutional conditions when designing European wellbeing policies.