1. Spatial Comprehensive Well-Being Composite Indicators Based on Bayesian Latent Factor Model: Evidence from Italian Provinces.
- Author
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Montorsi, Carlotta and Gigliarano, Chiara
- Subjects
- *
WELL-being , *WEIGHING instruments , *TIME series analysis , *PROVINCES - Abstract
This paper proposes spatial comprehensive composite indicators to evaluate the well-being levels and ranking of Italian provinces with data from the Equitable and Sustainable Well-Being dashboard. We use a method based on Bayesian latent factor models, which allow us to include spatial dependence across Italian provinces, quantify uncertainty in the resulting estimates, and estimate data-driven weights for elementary indicators. The results reveal that our data-driven approach changes the resulting composite indicator rankings compared to those produced by traditional composite indicators' approaches. Estimated social and economic well-being is unequally distributed among southern and northern Italian provinces. In contrast, the environmental dimension appears less spatially clustered, and its composite indicators also reach above-average levels in the southern provinces. The time series of well-being composite indicators of Italian macro-areas shows clustering and macro-areas discrimination on larger territorial units. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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