Spatial Comprehensive Well-Being Composite Indicators Based on Bayesian Latent Factor Model: Evidence from Italian Provinces

Carlotta Montorsi () and Chiara Gigliarano ()
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Carlotta Montorsi: Luxembourg Institute of Socio-Economic Research (LISER) and University of Luxembourg
Chiara Gigliarano: LIUC-Università Cattaneo

Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 2024, vol. 175, issue 2, No 2, 347-383

Abstract: 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.

Keywords: Well-being; Composite indicator; Spatial analysis; Bayesian latent factor; Italian provinces (search for similar items in EconPapers)
JEL-codes: C11 D63 I31 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11205-023-03285-5

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