Global Spatial Patterns of Unequal Longevity Using Geographically Weighted Panel Regression Model

Andi Rosilala, Achmad Fauzan, Roza Azizah Primatika

Abstract

Global disparities in life expectancy remain a major concern in public health research, particularly due to the spatially heterogeneous effects of socioeconomic and environmental determinants. This study investigates global patterns of unequal longevity using a geographically weighted panel regression (GWPR) model applied to balanced panel data from 139 countries over the period 2014–2023. Life expectancy at birth is modeled as a function of gross domestic product (GDP) per capita, health expenditure per capita, secondary school enrollment, PM₂.₅ air pollution, and renewable energy consumption, with data sourced from the World Development Indicators. Exploratory spatial analysis using Moran’s I and local indicators of spatial association (LISA) reveals strong global and local spatial autocorrelation in life expectancy and its determinants, indicating violations of spatial stationarity. Benchmark panel model selection tests favor a two-way fixed effects model; however, diagnostic results reveal significant heteroskedasticity, autocorrelation, and non-normality of residuals. GWPR with an adaptive Bisquare kernel is subsequently employed to capture spatially varying relationships. The results demonstrate substantial geographic heterogeneity in both the magnitude and significance of covariate effects, with GWPR markedly outperforming the global panel model in terms of goodness-of-fit (global R² = 0.989) and residual behavior. Policy-relevant findings highlight that the impacts of economic development, health investment, education, pollution, and renewable energy adoption on longevity differ widely across regions. These results underscore the limitations of one-size-fits-all global health policies and emphasize the importance of geographically targeted, context-sensitive interventions to reduce global inequalities in life expectancy.

Keywords: Geographically weighted panel regression, life expectancy, adaptive bisquare, socioeconomic and environmental determinants.

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References

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DOI: https://doi.org/10.13057/ijas.v9i1.114551

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