Explaining Regional Mortality Differences with an Economic-Neural Model: Evidence from European NUTS-2 Regions

Donatien Hainaut ()
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Donatien Hainaut: Université catholique de Louvain, LIDAM/ISBA, Belgium

No 2026009, LIDAM Discussion Papers ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Abstract: This article introduces a novel framework for explaining regional mortality differences across European NUTS-2 areas using macroeconomic indicators. Because regional death rates are substantially noisier than national aggregates, we model age-specific mortality as a smooth B-spline surface whose coefficients are predicted by a feed-forward neural network. The network takes as inputs a set of interpretable regional factors, including GDP per capita, purchasing power, employment rate, educational attainment, and NO2 emissions. Model parameters are estimated via maximization of the Poisson log-likelihood, and the methodology is applied to French regional mortality data. Compared with the LiLee multi-population framework, the proposed approach offers several advantages. First, it provides an interpretable link between economic conditions and mortality, allowing the impact of policy-relevant variables to be quantied. Second, the combination of neural networks with B-splines yields smooth, stable mortality curves and avoids the overtting often observed in non-parametric regional models. Finally, the model is suciently robust for long-term mortality forecasting and actuarial applications such as life expectancy projections and annuity valuation.

Keywords: Mortality; neural networks; Lee-Carter model; multi-group mortality; life insurance (search for similar items in EconPapers)
Pages: 26
Date: 2026-04-08
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