Utilize este identificador para referenciar este registo: http://hdl.handle.net/10362/129531
Título: Linking Pensions to Life Expectancy: Tackling Conceptual Uncertainty through Bayesian Model Averaging
Autor: Bravo, Jorge M.
Ayuso, Mercedes
Palavras-chave: Sustainability factor
Retirement age
Bayesian Model Averaging
Pensions
Life expectancy
Mortality forecasting
Redistribution
Policymaking under uncertainty
Mathematics(all)
SDG 3 - Good Health and Well-being
Data: 19-Dez-2021
Resumo: Linking pensions to longevity developments at retirement age has been one of the most common policy responses to pension schemes and aging populations. The introduction of automatic stabilizers is primarily motivated by cost containment objectives, but there are other dimensions of welfare restructuring in the politics of pension reforms, including recalibration, rationalization, and blame avoidance for unpopular policies that involve retrenchments. This paper examines the policy designs and implications of linking entry pensions to life expectancy developments through sustainability factors or life expectancy coefficients in Finland, Portugal, and Spain. To address conceptual and specification uncertainty in policymaking, we propose and apply a Bayesian model averaging approach to stochastic mortality modeling and life expectancy computation. The results show that: (i) sustainability factors will generate substantial pension entitlement reductions in the three countries analyzed; (ii) the magnitude of the pension losses depends on the factor design; (iii) to offset pension cuts and safeguard pension adequacy, individuals will have to prolong their working lives significantly; (iv) factor designs considering cohort longevity markers would have generated higher pension cuts in countries with increasing life expectancy gap.
Descrição: Bravo, J. M., & Ayuso, M. (2021). Linking Pensions to Life Expectancy: Tackling Conceptual Uncertainty through Bayesian Model Averaging. Mathematics, 9(24), 1-27. [3307]. https://doi.org/10.3390/math9243307 --------------------------------------------------- Funding: This research was funded by Portuguese national science funds through FCT under the grant UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC) (J.M. Bravo), by the Spanish Ministry of Science and Innovation for funding received under grant PID2019-105986GBC21 (M. Ayuso) and by Secretaria d’Universitats i Recerca del departament d’Empresa i Coneixement de la Generalitat de Catalunya for funding received under grant 2020-PANDE-00074.
Peer review: yes
URI: http://hdl.handle.net/10362/129531
DOI: https://doi.org/10.3390/math9243307
ISSN: 2227-7390
Aparece nas colecções:NIMS: MagIC - Artigos em revista internacional com arbitragem científica

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