Linking Pensions to Life Expectancy: Tackling Conceptual Uncertainty through Bayesian Model Averaging
Open accessAbstract
Linking pensions to longevity developments at retirement age has been one of the most common policy responses of pension schemes to 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.
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The customary approach to data-driven modelling is to neglect model risk and select a single 'best' model from a set of candidate learners using statistical criteria.
“The customary approach to data-driven modelling is to neglect model risk and pursue a 'winner-takes-all' perspective by which, for each dataset, a unique believed to be the 'best' model is selected from a set of candidate (weak) learners using some method or statistical criteria (goodness-of-fit, predictive), see Bravo and Ayuso (2021).”
From Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting (2023) · cites this paper as Bravo and Ayuso (2021)
The Introduction describes a standard “winner-take-all” approach that selects one best model using statistical criteria and neglects model uncertainty. It makes this claim specifically about age-specific mortality forecasting, not data-driven modelling generally; it also calls the alternatives candidate approaches rather than weak learners. (LLM, conf=0.84)
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Cites
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Automatic Indexation of the Pension Age to Life Expectancy: When Policy Design Matters
Ayuso, M., Bravo, J. M., Holzmann, R., & Palmer, E. (2021) · Risks · doi:10.3390/risks9050096 · Cited by 44 · see citing works
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Cited by
Sorted by citations
Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting
Clemente, C., Guerreiro, G. R., & Bravo, J. M. (2023) · Risks · doi:10.3390/risks11090163 · Cited by 28 · see citing works
Self-citation
- Authors
- Bravo, J. M., & Ayuso, M.
- Affiliations
- MagIC (PT)
- Universitat de Barcelona (ES)
- Corresponding author
- Bravo, Jorge M.
- Venue
- Mathematics
- Publisher
- MDPI AG
- Published
- 2021-12-19
- Volume
- 9
- Issue
- 24
- Pages
- 3307
- ISSN
- 2227-7390 (ISSN-L)
- OpenAlex
- W4200403779
- Cited by
- 18
- References
- 3
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 4 · 2023: 4 · 2022: 4
- FWCI
- 4.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Mathematics (publishedVersion, cc-by)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Fundação para a Ciência e Tecnologia (UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC))
- Ministerio de Ciencia e Innovación España (PID2019- 834 105986GB-C21)
- Secretaria d’Universitats i Recerca del departament d’Empresa i 835 Coneixement de la Generalitat de Catalunya (2020-PANDE-00074)
- Topics
- Social Sciences › Demography › Insurance, Mortality, Demography, Risk Management
- Social Sciences › Business, Management and Accounting › Accounting › Financial Literacy, Pension, Retirement Analysis
- Social Sciences › Demography › Retirement, Disability, and Employment
- Keywords
- pensions, life expectancy, automatic stabilizers, sustainability factors, population aging, pension reform, pension adequacy, working life extension, Bayesian model averaging, Finland, Portugal, Spain
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