Subnational Old-Age Mortality Modeling: Accounting for Underreporting in a Bayesian Framework

ARC Centre of Excellence in Population Ageing Research Working Paper 2020/23

38 Pages Posted: 29 Oct 2020

See all articles by Qian Lu

Qian Lu

Renmin University of China - School of Statistics

Katja Hanewald

UNSW Sydney - School of Risk & Actuarial Studies and ARC Centre of Excellence in Population Ageing Research (CEPAR)

Andres Villegas

University of New South Wales (UNSW)

Xiaojun Wang

Renmin University of China - School of Statistics

Date Written: September 3, 2020

Abstract

Accurate old-age mortality projections for subnational areas are important for assessing health outcomes and valuing pension liabilities. However, subnational mortality data often face small sample sizes at older ages. In some countries, the underreporting of deaths and population numbers poses additional problems. We propose a new Bayesian framework for old-age mortality that allows for deaths underreporting by introducing a reporting probability, which is defined as the ratio of reported deaths to real deaths and uses informative priors derived from demographic death distribution methods. We show that the proposed modeling framework works well for province-level old-age mortality data (ages 60–99) in China over 1982–2010. Compared to a more conventional framework that assumes the reported data are accurate and uses reported mortality data directly, the proposed framework provides a better fit, with a lower deviance information criterion. The proposed framework generates a reasonable mortality curvature and coherent forecasts for subpopulations with sparse or incomplete mortality data.

Keywords: Old-age mortality, Subnational modeling, Bayesian framework, Death underreporting

JEL Classification: G22, J11

Suggested Citation

Lu, Qian and Hanewald, Katja and Villegas, Andres and Wang, Xiaojun, Subnational Old-Age Mortality Modeling: Accounting for Underreporting in a Bayesian Framework (September 3, 2020). ARC Centre of Excellence in Population Ageing Research Working Paper 2020/23, Available at SSRN: https://ssrn.com/abstract=3687266 or http://dx.doi.org/10.2139/ssrn.3687266

Qian Lu

Renmin University of China - School of Statistics ( email )

No.59 Zhongguancun Street, Renmin University
Beijing, 100872
China

Katja Hanewald

UNSW Sydney - School of Risk & Actuarial Studies and ARC Centre of Excellence in Population Ageing Research (CEPAR) ( email )

School of Risk & Actuarial Studies
UNSW Sydney
Sydney, New South Wales NSW 2052
Australia

Andres Villegas

University of New South Wales (UNSW)

Kensington
High St
Sydney, NSW 2052
Australia

Xiaojun Wang (Contact Author)

Renmin University of China - School of Statistics ( email )

No.59 Zhongguancun Street, Renmin University
Beijing, 100872
China

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