Phantoms never die: living with unreliable population data

Cairns, A.J.G.; Blake, D.; Dowd, K.; Kessler, A.

doi:10.1111/rssa.12159

Phantoms never die: living with unreliable population data

Cairns, A.J.G.; Blake, D.; Dowd, K.; Kessler, A.

Authors

A.J.G. Cairns

D. Blake

Professor Kevin Dowd kevin.dowd@durham.ac.uk
Professor

A. Kessler

Abstract

The analysis of national mortality trends is critically dependent on the quality of the population, exposures and deaths data that underpin death rates. We develop a framework that allows us to assess data reliability and to identify anomalies, illustrated, by way of example, using England and Wales population data. First, we propose a set of graphical diagnostics that help to pinpoint anomalies. Second, we develop a simple Bayesian model that allows us to quantify objectively the size of any anomalies. Two-dimensional graphical diagnostics and modelling techniques are shown to improve significantly our ability to identify and quantify anomalies. An important conclusion is that significant anomalies in population data can often be linked to uneven patterns of births of people in cohorts born in the distant past. In the case of England and Wales, errors of more than 9% in the estimated size of some birth cohorts can be attributed to an uneven pattern of births. We propose methods that can use births data to improve estimates of the underlying population exposures. Finally, we consider the effect of anomalies on mortality forecasts and annuity values, and we find significant effects for some cohorts. Our methodology has general applicability to other sources of population data, such as the Human Mortality Database.

Citation

Cairns, A., Blake, D., Dowd, K., & Kessler, A. (2016). Phantoms never die: living with unreliable population data. Journal of the Royal Statistical Society: Series A, 179(4), 975-1005. https://doi.org/10.1111/rssa.12159

Journal Article Type	Article
Acceptance Date	Jan 25, 2016
Online Publication Date	Jan 25, 2016
Publication Date	Oct 1, 2016
Deposit Date	Jan 26, 2016
Publicly Available Date	Jan 27, 2016
Journal	Journal of the Royal Statistical Society: Series A
Print ISSN	0964-1998
Electronic ISSN	1467-985X
Publisher	Royal Statistical Society
Peer Reviewed	Peer Reviewed
Volume	179
Issue	4
Pages	975-1005
DOI	https://doi.org/10.1111/rssa.12159
Keywords	Baby boom, Cohort–births–deaths exposures methodology, Convexity adjustment ratio, Deaths, Graphical diagnostics, Population data.
Public URL	https://durham-repository.worktribe.com/output/1414417

Files

Accepted Journal Article (1.6 Mb)
PDF

Copyright Statement
© 2016 The Author Journal of the Royal Statistical Society: Series A (Statistics in Society) Published by John Wiley & Sons Ltd on behalf of the Royal Statistical Society. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.