Unconventional Approaches to Mortality Estimation Assessment of Developing Country and Historical Data for Inclusion in HMD Kenneth Hill Ian Timæus

The Issue: HMD is built from components needed to calculate Occurrence-Exposure rates Comparability Flexibility Accessibility Reproducibility Most developing countries do not have standard data for calculating O-E rates How well do alternative ways for estimating mortality in LDC’s fit with HMD?

Broad Approaches Evaluation and adjustment of registered deaths Alternative data collection mechanisms Indirect methods based on survival of close relatives Intercensal survival

Evaluation and Adjustment of Registered Deaths Evaluation methods compare mortality rates by age with the age distribution Early methods assumed population stability More recent methods do not assume stability, but require 2 census enumerations and still make strong assumptions: Net migration is either negligible or known (but recent exploration of ways of measuring migration) Errors of omission (from population, deaths) are invariant by age

Evaluation and Adjustment Methods Growth Balance methods compare the observed death rate age x+ to a residual estimate obtained by subtracting growth rate x+ from entry rate x+ Synthetic Extinct Generations methods compare estimate of population age x derived from deaths over age x and growth rates x+ to observed population age x (N.B. SEG assumes no change in census coverage)

Death reporting and census coverage England & Wales, 1841-1911

England and Wales - data sources Deaths – General Register Office annual reports Census counts – 1841 and every 10 years to 1911 (HMD database contains population estimates for 1 January each year) Compare deaths in each 11-year interval with the bordering population estimates e.g. 1/1/41-31/12/51

Methods of analysis England and Wales experienced significant net emigration throughout the 19th and early 20th centuries Three series of Generalized Growth Balance estimates: no migration Dudley Baines’ (1985) migration estimates GGB method applied iteratively to estimate migration Final estimates from Bennett-Horiuchi method after adjusting for changing census coverage

GGB – initial fit (ignoring migration) Men, 1841-1851

GGB – estimating migration Men, 1841-1851

GGB – final estimates Men, 1841-1851

Bennett-Horiuchi method Men, 1841-1851

Net migration from England & Wales Baines’ and GGB estimates

Estimated census coverage (relative to 1841)

Completeness of death registration (relative to bordering population estimates)

Conclusions – England and Wales Even fairly small net migration flows introduce significant uncertainty into evaluation of the relative completeness of deaths and population data In E&W 1841-1911, neither source suffers from major deficiencies Conventional wisdom about the scale of emigration during the 19th century leads to the view that the deaths data are about 5 per cent more complete than the census data throughout the period If 1 million more people than this emigrated, the data series can be largely reconciled, suggesting that the unadjusted death rates are fairly accurate

Alternative Data Collection Methods Sample registration systems Deaths by age and sex in recent period collected by a population census Amenable to same methods of evaluation as death registration May be less susceptible to selection bias

Survival of Close Relatives Mothers’ reports of survival of children: Aggregate Individual (birth history) Children’s reports of survival of parents: All respondents’ reports of survival of siblings Individual (sibling history) - usually limited to respondents of reproductive age but collection from all respondents could permit more formal evaluation

Intercensal Survival Methods Standard survivorship ratios Cumulative mortality through growth rates (Preston and Bennett) Very sensitive to change in census coverage and age exaggeration

Conclusions: Potential for inclusion in HMD Evaluated vital registration or similar data plus birth histories for child mortality best meet criteria: What inclusion criteria to apply? Selection biases Sibling survival (for recent period from large surveys) might prove acceptable Indirect approaches are not promising