Workshop on Demographic Analysis and Evaluation

1. Workshop on Demographic Analysis and Evaluation

2. Mortality: ConstructingLife Tables from Fragmented Data Using Model Life Tables

3. Constructing a Life Table from Fragmentary Data • Mortality indicators of good quality tend to be limited • to certain age groups. • A census may provide reported deaths for all ages in a population while a demographic survey may only provide child survivorship data supporting estimation of mortality. • However, survey data available for a few ages may reflect mortality levels more accurately than census data. • And such “fragmentary” data can be used to develop life tables that measure mortality for all age groups.

4. Recall from “Model Life Tables” Lecture… • Model life table functions have been constructed so that, if a value for one life table function is available, the rest of the life table can be estimated. • For example, if an estimate of e(x) or q(x) is available for one age, q(x) values for other ages can be interpolated using model life tables. • Similarly, if mortality rates for an actual population are severely distorted because of errors in the data, these rates can be smoothed using model life tables.

5. Constructing a Life Table from Fragmentary Data • The United Nations MORTPAK program MATCH utilizes an index life table indicator together with an empirical or model pattern of dying across all ages to generate abridged life tables. • For selected sex and age, MATCH can utilize: • a central death rate, m(x) • the probability of dying, q(x) • survivors to an exact age, l(x) • life expectancy, e(x)

6. Constructing a Life Table from Fragmentary Data Spreadsheet: MATCH.xls or MATCH_BS.xls

7. Synthesizing Information on Mortality to Select a Pattern • If the age distribution of mortality is limited, on what basis can we make assumptions about the age pattern of mortality and, in turn, incorporate what we do have into MATCH? • By comparing the mortality across ages to model life tables, we can make assumptions about the distribution of mortality among ages with missing data.

8. United Nations and Coale-Demeny West MLT Patterns Compared, Females at e(0) = 60 & 70 Years

9. United Nations and Coale-Demeny West MLT Patterns Compared, Females at e(0) = 60 & 70 Years

10. United Nations and Coale-Demeny West MLT Patterns Compared, Females at e(0) = 60 & 70 Years

11. Synthesizing Information on Mortality to Select a Pattern • Both COMPAR and DHSQCOM calculate life expectancies at birth corresponding to reported age-specific death rates or probabilities of dying for each of the five United Nations models and four Coale-Demeny models. • The smaller the variability in the corresponding life expectancies at birth across age groups, the closer the empirical measures are to a specific regional model. • Both programs provide indices measuring the dispersion of e(0) values across age groups

12. Indices • The median e(0) is calculated and an index formed as the sum of the absolute values of age-specific e(0) deviations from the median. The index with the smallest value indicates the life table most closely matching the empirical data. • COMPAR provides indices for all age groups, the broad age group under-10 and the broad age group over-10. It also provides the difference between the under-10 and 10+ scores. • DHSQCOM provides one index measuring the difference between the e(0) for age 0 and the e(0) for age group 1-4. DHSQCOM also ranks the models on that difference.