Preparing &
presenting
demographic
information: 1
(Session 05)
SADC Course in Statistics
Learning Objectives
At the end of this session, you will be able to
• interpret and use conventions about agegrouping of demographic data
• sensibly choose the types of breakdown of
general population samples needed for
various demographic purposes
• discuss some of the issues involved in
choosing how much detail to report
• explain the need to think of demographic
data in age, period & cohort terms
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Age-grouping conventions: 1
Quite often data from major sources is
presented “by single year of age”, but if
(i) this is overwhelming for users, or
(ii) Data don’t justify so much disaggregation,
broader age groups are used.
Relatively high mortality amongst the very
young means we often use birth up to 1st
birthday, 1st birthday to 5th, then 5-year
age-groups until a high age e.g. small popn
of people 80+ are all put in 1 group.
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Age-grouping conventions: 2
5-year age-groups easy to follow in decimal
system. Very rough categories often used
in general population studies:0 - <5 pre-school;
5 - <15 school age;
15 - <50 female fertile years;
15 - <60 potentially economically active;
60+ retired; 80+ aged.
How do these fit in in your country? Discuss.
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Age-grouping conventions: 3
In relation to general population studies, note
that comparison between studies & data
sources is hampered if people use other
than 5-year bands.
In specific settings, e.g. of school-related
topics, you must fit local framework e.g. if
school starts at age 6, 0-<6 is “pre-school”
e.g. for attainment measurement might
use grades, not ages, as categories.
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Age-misreporting
Historically there was a tendency for older
people, & the educationally-disadvantaged
to report ages vaguely. Standard measures
e.g. Myers’ index are used to quantify ageheaping. Frequent tendency is to round
ages to numbers ending in 0 or 5, and using
5-year age-groups alleviates any distorting
effects on population composition.
Do you think five-year age-gps would better
correct age-misreporting if centred at 5 and
0, or wd confusion created exceed gains?
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Disaggregation: 1
Specific rates are those where the population
reported is sub-divided e.g. by age &/or
sex. Usually a “crude” rate e.g. Crude
Birth Rate is not subdivided at all, but of
course Age-Specific Death Rates (ASDR)
are crude with respect to sex, unless they
are separated for M & F [then Age- and
Sex-Specific].
Note that all such rates have so far been
discussed on a national basis
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Disaggregation: 2
For many studies internal to one country, it is
valuable to sub-divide results by region or
district (administrative areas) and/or by
other stratification variables e.g. often
metropolitan/other urban/rural or e.g.
Buddhist/Christian/Hindu/Muslim/other.
In specific settings this needs to fit analysis
needs e.g. rainfed/irrigated in rural
livelihoods studies.
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Disaggregation: 3
Note that where data source is a sample
survey, not a full census, numbers are
soon cut down in disaggregated categories,
e.g. with a representative general
population sample of N= 5,000 people,
25 to 29-year-old rural males might ≈
1/
1
1
1
20 of /2 of /3 i.e. /120 ~ about 40 people.
Results then subject to very large sampling
variability. Only report estimates for such
gps if accompanied by error estimates &/or
warnings!
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Reporting detailed data: 1
Demography has a bad
reputation for generating
mountains of detailed
data.
You can’t read/assimilate it
when presented like this 
on screen.
Apologies to UK National
Stats Office who never
intended “Child Health
Statistics” to appear like
this. See next slide!
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Reporting detailed data: 2
Distinguish between (i) presentation tables
with very few key numbers, boldly/clearly
presented & well-explained for general
audience, and (ii) reference tables giving
detailed disaggregated numbers for those
who need to look up and use.
http://www.statistics.gov.uk/downloads/theme_health/Child_Health_book_v4.pdf
is a very good downloadable “data” book;
easy access to, and summary of, reference
data relating to UK child health and contact
points to get more on any theme.
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Reporting detailed data: 3
A very common consideration in demography
is the reporting of figures (e.g. diseasespecific death rates) repeatedly for each yr.
Time series graphs are useful to display a
general trend or compare a few trend lines.
N.B trends meaningful only if data collected
very consistently at constant high quality. If
definitions (e.g. diagnostic criteria, reporting
requirements) change, there is a break in
the series. Take care linking data across
any breaks.
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The age-period-cohort problem
Demographic data presentation always faces
groupings & approximations. A specialised
one is that e.g. the cohort born in 1953 will
each be 30 as of their birthdays in 1983.
With age vs time graph, cohort is a diagonal
line. The three concepts are always
entangled in this way. Example below:-
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Example: Age-Period-Cohort diagram
Line shows
late stages of
the life of
Mr. X, born
1/11/1953 ..
became 30 on
1/11/1983 ..
died aged 31
yrs 7 mths on
1/6/1985
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Age-Period-Cohort data: 1
Statistics giving male population
size by age for mid-1984,
corresponding to 1 square
in diagram, wd include Mr. X
as a 30-yr-old.
The population from the 1953
birth cohort alive in mid-1984
wd. include Mr.X but as part of
the black lozenge shape,
not a square, in the diagram.
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Age-Period-Cohort data: 2
Strictly speaking Mr. X’s untimely
death belongs in a single triangle
of the diagram.
To have all data
organised so that we can draw out
Age by Period OR Age by Cohort figures, we
need the original data doubly classified in
this triangular way.
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Practical work follows to
ensure learning objectives
are achieved…
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