Demographic challenges
and statistical developments
Kim Dunstan, Senior Demographer
Population topics
Patterns and processes:
Population size and change
Fertility
Mortality
Movement of people
Geographic base (national, regional, and local)
Different population types (residents, visitors)
Composition – age, sex, ethnicity, etc
Theories and practicalities
Demographic theory
• Demographic transition
• Epidemiological transition
Statistical models
• Cohort component methods
• Life tables
Statistical standards and classifications
Basic population equation
Pt 1  Pt  B  D  I  O
Natural increase
Net migration
Pt+1 Population at end of time period
Pt
Population at start of time period (base population)
B
Births during time period
D
Deaths during time period
I
In-migration (arrivals) during time period
O
Out-migration (departures) during time period
A very mobile population
4.9 million arrivals into NZ each year
4.9 million departures from NZ each year
Up to ¼ million visitors from overseas in NZ on any given day
Up to 200,000 NZ residents ‘temporarily’ overseas on any
given day
Roughly 1 million overseas-born living in NZ
At least 600,000 NZ-born living overseas
Over half of NZ’s population changes address within 5 years
Seasonal and diurnal flows with work, study, leisure and
holidays
How to measure local populations?
Especially measuring internal migration
67 local councils; 2,000+ area units (‘suburbs’)
Traditional periodic census
Sample surveys
Administrative data sources
• Data collected for administrative reasons
Estimating local populations
Established administrative data sources
Birth and death registrations
• High coverage
• Lag between birth and registration
• Some vague, incomplete and temporary addresses
International travel and migration
• Virtually all movements covered
• Actual length of stay/absence ≠ intended
• Some vague, incomplete and temporary addresses
Residential building consents
• Demolitions not well covered
• No information on onset and extent of inhabitation (eg holiday
homes, number of occupants)
Estimating local populations (cont.)
Established administrative data sources
Electoral enrolments
•
•
•
•
High coverage above age 30 years
Excludes people under 18 years and those ineligible to vote
Includes some people living overseas
Usual address ≠ electoral address
School rolls
• High coverage at compulsory school ages (6–16 years)
• School location ≠ usual address of student
• Students from overseas may not be residents
Territorial authority annual consultation
• Local insight into factors affecting population
• Generally qualitative
Alternative data sources
High potential usefulness
Health service data (PHO enrolments)
• Covers all ages
• Stock and flow/transition data available
• Differential coverage by age/sex/ethnicity
• Includes some people living overseas
• Lag between moving and recording change of address
PHO enrolments v ERP
New Zealand, mid-2011
Alternative data sources
High potential usefulness
Linked employer-employee data (LEED)
• High coverage above age 20 years
• Stock and flow/transition data available
• Includes some people living overseas
• Usual address ≠ LEED address (eg workplace, PO boxes)
• Lag between moving and recording change of address
LEED v ERP
New Zealand, mid-2011
How to model local populations from
multiple imperfect data sources
Subjective interpretation
Simple weights of different data sources
• by age-sex
• stock data, or changes in stocks
Multiple regression
Bayesian modelling
Bayesian population estimation model
Component
Description
Observed directly?
Demographic accounts
Complete description of births, deaths, migration
and population stocks, by age, sex, region and
year, during the period of interest
No
Statistical formulae for
births, deaths and
migration
Formulae that describe age patterns, regional
variation, and time trends in births, deaths,
internal migration and external migration
No
Data sources
All administrative, survey and census data used in
population estimation, such as census counts,
vital registration, arrivals and departures, school
enrolments, housing consents, etc.
Yes
Statistical formulae
linking data sources
and demographic
accounts
Formulae that use values from the demographic
accounts to predict values observed in the data
sources (eg that use numbers of people aged 5–
10 from the demographic accounts to predict
observed primary school enrolments)
No
Inference
Unknown components derived using Bayesian
Markov chain Monte Carlo (MCMC) methods
Result is a set of simulated values
Summarised by percentiles and measures of
uncertainty
Advantages
Deals easily with inaccurate input data
Deals easily with irregular input data
Measures of uncertainty
Automation and efficiency
Privacy and data management
Extension to projections and other estimation problems
Difficulties
Theoretical – relatively new application in demography
Practical – large volumes of data can affect efficiency
and speed of model
Conceptual – more complex, less transparent?
NZ’s 65+ population
2009-base official projections and experimental stochastic projections
Checks using electoral enrolment data
19
Benefits of embedding statistical
models in demography
Managing and utilising multiple large datasets
Transparency and replicability
Measures of uncertainty
What statistical skills are needed?
Data linking and integration
Efficient manipulation of large datasets
Measuring and conveying uncertainty
Data visualisation